Applied Chemical Engineering
type: channel
description: Applied Chemical Engineering (ACE) is an international open-access academic journal dedicated to publishing highly professional research in all fields related to chemical engineering. All manuscripts are subjected to a rigorous double-blind peer review process, to ensure quality and originality. We are interested inthe original research discoveries. This journal also features a wide range of research in ancillary areas relevant to chemistry. ACE publishes original research articles, review articles, editorials, case reports, letters, brief commentaries, perspectives, methods, etc. The research topics of ACE include but are not limited to: 1. Analytical Chemistry 2. Chemical Engineering 3. Materials chemistry 4. Material synthesis 5. Catalysis 6. Process chemistry and technology 7. Quantum chemistry method 8. Environmental chemical engineering 9. Bio-energy, resources, pollution 10.Reaction kinetics 11. Nanotechnology and bioreactors 12. Surface, coating and film Starting from Volume 7, Issue 2 of 2024, Applied Chemical Engineering (ACE) will be published by Arts and Science Press Pte. Ltd. Please turn to the journal website for new submissions.
publisher: Arts and Science Press Pte. Ltd.
language: en-US
publication name: Applied Chemical Engineering
issn: 2578-2010
copyright: The Author(s) warrant that permission to publish the article has not been previously assigned elsewhere. Author(s) shall retain the copyright of their work and grant the Journal/Publisher right for the first publication with the work simultaneously licensed under: OA - Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). This license allows for the copying, distribution and transmission of the work, provided the correct attribution of the original creator is stated. Adaptation and remixing are also permitted. This license intends to facilitate free access to, as well as the unrestricted reuse of, original works of all types for non-commercial purposes.
items: Machine Learning Approaches for Biomass Resource Mapping and Sustainable Energy Planning
items: Engineering Application of Advanced Adsorbents in Urban Underground Sewage Treatment: Chemical Mechanisms of Pollutant Removal
items: Deep learning and Multi-Sensor Remote Sensing for predicting Atlas cedar resilience: Integrating Landsat-8, Sentinel-2, and Field Inventories within an AI-Driven Ecological Monitoring Framework
items: AI-Driven Optimization of Bio-Energy Systems: Models for Resource Assessment and Emission Reduction
items: Extraction of bioactive phenolic compounds from yerba mate leaves using ultrasound: HPLC profiling and statistical analysis of variables
items: Analytical validation of a UV–Vis spectrophotometric method for precise and sensitive biochemical quantification
items: Synthesis and Characterization New Polymeric Drugs and Study Their Controlled Drug Release
items: Physicochemical Characterization of KOH-Activated Rice Husk–Derived Carbon toward Sustainable Anode Materials for Lithium-Ion Batteries
items: Analysis of semen quality, sperm DNA fragmentation and heavy metals in urine in 1202 males of premarital medical examination
items: Exploring the dynamic interplay of export, energy imports, capital formation and renewable energy use in Nepal
items: Waste Tire Rubber–Derived Activated Carbon for Wastewater Treatment
items: Synthesis and Characterization of a Cu(II) complex using a New Schiff base-Azo Reagent (HTYPIM)
items: Study of the effect of doped methyl blue and methylene blue dyes on the Optical and structural properties of (PMMA-CuO) thin films
items: A novel method for the remediation of environmental impacts of oil spills using poly carboxymethylcellulose coated (Fe2O3, Al2O3, and Ag) nanoparticles
items: Synthesis of Hydroxyapatite/Chitosan–Glutamic Acid Nanocomposite for Highly Efficient Removal of Congo Red Dye from Water
items: Multi-material Al–Ni–316L functionally graded composites for turbine blade applications: Fabrication, mechanical characterization and fatigue behavior
items: Deep Learning for Real-Time Detection of Pollutants in Bio-Energy Production and Utilization Systems
items: Effects of climate change on the food security of Morocco: A case study of the Bouregreg watershed
items: Synthesis, Characterisation and Application of New Amino Acid Proline Derivatives
items: Evaluation of Biochemical Markers in Iraqi Women Patients with Ovarian Cancer
items: AI-Based Pollution Monitoring in Bio-Energy Production Chains: Methods, Applications, and Gaps
items: Electronic, Optical, Vibrational, and Interaction Properties of Gefitinib and Quercetin: An Integrated DFT and Docking Study with Relevance to Applied Chemical Engineering
items: Green Analytical Approach: Determination of Polyphenols and Mineral Content in Pomegranate Peel Using ICP-MS, UV-Vis, and HPLC/LC-MS.
items: Sustainable synthesis of nano-hydroxyapatite from chicken bones: Effect of microwave treatment on structural and electrochemical performance
items: Analytical Chemistry Evaluation of Additive-Induced Stability in Polymer FDM-AM Filaments
items: Heterogeneous Fenton - Based Treatment for Petroleum Refinery Wastewater: A Systematic Review
items: Low-Carbon design of offshore wind turbine foundations and optimization of multi-energy complementary new energy systems based on smart grid collaboration
items: Functional Role of Female Sex Hormones and Receptors in Cervical Carcinoma
items: Removal of contaminants from wastewater by sedimentation in a circular secondary clarifier
items: Polythermal phase equilibria in the Na2CO3-LiCl-H2O ternary system and crystallization of lithium carbonate
items: Ensuring Drug Quality for Public Health: A Comparative HPLC and UV-Vis Study of Iraqi Market Furosemide Analysis Aligned with SDG 3
items: One-pot synthesis of triazole and oxadiazoline derivatives from naproxen: A sustainable approach in green chemistry
items: Profit share dynamics in oil and gas industry: Assessing the effects of oil price, production volume, and cost recovery on government and contractor entitlement
items: Assessment of Lead and Iron Contamination in Soil and Plants in Al-Kifil, Babylon Governorate, Iraq
items: Methanolic extraction of three plants and their biological activity against different pathogenic bacteria
items: Glycolytic Enzyme Profile in Beta-Thalassemia Major: Coordinated Alterations in Hexokinase, Pyruvate Kinase, and Phosphofructokinase with Implications for Therapeutic Targeting
items: AI Models for Life-Cycle Assessment of Bio-Energy Technologies and Pollution Quantification
items: Enhancing performance of planar structure single unit 800 °C O2-SOFC at atmospheric pressure: A numerical modeling approach for electric power production
items: Machine Learning Strategies for Enhancing Syngas Quality, Biofuel Stability, and Air Pollution Control in Bio-Energy Plants
items: Formulation and Statistical Optimisation of Liposome-Loaded Glimepiride Using Design-Expert® Software
items: AI-Supported Forecasting of Biomass Availability under Changing Environmental and Resource Conditions
items: Machine learning for waste-to-energy processes: Resource evaluation, conversion efficiency, and environmental effects
items: Comparative Evaluation of Activated and Non-Activated Sunflower Seed Husks surface for removal of Toxic Industrial Dyes
items: Smart Biorefineries: Machine Learning for Process Control, Resource Utilization, and Emission Monitoring
items: Green synthesis of silver nanoparticles (CC-Ag NPs) utilizing the cymbopogon citratus extract and studying the antibacterial activity
items: Quantitative Chromatographic Analysis of Volatile Compounds Released during Polymer FDM-AM
items: Spectrophotometric Determination of Iron (III) in Anti-Anemia Drugs and Biological Evaluation of HMNTDABA as an Anticancer Agent
items: Toward a conceptual framework for digital transformation in chemical engineering education: The role of MOOCs in fostering intercultural communicative competence
items: A review of the past decade of Azlactones: Synthesis, Reactions, Biomedical and Pharmaceutical applications

Machine Learning Approaches for Biomass Resource Mapping and Sustainable Energy Planning
type: item
description: Biomass resource mappings are essential tasks for sustainable energy planning, since it offers information on the potential supply, geographical availability of resources, plant sitting, transportation opportunities and roadmap towards long term renewable energy concepts that have policy relevance. Its relevance is increasing as countries are embracing low carbon economy’s roadmaps which demand for reliable spatial quantitative estimations of forest and waste residues–based biomass potentials. Despite the significant headway, there are still some loopholes in applying remote sensing and machine learning techniques. These limitations comprise scarcity of good quality field data for model calibration, poor integration of socio–economic drivers and difficulties in representing fine–scale spatial variability that hinder accurate estimate of yields at different spatial levels. This paper surveys recent machine learning methods for AGB estimation, discusses their methodological limitations, and proposes future research avenues toward scalable and robust forest biomass mapping. A combination of satellite observations, GIS–based layers and ground inventory data sets are included in the analysis as well as a variety of regression, tree based, kernel based, neural network, deep learning and hybrid modelling approaches over various land coverage areas. According to the previous works, evidence is gathered from the surveyed studies that ensemble and deep learning approaches can enhance prediction performance on multi–source data; GIS–machine learning integration contributes to better site selection and logistics analysis. The results also demonstrate the potential for a combined framework that exploits transfer learning approaches and digital twin methodologies to reduce prediction uncertainty, especially in low–data areas. Such information could help support rational decision–making activities for policymakers, planners and industry actors that consider the role of bioenergy i…
creator: Smita Desai
creator: Sushama Shirke
creator: Vishvas V. Kalunge
creator: Sireesha Koneru
creator: Gaurav Raju Khobragade
creator: Vidhi Rajendra Kadam
creator: Shyamsing Thakur
creator: Govindarajan Murali
creator: Anant Sidhappa Kurhade
rights: Copyright (c) 2025 Smita Desai, Sushama Shirke, Vishvas V. Kalunge, Sireesha Koneru, Gaurav Raju Khobragade, Vidhi Rajendra Kadam, Shyamsing Thakur, Govindarajan Murali, Anant Sidhappa Kurhade http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2025-12-29
publication date: 2025-12-29
ending page: 5843
doi: 10.59429/ace.v9i1.5843

Engineering Application of Advanced Adsorbents in Urban Underground Sewage Treatment: Chemical Mechanisms of Pollutant Removal
type: item
description: The sewage treatment systems in the cities demand small and efficient technologies that can eliminate both traditional and emerging pollutants. However, the practical use of the high-technology adsorbents in real-life situations of municipal wastewater is still limited. Engineering usage of high-performance adsorbents, such as the granular activated carbon (GAC), activated biochar, and Fe₃O₄-functionalized biochar composites, was examined in this work using actual municipal sewage samples of large metropolitan regions in China and pilot-scale treatment units. Experiments on batch adsorption, fixed-bed column studies and a 60-day pilot-scale experiment on adsorption were carried out to assess the adsorption performance, kinetics, equilibrium behavior, and regeneration potential. Fe₃O₄- biochar composite demonstrated the best adsorption capacity to heavy metals and pharmaceuticals with an equilibrium adsorption capacity of 121.5mg g⁻¹ of Pb2+, 77.8mg g⁻¹ of Cd²⁺ and 132.6mg g⁻¹ of ciprofloxacin, whereas the GAC had a high adsorption capacity of methylene blue (210.3mg g⁻¹). The kinetic analysis showed that adsorption was correlated to pseudo-second-order models with correlation coefficients greater than 0.98, which is an indication of processes of chemisorption. The Langmuir isotherm models were best fitted to the Pb2+ adsorption process, with maximum adsorption capacity of 188.9mg g⁻¹ of Fe₃O₄- biochar. A thermodynamic was used to show that adsorption was spontaneous and endothermic with ΔG° of −12.4 to −18.7 kJ mol⁻¹ and ΔH° of 26.3 kJ mol⁻¹. Experiments involving continuous flow column proved the breakthrough times of Fe 3 O 4 biochar to be 25.8 h, GAC 18.5 h and biochar 12.3 h. Pilot-scale operation had removal efficiencies of 86.2% chemical oxygen demand, 91.6% Pb²⁺, 83.3% Cd²⁺, 95.2% methylene blue, and 91.8% ciprofloxacin. The efficiencies of adsorbent regeneration were found to be over 77 % following five adsorption-desorption cycles. The findings show that, f…
creator: Feng Song
creator: Karimov Tashmukhamed
rights: Copyright (c) 2026 Feng Song, Karimov Tashmukhamed http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-03-20
publication date: 2026-03-20
ending page: 5896
doi: 10.59429/ace.v9i1.5896

Deep learning and Multi-Sensor Remote Sensing for predicting Atlas cedar resilience: Integrating Landsat-8, Sentinel-2, and Field Inventories within an AI-Driven Ecological Monitoring Framework
type: item
description: Atlas cedar (Cedrus atlantica) forests in Morocco’s Middle Atlas are experiencing an accelerated decline due to combined climatic and human pressures. Building on previous work on forest transition modeling, this study presents a deep-learning–based framework designed to predict and monitor the ecological resilience of Atlas cedar ecosystems. Multi-sensor satellite images from Landsat-8 and Sentinel-2, combined with field inventory data from the Ain Leuh–Sidi M’Guild massif, were processed to evaluate vegetation health, canopy density, and regeneration potential from 2013 to 2024. A hybrid Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN–BiLSTM) model was built to capture both spatial and temporal patterns of forest loss and recovery. Spectral indices such as NDVI, NBR, NDMI, and SAVI were extracted and standardized, while terrain features (altitude, slope, aspect) and bioclimatic variables (temperature seasonality, precipitation during the driest quarter) were included in the model. The hybrid CNN–BiLSTM architecture achieved an overall prediction accuracy of 94.7%, surpassing traditional machine learning methods (Random Forest, SVM, and Gradient Boosting). The spatio-temporal projections reveal a notable decline (−62%) of high-density cedar stands in low-elevation areas, while upper-slope refugia show partial stability and higher regeneration likelihoods. These results demonstrate the potential of deep learning combined with high-resolution Earth observation data for real-time forest health monitoring and adaptive management. The developed framework provides an operational foundation for Morocco’s Forest Strategy 2020–2030, enabling proactive decision-making for climate-resilient reforestation and ecological restoration in Mediterranean mountain ecosystems.
creator: Anass Legdou
creator: Ayoub Souileh
creator: Achraf Mabrouk
creator: Said Lahssini
creator: Bouchra Nassih
creator: Aouatif Amine
rights: Copyright (c) 2026 Anass Legdou*, Ayoub Souileh, Achraf Mabrouk, Said Lahssini, Bouchra Nassih, Aouatif Amine http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-02-06
publication date: 2026-02-06
ending page: 5868
doi: 10.59429/ace.v9i1.5868

AI-Driven Optimization of Bio-Energy Systems: Models for Resource Assessment and Emission Reduction
type: item
description: The increasing complexity of bio-energy systems is a reason for the need of advanced analytical methods to enhance resource utilization, process stability and environmental performance. AI methods are popular in this domain, but many papers neglect concerns around data quality, interpretability, scalability and the low generalization potential of models toward plants and feedstocks different from those they were trained on. This paper intends to offer a systematic review on AI techniques available for biomass resource assessment, conversion-process optimization, and supply-chain planning and emission management. The review is structured adopting a rigorous review approach focusing on model, data set, optimization framework and hybrid method developed in the scope of bio-energy value chain. Highlights – The key findings are that AI improves the prediction of biomass availability and biogas/syngas yields, of feedstock properties and emission behavior; surrogate and hybrid models result in expedited simulation time and facilitate real-time decision making. The review also highlights an emerging trend with digital twins, remote sensing with application of machine learning, and federated learning in multi-plant optimization. These findings also have important implications for researchers, engineers and policy-makers who aim to develop robust low emissions bio-energy systems that are economically feasible.
creator: Sajal Suhane
creator: Rushali Rajaram Katkar
creator: Smita Suhane
creator: S. Sugumaran
creator: Santosh Bhauso Takale
creator: Surekha Dehu Khetree
creator: Shyamsing Thakur
creator: Shital Yashwant Waware
creator: Anant Sidhappa Kurhade
rights: Copyright (c) 2025 Sajal Suhane, Rushali Rajaram Katkar, Smita Suhane, S. Sugumaran, Santosh Bhauso Takale, Surekha Dehu Khetree, Shyamsing Thakur, Shital Yashwant Waware, Anant Sidhappa Kurhade http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2025-12-29
publication date: 2025-12-29
ending page: 5837
doi: 10.59429/ace.v9i1.5837

Extraction of bioactive phenolic compounds from yerba mate leaves using ultrasound: HPLC profiling and statistical analysis of variables
type: item
description: Extraction is critical step in extracting bioactive phytochemicals from plant sources and separating them. In this study, a Box-Benken (BBD) design was used, with 15 experiments conducted to optimize the ultrasonic extraction process. Three independent variables (ethanol concentration, temperature, and time) were examined to assess their effect on total extract yield (TYE) as the main response. The optimization aimed to maximize the extraction of active compounds from the resilient matrix of yerba mate (Ilex paraguariensis) leaves. Then, used high-performance liquid chromatography (HPLC) to determine levels of gallic acid, caffeine, quercetin, tannic acid, chlorogenic acid, and other compounds from yerba Mate leaf extract. This method was used to find out how temperature (25–60°C), ultrasonic time (20–40 min), and ethanol content (40–80%) affected the characteristics of yerba mate leaf extracts. The results indicated that the ideal temperature was 50°C, the optimal ultrasonic duration was 30 minutes, and the best extraction ratio was Ethanol/Water (v/v) 50:50, which produced the largest concentration of biologically active compounds without thermal degradation into alternative substances. Importantly, The Analytical Greenness, AGREE, metric was also used to measure the environmental impact, achieving a high score of 0.85. This demonstrates that the created process is not only accurate but also environmentally friendly and long-lasting, as it requires minimal energy and reagents.
creator: Rulla Sabah
creator: Nisreen Kais Abood
creator: Mustafa Hammadi
rights: Copyright (c) 2026 Rulla Sabah, Nisreen Kais Abood, Mustafa Hammadi http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-03-20
publication date: 2026-03-20
ending page: 5893
doi: 10.59429/ace.v9i1.5893

Analytical validation of a UV–Vis spectrophotometric method for precise and sensitive biochemical quantification
type: item
description: A UV–Visible spectrophotometric method was rigorously validated for precise and sensitive biochemical quantification, yielding excellent linearity, low detection limits, and high accuracy and precision across the full analytical range. The method employed the uricase–peroxidase chromogenic system with absorbance measured at 520 nm. Calibration was linear within 0.5–15.0 mg/dL (R² = 0.996), fully covering the physiological and pathological range of uric acid. The limit of detection (0.15 mg/dL) and limit of quantification (0.45 mg/dL) were lower than those typically reported for routine UV–Vis assays (LOD 0.3–0.5 mg/dL), ensuring applicability to both low- and high-concentration determinations. Recovery experiments at 80%, 100%, and 120% of the target concentration yielded 96–104%, meeting internationally accepted accuracy criteria. Precision was robust, with intra-day RSD values below 5% and inter-day values below 6%, surpassing the ≤10% variability commonly observed in conventional spectrophotometric assays. Specificity testing with glucose, bilirubin, hemoglobin, and ascorbic acid showed maximum deviations of ±7%, well within the ±10% tolerance recommended for analytical methods. Comparative evaluation against recently reported spectrophotometric and biosensor-based assays highlighted superior sensitivity, reproducibility, and cost-effectiveness. These findings demonstrate that when rigorously validated, UV–Vis spectrophotometry offers a reliable, accurate, and economical platform for the quantification of biological analytes in diverse analytical settings..
creator: Bashar Abdulazeez Mahmood
rights: Copyright (c) 2026 Bashar Abdulazeez Mahmood* http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-03-03
publication date: 2026-03-03
ending page: 5865
doi: 10.59429/ace.v9i1.5865

Synthesis and Characterization New Polymeric Drugs and Study Their Controlled Drug Release
type: item
description: Prodrug based on polymers are pictured macromolecules and these prodrugs performance unique angle to raise the solubility and pharmacokinetics and pharmacological properties. According to their benefits as anticancer drugs, the highlights focused on the synthesis of prodrug polymers and their applications in medicine field, So these polymers having degradable amide bonds linked to paracetamol and ester bonds bounded to ciprofloxacin as a material for drug delivery . The characterization of the resulting products were carried by FT-IR, 1H – NMR , XRD, thermal analyses (TG, DTG, DTA, DSC) swelling ratio , controlled drug release and Anti-oxidant as an application of these polymers.
creator: Wisam Abdul Jaleel Jawad
rights: Copyright (c) 2026 Wisam Abdul Jaleel Jawad http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-03-20
publication date: 2026-03-20
ending page: 5821
doi: 10.59429/ace.v9i1.5821

Physicochemical Characterization of KOH-Activated Rice Husk–Derived Carbon toward Sustainable Anode Materials for Lithium-Ion Batteries
type: item
description: Rice husk is an abundant agricultural residue with strong potential as a sustainable precursor for porous carbon materials. In this study, rice-husk-derived carbon was prepared via carbonization followed by chemical activation using potassium hydroxide (KOH) with a mild impregnation ratio of 1:1. The physicochemical properties of the resulting activated carbon were systematically investigated using FTIR, SEM–EDX, and N₂ adsorption–desorption analyses. FTIR revealed the presence of oxygen-containing surface functionalities (–OH, C=O, and C–O) and residual silica-related vibrations (Si–O), indicating surface functionalization induced by KOH activation and partial retention of silica from the precursor. SEM images showed a rough, etched surface with interconnected pore networks and localized mineral-rich domains, while EDX confirmed carbon and oxygen as dominant surface elements, accompanied by residual potassium and minor silicon species. The N₂ adsorption–desorption isotherm exhibited Type IV behavior according to the IUPAC classification, with an average pore diameter of 5.0495 nm, indicating a predominantly mesoporous structure and a moderate BET surface area of 12.44 m² g⁻¹. These results demonstrate that mild KOH activation enables the formation of mesoporous carbon with controlled surface area and retained mineral features derived from rice husk. The physicochemical characteristics obtained in this work provide a structural basis for the potential relevance of rice-husk-derived carbon toward sustainable anode material design in lithium-ion batteries, while electrochemical performance remains to be validated in future studies.
creator: Bunyamin
creator: Ima Winaningsih
creator: Deni Fajar Fitriyana
creator: Suryo Wiroyudho Wibowo
creator: Rizky Ilham Fadzillah
creator: Harianingsih
rights: Copyright (c) 2026 Bunyamin, Ima Winaningsih, Deni Fajar Fitriyana, Suryo Wiroyudho Wibowo, Rizky Ilham Fadzillah, Harianingsih http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-03-13
publication date: 2026-03-13
ending page: 5891
doi: 10.59429/ace.v9i1.5891

Analysis of semen quality, sperm DNA fragmentation and heavy metals in urine in 1202 males of premarital medical examination
type: item
description: Background: Studies have shown that trace elements may adversely affect male reproduction, even at low levels. The toxic effects of heavy metals on the reproductive system have been mainly studied in animal experiments, and epidemiological evidence for populations exposed to the general environment is limited and inconsistent. Objectives: Our aim in this study was to analyze the relationship between semen quality and multiple metals or metalloids (Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Cd, Pb, As and Se) in men undergoing premarital medical examinations (PME) and living in the same city. Methods: Among 1202 PME men, semen quality and sperm DNA integrity were measured by using flow cytometry. Urine heavy metals were tested using a mass spectrometer at our infertility clinic. The urinary levels of various metals and the sperm DNA fragmentation rate of men with normal sperm parameters (NSP) and abnormal sperm parameters (ASP) were compared. Results: Among1202 males, 42.0% (505/1202) were smokers, and 42.9% (516/1202) were alcohol users. A total of 594 men (594/1202, 49.4%) had NSP such as sperm concentration, total sperm, sperm motility and sperm morphology, whereas 608 men (608/1202, 50.6%) had some type of sperm-pathology (ASP); 600 had oligo- or astheno- or terato- or oligoasthenoteratozoospermia (OAT), 8 azoospermia 8/1202, 0.7%). The males of long-term outdoor work, smoking and drinking alcohol in ASP group were 252 (41.4%) , 308 (50.7%) and 283 (46.5%), respectively, and more than NSP group [(180, 30.3%), (33.1%) and (39.2%)] (P<0.05). There were no significant differences between ASP and NSP in semen volume, liquefaction time, pH, and round cells. The DNA fragmentation index shouwed a statistically significant difference between the two groups (13.3% ± 5.9%, 585 NSPs; vs 16.6% ± 14.1%, 585 ASPs, P<0.001). The mean concentrations of vanadium (V), chromium (Cr), cobalt (Co) and lead (Pb) and metalloid arsenic (As) were statistically significant between the two groups …
creator: Zhi-Da Shi
creator: Ye Zhao
rights: Copyright (c) 2026 Zhi-Da Shi, Ye Zhao* http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-01-20
publication date: 2026-01-20
ending page: 5859
doi: 10.59429/ace.v9i1.5859

Exploring the dynamic interplay of export, energy imports, capital formation and renewable energy use in Nepal
type: item
description: This study attempts to explore the determinants of Nepal's export performance with focus on the energy variable. The major aim of the study is to find the impact of rising use of renewable energy and growing energy import on export trade of landlocked country Nepal in the context of growing trade deficit after 1990 to till date. Auto Regressive Distributed Lag Model (ARDL) for the time series data from 1990 to 2021 reveal the positive contribution of renewable energy consumption, gross fixed capital formation and energy imports. However, exchange rate did not show the significant result. The findings further reveal the fact that Nepalese export industries are still highly vulnerable to energy shocks besides having high potential for the generation of renewable energy. Thus, increasing the speed of generation of renewable energy and transfer it for industrial use could help to minimize energy import shock, reduce energy cost and increase export competitiveness. The findings suggest that national trade strategies should be linked up with renewable energy use and policymakers should rethink about restructuring trade based exchange rate system in Nepal.
creator: Pramshu Nepal
creator: Pitri Raj Adhikari
creator: Keshav Raj Panthee
rights: Copyright (c) 2026 Pramshu Nepal, Pitri Raj Adhikari*, Keshav Raj Panthee http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-01-12
publication date: 2026-01-12
ending page: 5817
doi: 10.59429/ace.v9i1.5817

Waste Tire Rubber–Derived Activated Carbon for Wastewater Treatment
type: item
description: The adsorption performance of activated carbon derived from waste tire rubber for the removal of methyl orange dye from aqueous solutions was investigated in the present laboratory-scale experiment. The activated carbon was prepared from ground waste tire rubber via chemical activation using potassium hydroxide (KOH) and hydrochloric acid (HCl). The carbonization process was conducted at 300 °C for 1 h, followed by further heating at 800 °C for 2 h under a nitrogen atmosphere. Chemical activation was carried out using 4 M (mol L⁻¹) KOH or HCl, followed by a final activation step at 800 °C for 1 h. Surface morphology and textural properties were characterized using scanning electron microscopy (SEM) and Brunauer–Emmett–Teller (BET) analysis. The BET surface areas of HCl-activated and KOH-activated carbons reached 925 m² g⁻¹ and 1048 m² g⁻¹, respectively. Batch adsorption experiments were conducted at initial dye concentrations of 50–200 mg L⁻¹ and temperatures ranging from 20 to 35 °C. Maximum dye removal efficiencies of 97.5 % and 98.7 % were achieved for HCl-activated and KOH-activated carbons, respectively. Equilibrium data were well described by the Langmuir isotherm model, indicating monolayer adsorption on a relatively homogeneous surface. The results confirm that waste tire rubber is a promising and sustainable precursor for producing high-performance activated carbon for wastewater treatment applications.
creator: Khadeeja Hatif Yamer
creator: Abbas Khalaf Mohammad
rights: Copyright (c) 2026 Khadeeja Hatif Yamer*, Abbas Khalaf Mohammad http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-02-05
publication date: 2026-02-05
ending page: 5887
doi: 10.59429/ace.v9i1.5887

Synthesis and Characterization of a Cu(II) complex using a New Schiff base-Azo Reagent (HTYPIM)
type: item
description: In this study a new organic Schiff base-azo ligand was prepared using two steps the condensation and diazotization reaction, this ligand used for reacting with the copper (II) ion to form a copper complex and determination small amounts of it, the prepared ligand and complex were characterized by UV-Vis, FT-IR, 1HNMR spectra, also measured Molar electrical conductivity for complex. Ion copper (II) was determined by a rapid, sensitive, simple and cheap spectrophotometric method, the copper compound has a molar absorbance of (3.193×105) L.mol-1.cm-1, a Sandel sensitivity of (1.989×10-4) μg.cm-2, and a maximum absorbance of (420) nm, with a limit of detection of (0.0083) μg.mL-1 and a limit of quantitation of (0.027) μg.mL-1, the metal concentration obeys Beer's law within the range (0.04-2) μg.mL-1 with a correlation coefficient value of 0.9994 indicating the degree of linearity of the standard calibration for copper. In the complex, the molar ratio of the metal to the ligand was [1:2], the results indicate that the complex has a high stability constant of )1.8645×108 (mol.L-1, and these results showed that this method was more sensitive, more precise and accurate through the calculation of (Re, Erel, R.S.D) %.
creator: Muntadhar Adnan Rahman
creator: Muneer A. Al-Da’amy
rights: Copyright (c) 2026 Muntadhar Adnan Rahman*, Muneer A. Al-Da’amy http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-03-16
publication date: 2026-03-16
ending page: 5856
doi: 10.59429/ace.v9i1.5856

Study of the effect of doped methyl blue and methylene blue dyes on the Optical and structural properties of (PMMA-CuO) thin films
type: item
description: The two laser dyes used were methyl blue and methylene blue, which exhibited favourable optical properties. The linear optical properties were enhanced by incorporating methyl blue or methylene blue dyes into the copper oxide nanoparticles added to polymethyl methacrylate dissolved in Acetone, improving the optical properties important for future applications in laser materials. The prepared samples were examined by a UV-Vis spectrophotometer at wavelengths ranging from 200 to 800 nm. The results showed that the incorporation of methylene blue dye into the (CuO-PMMA) nanocomposite thin films significantly increased some optical properties, including absorbance, absorption coefficient, extinction coefficient, and refractive index. Meanwhile, the transmittance and energy gap decreased with increasing dye concentration. The addition of methyl blue dye had less effect on these properties and yielded values close to those of the nanocomposites that did not contain the dye. The structural properties of the nanocomposites were studied. The structural properties included Field emission scanning electron microscopy (FE-SEM) images and XRD analysis. The SEM images showed the distribution of copper oxide nanoparticles within the PMMA polymer and demonstrated the diffusion of laser dyes within the nanocomposites. Methyl blue dye forms rough, irregular aggregates on the surface, creates darker areas and reduces solubility. In contrast, methylene blue forms a uniform thin layer that covers the nanoparticles and enhances surface contact through hydrogen bonds, which masks the material crystallinity and affects its mechanical and optical properties.
creator: Fadhel H. Ali
creator: Ayed F.Musheer
creator: Hussein A. Madlool
creator: Nibras Hayder
rights: Copyright (c) 2026 Fadhel H. Ali1*, Ayed F.Musheer2, Hussein A. Madlool3, Nibras Hayder4 http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-01-05
publication date: 2026-01-05
ending page: 5814
doi: 10.59429/ace.v9i1.5814

A novel method for the remediation of environmental impacts of oil spills using poly carboxymethylcellulose coated (Fe2O3, Al2O3, and Ag) nanoparticles
type: item
description: Large-scale oil spills and the discharge of oily effluents from ships and industrial operations pose serious environmental threats and often result in significant economic consequences. Conventional cleanup methods remain largely ineffective and may themselves cause ecological damage, yet nanotechnology offers a promising alternative for addressing oil contamination. In this study, polycarboxymethycellulose-coated nanoparticles (Fe2O3, Al2O3, and Ag) were synthesized via a simple, cost-effective hydrothermal technique and applied to remove Basra crude oil (API-32) from synthetic seawater under realistic environmental conditions. The physicochemical properties of these materials were characterized using FTIR and SEM analyses, while fluorescence and proton nuclear magnetic resonance spectroscopy confirmed near-complete oil-water separation under optimized conditions. Gas chromatography-mass spectrometry results showed that approximately 100 percent of lower molecular weight alkanes (C9–C21) were eliminated within 6 hours, and extending treatment to 24 hours removed more than 67 percent of C22–C25 alkanes. Among the tested materials, CMC-coated Ag nanoparticles demonstrated the highest removal efficiency at an optimal loading of 1.6 wt percent, with disintegration order following Ag > Fe2O3 > Al2O3. The superior performance of nano-Ag is attributed to its greater surface area, smaller particle size, homogeneous morphology, and strong oil adsorption capacity. Additionally, preliminary investigations assessed potential genotoxic effects and, for the first time, examined the ability of these nanoparticles to mitigate crude oil-induced genotoxicity in vivo, confirming their potential for rapid and effective oil removal.
creator: Khamael Abd Alsalam
creator: Mohamed Ali Mutar
rights: Copyright (c) 2026 Khamael Abd Alsalam*, Mohamed Ali Mutar http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-02-12
publication date: 2026-02-12
ending page: 5884
doi: 10.59429/ace.v9i1.5884

Synthesis of Hydroxyapatite/Chitosan–Glutamic Acid Nanocomposite for Highly Efficient Removal of Congo Red Dye from Water
type: item
description: Water pollution by synthetic dyes from industrial effluents poses serious environmental and health risks. This study reports the synthesis, characterization, and application of a novel hydroxyapatite/chitosan-glutamic acid composite (HAP-Cs-Glu) for Congo red (CR) dye removal from aqueous solutions. The composite was synthesized via chemical precipitation at 80°C and pH 10 with 5 hours aging. Characterization was performed using XRD, FESEM, TEM, BET, and FTIR techniques. XRD confirmed hydroxyapatite formation with low crystallinity and 12.27 nm average crystal size. FESEM and TEM revealed spherical particles (~45.77 nm) composed of needle-like nanocrystals. BET analysis showed a surface area of 58.958 m²/g, pore volume of 0.5538 cm³/g, and average pore diameter of 37.572 nm. FTIR confirmed successful functionalization with characteristic phosphate, amino, and carboxyl bands. Batch adsorption experiments investigated effects of contact time (15-75 min), adsorbent dose (0.03-0.15 g), pH (3-9), initial concentration (200-600 mg/L), and temperature (313-333 K). Optimum conditions were: 600 mg/L CR concentration, pH 3, 0.03 g adsorbent, 45 min contact time, and 60°C, achieving maximum adsorption capacity of 833.33 mg/g. Kinetic data followed pseudo-second-order model (R² = 0.9996). Langmuir isotherm provided better fit than Freundlich, indicating monolayer adsorption. Thermodynamic analysis revealed spontaneous (ΔG° < 0) and endothermic (ΔH° > 0) adsorption with positive entropy change. The HAP-Cs-Glu composite demonstrates excellent potential as an eco-friendly adsorbent for CR removal from contaminated water.
creator: Huda Mahdi
creator: Saher Ali
creator: Safa Ali
rights: Copyright (c) 2026 Huda Mahdi*, Saher Ali, Safa Ali http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-02-02
publication date: 2026-02-02
ending page: 5854
doi: 10.59429/ace.v9i1.5854

Multi-material Al–Ni–316L functionally graded composites for turbine blade applications: Fabrication, mechanical characterization and fatigue behavior
type: item
description: A turbine blade on an aircraft's jet engine is a component of the turbine section of turbojet engines. These blades extract energy from high-pressure gas flows and rising temperatures, making them subject to high-temperature gradients. Functionally graded materials are promising materials used in this research to improve blade performance in this challenging environment. This study analyzes the development, manufacturing, and characterization of multiple aluminium, nickel, and 316 steel alloys combined within multi-functionally graded materials that have been successfully fabricated using the powder metallurgical method. The three functionally graded material samples used in this study consist of five layers, starting with AL-NI (75% Ni to 25% Al) on one side and endi ng with Ni-AL and 316 steel (3.33% Ni to 33.33% Al to 33.33% steel) wt% on the other. After determining the mechanical characteristics of each layer of the functionally graded beam both before and after fatigue cracking, the natural frequency of the samples is calculated. As a result, it was found that the combination of 316L steel and particle concentration improved the mechanical properties of the Al-Ni alloy, making it a practical and lightweight alternative to steel structural elements. The 316L steel hybrid alloys showed favourable results in tensile tests and demonstrated stability in long-term fatigue tests. Additionally, the study found that fracture mechanics can accurately predict fatigue life, and that milled and blended Al-Ni-316L steel behaves similarly to a metal powder compact, with consolidation involving particle rearrangement and plastic deformation. The tensile tests carried out at 20°C demonstrated that Sample 2 had the best mechanical performance, with a yield strength of 679 MPa, ultimate tensile strength of 825 MPa, and elastic limit of 708 MPa, which are improvements of +12.6%, +11.7%, and +17.4% more than Sample 1, correspondingly. Fatigue-life experiments were carried out at a…
creator: Amjad M. Bader
creator: Mohanad S. Hasan
creator: Saad T. Faris
creator: Abdulwahab M. Al-Mushehdany
rights: Copyright (c) 2026 Amjad M. Bader, Mohanad S. Hasan2*, Saad T. Faris, Abdulwahab M. Al-Mushehdany http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-01-22
publication date: 2026-01-22
ending page: 5800
doi: 10.59429/ace.v9i1.5800

Deep Learning for Real-Time Detection of Pollutants in Bio-Energy Production and Utilization Systems
type: item
description: Bio-energy facilities are critical aspects of sustainability and low-carbon energy transitions, even though emitting pollutants from the combustion of biomass materials, anaerobic fermentation, and biofuel use is a serious environmental issue. Efficient real-time monitoring is the necessary requirement to maintain a clean energy production, satisfy regulations and care human's health. In most previous works, traditional monitoring methods are used which demonstrate obvious disadvantage with slow response time and poor flexibility of operation and unsatisfactory detection performance in a dynamic state; therefore, creating a gap that will only improve with further research for the intelligent in-situ pollution detecting application. The purpose of this project is to explore the use of deep learning to the real-time detection and monitoring of pollutants in bio-energy production/consumption systems. A comprehensive methodology is used to combine multi-sensor measurements of gas concentration, process parameters, and temporal response with state-of-the-art deep learning methods including convolutional and recurrent neural networks. The results suggest that the deep learning-based models provide remarkably high detection accuracy, efficiency and robustness compared to conventional approaches, leading to an earlier abnormal emission pattern detecting process. These findings indicated that intelligent monitoring system can help to achieve the optimized process control, emission reduction and predictive maintenance in bio-energy plant. Practical implications This work contributes directly to Sustainable Development Goals, namely SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation and Infrastructure), SDG 12 (Responsible Consumption and Production) and SDG 13 (Climate Action) by supporting cleaner bio-energy operations, environmental friendliness and sustainable industrial development.
creator: Hemlata Suresh Gaikwad
creator: Nidhi Sharma
creator: Shital Y. Solanke
creator: Swati Mukesh Dixit
creator: Anant Sidhappa Kurhade
rights: Copyright (c) 2026 Hemlata Suresh Gaikwad, Nidhi Sharma, Shital Y. Solanke, Swati Mukesh Dixit, Anant Sidhappa Kurhade http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-03-03
publication date: 2026-03-03
ending page: 5879
doi: 10.59429/ace.v9i1.5879

Effects of climate change on the food security of Morocco: A case study of the Bouregreg watershed
type: item
description: This study examines the impact of climate variability on agricultural systems in Morocco, with particular attention to wheat production and vegetation dynamics in a context of increasing temperature, decreasing and irregular rainfall, and heightened exposure to droughts and floods. This work aims to evaluate the magnitude and spatial extent of the combined effects of climate variability and agricultural and pastoral activities on the surface condition of the Bouregreg watershed. The specific objectives are to analyze recent climatic changes, assess their influence on plant-level alterations, and examine how agricultural and pastoral practices interact with climatic constraints to affect agricultural productivity. The study is based on an evaluation of climatic variability, including changes in temperature and rainfall patterns, alongside an analysis of vegetation dynamics as indicators of agricultural and environmental conditions within the Bouregreg watershed. Particular attention is given to inter-annual variability in agricultural yields and to the role of agricultural and pastoral activities in shaping vegetation responses to climatic stress. The analysis highlights a decline and increasing irregularity of rainfall, rising temperatures, and a high frequency of extreme climatic events, which collectively contribute to low average wheat yields and strong inter-annual variability in Morocco. Compared with other Mediterranean countries, Morocco exhibits limited technological progress in wheat production, which amplifies the sensitivity of agricultural output to climatic fluctuations. The findings indicate that national agriculture, particularly wheat production, is highly vulnerable to ongoing climatic changes. The strong dependence of agricultural performance on climate variability and land use practices suggests significant risks for the sustainability of agricultural systems and underscores the need for improved adaptation strategies to mitigate future impacts on…
creator: EL Houssaine Ouharba
creator: Khadija El-Moustaqim
creator: Ahmed Abarkan
creator: Aziza Lamchaimech
creator: Chaymae Benballa
creator: Zine EL Abidine Triqui
creator: Nissrine Majit
creator: Jamal Mabrouki
rights: Copyright (c) 2026 EL Houssaine Ouharba, Khadija El-Moustaqim, Ahmed Abarkan, Aziza Lamchaimech, Chaymae Benballa, Zine EL Abidine Triqui, Nissrine Majit, Jamal Mabrouki http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-02-03
publication date: 2026-02-03
ending page: 5851
doi: 10.59429/ace.v9i1.5851

Synthesis, Characterisation and Application of New Amino Acid Proline Derivatives
type: item
description: Five carrier drugs derived from proline were synthesised from proline 1 through an identical synthetic pathway: N-(carboxymethyl proline)-Ampicillin (A1), N-(carboxymethyl proline)-4-Amino antipyrine (A2), N-(carboxymethyl proline)-Cephalexin (A3), N-(carboxymethyl proline)-Ciprofloxacin (A4), and N-(carboxymethyl proline)-Sulfadiazine (A5). All intermediates and final products were identified using IR and 1H-NMR techniques. The antibacterial efficacy was assessed using the paper disc diffusion method. A strategy aimed at Gram-negative (Escherichia coli) and Gram-positive (Staphylococcus aureus) bacteria revealed that specific synthesised compounds demonstrated potential effectiveness against the evaluated pathogens compared to the commercially available antibiotic (Ampicillin). Molecular docking simulations were conducted for all synthesised compounds against DNA gyrase of Staphylococcus aureus and E. coli; the results indicated that the screened compounds possess potential activity for inhibiting DNA gyrase.
creator: Ameer Alaa Abdzaid
creator: Faris Hamood Mohammed
creator: Khudheyer Jawad Kadem
rights: Copyright (c) 2026 Ameer Alaa Abdzaid1, Faris Hamood Mohammed, Khudheyer Jawad Kadem http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-03-05
publication date: 2026-03-05
ending page: 5787
doi: 10.59429/ace.v9i1.5787

Evaluation of Biochemical Markers in Iraqi Women Patients with Ovarian Cancer
type: item
description: Ovarian cancer remains the most lethal gynecologic malignancy and a leading cause of cancer-related mortality among women, largely due to late-stage diagnosis and the lack of reliable early detection tools. This study evaluated selected biochemical markers in Iraqi women with epithelial ovarian cancer compared with those having benign ovarian tumors to assess their potential diagnostic value. The study included 100 women aged 30–65 years, divided into two groups based on histopathological diagnosis: 50 patients with epithelial ovarian cancer and 50 with benign ovarian tumors. Blood samples were analyzed for Human Epididymal Protein 4 (HE4), Carcinoembryonic Antigen (CEA), albumin, aspartate aminotransferase (AST), and glucose. HE4 levels were measured using enzyme-linked immunosorbent assay, while other parameters were determined using automated biochemical analyzers. Data on menopausal status and family history of ovarian cancer were also collected. Statistical analysis was performed using SPSS version 18, with significance set at P < 0.05. HE4 levels were significantly higher in ovarian cancer patients (128 ± 12.3 pg/mL) compared with those with benign tumors (54 ± 8.44 pg/mL; p < 0.001). Conversely, CEA and albumin levels were significantly lower in the cancer group, while AST showed limited discriminatory value and glucose levels did not differ significantly between groups. Additionally, ovarian cancer patients were more frequently postmenopausal and had a positive family history. These findings indicate that HE4 demonstrates superior diagnostic performance over traditional markers and supports its incorporation into diagnostic algorithms for ovarian cancer in Iraqi women, warranting further validation in larger, multi-center studies.
creator: Aqeela Hayder Majeed
creator: Henan Dh Skheel Aljebori
creator: Kawakeb N. A. Abdullae
creator: Montadher Ali Mahdi
creator: Kareem Salim Abod
rights: Copyright (c) 2026 Aqeela Hayder Majeed, Henan Dh Skheel Aljebori, Kawakeb N. A. Abdullae, Montadher Ali Mahdi, Kareem Salim Abod http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-02-13
publication date: 2026-02-13
ending page: 5877
doi: 10.59429/ace.v9i1.5877

AI-Based Pollution Monitoring in Bio-Energy Production Chains: Methods, Applications, and Gaps
type: item
description: The rapid growth of bio-energy production is closely aligned with global sustainability agendas, particularly the Sustainable Development Goals (SDGs) related to Affordable and Clean Energy (SDG 7), Industry, Innovation and Infrastructure (SDG 9), and Climate Action (SDG 13). Effective pollution monitoring across the bio-energy production chain is essential to ensure that renewable energy expansion does not lead to unintended environmental burdens. Current research largely treats artificial intelligence (AI) applications in environmental monitoring and bio-energy systems as separate domains, creating a research gap in integrated, process-wide frameworks that connect emission sources, sensor networks, data pipelines, and AI models across all production stages. The objective of this study is to critically review AI-based pollution monitoring approaches for bio-energy systems and to assess their capability to support sustainable and responsible energy production in line with SDG targets. The methodology involves a structured synthesis of recent literature on sensing technologies, data acquisition and preprocessing, machine learning and deep learning models, and hybrid physics-informed approaches applied from biomass handling to biofuel refining. The key findings show that AI-enabled monitoring improves real-time emission estimation, early detection of abnormal events, and short-term forecasting, supporting cleaner production pathways. At the same time, challenges related to sensor drift, data scarcity, model transferability, and interpretability limit large-scale adoption. The implications of this review highlight the need for open benchmark datasets, robust calibration strategies, and explainable AI models to strengthen regulatory trust, promote sustainable industrial practices, and contribute directly to achieving SDG-linked environmental and energy objectives.
creator: Madhuri Karad
creator: Nidhi Sharma
creator: Khaja Gulam Hussain
creator: Vakiti Sreelatha Reddy
creator: Madhuri Ghuge
creator: Sagar Arjun Dalvi
creator: Rupesh Gangadhar Mahajan
creator: Shital Yashwant Waware
creator: Anant Sidhappa Kurhade
rights: Copyright (c) 2026 Madhuri Karad, Nidhi Sharma, Khaja Gulam Hussain, Vakiti Sreelatha Reddy, Madhuri Ghuge, Sagar Arjun Dalvi, Rupesh Gangadhar Mahajan, Shital Yashwant Waware, Anant Sidhappa Kurhade http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-01-06
publication date: 2026-01-06
ending page: 5848
doi: 10.59429/ace.v9i1.5848

Electronic, Optical, Vibrational, and Interaction Properties of Gefitinib and Quercetin: An Integrated DFT and Docking Study with Relevance to Applied Chemical Engineering
type: item
description: We present a computational investigation of Gefitinib, a clinically approved EGFR inhibitor, and Quercetin, a bioactive flavonoid with anticancer potential, through an integrated density functional theory (DFT), time-dependent DFT (TD-DFT), and molecular docking framework. Geometry optimization at the B3LYP/6-31G(d,p) level yielded electronic and global reactivity descriptors, providing insights into molecular stability and interaction potential. Quercetin exhibited higher electronic stability, with a HOMO–LUMO gap of 5.22 eV compared to 4.24 eV for Gefitinib. TD-DFT simulations predicted key absorption bands at ~369 nm for Gefitinib and ~310 nm for Quercetin, correlating with their distinct electronic transitions. Docking studies against the EGFR tyrosine kinase domain (PDB: 4HJO) revealed stronger predicted binding for Quercetin (−8.9 kcal/mol) than Gefitinib (−5.5 kcal/mol), supported by hydrogen bonding and π–π stacking interactions. These findings highlight how computational chemistry techniques, widely applied in materials design and process optimization, can also provide multiscale insights into drug–target interactions. By integrating electronic structure analysis with molecular docking, this study demonstrates a transferable approach relevant to applied chemical engineering, bridging quantum chemistry with molecular interaction studies in pharmaceutical and materials science contexts.
creator: Ali Hayder Hamzah
creator: Hussein Odia
creator: Hamed A. Gatea
creator: Maithm A. Obaid
creator: Alzahraa S. Abdulwahid
creator: Hadil Hussain Hamza
creator: Mohannad Abdulrazzaq Gati
creator: Aseel M. aljeboree
creator: Ayad F. Alkaim
creator: Ali Aqeel Mahmood
rights: Copyright (c) 2025 Ali Hayder Hamzah, Hussein Odia, Hamed A. Gatea, Maithm A. Obaid, Alzahraa S. Abdulwahid, Hadil Hussain Hamza, Mohannad Abdulrazzaq Gati, Aseel M. aljeboree, Ayad F. Alkaim, Ali Aqeel Mahmood http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2025-12-31
publication date: 2025-12-31
ending page: 5772
doi: 10.59429/ace.v9i1.5772

Green Analytical Approach: Determination of Polyphenols and Mineral Content in Pomegranate Peel Using ICP-MS, UV-Vis, and HPLC/LC-MS.
type: item
description: Pomegranate peel is a natural considerable agro-industrial waste with high polyphenolics and essential minerals which can be used as functional ingredients. This work also suggests the green analytical approach comprising natural deep eutectic solvents (NaDES) coupled with UAE for effective recovery of polyphenols from pomegranate peel along with ICP-MS for multi-element mineral profiling and UV-Vis for total phenolic/flavonoid content and antioxidant activity. Method optimisation (DES composition, solvent:solid ratio, ultrasound time and temperature) and validation (recovery, precision, LOD/LOQ) are described. Expected outcomes include improved polyphenol yield with selected NaDES (e.g., glycerol:choline chloride or lactic acid-based systems) versus conventional ethanol, and accurate quantification of minerals (K, Ca, Mg, Fe, Zn, Cu, Mn) with limits of detection in the low µg•kg⁻¹ range by ICP-MS. Results will be discussed in light of green chemistry, circular economy, and potential nutraceutical applications.
creator: Mohammed Ali Hussein
creator: Fatima Ali Hussain
creator: Saja Farhan Abdullah
rights: Copyright (c) 2026 Mohammed Ali Hussein*, Fatima Ali Hussain, Saja Farhan Abdullah http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-03-16
publication date: 2026-03-16
ending page: 5873
doi: 10.59429/ace.v9i1.5873

Sustainable synthesis of nano-hydroxyapatite from chicken bones: Effect of microwave treatment on structural and electrochemical performance
type: item
description: Hydroxyapatite (HAp) is a bioactive calcium phosphate ceramic, which is the major inorganic constituent of natural teeth and bone. The current research paper involves the production of hydroxyapatite using chicken bone waste through a sustainable process and its pre- and post-processing with the use of microwave post-treatment. The originality of the work is the synthesis of a biogenic source of calcium with the irradiation of microwaves in order to adjust the structural and electrochemical characteristics of HAp. HAP1 was prepared as a sample by grinding and then by calcification, and then HAP2 was prepared by treating HAP1 with microwave. X-ray diffraction (XRD) ensured that the two samples are crystalline, and the microwave treatment caused slight changes in the peak and the size of crystallites. FESEM and TEM observations showed that HAP2 had smaller and more homogenous particles with lower agglomeration than HAP1. EDS identified the Ca, P, and O as the significant elements with minor traces of Mg and Na being biogenic in nature. Electrochemical characterization showed better ionic mobility, charge-transfer behavior, and capacitance of HAP1, but the treatment of microwave treatment raised the internal resistance and lowered ionic conductivity. In general, the research illustrates that microwave processing can increase the level of morphological homogeneity of biogenic HAp, but it has a comparable negative effect on the electrochemical activity.
creator: Noor Mustafa Kamal
creator: Hawraa Mohammed Sadiq
creator: Nuha Abdul-Saheb Ridha
creator: Hanaa Kadtem Egzar
creator: Baydaa Hamad Obaid
rights: Copyright (c) 2026 Noor Mustafa Kamal, Hawraa Mohammed, Sadiq Nuha Abdul-Saheb Ridha, Hanaa Kadtem Egzar, Baydaa Hamad Obaid http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-01-08
publication date: 2026-01-08
ending page: 5845
doi: 10.59429/ace.v9i1.5845

Analytical Chemistry Evaluation of Additive-Induced Stability in Polymer FDM-AM Filaments
type: item
description: The performance and longevity of polymer filaments in Fused Deposition Modeling Additive Manufacturing (FDM-AM) are highly dependent on their chemical and thermal stability, which can be significantly enhanced by the incorporation of functional additives. This study explores the role of stabilizers, plasticizers, nanofillers, and antioxidant agents in improving the structural integrity and printability of common polymers such as polylactic acid (PLA), polyethylene terephthalate glycol (PETG), and acrylonitrile butadiene styrene (ABS). Analytical chemistry techniques, including Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and gel permeation chromatography (GPC), are reviewed and applied as critical tools to evaluate molecular interactions, degradation kinetics, and additive dispersion within the polymer matrix. Results highlight that additives not only suppress thermo-oxidative degradation and moisture sensitivity but also influence glass transition behavior, crystallinity, and filament rheology, ultimately leading to improved dimensional accuracy and mechanical performance in printed parts. This work provides a comprehensive framework for correlating chemical stability with processing reliability in FDM-AM, offering insights for the development of next-generation, high-performance, and durable polymer filaments tailored for sustainable and industrial applications.
creator: Hala Hussain Kareem
creator: Ahmed Hussein Ahmed
creator: Abdul_mohsen Naji Al-mohesen
creator: Issa Mohammed Kadhim
creator: Muntadher Abed Hussein
creator: Nour Mohamd Rasla
creator: Muntadher Kadhem Sultan
creator: Duha Abed Almuhssen Muzahim
creator: Mustafa S. Shareef
rights: Copyright (c) 2025 Hala Hussain Kareem, Ahmed Hussein Ahmed, Abdul_mohsen Naji Al-mohesen, Issa Mohammed Kadhim, Muntadher Abed Hussein, Nour Mohamd Rasla, Muntadher Kadhem Sultan, Duha Abed Almuhssen Muzahim , Mustafa S. Shareef http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2025-12-31
publication date: 2025-12-31
ending page: 5752
doi: 10.59429/ace.v9i1.5752

Heterogeneous Fenton - Based Treatment for Petroleum Refinery Wastewater: A Systematic Review
type: item
description: Environmentally friendly methods are recommended for the petroleum refining industries, for treatment of the generated wastewater. Ammonia, sulfides, phenol, oil and grease, and petroleum hydrocarbons, and other chemical compounds are only a few of the many pollutants found in effluent from petroleum refineries. Major disadvantages of conventional treatment systems for refinery effluent include low effectiveness, high operating and capital costs, and susceptibility to low toxicity and biodegradability. Refinery wastewater has been treated using a variety of techniques, such as chemical, biological, physical, and hybrid approaches. Advanced oxidation processes (AOPs) have elaborated improved efficacy. Because of its multiple uses, affordability, and environmental friendliness, the Fenton process has emerged as a desirable substitute for other AOPs in the removal of contaminants from water. Therefore, the full comprehension of the Fenton process in petroleum refinery wastewater management is the main subject of this study by going over the major developments in the catalytic oxidation of nano-based Fenton materials for the treatment of petroleum refinery wastewater. Nano-based particles are widely used in Fenton catalysis because of their cheap cost and plentiful supply, particularly α-Fe2O3, as well as their high removal efficiency of chemical oxygen demand (COD) and concentrations of phenol, with an average elimination efficiency surpassing 90%. The current review discusses current developments in the manufacture and use of Fenton catalysts that are heterogeneous for the reduction of organic contaminants. Since the reaction between solid Fenton catalysts and H2O2 can produce extremely reactive hydroxyl radicals (HO%), heterogeneous Fenton reactions have drawn a lot of interest in eliminating stubborn organic pollutants. The aim of this study was to thoroughly examine the studies that have been conducted to improve heterogeneous Fenton reactivity for effective applic…
creator: Saleem Falhi Mones
creator: Husham Mohammed Al-Tameemi
creator: Rafid K. Abbas
creator: Hassan Thamer Jasim
rights: Copyright (c) 2026 Saleem Falhi Mones, Husham Mohammed Al-Tameemi, Rafid K. Abbas, Hassan Thamer Jasim http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-01-22
publication date: 2026-01-22
ending page: 5871
doi: 10.59429/ace.v9i1.5871

Low-Carbon design of offshore wind turbine foundations and optimization of multi-energy complementary new energy systems based on smart grid collaboration
type: item
description: Offshore wind power, as a key clean energy in the energy transition, has significant advantages in resource abundance and stability. However, current offshore wind turbine foundation designs face high carbon emissions, and the variability of offshore power generation easily increases the operational costs of the power system. In the design of basic structures, the chemical properties of materials have a significant impact on carbon emissions, costs and performance. However, current research lacks in-depth exploration of common directions in applied chemistry such as materials, reactions and chemical processes. This study proposes a low-carbon design of offshore wind turbine foundations and an optimization model of multi-energy complementary new energy systems based on smart grid collaboration. The experimental results indicated that after optimization with the proposed algorithm, the carbon emissions reached 2087.2 tons. The average cost of the proposed model is 7531.67 dollars, and the power balance constraint during peak load periods is satisfied at a rate of 98.51%. The supply insufficiency during low load periods is only 0.72%, and both curtailment rate and unit output exceedance occurrences are improved. These findings suggest that the proposed model was capable of achieving an effective balance between economic efficiency and environmental performance of offshore wind turbine foundations and promote low-carbon and efficient development of offshore wind power and new energy systems
creator: Ningfeng Huang
rights: Copyright (c) 2026 Ningfeng Huang http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-01-20
publication date: 2026-01-20
ending page: 5838
doi: 10.59429/ace.v9i1.5838

Functional Role of Female Sex Hormones and Receptors in Cervical Carcinoma
type: item
description: Cervical carcinoma remains one of the most prevalent malignancies affecting women worldwide. While human papillomavirus (HPV) infection is a well-established etiological factor, growing evidence suggests that female sex hormones and their receptors may significantly influence the pathogenesis and progression of cervical cancer. Hormonal imbalances, particularly involving estrogen and progesterone, may impact tumor growth and immune modulation within the tumor microenvironment. This study investigates the functional role of selected female sex hormones and their corresponding receptors in Iraqi women diagnosed with cervical carcinoma. A total of 60 female participants were enrolled from gynecology and oncology departments in Baghdad and Karbala, Iraq. Thirty women with histologically confirmed cervical carcinoma were assigned to the patient group, while thirty age-matched healthy volunteers served as controls. Blood samples were collected under fasting conditions, and serum levels of sex hormone-binding globulin (SHBG), estradiol (E2), dehydroepiandrosterone sulfate (DHEAS), progesterone, estrogen receptor alpha (ERα), and progesterone receptor (PR) were quantified using standardized ELISA kits. Statistical comparisons were conducted using SPSS with significance set at p < 0.05. Significant hormonal differences were observed between patient and control groups. Estradiol levels were markedly lower in postmenopausal patients than in premenopausal patients, while DHEAS levels were significantly higher in the same comparison. SHBG and DHEAS levels were also elevated in patients compared to controls. Importantly, ERα levels were significantly decreased, while PR levels were significantly increased in cervical cancer patients relative to controls. A strong association was observed between cervical cancer diagnosis and positive family history (73.3% in patients vs. 13% in controls). Our findings suggest that imbalances in circulating sex hormones and alterations in receptor…
creator: Sura Basil Findakly
creator: Rasha Hamza Mnehil
creator: Kawakeb N. A. Abdullae
creator: Montadher Ali Mahdi
rights: Copyright (c) 2026 Sura Basil Findakly, Rasha Hamza Mnehil, Kawakeb N. A. Abdullae, Montadher Ali Mahdi http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-02-27
publication date: 2026-02-27
ending page: 5894
doi: 10.59429/ace.v9i1.5894

Removal of contaminants from wastewater by sedimentation in a circular secondary clarifier
type: item
description: In wastewater treatment, a circular clarifier is used to separate wastewater into sludge and effluent in the primary treatment process, and it is one of the core instruments in the secondary treatment process for sludge sedimentation. Since biological sludge sedimentation has strong spatially and temporally non-uniform characteristics and a relative float settling velocity, a separation structure is applied to induce a uniform flow field to improve performance. This clarifier also requires an evacuation system for frequent solid removal. The source of power consumption with the clarifier is the induced flow energy. The result is that the effluent from the clarifier will have low turbidity with a high removal efficiency of contaminants. High removal efficiency is very important to meet the standard 'B' in the Department of Environment's effluent standard. This study will use numerical analysis (CFD) to simulate and validate the results from experimental work. By using CFD, it is hoped that this study can provide a better understanding of the hydraulic behavior and provide a scientific basis for the design process of a circular clarifier for sedimentation. A circular clarifier is crucial equipment in the primary treatment of wastewater. In this study, a circular clarifier with improved structural designs, for which a set of baffle plates with appropriate dimensions and locations generating reduced flow energy, was developed. CFD simulations of contaminant sedimentation were coupled with an Artificial Fish Swarm Algorithm optimization workflow to maximize removal efficiency under operational and hydraulic constraints. This clarifier demonstrated satisfactory performance in sediment separation. Trace back simulations and artificial fish swarm algorithms were applied to determine the optimal design of the clarifier. With these optimal structural dimensions, the clarifier could effectively minimize the sludge volume accumulation rate and shorten the time required for slud…
creator: Iqbal Khalaf Erabee
creator: Hayder Abdulhasan Lafta
creator: Mustafa M. Mansour
creator: Alaa M. Lafta
rights: Copyright (c) 2026 Iqbal Khalaf Erabee, Hayder Abdulhasan Lafta, Mustafa M. Mansour,*, Alaa M. Lafta http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-02-11
publication date: 2026-02-11
ending page: 5867
doi: 10.59429/ace.v9i1.5867

Polythermal phase equilibria in the Na2CO3-LiCl-H2O ternary system and crystallization of lithium carbonate
type: item
description: Understanding the phase equilibria and solubility behavior in multicomponent salt-water systems is essential for the development of efficient separation and resource recovery technologies, especially for lithium-containing compounds. However, reliable phase equilibrium data for the Na2CO3–LiCl–H2O system remain limited over a wide range of temperatures and concentrations. In this study, solubility relationships and phase transformations in the sodium carbonate–lithium chloride–water system were systematically studied using a visual polythermal method over a wide range of temperatures and compositions. The resulting phase diagram describes distinct crystallization regions corresponding to ice, Na2CO3∙10H2O, Na2CO3∙7H2O, LiCl∙5H2O, LiCl∙2H2O, LiCl∙H2O, and the newly identified Li2CO3 phase. The formation and stability of the new lithium carbonate phase were confirmed using combined chemical and physicochemical analysis, including infrared spectroscopy and X-ray diffraction, ensuring the reliability of the obtained equilibrium data. Characteristic absorption bands in the IR spectrum observed at 1437.03 cm-1 and 869.93 cm-1 were assigned to carbonate functional groups, providing further evidence for the formation of Li2CO3. The polythermal diagram shows that a significant portion of the system is occupied by crystallization fields, indicating the initially low solubility of lithium carbonate. This behavior highlights the possibility of selectively separating Li2CO3 from saturated solutions through controlled evaporation. Overall, the results provide fundamental thermodynamic insights and practical recommendations for lithium recovery processes and the development of effective separation strategies in aqueous carbonate-chloride systems.
creator: Bakhodir Abdullayev
creator: Dilnoza Axmedova
creator: Jurabek Bozorov
creator: Sarvar Abdiyev
creator: Sanat Samatov
creator: Erkin Yakubov
creator: Khusniddin Botirov
creator: Murodullo Rakhimov
creator: Elbek Mavlanov
creator: Tolmas Olimov
creator: Odil Mamajonov
creator: Mansur Axmedov
creator: Miyasar Zhumanova
creator: Shavkat Umarov
creator: Sarvigul Khujanazarova
creator: Murodjon Samadiy
rights: Copyright (c) 2026 Bakhodir Abdullayev, Dilnoza Axmedova, Jurabek Bozorov, Sarvar Abdiyev, Sanat Samatov, Erkin Yakubov, Khusniddin Botirov, Murodullo Rakhimov, Elbek Mavlanov, Tolmas Olimov, Odil Mamajonov, Mansur Axmedov, Miyasar Zhumanova, Shavkat Umarov, Sarvigul Khujanazarova, Murodjon Samadiy http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-03-02
publication date: 2026-03-02
ending page: 5892
doi: 10.59429/ace.v9i1.5892

Ensuring Drug Quality for Public Health: A Comparative HPLC and UV-Vis Study of Iraqi Market Furosemide Analysis Aligned with SDG 3
type: item
description: Reliable furosemide quantification in pharmaceutical formulations is critical to ensuring drug quality and regulatory compliance. The purpose of this study was to develop and compare spectrophotometric and high-performance liquid chromatography (HPLC) methods for determining furosemide in pharmaceutical dosage forms found in local pharmacies. Standard solutions of furosemide in the concentration range of 2-30 ppm were prepared to create calibration curves using UV-visible spectrophotometry and HPLC methods. Under optimum experimental conditions, the proposed methods' analytical performance was assessed in terms of linearity, limit of detection (LOD), limit of quantification (LOQ), molar absorptivity, and Sandell's sensitivity. The spectrophotometric method was linear, with a correlation coefficient (R²) of 0.999 and an absorption coefficient of 0.0596 at a wavelength of 275 nm. The calculated LOD and LOQ values were 0.721 ppm and 1.069 ppm. A C18 column with a mobile phase of water, ethanol, and acetonitrile was used for chromatographic analysis under controlled temperature conditions. The results of both analytical methods indicated that the measured drug content in all tested pharmaceutical samples was consistent with the labeled amounts. The developed methods demonstrated satisfactory accuracy, sensitivity, and applicability for routine quality control of furosemide in pharmaceutical formulations, resulting in dependable and cost-effective analytical approaches for laboratory use.
creator: Salam Mohammed Nasser
creator: Rusl Mahdi Obaid
creator: Ahmed Ali Alkarimi
rights: Copyright (c) 2026 Salam Mohammed Nasser, Rusl Mahdi Obaid, Ahmed Ali Alkarimi* http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-03-03
publication date: 2026-03-03
ending page: 5860
doi: 10.59429/ace.v9i1.5860

One-pot synthesis of triazole and oxadiazoline derivatives from naproxen: A sustainable approach in green chemistry
type: item
description: This study presents a novel, one-pot synthetic strategy for preparing triazole and oxadiazoline derivatives directly from naproxen. This approach aligns with the principles of green chemistry, aiming to enhance synthetic efficiency by minimizing reaction steps and reducing waste. By eliminating the need for multiple isolation and purification stages, this method offers a sustainable alternative to conventional multi-step procedures. The synthesized compounds underwent structural confirmation using a suite of spectroscopic techniques, specifically Fourier-Transform Infrared spectroscopy, Proton Nuclear Magnetic Resonance spectroscopy, and Carbon-13 Nuclear Magnetic Resonance spectroscopy and CNMR dept 135 and CNMR dept 90. Further analysis supported their potential as anti-inflammatory agents through molecular docking studies. These studies demonstrated strong binding affinities of the compounds to the cyclooxygenase-2 (COX-2) enzyme, suggesting a favorable mechanism of action for anti-inflammatory activity. Additionally, their acute toxicity was assessed by determining the LD50 values, providing preliminary data on their safety profile. Collectively, the new derivatives exhibited promising multi-target activity. The synthesized compounds exhibit potent broad-spectrum antimicrobial effects, demonstrating significant efficacy against both “Gram-positive bacteria, Staphylococcus aureus and Staphylococcus epidermidis”, and “Gram-negative bacteria, including Klebsiella species and Escherichia coli. Furthermore, they show promising antifungal activity against the pathogenic yeast Candida albicans”. This research demonstrates that a sustainable, one-pot synthesis can efficiently generate new compounds with valuable biological properties. Sustained-release naproxen derivatives show significant potential for future development in medicinal chemistry. This work highlights the constructive collaboration between green chemistry principles and the discovery of novel therapeutic …
creator: Nagham Majid Abdulhassan
creator: Abdull Jabar Attia
creator: Falah S. Al-Fartusie
rights: Copyright (c) 2026 Nagham Majid Abdulhassan*, Abdull Jabar Attia, Falah S. Al-Fartusie http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-01-05
publication date: 2026-01-05
ending page: 5820
doi: 10.59429/ace.v9i1.5820

Profit share dynamics in oil and gas industry: Assessing the effects of oil price, production volume, and cost recovery on government and contractor entitlement
type: item
description: This study focuses on how to optimize the crude oil lifting management within the framework of production-sharing contracts (PSC) on XYZ working area in Indonesia, emphasizing the critical role of cost recovery in ensuring financial sustainability and equitable profit distribution between the government and contractors. Crude oil lifting, defined as the extraction of oil from the reservoir and sales to buyer according to their respective entitlement, must be strategically managed to minimize the over-lifting or under-lifting gap, each of which could lead to significant financial consequences. Over-lifting, where the contractor extracts more than their fair share, reduces the oil available to the government, while under-lifting, where the contractor extracts less than their entitled share, delays cost recovery and affects the contractor’s financial position. In this study, the lifting volume is 47,2 Million Bbls, with a cost recovery value of 221,5 thousand USD. The base profit sharing is 46.4% for the government and 53.6% for the contractor, with the FTP (First Tranche Petroleum) set at 20% and the Domestic Market Obligation (DMO) at 25%. The study evaluates the impact of oil price fluctuations (ranging from 20 USD to 100 USD per barrel) and changes in lifting volume (from 20,000 Bbls to 100,000 Bbls) on profit-sharing dynamics. Revenue generated at a price of 20 USD per barrel amounts to 943,5 Million USD, with an Estimated Taxable Base (ETBS) of 533,4 Million USD, and the government entitlement at 48.9%, while the contractor’s entitlement is 51.1%. The findings suggest that fluctuations in oil prices and lifting volumes significantly influence profit-sharing arrangements, with the government’s share increasing as oil prices rise. This study proposes strategies to optimize lifting volumes by projecting the Government entitlement to guide the government lifting volume, so getting the minimum over-lifting or under-lifting gap between contractor's and Government, ulti…
creator: Dody Susanto
creator: Asep Handaya Saputra
creator: Andy Noorsaman Sommeng
rights: Copyright (c) 2026 Dody Susanto*, Asep Handaya Saputra, Andy Noorsaman Sommeng http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-03-13
publication date: 2026-03-13
ending page: 5890
doi: 10.59429/ace.v9i1.5890

Assessment of Lead and Iron Contamination in Soil and Plants in Al-Kifil, Babylon Governorate, Iraq
type: item
description: Heavy metal contamination in agricultural soil poses a significant threat to food safety and public health. This study investigated the concentrations of Lead (Pb) and Iron (Fe) in various plant species and their associated soils at the Al-Kifil site compared to a control site (Al-Ibrahimiya). Soil and plant samples (Onion, Swiss Chard, Broad Beans, Spinach, Garden Cress, and Barley) were analyzed for Pb and Fe content. Soil physicochemical properties, including pH, Salinity (EC), and Organic Matter (OM), were evaluated to determine their influence on metal mobility. All plant samples from Al-Kifil exceeded the FAO/WHO permissible limit for Pb (0.3 ppm), with Swiss chard and Spinach reaching 8.82 ppm and 8.46 ppm, respectively. Soil Pb at Al-Kifil (up to 99.83 ppm) exceeded the Kabata-Pendias safety threshold (70 ppm). Transfer Factor (TF) analysis identified Garden cress (0.478) and Swiss Chard (0.405) as the most efficient accumulators. The lower soil pH at Al-Kifil (5.93) was identified as a key driver for increased metal bioavailability compared to the control (7.11). This suggests a synergistic relationship driven by both soil chemistry (acidity) and plant physiology: The Al-Kifil site presents a severe Pb toxicity risk. Immediate soil remediation via liming and restricted cultivation of leafy accumulator crops are required to mitigate health risks to the local population.
creator: Hawraa F.A. Al-Jebory
creator: Mohammed A.H. Al-Sadi
creator: Alaa K.H. Al-Khalaf
rights: Copyright (c) 2026 Hawraa F.A. Al-Jebory, Mohammed A.H. Al-Sadi*, Alaa K.H. Al-Khalaf http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-03-16
publication date: 2026-03-16
ending page: 5857
doi: 10.59429/ace.v9i1.5857

Methanolic extraction of three plants and their biological activity against different pathogenic bacteria
type: item
description: The increased new and remerging infectious diseases, need to discovery and urgent of new microbial compounds having diverse chemical structures and novel mechanism have also been. The study focused on determining the antibacterial properties of pomegranate peels (Punica granatum), Cordia myxa fruits, and Citrullus colosynthis fruits, against pathogenic bacteria E.coli, Pseudomonas aeruginosa as Gram-negative and Staphylococcus aureus as Gram-negative. Also the antioxidant as free radical scavenge to the plants The plant parts were collected, shade dried and powdered and subjected to extraction by methanol maceration as solvents. The extracts were used to determine the antibacterial activity by agar well diffusion method and MIC assay. The results showed that the plant extracts inhibited the growth of both Gram-positive and negative bacteria, Gram-negative bacteria were more sensitive. The inhibition zone (in millimeters) was greater for Gram-negative bacteria (17 mm) than for Gram-positive bacteria (0 mm) with Cordia myxa. Punica peel and Citrullus colocynthis fruits extracts exhibited high biological activity against all types of bacteria at concentrations of 0.1, 0.5, and 1 mg/mL, respectively. Cordia myxa had the highest percentage of free radical scavenging, followed by Punica and then Citrullus colocynthis. On the basis of the Data obtained it is clear that plant extracts can be used as effective herbal cure against human pathogens.
creator: Haider Abbas AbdulRedha
creator: Nawras Alwan Al- Barqawi
creator: Najla Saleh Mahdi
creator: Ahmed A. A. Madhloom
creator: Zahraa Yosif Motaweq
rights: Copyright (c) 2026 Haider Abbas AbdulRedha , Nawras Alwan Al- Barqawi , Najla Saleh Mahdi, Ahmed A. A. Madhloom , Zahraa Yosif Motaweq http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-01-22
publication date: 2026-01-22
ending page: 5815
doi: 10.59429/ace.v9i1.5815

Glycolytic Enzyme Profile in Beta-Thalassemia Major: Coordinated Alterations in Hexokinase, Pyruvate Kinase, and Phosphofructokinase with Implications for Therapeutic Targeting
type: item
description: Background: Mature erythrocytes depend exclusively on glycolysis for ATP production, making glycolytic enzymes critical for red blood cell survival. Beta-thalassemia major is characterized by oxidative stress and metabolic perturbations that may affect glycolytic enzyme function. Objective: To characterize the glycolytic enzyme profile (hexokinase [HK], pyruvate kinase [PK], phosphofructokinase [PFK]) and PFKP gene expression in beta-thalassemia major patients and to evaluate inter-enzyme correlations indicative of coordinated metabolic regulation. Methods: This case-control study included 42 patients with beta-thalassemia major and 30 healthy controls. Serum enzyme levels were measured by ELISA, and PFKP gene expression was assessed using RT-qPCR with the 2⁻ΔΔCt method. Results: Patients demonstrated significantly elevated HK (327.71 ± 282.40 vs. 131.47 ± 158.67 pg/mL; p = 0.0012) and PFK (1.83 ± 1.52 vs. 0.51 ± 0.53 ng/mL; p < 0.0001). A very strong positive correlation existed between HK and PFK (r = 0.874, p < 0.0001), with moderate correlations among all enzymes. PFKP gene expression did not correlate with serum protein levels (r = 0.006, p = 0.968). Conclusion: Beta-thalassemia major exhibits coordinated elevation of glycolytic enzymes, with PFK showing the highest diagnostic potential. The discordance between gene expression and protein levels suggests post-transcriptional regulation. These findings support therapeutic strategies targeting glycolytic metabolism, including pyruvate kinase activators. HIGHLIGHTS Hexokinase and phosphofructokinase are significantly elevated in patients with beta-thalassemia major. A very strong correlation between HK and PFK (r = 0.874) suggests coordinated regulation of glycolysis. All three glycolytic enzymes show significant intercorrelations (r = 0.509–0.874). PFKPgene expression does not correlate with serum PFK protein levels, indicating post-transcriptional control. Findings support the use of pyruvate kinase activators a…
creator: Karima Jalil Hamza
creator: Maysoon Khalid Hussein
rights: Copyright (c) 2026 Karima Jalil Hamza*, Maysoon Khalid Hussein http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-02-12
publication date: 2026-02-12
ending page: 5885
doi: 10.59429/ace.v9i1.5885

AI Models for Life-Cycle Assessment of Bio-Energy Technologies and Pollution Quantification
type: item
description: Bio-energy systems are frequently promoted as low-carbon substitutes for fossil fuels and are closely related to SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action). Actual environmental performance, however, is contingent on supply-chain design, choice of feedstock, set-up of the technology and local operating conditions. The life-cycle assessment (LCA) is currently the predominant scientific instrument for assessing these impacts in a comprehensive approach linked to SDG 12 Responsible Consumption and Production. Traditional LCA, however, has to deal with a variety of challenges including data scarcity, spatial and temporal variations, and the necessity to analyze several scenarios depending on changing circumstances. In recent years, some of these limitations can be mitigated by using artificial intelligence (AI) and machine learning (ML) techniques. This approach facilitates inventory data extrapolation, gap filling and estimation of the nonlinear function between process variables and environmental parameters, all leading to more dynamic and data rich assessment which reflects SDG 9 (Industry, Innovation and Infrastructure). This review consolidates a summary of up-to-date studies on AI-enabled LCA approaches for bio-energy systems and pollution assessment methods that interface directly with sustainability evaluation. The paper describes a general description of key aspects related to bio-energy supply chains in LCAs regarding impact categories, including greenhouse gas (GHG), air pollutants, land use and water use with relevance to SDG 6-Clean Water and Sanitation and SDG 15-Life on Land. It subsequently provides an overview of AI and ML applications covering the full bio-energy life cycle, including aspects related to biomass resource assessment and feedstock production as well conversion, upgrading, refining, distribution to end use. Particular focus is given to works that integrate ML with LCA metrics for the prediction of environmental perform…
creator: Madhuri Karad
creator: Puja Gholap
creator: Ashwini Dhumal
creator: Vilas Suresh Mane
creator: N. Alangudi Balaji
creator: Kunal Ingole
creator: Rahul N. Patil
creator: Shital Yashwant Waware
creator: Anant Sidhappa Kurhade
rights: Copyright (c) 2026 Madhuri Karad, Puja Gholap, Ashwini Dhumal, Vilas Suresh Mane, N. Alangudi Balaji, Kunal Ingole, Rahul N. Patil, Shital Yashwant Waware, Anant Sidhappa Kurhade http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-01-20
publication date: 2026-01-20
ending page: 5855
doi: 10.59429/ace.v9i1.5855

Enhancing performance of planar structure single unit 800 °C O2-SOFC at atmospheric pressure: A numerical modeling approach for electric power production
type: item
description: Solid oxide fuel cells generate electricity with high electrical efficiency and flexibility in fuel use without emitting pollutants, such as carbon dioxide, nitrogen oxides, sulfur oxides and particulates. Nevertheless, they still face several obstacles and challenges that pose problems and questions driving researchers to find solutions. Numerical simulation is a key tool for integrating various physical fields, including charge dynamics, electrochemical pathways, chemical species kinetics, fluid flow, and energy transformations. The Butler-Volmer equation, in its various forms and approximations, is the most widely used equation for relating electrochemical pathways to current density in solid oxide fuel cell modeling. The main objective of this research is to model and simulate a high-temperature solid oxide fuel cell at atmospheric pressure, in order to test the effect of changing some properties values on the electrical power density produced. The results obtained revealed several techniques for enhancing the fuel cell performance, by improving the physical behaviors appropriate to each property on the corresponding side. It was found that fuel cell performance improves with increasing values of porosity rate, exchange current density, and pressure drop, and with decreasing both cell length and electrolyte thickness. Furthermore, since the electrolyte's conductivity class is oxygen ion carrier, the effect of parameters at the cathode side was more significant compared to the anode side. The results of this research are consistent with well-documented theories, and it is usable for developing other numerical models.
creator: Ismail Benchebiba
creator: Mohamed Mostefaoui
creator: Ahmed Nour El Islam Ayad
rights: Copyright (c) 2026 Ismail Benchebiba*, Mohamed Mostefaoui, Ahmed Nour El Islam Ayad http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-01-27
publication date: 2026-01-27
ending page: 5812
doi: 10.59429/ace.v9i1.5812

Machine Learning Strategies for Enhancing Syngas Quality, Biofuel Stability, and Air Pollution Control in Bio-Energy Plants
type: item
description: Bio-energy plants play a key role in the transition toward low-carbon energy systems, yet their large-scale deployment is constrained by variability in syngas quality, biofuel instability during storage, and fluctuating air pollutant emissions. This review examines how machine learning (ML) methods support improved decision-making across bio-energy value chains by linking multi-source data with predictive and adaptive control strategies. The study synthesizes recent advances in ML-based modelling for syngas composition prediction, biofuel stability assessment, and real-time emission monitoring and mitigation. Emphasis is placed on uncertainty-aware models and hybrid approaches that combine data-driven learning with process knowledge to address feedstock heterogeneity and dynamic operating conditions. The findings show that ML enhances operational efficiency, supports cleaner production, and improves system reliability by enabling proactive control rather than reactive adjustments. From a sustainability perspective, these outcomes directly contribute to SDG 7 (Affordable and Clean Energy) through higher efficiency and reliability of bio-energy systems, SDG 9 (Industry, Innovation, and Infrastructure) by promoting intelligent and resilient industrial processes, SDG 12 (Responsible Consumption and Production) via optimized resource use and reduced waste, and SDG 13 (Climate Action) through lower emissions and improved carbon performance. Overall, the review highlights ML as a practical decision-support tool for industry and policy stakeholders seeking resilient, data-driven pathways toward sustainable bio-energy deployment.
creator: Sonali Shrikant Patil
creator: Snehal Mayur Banarase
creator: Dinesh Keloth kaithari
creator: N. Bharathiraja
creator: Santosh Bhauso Takale
creator: Pratik V. Lepse
creator: Pushparaj Sunil Warke
creator: Muralidhar Ingale
creator: Anant Sidhappa Kurhade
rights: Copyright (c) 2026 Sonali Shrikant Patil, Snehal Mayur Banarase, Dinesh Keloth kaithari, N. Bharathiraja, Santosh Bhauso Takale, Pratik V. Lepse, Pushparaj Sunil Warke, Muralidhar Ingale Anant Sidhappa Kurhade http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-02-26
publication date: 2026-02-26
ending page: 5880
doi: 10.59429/ace.v9i1.5880

Formulation and Statistical Optimisation of Liposome-Loaded Glimepiride Using Design-Expert® Software
type: item
description: Background: Glimepiride is a popular third-generation sulfonylurea used to treat type 2 diabetes. Its therapeutic effectiveness is, however, limited by its fluctuating bioavailability and poor water solubility. Liposomal drug delivery techniques offer an attractive means of improving the stability, solubility, and bioavailability of poorly soluble drugs like Glimepiride. Objective: This study seeks to develop and statistically optimise Glimepiride-loaded liposomes utilising Design-expert® software as part of an extensive pre-formulation analysis. Method: Liposomes were synthesised utilising the thin-film hydration method, adjusting critical formulation variables like phospholipid content and cholesterol proportion. Utilizing a statistical experimental design methodology, the impact of these factors on key quality measures, including loading capacity (DL%), entrapment efficacy (EE%), zeta potential, particle size, and polydispersity index (PDI), was assessed. The Design-expert ® software facilitated model generation, optimisation, and response surface analysis. Results: A statistical investigation revealed that the formulation factors had a substantial effect on the liposomes' properties. Zeta potential of -28.85 mV, polydispersity index (PDI) of 0.025, particle size of 71 nm, drug loading (DL%) of 7.53%, and entrapment efficiency (EE%) of 35% were all characteristics of the optimized liposomal formulation. The regression models demonstrated robust predictive capability, and the design space was effectively delineated for subsequent formulation development. Conclusion: This preformulation study illustrated the successful application of statistical design in optimising Glimepiride-loaded liposomes. These findings establish a foundation for subsequent studies concerning in vitro release and stability testing in the forthcoming phase of formulation development.
creator: Wafaa Nahi
creator: Sarmad Al-Edresi
creator: Rafid Mohammed Ali Hassan wasfi
rights: Copyright (c) 2026 Wafaa Nahi, Sarmad Al-Edresi, Rafid Mohammed Ali Hassan wasfi http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-02-05
publication date: 2026-02-05
ending page: 5794
doi: 10.59429/ace.v9i1.5794

AI-Supported Forecasting of Biomass Availability under Changing Environmental and Resource Conditions
type: item
description: Reliable forecasting of biomass availability is essential for sustainable bioenergy planning, climate mitigation, and efficient resource management. Biomass production is influenced by complex interactions among climate variability, land use, management practices, and socioeconomic drivers, which limits the effectiveness of conventional empirical and process-based models. This study reviews recent advances in artificial intelligence (AI) and machine learning approaches for biomass availability forecasting under dynamic environmental and resource conditions. Emphasis is placed on models that integrate multi-source data, including remote sensing, field observations, climate records, management inputs, and socioeconomic indicators. The reviewed literature shows that AI-based methods capture nonlinear and spatiotemporal relationships more effectively than traditional approaches, resulting in improved prediction accuracy, scalability, and adaptability across regions. Ensemble, hybrid, and probabilistic frameworks further support uncertainty-aware forecasting, which is critical for policy formulation and industrial decision-making. From a sustainability perspective, AI-supported biomass forecasting contributes directly to several United Nations Sustainable Development Goals, particularly SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action). By supporting informed decision-making, resilient biomass supply chains, and risk-aware planning, AI-based forecasting frameworks provide a practical pathway toward sustainable and climate-resilient bioenergy systems.
creator: Sonali Shrikant Patil
creator: P. Ramani
creator: Snehal Mayur Banarase
creator: Prafulla O. Bagde
creator: Pushparaj Sunil Warke
creator: N. Alangudi Balaji
creator: Muralidhar Ingale
creator: Shital Yashwant Waware
creator: Anant Sidhappa Kurhade
rights: Copyright (c) 2026 Sonali Shrikant Patil, P. Ramani, Snehal Mayur Banarase, Prafulla O. Bagde, Pushparaj Sunil Warke, N. Alangudi Balaji, Muralidhar Ingale, Shital Yashwant Waware, Anant Sidhappa Kurhade http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-02-26
publication date: 2026-02-26
ending page: 5878
doi: 10.59429/ace.v9i1.5878

Machine learning for waste-to-energy processes: Resource evaluation, conversion efficiency, and environmental effects
type: item
description: Waste-to-energy (WtE) technologies are increasingly important for sustainable waste management and circular economy practices, as they enable recovery of energy from municipal, agricultural, and industrial wastes while reducing landfill use and associated emissions. Despite this relevance, existing research on machine learning (ML) applications in WtE systems remains fragmented, with most studies addressing individual processes, specific algorithms, or isolated performance metrics, and lacking an integrated perspective across the full value chain. The objective of this work is to provide a comprehensive review of machine learning applications in WtE systems, covering resource evaluation, conversion efficiency, and environmental effects within a unified framework. The study is based on a systematic analysis of recent peer-reviewed literature reporting experimental validation or applied modeling in incineration, gasification, pyrolysis, and anaerobic digestion processes. The review indicates that machine learning models successfully capture the nonlinear and time-varying behavior of WtE systems, allowing accurate prediction of waste generation and composition, heating value, biogas yield, process efficiency, and pollutant emissions. Tree-based ensembles and neural networks show strong performance in feedstock assessment and conversion modeling, while data-driven soft sensors and surrogate models support real-time emission prediction and life-cycle impact evaluation. These findings demonstrate that machine learning offers practical benefits for improving operational stability, energy recovery, and environmental compliance in WtE plants, while also highlighting persistent challenges related to data quality, model transferability, and interpretability that should guide future research and deployment.
creator: Swapnil S. Chaudhari
creator: Kundan Kale
creator: Manisha Raghuvanshi
creator: Torana Kamble
creator: Ramsing Thakur
creator: Sagar Arjun Dalvi
creator: Prashant Ashok Patil
creator: Shital Yashwant Waware
creator: Anant Sidhappa Kurhade
rights: Copyright (c) 2026 Swapnil S. Chaudhari, Kundan Kale, Manisha Raghuvanshi, Torana Kamble, Ramsing Thakur, Sagar Arjun Dalvi, Prashant Ashok Patil, Shital Yashwant Waware, Anant Sidhappa Kurhade http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-01-06
publication date: 2026-01-06
ending page: 5850
doi: 10.59429/ace.v9i1.5850

Comparative Evaluation of Activated and Non-Activated Sunflower Seed Husks surface for removal of Toxic Industrial Dyes
type: item
description: The adsorption of the potentially toxic industrial dyes, Acid Red 18 (AR18), Acid Yellow 23 (AY23), Reactive Yellow 84 (RY84), and Reactive Black 5 (RB5). was assessed in this work using non-activated and activated sunflower seed husks as environmentally sound adsorbents. Hydrochloric, phosphoric and sulfuric acids activated the sunflower seed husk, and the best chemical activation method was determined by surface morphology and the adsorption performance. The optimal temperature for thermal treatment to convert biomass into activated carbon has been established. The structural and chemical characteristics of the adsorbents were investigated by the characterization techniques such as Field Emission Scanning Electron Microscopy (FESEM), Energy Dispersive X-ray Spectroscopy (EDX), Transmission Electron Microscopy (TEM), and Fourier-Transform Infrared Spectroscopy (FTIR). The percentage removals of Reactive Yellow, Reactive Black 5, Acid Red, and Acid Yellow 23 were determined to be (95.02%, 90.00%, 85.00% and 70.00% for the acid-activated sunflower seed husks. These values were significantly higher than the adsorption capacities of non-activated husks, 80.02%, 75.27%, 55.70%, and 45.00%, respectively, for the corresponding dyes. The findings demonstrate an improvement in the properties of acid-activated sunflower seed husks in the adsorption of dyes (Reactive Black 5 and Acid Red), reflecting the influence of acid activation on expansion of the specific surface area and availability of functional groups. Adsorption efficiency was highest under acidic conditions due to enhanced electrostatic attraction between the adsorbent surface and dye molecules. Reactive dyes showed greater sensitivity to pH variation, with efficiency dropping sharply in alkaline media. In contrast, Acid dyes—particularly Acid Yellow—retained higher performance across the pH range, indicating additional binding mechanisms beyond electrostatic interactions.
creator: Uday Abdul-Reda Hussein
creator: Hayder Hamid Abbas Al-Anbari
creator: Turki Meften Saad
creator: Abed J. Kadhim
creator: Fadhil M. Abid
creator: Aseel M. Aljeboree
creator: Ayad F. Alkaim
rights: Copyright (c) 2025 Uday Abdul-Reda Hussein, Hayder Hamid Abbas Al-Anbari, Abed J. Kadhim, Fadhil M. Abid, Aseel M. Aljeboree, Ayad F. Alkaim http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2025-12-31
publication date: 2025-12-31
ending page: 5781
doi: 10.59429/ace.v9i1.5781

Smart Biorefineries: Machine Learning for Process Control, Resource Utilization, and Emission Monitoring
type: item
description: Bio-refineries are central to achieving a sustainable bioeconomy by converting renewable biomass into fuels, chemicals, and energy, directly supporting global climate and resource-efficiency targets. Their operation remains challenging due to nonlinear process behavior, feedstock variability, and the need to simultaneously address productivity and environmental compliance. While machine learning (ML) has been increasingly applied to individual bio-refinery operations, existing studies lack an integrated perspective that links process control, resource use optimization, and emission monitoring with sustainability objectives defined under the Sustainable Development Goals (SDGs). The objective of this review is to analyze and synthesize recent advances in ML applications for smart bio-refineries, with a focus on improving process efficiency (SDG 9: Industry, Innovation and Infrastructure), reducing energy and water consumption (SDG 7: Affordable and Clean Energy; SDG 6: Clean Water and Sanitation), and minimizing environmental emissions (SDG 12: Responsible Consumption and Production; SDG 13: Climate Action). A structured review methodology was adopted, covering peer-reviewed studies on supervised, unsupervised, and reinforcement learning methods applied to fermentation, thermochemical conversion, heat integration, and emission monitoring. The review shows that ML models, including artificial neural networks, ensemble methods, and hybrid ML–physics frameworks, enhance predictive accuracy, stabilize process operation, and enable proactive emission control. Reported case studies demonstrate measurable reductions in energy demand, improved resource utilization, and better compliance with emission limits. These findings underline the role of ML as a practical enabler of sustainable, low-emission bio-refineries. By linking operational performance with SDG-oriented outcomes, this review provides a clear framework for deploying ML technologies to support environmentally resp…
creator: Sonali Shrikant Patil
creator: Madhuri Karad
creator: S. Manjula Gandhi
creator: Mahesh Ganpat Bhong
creator: Kiran Dattatray Devade
creator: Vishakha Avinash Mahajan
creator: Ghanasham Chandrakant Sarode
creator: Ganesh Patil
creator: Anant Sidhappa Kurhade
rights: Copyright (c) 2026 Sonali Shrikant Patil, Madhuri Karad, S. Manjula Gandhi, Mahesh Ganpat Bhong, Kiran Dattatray Devade, Vishakha Avinash Mahajan, Ghanasham Chandrakant Sarode, Ganesh Patil, Anant Sidhappa Kurhade http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-03-05
publication date: 2026-03-05
ending page: 5874
doi: 10.59429/ace.v9i1.5874

Green synthesis of silver nanoparticles (CC-Ag NPs) utilizing the cymbopogon citratus extract and studying the antibacterial activity
type: item
description: CC-Ag NPs (Silver nanoparticles) were synthesized from the leaf extract of Cymbopogon citratus. The biosynthesized silver nanoparticles were characterized by different analytical techniques. A characteristic absorption peak at 432 nm was observed which confirmed the surface plasmon resonance in silver nanoparticles (AgNPs). FTIR analysis showed the presence of bioactive compounds that take part in reduction and stabilization processes. The results of XRD analysis showed a crystalline phase formation only corresponding to silver nanoparticles. AFM and SEM analyses revealed that most of the particles are spherical with an average particle size of about 40 nm. The biosynthesized silver nanoparticles showed strong antibacterial effects against different pathogenic bacteria that covered both Gram-positive and Gram-negative strains. These included Proteus mirabilis, Bacillus subtilis, Staphylococcus aureus, Escherichia coli, Klebsiella pneumoniae, Vibrio cholerae, Vibrio parahaemolyticus and Salmonella enteritidis. Among all these tested organisms, the maximum zone of inhibition was observed against Vibrio parahaemolyticus with a diameter of 34.67mm. The concentration of the synthesized AgNPs solution used in these experiments was 1 mM.
creator: Sahar A. Mamoori
creator: Ameer S. Muttaleb
creator: Issa Farhan D.
creator: Ehab K. Obaid
creator: Ali Jabbar Radhi
creator: Amjed Mirza Oda
rights: Copyright (c) 2026 Sahar A. Mamoori, Ameer S. Muttaleb, Issa Farhan D., Ehab K. Obaid, Ali Jabbar Radhi, Amjed Mirza Oda* http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-01-26
publication date: 2026-01-26
ending page: 5847
doi: 10.59429/ace.v9i1.5847

Quantitative Chromatographic Analysis of Volatile Compounds Released during Polymer FDM-AM
type: item
description: The release of volatile organic compounds (VOCs) during fused deposition modeling additive manufacturing (FDM-AM) has become a critical concern due to its implications for occupational health, indoor air quality, and material performance. In this study, a quantitative chromatographic analysis was conducted to characterize and evaluate VOC emissions from commonly used thermoplastic filaments during FDM-AM. Gas chromatography coupled with mass spectrometry (GC–MS) was employed to separate and identify volatile fractions, while flame ionization detection (FID) provided quantitative assessment of emission concentrations. Representative results revealed the presence of styrene, ethylbenzene, formaldehyde, acetaldehyde, and other low-molecular-weight aldehydes and ketones, with emission profiles varying significantly across polymer types such as ABS, PLA, and PETG. Peak intensities correlated strongly with extrusion temperature, suggesting that process parameters directly influence VOC release. Comparative analysis indicated that ABS exhibited the highest emission intensity, dominated by aromatic hydrocarbons, while PLA produced lower total VOCs but higher proportions of lactide-derived species. The findings underscore the necessity of systematic monitoring of VOCs in FDM-AM environments and provide quantitative evidence for optimizing process conditions and implementing adequate ventilation systems. This work establishes a framework for linking chromatographic signatures of volatile compounds with material choice and processing parameters, contributing to safer and more sustainable additive manufacturing practices.
creator: Ali Raqee Abdulhadi
creator: Aqeel Al-Hilali
creator: Al-Dily Kareem
creator: Hayder Hamid Abbas Al-Anbari
creator: Salah Abdulhadi Salih
creator: Ammar S. Al Khafaji
creator: Sameh Hussein Hamo
creator: Duha Abed Almuhssen Muzahim
creator: Shurooq Sabah Hussein
rights: Copyright (c) 2025 Ali Raqee Abdulhadi, Aqeel Al-Hilali, Al-Dily Kareem, Hayder Hamid Abbas Al-Anbari, Salah Abdulhadi Salih, Ammar S. Al Khafaji, Sameh Hussein Hamo, Duha Abed Almuhssen Muzahim, Shurooq Sabah Hussein http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
ending page: 5753
doi: 10.59429/ace.v9i1.5753

Spectrophotometric Determination of Iron (III) in Anti-Anemia Drugs and Biological Evaluation of HMNTDABA as an Anticancer Agent
type: item
description: A sensitive and accurate spectrophotometric method was developed for the determination of iron (III) using the azo reagent 2-hydroxy-5-(5-(4-methoxy-3-nitrophenyl)-1,3,4-thiadiazol-2-yl) diazenyl) benzoic acid (HMNTDABA). The reagent was prepared and characterized using UV-Visible spectroscopy, Fourier transform infrared spectroscopy (FT-IR), 1H-NMR spectroscopy, X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and elemental analysis (CHNS). The complexation reaction between HMNTDABA and Fe(III) proceeds instantaneously at an optimal wavelength of 485 nm and pH 3.5, yielding a stable yellow complex with a detection limit of 0.104 ppm. The proposed method exhibited excellent precision (S.D. = 0.0012, R.S.D. = 0.17%) and a recovery rate of 97.36%. The stability constant for the 1:2 metal-to-reagent complex was calculated to be 15 × 108 L2.mol−2, with linearity observed across the concentration range of 1–40 ppm. Additionally, the biological activity of HMNTDABA was evaluated against MCF-7 breast cancer cells, revealing concentration-dependent cytotoxic effects and characteristic apoptotic morphological changes. The proposed spectrophotometric method was successfully applied for the quantitative determination of iron(III) in commercial pharmaceutical formulations, including Ferro Strong syrup and Supervit tablets, with results comparable to those obtained by atomic absorption spectroscopy.
creator: Ayah Kamil Shanbir
creator: Shaimaa Mohsen Essa
rights: Copyright (c) 2026 Ayah Kamil Shanbir, Shaimaa Mohsen Essa http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-02-02
publication date: 2026-02-02
ending page: 5872
doi: 10.59429/ace.v9i1.5872

Toward a conceptual framework for digital transformation in chemical engineering education: The role of MOOCs in fostering intercultural communicative competence
type: item
description: The creation, delivery, and dissemination of chemical engineering knowledge in a globally interconnected setting are being redefined by the digital transformation of higher education. Massive Open Online Courses (MOOCs) have emerged as important venues for increasing access to engineering education while fostering interdisciplinary cooperation and cross-cultural learning. In order to investigate how MOOCs promote digital transformation and Intercultural Communicative Competence (ICC) in chemical engineering education, this study systematically reviews 24 peer-reviewed articles (2020-2025) using the PRISMA methodology. According to research, MOOCs foster educational innovation by incorporating flipped learning, simulation-based experimentation, and group projects that improve students' technical and intercultural competencies. In order to solve sustainability and process-design issues, they also enable interdisciplinary integration by linking chemical, computational, and environmental disciplines. However, enduring obstacles include unequal access to technology, a lack of established ICC evaluation tools, and poor cultural contextualisation limit wider impact. This review offers a conceptual framework for a Global Chemical Engineering Education Ecosystem based on constructivist, connectivist, and intercultural communication theories. MOOCs are positioned as socio-technical environments that connect digital, interdisciplinary, and intercultural learning processes. The study comes to the conclusion that pedagogical intentionality, institutional backing, and inclusive digital infrastructures that train internationally competent and morally conscious engineers are necessary for MOOCs to have their revolutionary potential.
creator: Dejuan Mao
creator: Fathiyah Mohd Kamaruzaman
creator: Ahmad Zamri Mansor
rights: Copyright (c) 2026 Dejuan Mao, Fathiyah Mohd Kamaruzaman*, Ahmad Zamri Mansor http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-01-05
publication date: 2026-01-05
ending page: 5822
doi: 10.59429/ace.v9i1.5822

A review of the past decade of Azlactones: Synthesis, Reactions, Biomedical and Pharmaceutical applications
type: item
description: Azlactones (2,4-disubstituted oxazol-5-ones) are pivotal building blocks in pharmaceutical design due to their dual electrophilic-nucleophilic reactivity and structural plasticity. Their strained ring system enables diverse ring-opening pathways, facilitating rapid construction of peptidomimetics and bioactive conjugates. Recent studies demonstrate potent antibacterial and antifungal properties such as pyridinyl-functionalized azlactones exhibit efficacy against MRSA (MIC = 0.78 µg/mL), while azlactone-triazole hybrids inhibit Candida auris (MIC = 1.56 µg/mL) and Aspergillus fumigatus via ergosterol synthesis disruption. Additionally, spiro-azlactones show anticancer potential against MCF-7 and A549 cell lines through DNA interaction, tubulin polymerization inhibition, and cell cycle disruption. The spirocyclic structure enhances selectivity, target affinity, and metabolic stability.
creator: Mohammed R. Mohammed
creator: Zubaida khaddar Elias
creator: Shakir Mahmood Saied
creator: Mohanad Y. Saleh
rights: Copyright (c) 2026 Mohammed R. Mohammed, Zubaida khaddar Elias2, Shakir Mahmood Saied, Mohanad Y. Saleh http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-03-10
publication date: 2026-03-10
ending page: 5852
doi: 10.59429/ace.v9i1.5852