Journal Description
ChemEngineering
ChemEngineering
is an international, peer-reviewed, open access journal on the science and technology of chemical engineering, published bimonthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), Inspec, CAPlus / SciFinder, and other databases.
- Journal Rank: JCR - Q2 (Engineering, Chemical) / CiteScore - Q1 (General Engineering )
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 29.6 days after submission; acceptance to publication is undertaken in 5.7 days (median values for papers published in this journal in the first half of 2025).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
3.4 (2024);
5-Year Impact Factor:
3.1 (2024)
Latest Articles
Research on Deep Separation Technology of Multi–Source By–Products in Coking Coal
ChemEngineering 2025, 9(4), 92; https://doi.org/10.3390/chemengineering9040092 - 18 Aug 2025
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This study proposes considering the effective re–benefication of coal middlings and other such considered waste materials as a way to ensure that clean coal in coal by–products can be extracted and effectively utilized, saving costs and reducing coal waste. To quantify the clean–coal
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This study proposes considering the effective re–benefication of coal middlings and other such considered waste materials as a way to ensure that clean coal in coal by–products can be extracted and effectively utilized, saving costs and reducing coal waste. To quantify the clean–coal yield and ash reduction that can be achieved by re–beneficiating four typical by–product streams from the Guobei Coal Preparation Plant (6 Mt a−1) were used for the study. Coking–coal middlings, flotation tailings, and pressure–filter cakes from preparation plants still contain 30–60% combustible matter. Re–beneficiation techniques have been considered to recover this often-wasted coal, reduce waste rock disposal, and cut greenhouse–gas emissions per ton of clean coal produced. Representative samples (n = 4) were collected, sample size–classified as (fine coal particles ≤0.5 mm and coarse particles ≥) and subjected to (i) magnetite removal, (ii) laboratory froth flotation (diesel 507 g t−1, sec–octanol 103 g t−1), and (iii) fine and large particle density separation at 1.3–1.8 g cm−3 ZnCO3 media. Clean–coal yield and ash were measured for each stream and the coal’s particle liberation was examined by SEM. Crushing, grinding and liberation equipment and techniques that aid in the treatment of coal and the re–beneficiation of coal middlings and tailings. The key findings recorded during the experiment are as follows: Flotation of <0.5 mm fractions delivered 46.9–58.3% clean–coal yield at 10.3–17.0% ash. Density separation of 0.5–1.0 mm middlings peaked at 1.4–1.5 g cm−3, yielding 34.2% clean coal at 15–18.4% ash. Scanning Electron Microscope analysis confirmed partial liberation as results from re–grinding + second flotation which increased yield by a further 8–12%. A calculated theoretical examination of the preliminary cost–benefit analysis indicates ≈36 CNY t−1≈9 million CNY a−1 in saved disposal costs alone. savings in disposal and 0.25 Mt a−1 additional clean coal for the Guobei plant. The research presented in this paper highlights the current work by Anhui University of Science and technology in collaboration with Guobei coal preparation plant and the results therein achieved.
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Open AccessArticle
Exploring the Physicochemical and Toxicological Study of G-Series and A-Series Agents Combining Molecular Dynamics and Quantitative Structure–Activity Relationship
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Michail Chalaris, Antonios Koufou, Sotiria Anastasiou, Pantelis-Alexandros Roupas and Georgios Nikolaou
ChemEngineering 2025, 9(4), 91; https://doi.org/10.3390/chemengineering9040091 - 18 Aug 2025
Abstract
This study explores the physicochemical and toxicological properties of six G-series and A-series chemical warfare agents (Sarin, Soman, Tabun, A230, A232, and A234) using an integrated computational approach combining molecular dynamics (MD) simulations and Quantitative Structure–Activity Relationship (QSAR) modeling. For the A-series nerve
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This study explores the physicochemical and toxicological properties of six G-series and A-series chemical warfare agents (Sarin, Soman, Tabun, A230, A232, and A234) using an integrated computational approach combining molecular dynamics (MD) simulations and Quantitative Structure–Activity Relationship (QSAR) modeling. For the A-series nerve agents, both Ellison–Hoenig and Mirzayanov structural proposals were examined. MD simulations (10 ns, NPT ensemble) provided key thermodynamic properties, including density, molar heat capacity, and diffusivity. Simulated densities for G-agents (e.g., Sarin: 1.09 g/cm3, Soman: 1.03 g/cm3) and A-agents (e.g., A230: 1.608 g/cm3, Ellison–Hoenig model) closely matched experimental data. Heat capacities ranged from 258 to 462 J/mol·K, and self-diffusion coefficients revealed lower mobility for A-agents, especially under the Ellison–Hoenig configurations. QSAR modeling focused on lipophilicity (LogP) and acute toxicity (LD50). Predicted LD50 values ranged from 0.012 to 0.017 mg/kg for G-agents and up to 1.23 mg/kg for A-agents. A-234 showed the highest lipophilicity (LogP = 2.97) and toxicity (LD50 = 0.51 mg/kg) within its group. Additional descriptors, such as molecular weight and polar surface area, supported toxicity predictions. Strong correlations emerged between MD-derived properties and QSAR outputs, validating the integrated approach. The combined use of MD and QSAR techniques provided a comprehensive view of the agents’ environmental behavior and toxicological impact, supporting safer assessment strategies and reinforcing the importance of multidisciplinary modeling for chemical threat mitigation.
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(This article belongs to the Topic Artificial Intelligence and Automation in Chemical Engineering)
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Bio-Based Nanocellulose Piezocatalysts: PH-Neutral Mechanochemical Degradation of Multipollutant Dyes via Ambient Vibration Energy Conversion
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Zhaoning Yang, Zihao Yang, Xiaoxin Shu, Wenshuai Chen, Jiaolong Liu, Keqing Chen and Yanmin Jia
ChemEngineering 2025, 9(4), 90; https://doi.org/10.3390/chemengineering9040090 - 15 Aug 2025
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Piezoelectric catalytic technology has attracted much attention in the field of dye wastewater treatment, in which inorganic piezoelectric materials have been widely studied. Its core mechanism involves utilizing the piezoelectric effect to generate positive and negative charges, which react with oxygen ions and
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Piezoelectric catalytic technology has attracted much attention in the field of dye wastewater treatment, in which inorganic piezoelectric materials have been widely studied. Its core mechanism involves utilizing the piezoelectric effect to generate positive and negative charges, which react with oxygen ions and hydroxyl radicals, respectively, to generate reactive oxygen species to degrade organic pollutants. Currently, while organic piezoelectric catalysts theoretically offer significant advantages such as low cost and high processability, there has been a notable lack of research in this area, which presents an innovative opportunity for the exploration of new organic piezoelectric catalytic materials. In this study, new research using natural nanocellulose (FC) suspension as an efficient organic piezoelectric catalyst is reported for the first time. The experimental results showed that the catalyst exhibited excellent degradation performance for Rhodamine B (RhB), Acid Orange 7 (AO7), and Methyl Orange (MO) under ultrasonic vibration (40 kHz, 200 W): the degradation rates reached 95.4%, 72.4%, and 31.2%, respectively, for 150 min, and the corresponding first-order reaction kinetic constants were 0.0205, 0.00858, and 0.00249 min−1, respectively. It is noteworthy that the RhB solution can achieve the optimal degradation efficiency without adjustment under neutral initial pH conditions, which significantly enhances the practical application feasibility. The experimental results showed that the catalyst, with a measurable piezoelectric coefficient (d33 = 4.4 pm/V), exhibited excellent degradation performance for Rhodamine B (RhB), Acid Orange 7 (AO7), and Methyl Orange (MO) under ultrasonic vibration (40 kHz, 200 W). This organic piezoelectric catalyst, based on renewable biomass, innovatively converts mechanical vibration energy in the environment into the power to degrade pollutants. It not only expands the application boundaries of organic piezoelectric materials but also provides a new solution for sustainable water treatment technology, demonstrating extremely promising application prospects in the field of green and environmentally friendly water treatment.
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Open AccessArticle
Modeling of Temperature and Moisture Dynamics in Corn Storage Silos with and Without Aeration Periods in Three Dimensions
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F. I. Molina-Herrera, H. Jiménez-Islas, M. A. Sandoval-Hernández, N. E. Maldonado-Sierra, C. Domínguez Campos, L. Jarquín Enríquez, F. J. Mondragón Rojas and N. L. Flores-Martínez
ChemEngineering 2025, 9(4), 89; https://doi.org/10.3390/chemengineering9040089 - 15 Aug 2025
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This study analyzes the dynamics of temperature and moisture in a cylindrical silo with a conical roof and floor used for storing corn in the Bajío region of Mexico, considering conditions both with and without aeration. The model incorporates external temperature fluctuations, solar
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This study analyzes the dynamics of temperature and moisture in a cylindrical silo with a conical roof and floor used for storing corn in the Bajío region of Mexico, considering conditions both with and without aeration. The model incorporates external temperature fluctuations, solar radiation, grain moisture equilibrium with air humidity through the sorption isotherm (water activity), and grain respiration to simulate real storage conditions. The model is based on continuity, momentum, energy, and moisture conservation equations in porous media. This model was solved using the finite element method (FEM) to evaluate temperature and interstitial humidity variations during January and May, representing cold and warm environmental conditions, respectively. The simulations show that, without aeration, grain temperature progressively accumulates in the center and bottom region of the silo, reaching critical values for safe storage. In January, the low ambient temperature favors the natural dissipation of heat. In contrast, in May, the combination of high ambient temperatures and solar radiation intensifies thermal accumulation, increasing the risk of grain deterioration. However, implementing aeration periods allowed for a reduction in the silo’s internal temperature, achieving more homogeneous cooling and reducing the threats of mold and insect proliferation. For January, an airflow rate of 0.15 m3/(min·ton) was optimal for maintaining the temperature within the safe storage range (≤17 °C). In contrast, in May, neither this airflow rate nor the accumulation of 120 h of aeration was sufficient to achieve optimal storage temperatures. This indicates that, under warm conditions, the aeration strategy needs to be reconsidered, assessing whether a higher airflow rate, longer periods, or a combination of both could improve heat dissipation. The results also show that interstitial relative humidity remains stable with nocturnal aeration, minimizing moisture absorption in January and preventing excessive drying in May. However, it was identified that aeration period management must be adaptive, taking environmental conditions into account to avoid issues such as re-wetting or excessive grain drying.
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Open AccessArticle
Electrochemical Sensors Based on Track-Etched Membranes for Rare Earth Metal Ion Detection
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Nurdaulet Zhumanazar, Arman B. Yeszhanov, Galina B. Melnikova, Ainash T. Zhumazhanova, Sergei A. Chizhik and Ilya V. Korolkov
ChemEngineering 2025, 9(4), 88; https://doi.org/10.3390/chemengineering9040088 - 15 Aug 2025
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Electrochemical sensors have been developed based on polyethylene terephthalate track-etched membranes (PET TeMs) modified by photograft copolymerization of N-vinylformamide (N-VFA) and trimethylolpropane trimethacrylate (TMPTMA). The modification, structure and properties of the modified PET TeMs were thoroughly characterized using scanning electron microscopy (SEM) and
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Electrochemical sensors have been developed based on polyethylene terephthalate track-etched membranes (PET TeMs) modified by photograft copolymerization of N-vinylformamide (N-VFA) and trimethylolpropane trimethacrylate (TMPTMA). The modification, structure and properties of the modified PET TeMs were thoroughly characterized using scanning electron microscopy (SEM) and atomic force microscopy (AFM), thermogravimetric analysis (TGA), Fourier-transform infrared (FTIR) spectroscopy, gas permeability measurements and contact angle analysis. Optimal membrane modification was achieved using C = 10% (N-VFA), 60 min of UV irradiation and a UV lamp distance of 10 cm. Furthermore, the modified membranes were implemented in a two-electrode configuration for the determination of Eu3+, Gd3+, La3+ and Ce3+ ions via square-wave anodic stripping voltammetry (SW-ASV). The sensors exhibited a linear detection range from 10−7 M to 10−3 M, with limits of detection of 1.0 × 10−6 M (Eu3+), 6.0 × 10−6 M (Gd3+), 2.0 × 10−4 M (La3+) and 2.5 × 10−5 M (Ce3+). The results demonstrated a significant enhancement in electrochemical response due to the grafted PET TeMs-g-N-PVFA-TMPTMA structure, and the sensor showed practical applicability and consistent performance in detecting rare earth ions in tap water.
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Open AccessArticle
Robust Nonlinear Soft Sensor for Online Estimation of Product Compositions in Heat-Integrated Distillation Column
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Nura Musa Tahir, Jie Zhang and Matthew Armstrong
ChemEngineering 2025, 9(4), 87; https://doi.org/10.3390/chemengineering9040087 - 11 Aug 2025
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This paper proposes the development of a robust nonlinear soft sensor for online estimation of product compositions in a Heat-Integrated Distillation Column (HIDiC). Traditional composition analyzers, such as gas chromatographs, are costly and suffer from long measurement delays, making them inefficient for real-time
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This paper proposes the development of a robust nonlinear soft sensor for online estimation of product compositions in a Heat-Integrated Distillation Column (HIDiC). Traditional composition analyzers, such as gas chromatographs, are costly and suffer from long measurement delays, making them inefficient for real-time monitoring and control. To address this, data-driven soft sensors are developed using tray temperature data obtained from a high-fidelity dynamic HIDiC simulation. The study investigates both linear and nonlinear modeling strategies for composition estimation, including principal component regression (PCR), artificial neural networks (ANNs), and, for the first time in HIDiC modeling, a Bidirectional Long Short-Term Memory (BiLSTM) network. The objective is to evaluate the capability of each method for accurate estimation of product compositions in a HIDiC. The results demonstrate that the BiLSTM-based soft sensor significantly outperforms conventional methods and offers strong potential for enhancing real-time composition estimation and control in HIDiC systems.
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Open AccessArticle
A Glycerol Acetylation Study on a Tin Ferrite Nanocatalyst
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Rami Doukeh, Andreea-Cătălina Joe, Ion Onuțu, Iuliana Veronica Ghețiu, Marian Băjan, Gabriel Vasilievici, Dorin Bomboș, Abeer Baioun, Cașen Panaitescu, Ionuț Banu and Romuald Győrgy
ChemEngineering 2025, 9(4), 86; https://doi.org/10.3390/chemengineering9040086 - 8 Aug 2025
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In this study, a novel magnetic nanocatalyst based on tin ferrite (SnFe2O4) was synthesized via a chemical co-precipitation method and thoroughly characterized using XRD, SEM, TGA-DTG, BET, FTIR, and FTIR-pyridine techniques. The catalyst exhibited high crystallinity, a mesoporous structure
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In this study, a novel magnetic nanocatalyst based on tin ferrite (SnFe2O4) was synthesized via a chemical co-precipitation method and thoroughly characterized using XRD, SEM, TGA-DTG, BET, FTIR, and FTIR-pyridine techniques. The catalyst exhibited high crystallinity, a mesoporous structure with a specific surface area of 79.7 m2/g, and well-defined Lewis and Brønsted acid sites. Its catalytic performance was assessed in the esterification of glycerol with acetic acid to produce monoacetin (MAG), diacetin (DAG), and triacetin (TAG). A Box–Behnken experimental design was employed to evaluate the influence of temperature, catalyst loading, and the acetic-acid-to-glycerol molar ratio on product distribution and glycerol conversion. Statistical analysis and regression modeling revealed excellent predictive accuracy (R2 > 0.99), confirming the robustness of the model. Optimal reaction conditions (110 °C, 2 wt.% catalyst, and AA/GLY ratio of 3.6) yielded a maximum glycerol conversion of 93.2% and a combined DAG and TAG yield of ~59.1%. These results demonstrate the high efficiency and selectivity of the synthesized SnFe2O4 catalyst, making it a promising candidate for sustainable glycerol valorization.
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(This article belongs to the Special Issue Catalytic Reactions and Development of (Bio)Chemical Processes for Synthesizing Value Added Compounds)
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Graph Neural Networks for Sustainable Energy: Predicting Adsorption in Aromatic Molecules
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Hasan Imani Parashkooh and Cuiying Jian
ChemEngineering 2025, 9(4), 85; https://doi.org/10.3390/chemengineering9040085 - 6 Aug 2025
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The growing need for rapid screening of adsorption energies in organic materials has driven substantial progress in developing various architectures of equivariant graph neural networks (eGNNs). This advancement has largely been enabled by the availability of extensive Density Functional Theory (DFT)-generated datasets, sufficiently
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The growing need for rapid screening of adsorption energies in organic materials has driven substantial progress in developing various architectures of equivariant graph neural networks (eGNNs). This advancement has largely been enabled by the availability of extensive Density Functional Theory (DFT)-generated datasets, sufficiently large to train complex eGNN models effectively. However, certain material groups with significant industrial relevance, such as aromatic compounds, remain underrepresented in these large datasets. In this work, we aim to bridge the gap between limited, domain-specific DFT datasets and large-scale pretrained eGNNs. Our methodology involves creating a specialized dataset by segregating aromatic compounds after a targeted ensemble extraction process, then fine-tuning a pretrained model via approaches that include full retraining and systematically freezing specific network sections. We demonstrate that these approaches can yield accurate energy and force predictions with minimal domain-specific training data and computation. Additionally, we investigate the effects of augmenting training datasets with chemically related but out-of-domain groups. Our findings indicate that incorporating supplementary data that closely resembles the target domain, even if approximate, would enhance model performance on domain-specific tasks. Furthermore, we systematically freeze different sections of the pretrained models to elucidate the role each component plays during adaptation to new domains, revealing that relearning low-level representations is critical for effective domain transfer. Overall, this study contributes valuable insights and practical guidelines for efficiently adapting deep learning models for accurate adsorption energy predictions, significantly reducing reliance on extensive training datasets.
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Open AccessReview
Ternary Choline Chloride-Based Deep Eutectic Solvents: A Review
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Abdulalim Ibrahim, Marc Mulamba Tshibangu, Christophe Coquelet and Fabienne Espitalier
ChemEngineering 2025, 9(4), 84; https://doi.org/10.3390/chemengineering9040084 - 6 Aug 2025
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Ternary choline chloride-based deep eutectic solvents (TDESs) exhibit unique physicochemical properties, including lower viscosities, lower melting points, higher thermal stabilities, and enhanced solvations compared to binary deep eutectic solvents (BDESs). Although BDESs have been widely studied, the addition of a third component in
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Ternary choline chloride-based deep eutectic solvents (TDESs) exhibit unique physicochemical properties, including lower viscosities, lower melting points, higher thermal stabilities, and enhanced solvations compared to binary deep eutectic solvents (BDESs). Although BDESs have been widely studied, the addition of a third component in TDESs offers opportunities to further optimize their performance. This review aims to evaluate the physicochemical properties of TDESs and highlight their potential applications in sustainable industrial processes compared to BDESs. A comprehensive analysis of the existing literature was conducted, focusing on TDES properties, such as phase behavior, density, viscosity, pH, conductivity, and the effect of water, along with their applications in various fields. TDESs demonstrated superior physicochemical characteristics compared to BDESs, including improved solvation and thermal stability. Their applications in biomass conversion, CO2 capture, heavy oil upgrading, refrigeration gases, and as solvents/catalysts in organic reactions show significant promise for enhancing process efficiency and sustainability. Despite their advantages, TDESs face challenges including limited predictive models, potential instability under certain conditions, and scalability hurdles. Overall, TDESs offer significant potential for advancing sustainable and efficient chemical processes for industrial applications.
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Open AccessArticle
Molecular Dynamics Simulation of PFAS Adsorption on Graphene for Enhanced Water Purification
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Bashar Awawdeh, Matteo D’Alessio, Sasan Nouranian, Ahmed Al-Ostaz, Mine Ucak-Astarlioglu and Hunain Alkhateb
ChemEngineering 2025, 9(4), 83; https://doi.org/10.3390/chemengineering9040083 - 1 Aug 2025
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The contamination of drinking water by per- and polyfluoroalkyl substances (PFASs) presents a global concern due to their extreme persistence, driven by strong C–F bonds. This study investigated the potential of graphene as a filtration material for PFAS removal, focusing on six key
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The contamination of drinking water by per- and polyfluoroalkyl substances (PFASs) presents a global concern due to their extreme persistence, driven by strong C–F bonds. This study investigated the potential of graphene as a filtration material for PFAS removal, focusing on six key compounds regulated by the U.S. EPA: PFOA, PFNA, GenX, PFBS, PFOS, and PFHxS. Using molecular simulations, adsorption energy, diffusion coefficients, and PFAS-to-graphene distances were analyzed. The results showed that adsorption strength increased with molecular weight; PFOS (500 g/mol) exhibited the strongest adsorption (−171 kcal/mol). Compounds with sulfonic acid head groups (e.g., PFOS) had stronger interactions than those with carboxylate groups (e.g., PFNA), highlighting the importance of head group chemistry. Shorter graphene-to-PFAS distances also aligned with higher adsorption energies. PFOS, for example, had the shortest distance at 8.23 Å (head) and 6.15 Å (tail) from graphene. Diffusion coefficients decreased with increasing molecular weight and carbon chain length, with lower molecules like PFBS (four carbon atoms) diffusing more rapidly than heavier ones like PFOS and PFNA. Interestingly, graphene enhanced PFAS mobility in water, likely by disrupting the water structure and lowering intermolecular resistance. These results highlight graphene’s promise as a high-performance material for PFAS removal and future water purification technologies.
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Open AccessArticle
Optimization of Developed TiO2 NWs-Fe2O3 Modified PES Membranes for Efficient NBB Dye Removal
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Mouna Mansor Hussein, Qusay F. Alsalhy, Mohamed Gar Alalm and M. M. El-Halwany
ChemEngineering 2025, 9(4), 82; https://doi.org/10.3390/chemengineering9040082 - 1 Aug 2025
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Current work investigates the fabrication and performance of nanocomposite membranes, modified with varying concentrations of hybrid nanostructures comprising titanium nanowires coated with iron nanoparticles (TiO2 NWs-Fe2O3), for the removal of Naphthol Blue Black (NBB) dye from industrial wastewater.
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Current work investigates the fabrication and performance of nanocomposite membranes, modified with varying concentrations of hybrid nanostructures comprising titanium nanowires coated with iron nanoparticles (TiO2 NWs-Fe2O3), for the removal of Naphthol Blue Black (NBB) dye from industrial wastewater. A series of analytical tools were employed to confirm the successful modification including scanning electron microscopy and EDX analysis, porosity and hydrophilicity measurements, Fourier-transform infrared spectroscopy, and X-Ray Diffraction. The incorporation of TiO2 NWs-Fe2O3 has enhanced membrane performance significantly by increasing the PWF and improving dye retention rates of nanocomposite membranes. At 0.7 g of nanostructure content, the modified membrane (M8) achieved a PWF of 93 L/m2·h and NBB dye rejection of over 98%. The flux recovery ratio (FRR) analysis disclosed improved antifouling properties, with the M8 membrane demonstrating a 73.4% FRR. This study confirms the potential of TiO2 NWs-Fe2O3-modified membranes in enhancing water treatment processes, offering a promising solution for industrial wastewater treatment. These outstanding results highlight the potential of the novel PES-TiO2 NWs-Fe2O3 membranes for dye removal and present adequate guidance for the modification of membrane physical properties in the field of wastewater treatment.
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(This article belongs to the Special Issue New Advances in Chemical Engineering)
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Open AccessArticle
Recyclable Platinum Nanocatalyst for Nitroarene Hydrogenation: Gum Acacia Polymer-Stabilized Pt Nanoparticles with TiO2 Support
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Supriya Prakash, Selvakumar Ponnusamy, Jagadeeswari Rangaraman, Kundana Nakkala and Putrakumar Balla
ChemEngineering 2025, 9(4), 81; https://doi.org/10.3390/chemengineering9040081 - 30 Jul 2025
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Platinum has emerged as an optimal catalyst for the selective hydrogenation of nitroarenes owing to its high hydrogenation activity, selectivity, and stability. In this study, we report the fabrication of platinum nanoparticles stabilized on a composite support consisting of gum acacia polymer (GAP)
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Platinum has emerged as an optimal catalyst for the selective hydrogenation of nitroarenes owing to its high hydrogenation activity, selectivity, and stability. In this study, we report the fabrication of platinum nanoparticles stabilized on a composite support consisting of gum acacia polymer (GAP) and TiO2. It was engineered for the targeted reduction of nitroarenes to arylamines via selective hydrogenation in methanol at ambient temperature. The non-toxic and biocompatible properties of GAP enable it to act as a reducing and stabilizing agent during synthesis. The synthesized nanocatalyst was characterized using X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Morphological and structural analyses revealed that the fabricated catalyst consisted of minuscule Pt nanoparticles integrated within the GAP framework, accompanied by the corresponding TiO2 nanoparticles. Inductively coupled plasma optical emission spectrometry (ICP-OES) was employed to ascertain the Pt content. The mild reaction conditions, decent yields, trouble-free workup, and facile separation of the catalyst make this method a clean and practical alternative to nitroreduction. Selective hydrogenation yielded an average arylamine production of 97.6% over five consecutive cycles, demonstrating the stability of the nanocatalyst without detectable leaching.
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Open AccessArticle
Universal Prediction of CO2 Adsorption on Zeolites Using Machine Learning: A Comparative Analysis with Langmuir Isotherm Models
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Emrah Kirtil
ChemEngineering 2025, 9(4), 80; https://doi.org/10.3390/chemengineering9040080 - 28 Jul 2025
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The global atmospheric concentration of carbon dioxide (CO2) has exceeded 420 ppm. Adsorption-based carbon capture technologies, offer energy-efficient, sustainable solutions. Relying on classical adsorption models like Langmuir to predict CO2 uptake presents limitations due to the need for case-specific parameter
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The global atmospheric concentration of carbon dioxide (CO2) has exceeded 420 ppm. Adsorption-based carbon capture technologies, offer energy-efficient, sustainable solutions. Relying on classical adsorption models like Langmuir to predict CO2 uptake presents limitations due to the need for case-specific parameter fitting. To address this, the present study introduces a universal machine learning (ML) framework using multiple algorithms—Generalized Linear Model (GLM), Feed-forward Multilayer Perceptron (DL), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosted Trees (GBT)—to reliably predict CO2 adsorption capacities across diverse zeolite structures and conditions. By compiling over 5700 experimentally measured adsorption data points from 71 independent studies, this approach systematically incorporates critical factors including pore size, Si/Al ratio, cation type, temperature, and pressure. Rigorous Cross-Validation confirmed superior performance of the GBT model (R2 = 0.936, RMSE = 0.806 mmol/g), outperforming other ML models and providing comparable performance with classical Langmuir model predictions without separate parameter calibration. Feature importance analysis identified pressure, Si/Al ratio, and cation type as dominant influences on adsorption performance. Overall, this ML-driven methodology demonstrates substantial promise for accelerating material discovery, optimization, and practical deployment of zeolite-based CO2 capture technologies.
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Open AccessArticle
Adsorption of Methylene Blue on Metakaolin-Based Geopolymers: A Kinetic and Thermodynamic Investigation
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Maryam Hmoudah, Rosanna Paparo, Michela De Luca, Michele Emanuele Fortunato, Olimpia Tammaro, Serena Esposito, Riccardo Tesser, Martino Di Serio, Claudio Ferone, Giuseppina Roviello, Oreste Tarallo and Vincenzo Russo
ChemEngineering 2025, 9(4), 79; https://doi.org/10.3390/chemengineering9040079 - 25 Jul 2025
Abstract
Metakaolin-based geopolymers with different molar ratios of Si/Al were synthesized and utilized as an efficient adsorbent for the removal of methylene blue (MB) as a model cationic dye from aqueous solution. Various analytical techniques were employed to characterize the synthesized geopolymers. The influence
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Metakaolin-based geopolymers with different molar ratios of Si/Al were synthesized and utilized as an efficient adsorbent for the removal of methylene blue (MB) as a model cationic dye from aqueous solution. Various analytical techniques were employed to characterize the synthesized geopolymers. The influence of the main operation conditions on the adsorption kinetics of MB onto the geopolymer was examined under various operating conditions. Results showed a significant maximum MB adsorption capacity at the temperature of 30 °C for all four types of geopolymers studied (designated as A, B, C, and D) up to 35.3, 23.6, 25.5, and 19.0 mg g−1, respectively. The corresponding order of Si/Al ratio was A < C < B < D. Adsorption kinetics was so fast and reached equilibrium in 10 min, and the experimental results were described using the adsorption dynamic intraparticle model (ADIM). The equilibrium data for MB removal was in agreement with the Langmuir isotherm.
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(This article belongs to the Special Issue New Advances in Chemical Engineering)
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Open AccessArticle
Industry 5.0 and Digital Twins in the Chemical Industry: An Approach to the Golden Batch Concept
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Andrés Redchuk and Federico Walas Mateo
ChemEngineering 2025, 9(4), 78; https://doi.org/10.3390/chemengineering9040078 - 25 Jul 2025
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In the context of industrial digitalization, the Industry 5.0 paradigm introduces digital twins as a cutting-edge solution. This study explores the concept of digital twins and their integration with the Industrial Internet of Things (IIoT), offering insights into how these technologies bring intelligence
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In the context of industrial digitalization, the Industry 5.0 paradigm introduces digital twins as a cutting-edge solution. This study explores the concept of digital twins and their integration with the Industrial Internet of Things (IIoT), offering insights into how these technologies bring intelligence to industrial settings to drive both process optimization and sustainability. Industrial digitalization connects products and processes, boosting the productivity and efficiency of people, facilities, and equipment. These advancements are expected to yield broad economic and environmental benefits. As connected systems continuously generate data, this information becomes a vital asset, but also introduces new challenges for industrial operations. The work presented in this article aims to demonstrate the possibility of generating advanced tools for process optimization. This, which ultimately impacts the environment and empowers people in the processes, is achieved through data integration and the development of a digital twin using open tools such as NodeRed v4.0.9 and Python 3.13.5 frameworks, among others. The article begins with a conceptual analysis of IIoT and digital twin integration and then presents a case study to demonstrate how these technologies support the principles of the Industry 5.0 framework. Specifically, it examines the requirements for applying the golden batch concept within a biological production environment. The goal is to illustrate how digital twins can facilitate the achievement of quality standards while fostering a more sustainable production process. The results from the case study show that biomaterial concentration was optimized by approximately 10%, reducing excess in an initially overdesigned process. In doing so, this paper highlights the potential of digital twins as key enablers of Industry 5.0—enhancing sustainability, empowering operators, and building resilience throughout the value chain.
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Open AccessArticle
Nanoscale Nickel Oxide: Synthesis, Characterization, and Impact on Antibacterial Activity Against Representative Microorganisms
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Daniela Istrate, Mihai Oproescu, Ecaterina Magdalena Modan, Sorin Georgian Moga, Denis Aurelian Negrea and Adriana-Gabriela Schiopu
ChemEngineering 2025, 9(4), 77; https://doi.org/10.3390/chemengineering9040077 - 25 Jul 2025
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Among the various available synthesis approaches, hydrolytic precipitation offers a simple, cost-effective, and scalable route for producing phase-pure NiO with a controlled morphology and crystallite size. However, the influence of calcination temperature on its crystalline phase, particle size, and antimicrobial activity remains an
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Among the various available synthesis approaches, hydrolytic precipitation offers a simple, cost-effective, and scalable route for producing phase-pure NiO with a controlled morphology and crystallite size. However, the influence of calcination temperature on its crystalline phase, particle size, and antimicrobial activity remains an active field of research. This study aims to investigate the structural, morphological, and antibacterial properties of NiO nanoparticles synthesized via hydrolytic methods and thermally treated at different temperatures. XRD data indicate the presence of the hexagonal crystallographic phase of NiO (space group 166: R-3m), a structural variant less commonly reported in the literature, stabilized under mild hydrolytic synthesis conditions. The average crystallite size increases significantly from 4.97 nm at 300 °C to values of ~17.8 nm at 500–700 °C, confirming the development of the crystal lattice. The ATR-FTIR analysis confirms the presence of the characteristic Ni–O band for all samples, positioned between 367 and 383 cm−1, with a reference value of 355 cm−1 for commercial NiO. The displacements and variations in intensity reflect a thermal evolution of the crystalline structure, but also an important influence of the size of the crystallites and the agglomeration state. The results reveal a systematic evolution in particle morphology from porous, flake-like nanostructures at 300 °C to dense, well-faceted polyhedral crystals at 900 °C. With an increasing temperature, particle size increases. EDS spectra confirm the high purity of the NiO phase across all samples. Additionally, the NiO nanoparticles exhibit calcination-temperature-dependent antibacterial activity, with the complete inhibition of Escherichia coli and Enterococcus faecalis observed after 24 h for the sample calcined at 300 °C and over 90% CFU reduction within 4 h. A significant reduction in E. faecalis viability across all samples indicates time- and strain-specific bactericidal effects. Due to its remarkable multifunctionality, NiO has emerged as a strategic nanomaterial in fields ranging from energy storage and catalysis to antimicrobial technologies, where precise control over its structural phase and particle size is essential for optimizing performance.
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Open AccessArticle
Vanillin Quantum–Classical Photodynamics and Photostatic Optical Spectra
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Vladimir Pomogaev and Olga Tchaikovskaya
ChemEngineering 2025, 9(4), 76; https://doi.org/10.3390/chemengineering9040076 - 23 Jul 2025
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Vanillin photoinduced deprotonation was evaluated and analyzed. Vibronic states and transitions were computationally investigated. Optimizations and vertical electron transitions in the gas phase and with the continuum solvation model were computed using the time-dependent density functional theory. Static absorption and emission (photostatic optical)
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Vanillin photoinduced deprotonation was evaluated and analyzed. Vibronic states and transitions were computationally investigated. Optimizations and vertical electron transitions in the gas phase and with the continuum solvation model were computed using the time-dependent density functional theory. Static absorption and emission (photostatic optical) spectra were statistically averaged over the excited instantaneous molecular conformers fluctuating on quantum–classical molecular dynamic trajectories. Photostatic optical spectra were generated using the hybrid quantum–classical molecular dynamics for explicit solvent models. Conical intersection searching and nonadiabatic molecular dynamics simulations defined potential energy surface propagations, intersections, dissipations, and dissociations. The procedure included mixed-reference spin–flip excitations for both procedures and trajectory surface hopping for photodynamics. Insignificant structural deformations vs. hydroxyl bond cleavage followed by deprotonation were demonstrated starting from different initial structural conditions, which included optimized, transition state, and several other important fluctuating configurations in various environments. Vanillin electronic structure changes were illustrated and analyzed at the key points on conical intersection and nonadiabatic molecular dynamics trajectories by investigating molecular orbital symmetry and electron density difference. The hydroxyl group decomposed on transition to a σ-molecular orbital localized on the elongated O–H bond.
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Open AccessArticle
Application of Risk Management in Applied Engineering Projects in a Petrochemical Plant Producing Polyvinyl Chloride in Cartagena, Colombia
by
Juan Pablo Bustamante Visbal, Rodrigo Ortega-Toro and Joaquín Alejandro Hernández Fernández
ChemEngineering 2025, 9(4), 75; https://doi.org/10.3390/chemengineering9040075 - 21 Jul 2025
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Risk management is crucial in engineering projects, especially in highly complex environments like petrochemical plants producing polyvinyl chloride (PVC). This study proposes a tailored risk management model, using analytic hierarchy process (AHP) and linear regression analysis, alongside MS Excel and IBM SPSS®
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Risk management is crucial in engineering projects, especially in highly complex environments like petrochemical plants producing polyvinyl chloride (PVC). This study proposes a tailored risk management model, using analytic hierarchy process (AHP) and linear regression analysis, alongside MS Excel and IBM SPSS® version 23, to identify, assess, and prioritize key risks. Surveys and interviews revealed seven management factors (budget, schedule, safety, productivity, contracting, quality, and environment) and 18 critical risks, including design errors and procurement delays. The model quantifies risk impacts, provides a regression equation for risk classification, and supports effective mitigation strategies. Based on this model, decision-making can be facilitated for the implementation of effective mitigation strategies. It also promotes continuous improvement, optimizing economic resources and minimizing environmental impacts, addressing a research gap in Colombia’s petrochemical sector and paving the way for broader industrial applications.
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Open AccessArticle
The Cascade Transformation of Furfural to Cyclopentanone: A Critical Evaluation Concerning Feasible Process Development
by
Christian A. M. R. van Slagmaat
ChemEngineering 2025, 9(4), 74; https://doi.org/10.3390/chemengineering9040074 - 19 Jul 2025
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Furfural is a fascinating bio-based platform molecule that can be converted into useful cyclic compounds, among others. In this work, the hydrogenative rearrangement-dehydration of furfural towards cyclopentanone using a commercially available Pt/C catalyst was investigated in terms of its reaction performance to assess
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Furfural is a fascinating bio-based platform molecule that can be converted into useful cyclic compounds, among others. In this work, the hydrogenative rearrangement-dehydration of furfural towards cyclopentanone using a commercially available Pt/C catalyst was investigated in terms of its reaction performance to assess its feasibility as an industrial process. However, acquiring an acceptable cyclopentanone yield proved very difficult, and the reaction was constrained by unforeseen parameters, such as the relative liquid volume in the reactor and the substrate concentration. Most strikingly, the sacrificial formation of furanoic oligomers that precipitated onto the catalyst’s surface was a troublesome key factor that mediated the product’s selectivity versus the carbon mass balance. By applying a biphasic water–toluene solvent system, the yield of cyclopentanone was somewhat improved to a middling 59%, while tentatively positive distributions of reaction components over these solvent phases were observed, which could be advantageous for anticipated down-stream processing. Overall, the sheer difficulty of controlling this one-pot cascade transformation towards a satisfactory product output under rather unfavorable reaction parameters renders it unsuitable for industrial process development, and a multi-step procedure for this chemical transformation might be considered instead.
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Fluorescent Moieties Through Alkaline Treatment of Graphene Oxide: A Potential Substitute to Replace CRM in wLEDS
by
Maria Lucia Protopapa, Emiliano Burresi, Martino Palmisano and Emanuela Pesce
ChemEngineering 2025, 9(4), 73; https://doi.org/10.3390/chemengineering9040073 - 18 Jul 2025
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White-light-emitting diodes (wLEDs) are central to next-generation lighting technologies, yet their reliance on critical raw materials (CRMs), such as rare-earth elements, raises concerns regarding sustainability and supply security. In this work, we present a simple, low-cost method to produce photoluminescent carbon-based nanostructures—known as
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White-light-emitting diodes (wLEDs) are central to next-generation lighting technologies, yet their reliance on critical raw materials (CRMs), such as rare-earth elements, raises concerns regarding sustainability and supply security. In this work, we present a simple, low-cost method to produce photoluminescent carbon-based nanostructures—known as oxidative debris (OD)—via alkaline treatment of graphene oxide (GO) using KOH solutions ranging from 0.04 M to 1.78 M. The resulting OD, isolated from the supernatant after acid precipitation, exhibits strong and tunable photoluminescence (PL) across the visible spectrum. Emission peaks shift from blue (~440 nm) to green (~500 nm) and yellow (~565 nm) as a function of treatment conditions, with excitation wavelengths between 300 and 390 nm. Optical, morphological. and compositional analyses were performed using UV-Vis, AFM, FTIR, and Raman spectroscopy, confirming the presence of highly oxidized aromatic domains. The blue-emitting (S2) and green/yellow-emitting (R2) fractions were successfully separated and characterized, demonstrating potential color tuning by adjusting KOH concentration and treatment time. This study highlights the feasibility of reusing GO-derived byproducts as sustainable phosphor alternatives in wLEDs, reducing reliance on CRMs and aligning with green chemistry principles.
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