{"title":"Issue Information ‐ Table of Contents","authors":"","doi":"10.1002/aic.70288","DOIUrl":"https://doi.org/10.1002/aic.70288","url":null,"abstract":"","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"83 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146145994","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}
Zixuan Zhang, Xiaowei Song, Jiaming Li, Yujiao Zeng, Yaling Nie, Min Zhu, Dongyun Lu, Yibo Zhang, Xin Xiao, Jie Li
Black‐box optimization (BBO) involves functions that are unknown, inexact, and/or expensive‐to‐evaluate. Existing BBO algorithms face several challenges, including high computational cost from extensive evaluations, difficulty in handling complex constraints, lacking theoretical convergence guarantees, and/or instability due to large solution quality variation. In this work, a machine learning‐powered feasible path optimization framework (MLFP) is proposed for general BBO problems including complex constraints. An adaptive sampling strategy is first proposed to explore optimal regions and pre‐filter potentially infeasible points to reduce evaluations. Machine learning algorithms are leveraged to develop surrogates of black‐boxes. The feasible path algorithm is employed to accelerate theoretical convergence by updating independent variables rather than all. Computational studies demonstrate MLFP can rapidly and robustly converge around the KKT point, even training surrogates with small datasets. MLFP is superior to the state‐of‐the‐art BBO algorithms, as it stably obtains the same or better solutions with fewer evaluations for benchmark examples.
{"title":"Machine learning‐powered feasible path framework with adaptive sampling for black‐box optimization","authors":"Zixuan Zhang, Xiaowei Song, Jiaming Li, Yujiao Zeng, Yaling Nie, Min Zhu, Dongyun Lu, Yibo Zhang, Xin Xiao, Jie Li","doi":"10.1002/aic.70239","DOIUrl":"https://doi.org/10.1002/aic.70239","url":null,"abstract":"Black‐box optimization (BBO) involves functions that are unknown, inexact, and/or expensive‐to‐evaluate. Existing BBO algorithms face several challenges, including high computational cost from extensive evaluations, difficulty in handling complex constraints, lacking theoretical convergence guarantees, and/or instability due to large solution quality variation. In this work, a machine learning‐powered feasible path optimization framework (MLFP) is proposed for general BBO problems including complex constraints. An adaptive sampling strategy is first proposed to explore optimal regions and pre‐filter potentially infeasible points to reduce evaluations. Machine learning algorithms are leveraged to develop surrogates of black‐boxes. The feasible path algorithm is employed to accelerate theoretical convergence by updating independent variables rather than all. Computational studies demonstrate MLFP can rapidly and robustly converge around the KKT point, even training surrogates with small datasets. MLFP is superior to the state‐of‐the‐art BBO algorithms, as it stably obtains the same or better solutions with fewer evaluations for benchmark examples.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"40 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146145993","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}
Biocatalysis has emerged as a cornerstone of sustainable manufacturing, yet conventional modes are hindered by inherent limitations such as metabolic interference, mass transfer barriers, and instability. This study presented a novel platform using fragmented Gluconobacter oxydans, combining the autonomous cofactor regeneration of whole cells with the superior substrate accessibility of free enzymes. It was observed that subcellular membrane fragments retain dehydrogenase activity and an intact electron transport chain (ETC), with a critical size threshold (37,300–250,000 g centrifugal force) systematically validated for sustaining this function. The fragmented cell system eliminates carbon diversion by decoupling catalysis from central metabolism, achieving near-complete substrate conversion across multiple dehydrogenase substrates. Furthermore, artificial electron transfer experiments confirmed the essential role of ETC coupling in the catalytic mechanism. A fully functional FCM system could serve as a scalable and efficient biocatalytic tool for industrial bioconversion processes.
{"title":"Harnessing fragmented cells as biocatalysts: The critical size for sustaining the integrity of the electron transport chain","authors":"Xia Hua, Wei Hu, Yating Hu, Sang-Hyun Pyo, Yong Xu","doi":"10.1002/aic.70281","DOIUrl":"https://doi.org/10.1002/aic.70281","url":null,"abstract":"Biocatalysis has emerged as a cornerstone of sustainable manufacturing, yet conventional modes are hindered by inherent limitations such as metabolic interference, mass transfer barriers, and instability. This study presented a novel platform using fragmented <i>Gluconobacter oxydans</i>, combining the autonomous cofactor regeneration of whole cells with the superior substrate accessibility of free enzymes. It was observed that subcellular membrane fragments retain dehydrogenase activity and an intact electron transport chain (ETC), with a critical size threshold (37,300–250,000 <i>g</i> centrifugal force) systematically validated for sustaining this function. The fragmented cell system eliminates carbon diversion by decoupling catalysis from central metabolism, achieving near-complete substrate conversion across multiple dehydrogenase substrates. Furthermore, artificial electron transfer experiments confirmed the essential role of ETC coupling in the catalytic mechanism. A fully functional FCM system could serve as a scalable and efficient biocatalytic tool for industrial bioconversion processes.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"45 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135345","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 optically transparent, dynamic repellent coating effectively prevents the wetting of water and oily liquids on surfaces that are highly desirable but still challenging to integrate the characteristics of environmental friendliness and application adaptability. In this study, an environmentally friendly process combining silicone‐modified acrylate emulsion polymerization with silica sol was employed to develop an organic–inorganic dual‐continuous water‐based coating. The coating demonstrates anti‐smudge, high transparency, and cooling functionalities. It ensures that various liquids slide off the surface without leaving residues, showcasing superior anti‐smudge performance. Additionally, it exhibits excellent durability, chemical stability, and compatibility with diverse substrates. Based on molecular infrared absorption characteristics, this coating achieves emissivity rates of 88.56% and 87.52% in the two atmospheric transparent windows. Outdoor verification experiments conducted under intense sunlight, along with application tests on solar panels, indicate average cooling performances of 5.5 and 3°C. This coating exhibits significant potential for application in architectural glass and photovoltaic panels.
{"title":"Waterborne transparent anti‐smudge coating with cooling performance via molecular engineering","authors":"Yueyan Liang, Wantong Lin, Xiubin Xu, Linjie Wei, Yintong Lin, Jiahui Tang, Jianwei Liu, Wenqi Kuang, Xu Wu, Xiaoqiang Chen, Xiaojun Peng","doi":"10.1002/aic.70276","DOIUrl":"https://doi.org/10.1002/aic.70276","url":null,"abstract":"The optically transparent, dynamic repellent coating effectively prevents the wetting of water and oily liquids on surfaces that are highly desirable but still challenging to integrate the characteristics of environmental friendliness and application adaptability. In this study, an environmentally friendly process combining silicone‐modified acrylate emulsion polymerization with silica sol was employed to develop an organic–inorganic dual‐continuous water‐based coating. The coating demonstrates anti‐smudge, high transparency, and cooling functionalities. It ensures that various liquids slide off the surface without leaving residues, showcasing superior anti‐smudge performance. Additionally, it exhibits excellent durability, chemical stability, and compatibility with diverse substrates. Based on molecular infrared absorption characteristics, this coating achieves emissivity rates of 88.56% and 87.52% in the two atmospheric transparent windows. Outdoor verification experiments conducted under intense sunlight, along with application tests on solar panels, indicate average cooling performances of 5.5 and 3°C. This coating exhibits significant potential for application in architectural glass and photovoltaic panels.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"70 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129352","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}
Chengmin Gui, Abbas Ahmad Kassem, Yan Cui, Shuai Li, Zhigang Lei, Sophie Fourmentin, Dong Xiang
The absorption performance of fatty acid-based deep eutectic solvents (DESs) for volatile organic compounds (VOCs) of varying hydrophobicity was investigated through a combination of experiments and molecular simulations. Partition coefficient experiments demonstrated the DES composed of tetrabutylammonium bromide (TBAB) and octanoic acid (OCA) in a molar ratio of 1:2 exhibits dual-functional absorption performance, effectively absorbing both weakly hydrophobic VOCs (e.g., dichloromethane and chloroform) and strongly hydrophobic VOCs (e.g., limonene and octamethylcyclotetrasiloxane). Fatty acid-based DESs retained both absorption performance and structural stability in five absorption–desorption cycles. Both VOC and solvent hydrophobicity are likely to positively affect absorption performance, with higher hydrophobicity leading to stronger absorption ability. Molecular simulations uncover that VOC absorption in TBAB:OCA can be attributed to three mechanisms: (1) large free volume of TBAB:OCA, (2) electrostatic interactions between TBAB and weakly hydrophobic VOCs that promote solubilization, and (3) Van der Waals interactions between OCA and strongly hydrophobic VOC that enhance absorption.
{"title":"Fatty acid-based deep eutectic solvents for efficient absorption of VOCs: Hydrophobicity and molecular mechanism study","authors":"Chengmin Gui, Abbas Ahmad Kassem, Yan Cui, Shuai Li, Zhigang Lei, Sophie Fourmentin, Dong Xiang","doi":"10.1002/aic.70287","DOIUrl":"https://doi.org/10.1002/aic.70287","url":null,"abstract":"The absorption performance of fatty acid-based deep eutectic solvents (DESs) for volatile organic compounds (VOCs) of varying hydrophobicity was investigated through a combination of experiments and molecular simulations. Partition coefficient experiments demonstrated the DES composed of tetrabutylammonium bromide (TBAB) and octanoic acid (OCA) in a molar ratio of 1:2 exhibits dual-functional absorption performance, effectively absorbing both weakly hydrophobic VOCs (e.g., dichloromethane and chloroform) and strongly hydrophobic VOCs (e.g., limonene and octamethylcyclotetrasiloxane). Fatty acid-based DESs retained both absorption performance and structural stability in five absorption–desorption cycles. Both VOC and solvent hydrophobicity are likely to positively affect absorption performance, with higher hydrophobicity leading to stronger absorption ability. Molecular simulations uncover that VOC absorption in TBAB:OCA can be attributed to three mechanisms: (1) large free volume of TBAB:OCA, (2) electrostatic interactions between TBAB and weakly hydrophobic VOCs that promote solubilization, and (3) Van der Waals interactions between OCA and strongly hydrophobic VOC that enhance absorption.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"11 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135346","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}
Gul Hameed, Tao Chen, Antonio del Rio Chanona, Lorenz T. Biegler, Michael Short
Gray-box optimization, where parts of optimization problems are represented by algebraic models while others are treated as black-box models lacking analytic derivatives, remains a challenge. Trust-region (TR) methods provide a robust framework for gray-box problems through local reduced models (RMs) for black-box components, but they are complex and require extensive parameter tuning. Motivated by recent advances in funnel-based convergence theory for nonlinear optimization, we propose a novel TR funnel algorithm for gray-box optimization, replacing the filter acceptance criterion with a uni-dimensional funnel, maintaining a monotonically decreasing upper bound on approximation error of local black-box RMs. A global convergence proof to a first-order critical point is established. The algorithm, implemented open-source in Pyomo, supports multiple RM forms and globalization strategies (filter or funnel). Benchmark tests show the TR funnel algorithm achieves comparable and often improved performance relative to the classical TR filter method, thus providing a simpler, effective alternative for gray-box optimization.
{"title":"A trust-region funnel algorithm for gray-box optimization","authors":"Gul Hameed, Tao Chen, Antonio del Rio Chanona, Lorenz T. Biegler, Michael Short","doi":"10.1002/aic.70258","DOIUrl":"https://doi.org/10.1002/aic.70258","url":null,"abstract":"Gray-box optimization, where parts of optimization problems are represented by algebraic models while others are treated as black-box models lacking analytic derivatives, remains a challenge. Trust-region (TR) methods provide a robust framework for gray-box problems through local reduced models (RMs) for black-box components, but they are complex and require extensive parameter tuning. Motivated by recent advances in funnel-based convergence theory for nonlinear optimization, we propose a novel TR funnel algorithm for gray-box optimization, replacing the filter acceptance criterion with a uni-dimensional funnel, maintaining a monotonically decreasing upper bound on approximation error of local black-box RMs. A global convergence proof to a first-order critical point is established. The algorithm, implemented open-source in Pyomo, supports multiple RM forms and globalization strategies (filter or funnel). Benchmark tests show the TR funnel algorithm achieves comparable and often improved performance relative to the classical TR filter method, thus providing a simpler, effective alternative for gray-box optimization.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"91 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135347","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}
Dongxian Li, Xiaocao Shan, Xianzhi Meng, Jia Wang, Jianchun Jiang, Arthur J. Ragauskas
Polyolefin waste hydrogenolysis is constrained by high H2 pressure, heat–mass transfer limitations, and broad product distributions. Here, a tandem hydropyrolysis–hydrogenolysis strategy decouples polymer depolymerization from hydrogenation, enabling selective low-pressure upgrading of polyethylene (PE) and polypropylene (PP) to jet fuel-range hydrocarbons (C8–C16). Morphology-tuned Co3O4 catalysts form Co@CoO core–shell structures enriched with oxygen vacancies, facilitating H2 dissociation and selective C–C cleavage. Under optimized conditions (540°C, 200°C, catalyst-to-feedstock mass ratio (C/F) of 4, 1.8 bar H2), an 82.6% liquid yield with 83.7% jet-fuel selectivity was achieved using standard PE powders, while real medical plastics gave 75%–78% yields. PE mainly produced linear alkanes, whereas PP yielded branched products, enabling fuel tuning. Density functional theory calculations show that oxygen vacancies lower the H2 dissociation barrier and promote non-terminal C–C bond activation in n-butane. This tandem route offers a scalable, non-noble pathway to jet fuel from polyolefin waste.
{"title":"Tandem hydropyrolysis–hydrogenolysis of polyolefin wastes over morphology-tuned Co3O4 for jet-fuel hydrocarbons","authors":"Dongxian Li, Xiaocao Shan, Xianzhi Meng, Jia Wang, Jianchun Jiang, Arthur J. Ragauskas","doi":"10.1002/aic.70286","DOIUrl":"https://doi.org/10.1002/aic.70286","url":null,"abstract":"Polyolefin waste hydrogenolysis is constrained by high H<sub>2</sub> pressure, heat–mass transfer limitations, and broad product distributions. Here, a tandem hydropyrolysis–hydrogenolysis strategy decouples polymer depolymerization from hydrogenation, enabling selective low-pressure upgrading of polyethylene (PE) and polypropylene (PP) to jet fuel-range hydrocarbons (C<sub>8</sub>–C<sub>16</sub>). Morphology-tuned Co<sub>3</sub>O<sub>4</sub> catalysts form Co@CoO core–shell structures enriched with oxygen vacancies, facilitating H<sub>2</sub> dissociation and selective C–C cleavage. Under optimized conditions (540°C, 200°C, catalyst-to-feedstock mass ratio (<i>C</i>/<i>F</i>) of 4, 1.8 bar H<sub>2</sub>), an 82.6% liquid yield with 83.7% jet-fuel selectivity was achieved using standard PE powders, while real medical plastics gave 75%–78% yields. PE mainly produced linear alkanes, whereas PP yielded branched products, enabling fuel tuning. Density functional theory calculations show that oxygen vacancies lower the H<sub>2</sub> dissociation barrier and promote non-terminal C–C bond activation in <i>n</i>-butane. This tandem route offers a scalable, non-noble pathway to jet fuel from polyolefin waste.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"241 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135348","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}
Despite extensive modeling efforts in extraction research, transient column models are rarely applied in industry due to concerns regarding parameter identifiability and model reliability. To address this, we analyzed uncertainty propagation from estimated parameters in a previously introduced column model and assessed identifiability via ill-conditioning analysis. Extrapolation was evaluated by estimating parameters from fluid dynamic experiments and validating with mass transfer data. Using a DN50 pulsed sieve tray column, parameters <span data-altimg="/cms/asset/d8c9718a-a6c5-45ba-a58d-5edfcbb2121e/aic70227-math-0001.png"></span><math altimg="urn:x-wiley:00011541:media:aic70227:aic70227-math-0001" display="inline" location="graphic/aic70227-math-0001.png"> <semantics> <mrow> <msub> <mi>d</mi> <mtext>stab</mtext> </msub> <mo>,</mo> <msub> <mi>d</mi> <mn>100</mn> </msub> </mrow> $$ {d}_{mathrm{stab}},{d}_{100} $$</annotation> </semantics></math>, and the swarm exponent <span data-altimg="/cms/asset/d537bc0b-64be-46d3-b69c-ba362b22db94/aic70227-math-0002.png"></span><math altimg="urn:x-wiley:00011541:media:aic70227:aic70227-math-0002" display="inline" location="graphic/aic70227-math-0002.png"> <semantics> <mrow> <mi>n</mi> </mrow> $$ n $$</annotation> </semantics></math> were estimated through maximum likelihood, as they directly affect online-measurable drop diameter <span data-altimg="/cms/asset/b42b4e7c-00a5-4a79-9a11-02c6ba161c95/aic70227-math-0003.png"></span><math altimg="urn:x-wiley:00011541:media:aic70227:aic70227-math-0003" display="inline" location="graphic/aic70227-math-0003.png"> <semantics> <mrow> <mi>d</mi> </mrow> $$ d $$</annotation> </semantics></math> and hold-up <span data-altimg="/cms/asset/8c5a6d45-2fe8-4dfc-b1ef-96555d009483/aic70227-math-0004.png"></span><math altimg="urn:x-wiley:00011541:media:aic70227:aic70227-math-0004" display="inline" location="graphic/aic70227-math-0004.png"> <semantics> <mrow> <msub> <mi>α</mi> <mi>y</mi> </msub> </mrow> $$ {alpha}_y $$</annotation> </semantics></math>. Incorporating these estimates, the model reproduced <span data-altimg="/cms/asset/ab38dae3-16f5-458b-800d-38d4039d2d3a/aic70227-math-0005.png"></span><math altimg="urn:x-wiley:00011541:media:aic70227:aic70227-math-0005" display="inline" location="graphic/aic70227-math-0005.png"> <semantics> <mrow> <mi>d</mi> </mrow> $$ d $$</annotation> </semantics></math>, <span data-altimg="/cms/asset/3ad3a44d-b268-4efd-9e50-ee44bd5718e1/aic70227-math-0006.png"></span><math altimg="urn:x-wiley:00011541:media:aic70227:aic70227-math-0006" display="inline" location="graphic/aic70227-math-0006.png"> <semantics> <mrow> <msub> <mi>α</mi> <mi>y</mi> </msub> </mrow> $$ {alpha}_y $$</annotation> </semantics></math>, and stationary and transient concentration profiles with relative errors below 10%. For optimization, we applied the model to minimize solvent demand for 99% product recovery. Uncertainty analysis showed coefficients of variation below 4%, confir
{"title":"Transient modeling of extraction columns: Parameter estimation, uncertainty analysis, and operation optimization","authors":"Andreas Palmtag, Jannik Dohmen, Andreas Jupke","doi":"10.1002/aic.70227","DOIUrl":"https://doi.org/10.1002/aic.70227","url":null,"abstract":"Despite extensive modeling efforts in extraction research, transient column models are rarely applied in industry due to concerns regarding parameter identifiability and model reliability. To address this, we analyzed uncertainty propagation from estimated parameters in a previously introduced column model and assessed identifiability via ill-conditioning analysis. Extrapolation was evaluated by estimating parameters from fluid dynamic experiments and validating with mass transfer data. Using a DN50 pulsed sieve tray column, parameters <span data-altimg=\"/cms/asset/d8c9718a-a6c5-45ba-a58d-5edfcbb2121e/aic70227-math-0001.png\"></span><math altimg=\"urn:x-wiley:00011541:media:aic70227:aic70227-math-0001\" display=\"inline\" location=\"graphic/aic70227-math-0001.png\">\u0000<semantics>\u0000<mrow>\u0000<msub>\u0000<mi>d</mi>\u0000<mtext>stab</mtext>\u0000</msub>\u0000<mo>,</mo>\u0000<msub>\u0000<mi>d</mi>\u0000<mn>100</mn>\u0000</msub>\u0000</mrow>\u0000$$ {d}_{mathrm{stab}},{d}_{100} $$</annotation>\u0000</semantics></math>, and the swarm exponent <span data-altimg=\"/cms/asset/d537bc0b-64be-46d3-b69c-ba362b22db94/aic70227-math-0002.png\"></span><math altimg=\"urn:x-wiley:00011541:media:aic70227:aic70227-math-0002\" display=\"inline\" location=\"graphic/aic70227-math-0002.png\">\u0000<semantics>\u0000<mrow>\u0000<mi>n</mi>\u0000</mrow>\u0000$$ n $$</annotation>\u0000</semantics></math> were estimated through maximum likelihood, as they directly affect online-measurable drop diameter <span data-altimg=\"/cms/asset/b42b4e7c-00a5-4a79-9a11-02c6ba161c95/aic70227-math-0003.png\"></span><math altimg=\"urn:x-wiley:00011541:media:aic70227:aic70227-math-0003\" display=\"inline\" location=\"graphic/aic70227-math-0003.png\">\u0000<semantics>\u0000<mrow>\u0000<mi>d</mi>\u0000</mrow>\u0000$$ d $$</annotation>\u0000</semantics></math> and hold-up <span data-altimg=\"/cms/asset/8c5a6d45-2fe8-4dfc-b1ef-96555d009483/aic70227-math-0004.png\"></span><math altimg=\"urn:x-wiley:00011541:media:aic70227:aic70227-math-0004\" display=\"inline\" location=\"graphic/aic70227-math-0004.png\">\u0000<semantics>\u0000<mrow>\u0000<msub>\u0000<mi>α</mi>\u0000<mi>y</mi>\u0000</msub>\u0000</mrow>\u0000$$ {alpha}_y $$</annotation>\u0000</semantics></math>. Incorporating these estimates, the model reproduced <span data-altimg=\"/cms/asset/ab38dae3-16f5-458b-800d-38d4039d2d3a/aic70227-math-0005.png\"></span><math altimg=\"urn:x-wiley:00011541:media:aic70227:aic70227-math-0005\" display=\"inline\" location=\"graphic/aic70227-math-0005.png\">\u0000<semantics>\u0000<mrow>\u0000<mi>d</mi>\u0000</mrow>\u0000$$ d $$</annotation>\u0000</semantics></math>, <span data-altimg=\"/cms/asset/3ad3a44d-b268-4efd-9e50-ee44bd5718e1/aic70227-math-0006.png\"></span><math altimg=\"urn:x-wiley:00011541:media:aic70227:aic70227-math-0006\" display=\"inline\" location=\"graphic/aic70227-math-0006.png\">\u0000<semantics>\u0000<mrow>\u0000<msub>\u0000<mi>α</mi>\u0000<mi>y</mi>\u0000</msub>\u0000</mrow>\u0000$$ {alpha}_y $$</annotation>\u0000</semantics></math>, and stationary and transient concentration profiles with relative errors below 10%. For optimization, we applied the model to minimize solvent demand for 99% product recovery. Uncertainty analysis showed coefficients of variation below 4%, confir","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"311 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135353","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}
Si-Qi Zhang, Yin-Ning Zhou, Jie Jin, Zheng-Hong Luo
Precise tailoring of chemical structures is fundamental to chemical product engineering. Star block copolymers, as advanced branched polymers, possess unique properties deriving from sequence structures. Herein, we propose a multiple matrix-based kinetic Monte Carlo (kMC) framework capable of capturing both complex topologies and sequence structures of star block copolymers. This approach employs primary matrices to record the structure of arms and cores, along with submatrices to map connections between each arm and its corresponding core. By integrating this kMC algorithm with the recently developed sequence structure descriptor, we simulate the atom transfer radical copolymerization of n-butyl acrylate and styrene to produce sequence-controlled star block copolymers. Kinetic analysis highlights the importance of the activation/deactivation equilibrium of the first monomer in the two-step batch copolymerization. Comprehensive optimization across three key conditions enables precise regulation of sequence structures while achieving a balance between time cost and monomer utilization efficiency.
{"title":"A multi-matrix kinetic Monte Carlo framework enabling the design of sequence-controlled star block copolymers","authors":"Si-Qi Zhang, Yin-Ning Zhou, Jie Jin, Zheng-Hong Luo","doi":"10.1002/aic.70278","DOIUrl":"https://doi.org/10.1002/aic.70278","url":null,"abstract":"Precise tailoring of chemical structures is fundamental to chemical product engineering. Star block copolymers, as advanced branched polymers, possess unique properties deriving from sequence structures. Herein, we propose a multiple matrix-based kinetic Monte Carlo (<i>k</i>MC) framework capable of capturing both complex topologies and sequence structures of star block copolymers. This approach employs primary matrices to record the structure of arms and cores, along with submatrices to map connections between each arm and its corresponding core. By integrating this <i>k</i>MC algorithm with the recently developed sequence structure descriptor, we simulate the atom transfer radical copolymerization of n-butyl acrylate and styrene to produce sequence-controlled star block copolymers. Kinetic analysis highlights the importance of the activation/deactivation equilibrium of the first monomer in the two-step batch copolymerization. Comprehensive optimization across three key conditions enables precise regulation of sequence structures while achieving a balance between time cost and monomer utilization efficiency.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"2 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135349","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 oxidative dehydrogenation of ethane (ODHE) represents an exothermic alternative to the energy-intensive steam cracking of ethane. In this work we evaluate ODHE conducted at higher temperatures (773–998 K) in the absence of any catalyst and develop a seven-step global kinetic model that carries a lower degree of parametrization than those previously reported, yet captures rate features over a relatively wide range of conditions. The model is regressed against a wide array of rate data measured under conditions of strict kinetic control and predicts accurately a plethora of rate data collated from the open literature. This study illuminates unique features of high-temperature gas phase ODHE when compared to catalytic ODHE, and points to gas phase ODHE as a plausible alternative to existing catalytic approaches to producing ethylene by means of oxidative chemistries.
{"title":"A global kinetic model for high temperature homogeneous oxidative dehydrogenation of ethane","authors":"Syed Ahsan Imam, Praveen Bollini, Vemuri Balakotaiah","doi":"10.1002/aic.70262","DOIUrl":"https://doi.org/10.1002/aic.70262","url":null,"abstract":"The oxidative dehydrogenation of ethane (ODHE) represents an exothermic alternative to the energy-intensive steam cracking of ethane. In this work we evaluate ODHE conducted at higher temperatures (773–998 K) in the absence of any catalyst and develop a seven-step global kinetic model that carries a lower degree of parametrization than those previously reported, yet captures rate features over a relatively wide range of conditions. The model is regressed against a wide array of rate data measured under conditions of strict kinetic control and predicts accurately a plethora of rate data collated from the open literature. This study illuminates unique features of high-temperature gas phase ODHE when compared to catalytic ODHE, and points to gas phase ODHE as a plausible alternative to existing catalytic approaches to producing ethylene by means of oxidative chemistries.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"16 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135364","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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