Pub Date : 2026-01-28DOI: 10.1021/acs.iecr.5c03017
Diego Balthasar Renner, , , Mengyao Li*, , , Joao Alcantara, , , Nuno Buxo Carinhas, , and , David Garcia,
Ensuring the quality and consistency of cell culture media is essential in biopharmaceutical manufacturing. This study investigates the application of inline Raman spectroscopy combined with machine learning algorithms for real-time characterization and release of cell culture media compositions. Raman spectroscopy, known for its ability to provide detailed molecular fingerprints through inelastic scattering, enables the noninvasive identification and quantification of Raman-active media components and the indirect estimation of certain non-Raman-active quality markers via correlation-based models. Our methodology involved the collection of Raman spectra from media mixtures with varying compositions, systematically altered through two experimental designs. These spectra were preprocessed and used to train Artificial Neural Networks (ANNs), which accurately predicted critical media markers based on both direct Raman signals and indirect correlations with Raman-detectable species, achieving R2 values of 0.988 (glucose), 0.985 (glutamine), 0.994 (osmolality), 0.994 (potassium), and 0.975 (sodium). Subsequently, K-Nearest Neighbors (KNN) models were employed to classify the media based on solution composition ranges. The KNN models achieved approximately 90% accuracy in classifying solution ranges, showcasing the potential of this combined approach for inline, real-time quality control of continous media preparations. This study underscores the effectiveness of integrating Raman spectroscopy and machine learning models within the Process Analytical Technology (PAT) framework to enhance media release and quality assurance in biopharmaceutical manufacturing.
{"title":"Cell Culture Media Release Using Inline Raman Spectroscopy and Artificial Neural Networks","authors":"Diego Balthasar Renner, , , Mengyao Li*, , , Joao Alcantara, , , Nuno Buxo Carinhas, , and , David Garcia, ","doi":"10.1021/acs.iecr.5c03017","DOIUrl":"10.1021/acs.iecr.5c03017","url":null,"abstract":"<p >Ensuring the quality and consistency of cell culture media is essential in biopharmaceutical manufacturing. This study investigates the application of inline Raman spectroscopy combined with machine learning algorithms for real-time characterization and release of cell culture media compositions. Raman spectroscopy, known for its ability to provide detailed molecular fingerprints through inelastic scattering, enables the noninvasive identification and quantification of Raman-active media components and the indirect estimation of certain non-Raman-active quality markers via correlation-based models. Our methodology involved the collection of Raman spectra from media mixtures with varying compositions, systematically altered through two experimental designs. These spectra were preprocessed and used to train Artificial Neural Networks (ANNs), which accurately predicted critical media markers based on both direct Raman signals and indirect correlations with Raman-detectable species, achieving <i>R</i><sup>2</sup> values of 0.988 (glucose), 0.985 (glutamine), 0.994 (osmolality), 0.994 (potassium), and 0.975 (sodium). Subsequently, K-Nearest Neighbors (KNN) models were employed to classify the media based on solution composition ranges. The KNN models achieved approximately 90% accuracy in classifying solution ranges, showcasing the potential of this combined approach for inline, real-time quality control of continous media preparations. This study underscores the effectiveness of integrating Raman spectroscopy and machine learning models within the Process Analytical Technology (PAT) framework to enhance media release and quality assurance in biopharmaceutical manufacturing.</p>","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"65 5","pages":"2502–2510"},"PeriodicalIF":3.9,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
5-Hydroxymethylfurfural (HMF) can be efficiently converted into valuable chemicals, such as 2,5-furandicarboxylic acid (FDCA), through controlled electrocatalytic processes. Electrocatalytic synthesis in flow electrolytic cells has become particularly promising for industrial-scale applications. This study developed a self-supported Ni(OH)2/NF catalyst fabricated on nickel foam via an acid-etching approach for continuous-flow HMF oxidation. Optimal performance was achieved at an electrolyte flow rate of 160 mL min–1, an applied potential of 1.65 V (vs RHE), a HMF concentration of 10 mmol L–1, and a temperature of 25 °C. The catalyst exhibited robust activity, achieving 70% HMF conversion with 40% FDCA yield per cycle. Over four consecutive 40 h cycles, the system produced a total of 0.51 g of FDCA, demonstrating the viability of electrocatalytic approaches for sustainable biomass conversion.
5-羟甲基糠醛（HMF）可以通过可控的电催化过程有效地转化为有价值的化学物质，如2,5-呋喃二羧酸（FDCA）。流动电解池中的电催化合成已成为工业规模应用的特别有前途的方法。本研究采用酸蚀法在泡沫镍上制备了一种用于连续流氧化HMF的自支撑Ni(OH)2/NF催化剂。在电解液流速为160 mL min-1、电压为1.65 V （vs RHE）、HMF浓度为10 mmol L-1、温度为25℃的条件下，获得了最佳性能。催化剂表现出强劲的活性，每循环可实现70%的HMF转化率和40%的FDCA收率。在连续四个40小时的循环中，该系统共产生0.51 g FDCA，证明了电催化方法可持续生物质转化的可行性。
{"title":"Investigation on the Electrocatalytic Oxidation of 5-Hydroxymethylfurfural in a Flow Electrolytic Cell Using Ni(OH)2/NF","authors":"YunYing Huo, , , Guang Pan, , , Yongle Zhang, , , Qiao Zhang*, , , Zhiting Liu, , , Guangxing Yang, , and , Feng Peng*, ","doi":"10.1021/acs.iecr.5c04331","DOIUrl":"10.1021/acs.iecr.5c04331","url":null,"abstract":"<p >5-Hydroxymethylfurfural (HMF) can be efficiently converted into valuable chemicals, such as 2,5-furandicarboxylic acid (FDCA), through controlled electrocatalytic processes. Electrocatalytic synthesis in flow electrolytic cells has become particularly promising for industrial-scale applications. This study developed a self-supported Ni(OH)<sub>2</sub>/NF catalyst fabricated on nickel foam via an acid-etching approach for continuous-flow HMF oxidation. Optimal performance was achieved at an electrolyte flow rate of 160 mL min<sup>–1</sup>, an applied potential of 1.65 V (vs RHE), a HMF concentration of 10 mmol L<sup>–1</sup>, and a temperature of 25 °C. The catalyst exhibited robust activity, achieving 70% HMF conversion with 40% FDCA yield per cycle. Over four consecutive 40 h cycles, the system produced a total of 0.51 g of FDCA, demonstrating the viability of electrocatalytic approaches for sustainable biomass conversion.</p>","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"65 5","pages":"2577–2586"},"PeriodicalIF":3.9,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multi-ingredient cleansing foams pose a combinatorial design challenge because many component ratios must be experimentally screened. We integrate DPD-derived descriptors with machine learning to enable prescreening and prioritization of formulations, thereby reducing exploratory batches and accelerating design cycles while maintaining a traceable physical rationale. The modeling descriptors─the hydrophilic fraction (fHI) and the solubility parameter contrast (Δδ) defined relative to the polyethylene glycol thresholds─probe amphiphilicity, while DPD-derived potential energy and pressure summarize mesoscale self-assembly. Using 430 historical recipes, we benchmark nested cross-validation under three generalization regimes: Points Out (random formulations), Mixtures Out (novel combinations of known ingredients), and Compounds Out (novel raw ingredients; polyols, including humectants and amphiphilic derivatives, in this data set). To prevent leakage, all preprocessing (imputation and scaling) is fit strictly on training folds only, and identical outer-CV partitions are held across feature conditions to enable paired comparisons. Incorporating modeling and simulation descriptors improves mean R2 from 0.665 to 0.716 (Points Out) and from 0.420 to 0.573 (Mixtures Out), and raises Compounds Out R2 from 0.023 to 0.341. Paired difference tests with HC3-robust OLS and Holm correction confirm statistically significant gains─small to moderate for Points Out and moderate to large for Mixtures and Compounds Out. Among algorithms, tree-based ensembles outperform linear, kernel, and neural baselines, reflecting nonlinear composition–property relations. This workflow operationalizes AI-assisted formulation design by triaging candidate recipes prior to wet-lab screening, enabling faster decision-making and tangible experimental savings while retaining physical interpretability via DPD-derived descriptors. Compounds out results apply only to polyols in the present data set; generalization beyond polyols is out-of-scope and will require larger, more diverse data sets and transfer learning.
{"title":"In Silico Prediction of Multicomponent Functional Material Formulations via Machine Learning Coupled with Molecular Simulation: A Case Study on Cleansing Foam Formulations","authors":"Masugu Hamaguchi, Takahiro Yokoyama, Hideki Miwake, Ryoichi Nakatake, Noriyoshi Arai","doi":"10.1021/acs.iecr.5c03748","DOIUrl":"https://doi.org/10.1021/acs.iecr.5c03748","url":null,"abstract":"Multi-ingredient cleansing foams pose a combinatorial design challenge because many component ratios must be experimentally screened. We integrate DPD-derived descriptors with machine learning to enable prescreening and prioritization of formulations, thereby reducing exploratory batches and accelerating design cycles while maintaining a traceable physical rationale. The modeling descriptors─the hydrophilic fraction (<i>f</i><sub>HI</sub>) and the solubility parameter contrast (Δδ) defined relative to the polyethylene glycol thresholds─probe amphiphilicity, while DPD-derived potential energy and pressure summarize mesoscale self-assembly. Using 430 historical recipes, we benchmark nested cross-validation under three generalization regimes: Points Out (random formulations), Mixtures Out (novel combinations of known ingredients), and Compounds Out (novel raw ingredients; polyols, including humectants and amphiphilic derivatives, in this data set). To prevent leakage, all preprocessing (imputation and scaling) is fit strictly on training folds only, and identical outer-CV partitions are held across feature conditions to enable paired comparisons. Incorporating modeling and simulation descriptors improves mean <i>R</i><sup>2</sup> from 0.665 to 0.716 (Points Out) and from 0.420 to 0.573 (Mixtures Out), and raises Compounds Out <i>R</i><sup>2</sup> from 0.023 to 0.341. Paired difference tests with HC3-robust OLS and Holm correction confirm statistically significant gains─small to moderate for Points Out and moderate to large for Mixtures and Compounds Out. Among algorithms, tree-based ensembles outperform linear, kernel, and neural baselines, reflecting nonlinear composition–property relations. This workflow operationalizes AI-assisted formulation design by triaging candidate recipes prior to wet-lab screening, enabling faster decision-making and tangible experimental savings while retaining physical interpretability via DPD-derived descriptors. Compounds out results apply only to polyols in the present data set; generalization beyond polyols is out-of-scope and will require larger, more diverse data sets and transfer learning.","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"31 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146056949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-28DOI: 10.1021/acs.iecr.5c03881
Najam Us Sahar Riyaz, Ahmed Ben Ali, Mazen Khaled, Ibnelwaleed Hussein, Saeed Al-Meer
Creating environmentally friendly corrosion inhibitors is vital for sustainable operations in the oil and gas industry. This study presents an integrated green chemistry and deep learning approach to design and test chitosan-grafted polyacrylamide (CsAM) as a biodegradable corrosion inhibitor for carbon steel in CO2-rich environments. A graph convolutional network, trained on a curated data set of over 70 inhibitors, predicted that CsAM could achieve approximately 84% inhibition at 200 ppm, guiding experimental efforts. Four CsAM formulations with different chitosan-to-polyacrylamide ratios were synthesized and characterized by FTIR, SEM, and contact angle analysis to demonstrate beneficial functional and surface-active properties. Electrochemical tests showed that the 1:30 CsAM ratio achieved an impressive 98% inhibition efficiency, acting as a mixed-type inhibitor with physisorption as the main adsorption mechanism. These findings demonstrate that combining AI-based molecular prediction with sustainable polymer synthesis can significantly accelerate the development of effective green inhibitors while lowering reliance on toxic alternatives. The proposed computational–experimental approach offers a scalable pathway to creating high-performance, environmentally friendly corrosion control solutions for industry use.
{"title":"Chitosan-Derived Green Corrosion Inhibitor for Carbon Steel in CO2-Saturated Media: A Graph Convolutional Network Approach","authors":"Najam Us Sahar Riyaz, Ahmed Ben Ali, Mazen Khaled, Ibnelwaleed Hussein, Saeed Al-Meer","doi":"10.1021/acs.iecr.5c03881","DOIUrl":"https://doi.org/10.1021/acs.iecr.5c03881","url":null,"abstract":"Creating environmentally friendly corrosion inhibitors is vital for sustainable operations in the oil and gas industry. This study presents an integrated green chemistry and deep learning approach to design and test chitosan-grafted polyacrylamide (CsAM) as a biodegradable corrosion inhibitor for carbon steel in CO<sub>2</sub>-rich environments. A graph convolutional network, trained on a curated data set of over 70 inhibitors, predicted that CsAM could achieve approximately 84% inhibition at 200 ppm, guiding experimental efforts. Four CsAM formulations with different chitosan-to-polyacrylamide ratios were synthesized and characterized by FTIR, SEM, and contact angle analysis to demonstrate beneficial functional and surface-active properties. Electrochemical tests showed that the 1:30 CsAM ratio achieved an impressive 98% inhibition efficiency, acting as a mixed-type inhibitor with physisorption as the main adsorption mechanism. These findings demonstrate that combining AI-based molecular prediction with sustainable polymer synthesis can significantly accelerate the development of effective green inhibitors while lowering reliance on toxic alternatives. The proposed computational–experimental approach offers a scalable pathway to creating high-performance, environmentally friendly corrosion control solutions for industry use.","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"7 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The catalytic synthesis of N,N′-dialkylureas from CO2 and amines offers a sustainable route for carbon utilization yet remains constrained by inefficient catalysis. Herein, a K+-doped CoAl-layered double oxide (K(0.3)-CoAl-LDO) is presented, which addresses this challenge through a pronounced photothermal effect. K+ doping triggers a charge compensation mechanism (2K+ → 2M3+ + Ov), generating abundant oxygen vacancies within an expanded lattice. These vacancies serve as Lewis bases for CO2 activation, while adjacent Co3+ cations function as Lewis acids for amine adsorption, establishing synergistic active sites that collectively lower the reaction energy barrier. Simultaneously, K+ doping narrows the bandgap to 1.73 eV and enhances charge separation, improving light absorption and electron transfer. As a result, K(0.3)-CoAl-LDO achieves a 68.81% yield of N,N′-dibutylurea under mild conditions (110 °C, 1.0 MPa)─a 2.4-fold increase over the undoped analogue. By using PEG 400 as a reaction-promoting medium, the urea yield can be further increased to 98.41%. The catalyst also demonstrates broad substrate generality and retains a high stability over five cycles. This work establishes a generalizable doping strategy for precisely engineering defect sites and acid–base pairs in layered oxides, providing a powerful blueprint for the rational design of advanced photothermal catalysts for efficient CO2 conversion under mild conditions.
{"title":"Boosting Photothermal Synergistic Catalysis of Amine Carbonylation with CO2 over K-Promoted Spinel Co2AlO4 Nanosheets from Hydrotalcite","authors":"Dalei Sun, Guoliang Lu, Hongyu Li, Fengjing Wu, Zhi-Wu Liang","doi":"10.1021/acs.iecr.5c04693","DOIUrl":"https://doi.org/10.1021/acs.iecr.5c04693","url":null,"abstract":"The catalytic synthesis of <i>N</i>,<i>N</i>′-dialkylureas from CO<sub>2</sub> and amines offers a sustainable route for carbon utilization yet remains constrained by inefficient catalysis. Herein, a K<sup>+</sup>-doped CoAl-layered double oxide (K(0.3)-CoAl-LDO) is presented, which addresses this challenge through a pronounced photothermal effect. K<sup>+</sup> doping triggers a charge compensation mechanism (2K<sup>+</sup> → 2M<sup>3+</sup> + O<sub>v</sub>), generating abundant oxygen vacancies within an expanded lattice. These vacancies serve as Lewis bases for CO<sub>2</sub> activation, while adjacent Co<sup>3+</sup> cations function as Lewis acids for amine adsorption, establishing synergistic active sites that collectively lower the reaction energy barrier. Simultaneously, K<sup>+</sup> doping narrows the bandgap to 1.73 eV and enhances charge separation, improving light absorption and electron transfer. As a result, K(0.3)-CoAl-LDO achieves a 68.81% yield of <i>N</i>,<i>N</i>′-dibutylurea under mild conditions (110 °C, 1.0 MPa)─a 2.4-fold increase over the undoped analogue. By using PEG 400 as a reaction-promoting medium, the urea yield can be further increased to 98.41%. The catalyst also demonstrates broad substrate generality and retains a high stability over five cycles. This work establishes a generalizable doping strategy for precisely engineering defect sites and acid–base pairs in layered oxides, providing a powerful blueprint for the rational design of advanced photothermal catalysts for efficient CO<sub>2</sub> conversion under mild conditions.","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"14 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ring-opening polymerization (ROP) of ε-caprolactone (ε-CL) provides an efficient route to synthesize the widely used biodegradable polymer poly(ε-caprolactone) (PCL). Compared to homogeneous catalysts, heterogeneous double metal cyanide (DMC) catalysts offer the advantages of easy separation and recyclability, thereby improving product purity for the polymer industry. In this work, Zn/Co DMC catalysts are synthesized from cobalt cyanic acid (H3[Co(CN)6]) and zinc 2-ethylhexanoate (Zn(EH)2) using methanol as a solvent. The structure and composition of the prepared DMC catalyst are determined with comprehensive characterizations (e.g., ICP, elemental analysis, FTIR, TGA, XRD, and XPS). Kinetic studies of ROP of ε-CL catalyzed by the prepared Zn/Co DMC catalysts with and without an external initiator are systemically investigated, and the corresponding kinetic equations are developed as well. Results show that coordinated methanol exclusively initiates polymerization without an external initiator. Adding an external benzyl alcohol initiator or increasing catalyst loading accelerates polymerization but reduces the average molar mass of the resulting polymers. Finally, by integrating structural features with polymerization kinetics, a reaction mechanism for DMC-catalyzed ε-CL ROP is proposed. This mechanism delineates the functional role of each component, establishing a theoretical framework for advancing DMC catalyst applications.
{"title":"Ring-Opening Polymerization of ε-Caprolactone Catalyzed by Zn/Co Double Metal Cyanide Catalysts: The Vital Role of Coordinated Methanol","authors":"Wei-Dong Fu, , , Jin-Jin Li*, , , Qi-Lin Li, , , Jie Jiang, , , Ling Zhao, , and , Zhenhao Xi*, ","doi":"10.1021/acs.iecr.5c04095","DOIUrl":"10.1021/acs.iecr.5c04095","url":null,"abstract":"<p >Ring-opening polymerization (ROP) of ε-caprolactone (ε-CL) provides an efficient route to synthesize the widely used biodegradable polymer poly(ε-caprolactone) (PCL). Compared to homogeneous catalysts, heterogeneous double metal cyanide (DMC) catalysts offer the advantages of easy separation and recyclability, thereby improving product purity for the polymer industry. In this work, Zn/Co DMC catalysts are synthesized from cobalt cyanic acid (H<sub>3</sub>[Co(CN)<sub>6</sub>]) and zinc 2-ethylhexanoate (Zn(EH)<sub>2</sub>) using methanol as a solvent. The structure and composition of the prepared DMC catalyst are determined with comprehensive characterizations (e.g., ICP, elemental analysis, FTIR, TGA, XRD, and XPS). Kinetic studies of ROP of ε-CL catalyzed by the prepared Zn/Co DMC catalysts with and without an external initiator are systemically investigated, and the corresponding kinetic equations are developed as well. Results show that coordinated methanol exclusively initiates polymerization without an external initiator. Adding an external benzyl alcohol initiator or increasing catalyst loading accelerates polymerization but reduces the average molar mass of the resulting polymers. Finally, by integrating structural features with polymerization kinetics, a reaction mechanism for DMC-catalyzed ε-CL ROP is proposed. This mechanism delineates the functional role of each component, establishing a theoretical framework for advancing DMC catalyst applications.</p>","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"65 5","pages":"2543–2553"},"PeriodicalIF":3.9,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-28DOI: 10.1021/acs.iecr.5c05138
Leibing Chen, , , Kairui Li, , , Jing Li*, , , Xinwei Du, , and , Haisheng Wei*,
The development of highly dispersed supported noble-metal catalysts is crucial for maximizing atomic utilization and enhancing catalytic performance. This work demonstrates a highly efficient Au–Pt bimetallic catalyst supported on 2-methylimidazole-modified ZnO (N-ZnO) for the chemoselective hydrogenation of nitroarenes. The modification creates strong anchoring sites for metal precursors, which, as confirmed by DFT calculations, effectively suppress metal aggregation and yield highly dispersed nanoparticles with an average size of 2.9 nm. The resulting Au–Pt/N-ZnO catalyst exhibits exceptional performance in the hydrogenation of p-chloronitrobenzene under mild conditions (50 °C and 0.5 MPa H2), achieving >99% conversion and 98.6% selectivity to p-chloroaniline, significantly outperforming its monometallic counterparts due to the synergistic effect. The catalyst also exhibited excellent recyclability and broad substrate applicability for various substituted nitroarenes. This work provides an effective strategy for fabricating highly efficient supported bimetallic catalysts through the organic ligand-mediated surface modification of metal oxide supports.
{"title":"Surface Engineering of ZnO with 2-Methylimidazole for Highly Dispersed Au–Pt Nanoparticles and Enhanced Hydrogenation Catalysis","authors":"Leibing Chen, , , Kairui Li, , , Jing Li*, , , Xinwei Du, , and , Haisheng Wei*, ","doi":"10.1021/acs.iecr.5c05138","DOIUrl":"10.1021/acs.iecr.5c05138","url":null,"abstract":"<p >The development of highly dispersed supported noble-metal catalysts is crucial for maximizing atomic utilization and enhancing catalytic performance. This work demonstrates a highly efficient Au–Pt bimetallic catalyst supported on 2-methylimidazole-modified ZnO (N-ZnO) for the chemoselective hydrogenation of nitroarenes. The modification creates strong anchoring sites for metal precursors, which, as confirmed by DFT calculations, effectively suppress metal aggregation and yield highly dispersed nanoparticles with an average size of 2.9 nm. The resulting Au–Pt/N-ZnO catalyst exhibits exceptional performance in the hydrogenation of <i>p</i>-chloronitrobenzene under mild conditions (50 °C and 0.5 MPa H<sub>2</sub>), achieving >99% conversion and 98.6% selectivity to <i>p</i>-chloroaniline, significantly outperforming its monometallic counterparts due to the synergistic effect. The catalyst also exhibited excellent recyclability and broad substrate applicability for various substituted nitroarenes. This work provides an effective strategy for fabricating highly efficient supported bimetallic catalysts through the organic ligand-mediated surface modification of metal oxide supports.</p>","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"65 5","pages":"2699–2705"},"PeriodicalIF":3.9,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fe-based Prussian blue analogues (Fe-PBAs) possess a high specific capacity as cathode materials for sodium-ion batteries (SIBs), yet framework instability and inherent Fe(CN)64– defects significantly hamper their practical application. Here, we employ an innovative high-entropy doping strategy to overcome these limitations. By substantially boosting the material’s configurational entropy (to 1.73 R), we dramatically enhanced its electrochemical performance, achieving exceptional full-cell results: 88.8% capacity retention after 1000 cycles at 150 mA g–1. Mössbauer spectroscopy revealed that high-entropy doping effectively regulates the spin state of Fe. ICP-OES analysis confirmed that this strategy significantly reduces Fe(CN)64– defects within the material. In situ XRD demonstrated that the high-entropy structure mitigates volume strain during charging and discharging. Furthermore, density functional theory (DFT) calculations indicated that the high-entropy design strengthens Fe–N bonds and the rigidity of Fe–C bonds, thereby stabilizing the framework structure.
铁基普鲁士蓝类似物（Fe- pbas）作为钠离子电池（sib）正极材料具有很高的比容量，但其结构不稳定性和固有的Fe(CN)64 -缺陷严重阻碍了其实际应用。在这里，我们采用一种创新的高熵掺杂策略来克服这些限制。通过大幅提高材料的构型熵（达到1.73 R），我们显著提高了其电化学性能，实现了出色的全电池结果：在150 mA g-1下循环1000次后，容量保持率为88.8%。Mössbauer光谱分析表明，高熵掺杂有效地调控了Fe的自旋态。ICP-OES分析证实，该策略显著降低了材料中的Fe(CN)64 -缺陷。原位XRD分析表明，高熵结构减轻了充放电过程中的体积应变。此外，密度泛函理论（DFT）计算表明，高熵设计增强了Fe-N键和Fe-C键的刚度，从而稳定了框架结构。
{"title":"Unveiling the Mechanism of High-Entropy Doping in Regulating Fe Spin State, Fe(CN)64– Defects, and Fe–N Bond Strength in Fe-Based Prussian Blue Analogues for Sodium-Ion Batteries","authors":"Yu-Yan Zhou, Hao-Tian Tong, Yan-Jiang Liu, Bing-Hao Wang, Ting-Liang Xie, Zhong-Yuan Huang, Shuang-Feng Yin","doi":"10.1021/acs.iecr.5c04999","DOIUrl":"https://doi.org/10.1021/acs.iecr.5c04999","url":null,"abstract":"Fe-based Prussian blue analogues (Fe-PBAs) possess a high specific capacity as cathode materials for sodium-ion batteries (SIBs), yet framework instability and inherent Fe(CN)<sub>6</sub><sup>4–</sup> defects significantly hamper their practical application. Here, we employ an innovative high-entropy doping strategy to overcome these limitations. By substantially boosting the material’s configurational entropy (to 1.73 R), we dramatically enhanced its electrochemical performance, achieving exceptional full-cell results: 88.8% capacity retention after 1000 cycles at 150 mA g<sup>–1</sup>. Mössbauer spectroscopy revealed that high-entropy doping effectively regulates the spin state of Fe. ICP-OES analysis confirmed that this strategy significantly reduces Fe(CN)<sub>6</sub><sup>4–</sup> defects within the material. In situ XRD demonstrated that the high-entropy structure mitigates volume strain during charging and discharging. Furthermore, density functional theory (DFT) calculations indicated that the high-entropy design strengthens Fe–N bonds and the rigidity of Fe–C bonds, thereby stabilizing the framework structure.","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"30 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146056950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Embedding Si nanoparticles in the graphite matrix to form silicon–carbon composite anodes is an effective approach to enhancing the battery performance of silicon anodes. However, poor adhesion at the graphite–silicon interface fails to fully accommodate silicon’s volume changes during cycling, causing the silicon–carbon composite to crack, consequently resulting in poor cycling stability. Here, we report a green and economical method to prepare nano-Si@carbon/graphite (Si@C/G) anode materials by encapsulating silicon nanoparticles within an industrial lignin-derived carbon shell to form core–shell Si@C nanoparticles, which are then embedded within a commercial graphite matrix to produce the Si@C/G composite. Compared to bare nano-Si, the Si@C nanoparticles exhibit stronger van der Waals interactions with graphite (−30.2 kcal/mol vs −24.7 kcal/mol) and a large interfacial contact area, attributed to efficient π–π stacking between the lignin-derived carbon shell and graphite. Additionally, the average adhesion force between Si@C nanoparticles and graphite (−1.039 ± 0.523 mN/m) is substantially greater than the adhesion force between Si and graphite (−0.369 ± 0.211 mN/m), confirming that the lignin-derived carbon coating dramatically enhances adhesion. This enhanced interface facilitates fast electron transport and contributes to the anode’s excellent mechanical stability. Furthermore, the graphite matrix buffers the overall volume expansion and boosts the conductive performance of the prepared anode. Consequently, the LIB employing the Si@C/G anode delivers 777.4 mAh·g–1 at a high current density of 5.0 A·g–1. The material also shows a notably stable cycling performance, maintaining a capacity of as high as 956 mAh·g–1 after 200 cycles at 1 A·g–1, corresponding to a capacity retention rate exceeding 77%. This study presents an economical strategy to fabricate next-generation Si/C anodes for LIBs while also offering a high-value utilization pathway for industrial lignin.
{"title":"Large-Scale Construction of Multiscale-Structured Nano-Si@C/Graphite Composites toward a High-Stability Lithium Ion Battery Anode","authors":"Yiqiang Sun, Shipeng Chen, Xihong Zu, Leyu Cai, Haiping Guo, Liheng Chen, Qiyu Liu, Jinxin Lin, Xueqing Qiu, Wenli Zhang","doi":"10.1021/acs.iecr.5c03951","DOIUrl":"https://doi.org/10.1021/acs.iecr.5c03951","url":null,"abstract":"Embedding Si nanoparticles in the graphite matrix to form silicon–carbon composite anodes is an effective approach to enhancing the battery performance of silicon anodes. However, poor adhesion at the graphite–silicon interface fails to fully accommodate silicon’s volume changes during cycling, causing the silicon–carbon composite to crack, consequently resulting in poor cycling stability. Here, we report a green and economical method to prepare nano-Si@carbon/graphite (Si@C/G) anode materials by encapsulating silicon nanoparticles within an industrial lignin-derived carbon shell to form core–shell Si@C nanoparticles, which are then embedded within a commercial graphite matrix to produce the Si@C/G composite. Compared to bare nano-Si, the Si@C nanoparticles exhibit stronger van der Waals interactions with graphite (−30.2 kcal/mol vs −24.7 kcal/mol) and a large interfacial contact area, attributed to efficient π–π stacking between the lignin-derived carbon shell and graphite. Additionally, the average adhesion force between Si@C nanoparticles and graphite (−1.039 ± 0.523 mN/m) is substantially greater than the adhesion force between Si and graphite (−0.369 ± 0.211 mN/m), confirming that the lignin-derived carbon coating dramatically enhances adhesion. This enhanced interface facilitates fast electron transport and contributes to the anode’s excellent mechanical stability. Furthermore, the graphite matrix buffers the overall volume expansion and boosts the conductive performance of the prepared anode. Consequently, the LIB employing the Si@C/G anode delivers 777.4 mAh·g<sup>–1</sup> at a high current density of 5.0 A·g<sup>–1</sup>. The material also shows a notably stable cycling performance, maintaining a capacity of as high as 956 mAh·g<sup>–1</sup> after 200 cycles at 1 A·g<sup>–1</sup>, corresponding to a capacity retention rate exceeding 77%. This study presents an economical strategy to fabricate next-generation Si/C anodes for LIBs while also offering a high-value utilization pathway for industrial lignin.","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"117 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Proton exchange membrane fuel cells (PEMFCs) are increasingly valued for their eco-friendly feature. Nevertheless, challenges such as restricted mass transfer and suboptimal water management have hindered its high-current-density performance. This study introduces a new Three-Dimensional Sinusoidal Twisted Flow Field (3D-STFF) for PEMFCs, and its performance is evaluated using computational fluid dynamics (CFD) modeling. The 3D-STFF incorporates a helical architecture that enhances reactant delivery, optimizes water evacuation, and reduces energy losses. Compared with parallel flow fields, CFD results reveal that the 3D-STFF improves the mass transfer and water management in PEMFCs, yielding a 22.6% boost in current density (0.532 A·cm–2) and a 15.0% increase in net power density (0.504 W·cm–2) within the medium-to-high voltage range (0.5–0.8 V), while maintaining a minimal pressure drop of 174.2 Pa at 353 K, 100% relative humidity, and 1 atm. The design ensures superior oxygen distribution with a nonuniformity index of 0.259 and an oxygen molar concentration of 6.45 mol·m–3, effectively mitigating downstream oxygen depletion. The 3D-STFF design generates periodic velocity oscillations (peak at 20.5 m·s–1), fostering enhanced lateral gas diffusion and consistent reactant supply. Additionally, the 3D-STFF demonstrates superior water management compared to other flow fields, reducing liquid accumulation at both the midchannel and outlet, thereby mitigating cathode flooding. The 3D-STFF presents a robust and effective approach to improve PEMFC performance, particularly under high-load operational conditions.
{"title":"A Novel Flow Field Design with Superimposed Vertical and Parallel Twisting for Enhanced PEMFC Performance","authors":"Bo Wang, , , Chuang Li, , , Mingyi Xu, , , Guihua Liu, , , Xiaohang Du*, , and , Jingde Li*, ","doi":"10.1021/acs.iecr.5c04221","DOIUrl":"10.1021/acs.iecr.5c04221","url":null,"abstract":"<p >Proton exchange membrane fuel cells (PEMFCs) are increasingly valued for their eco-friendly feature. Nevertheless, challenges such as restricted mass transfer and suboptimal water management have hindered its high-current-density performance. This study introduces a new Three-Dimensional Sinusoidal Twisted Flow Field (3D-STFF) for PEMFCs, and its performance is evaluated using computational fluid dynamics (CFD) modeling. The 3D-STFF incorporates a helical architecture that enhances reactant delivery, optimizes water evacuation, and reduces energy losses. Compared with parallel flow fields, CFD results reveal that the 3D-STFF improves the mass transfer and water management in PEMFCs, yielding a 22.6% boost in current density (0.532 A·cm<sup>–2</sup>) and a 15.0% increase in net power density (0.504 W·cm<sup>–2</sup>) within the medium-to-high voltage range (0.5–0.8 V), while maintaining a minimal pressure drop of 174.2 Pa at 353 K, 100% relative humidity, and 1 atm. The design ensures superior oxygen distribution with a nonuniformity index of 0.259 and an oxygen molar concentration of 6.45 mol·m<sup>–3</sup>, effectively mitigating downstream oxygen depletion. The 3D-STFF design generates periodic velocity oscillations (peak at 20.5 m·s<sup>–1</sup>), fostering enhanced lateral gas diffusion and consistent reactant supply. Additionally, the 3D-STFF demonstrates superior water management compared to other flow fields, reducing liquid accumulation at both the midchannel and outlet, thereby mitigating cathode flooding. The 3D-STFF presents a robust and effective approach to improve PEMFC performance, particularly under high-load operational conditions.</p>","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"65 5","pages":"2564–2576"},"PeriodicalIF":3.9,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146056952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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