Pub Date : 2025-12-23DOI: 10.1016/j.coche.2025.101217
Sven M Papidocha , Andreas Burger , Varinia Bernales , Alán Aspuru-Guzik
The design of small molecules with tailored properties is a central goal in chemistry and materials science. Recent advances in machine learning provide powerful tools to accelerate the pace of discovery. One promising avenue for acceleration involves the use of generative models that propose novel candidates for diverse optimization tasks. Despite their promise, these methods are often evaluated solely using computational benchmarks, and many studies fail to advance proposed candidates to experimental validation in the wet lab. A key reason for this gap, the elephant in the room, is the limited synthesizability of the generated molecules. In response, the community has recently developed various strategies to address this challenge and incorporate synthesizability into generative design workflows. In this opinion, we provide a comprehensive overview of recent contributions that explicitly tackle molecular synthesizability, highlighting notable advances. We also discuss key limitations of current approaches and outline promising directions for future research.
{"title":"The elephant in the lab: synthesizability in generative small-molecule design","authors":"Sven M Papidocha , Andreas Burger , Varinia Bernales , Alán Aspuru-Guzik","doi":"10.1016/j.coche.2025.101217","DOIUrl":"10.1016/j.coche.2025.101217","url":null,"abstract":"<div><div>The design of small molecules with tailored properties is a central goal in chemistry and materials science. Recent advances in machine learning provide powerful tools to accelerate the pace of discovery. One promising avenue for acceleration involves the use of generative models that propose novel candidates for diverse optimization tasks. Despite their promise, these methods are often evaluated solely using computational benchmarks, and many studies fail to advance proposed candidates to experimental validation in the wet lab. A key reason for this gap, the elephant in the room, is the limited <em>synthesizability</em> of the generated molecules. In response, the community has recently developed various strategies to address this challenge and incorporate synthesizability into generative design workflows. In this opinion, we provide a comprehensive overview of recent contributions that explicitly tackle molecular synthesizability, highlighting notable advances. We also discuss key limitations of current approaches and outline promising directions for future research.</div></div>","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"51 ","pages":"Article 101217"},"PeriodicalIF":6.8,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-22DOI: 10.1016/j.coche.2025.101208
Le Yuan , Saman Shafaei , Huimin Zhao
Artificial intelligence (AI)-driven enzyme property prediction enables rapid discovery and engineering of enzymes for a wide range of biotechnological and therapeutic applications. Here, we first introduce the key components in AI model development, including enzyme datasets, protein representation methods, and model architectures. We then highlight a variety of AI tools developed for the prediction of enzyme properties and functional annotations, including enzyme structure, kinetic parameters, substrate specificity, thermostability, solubility, Enzyme Commission number, and Gene Ontology term. Moreover, we describe representative downstream applications enabled by these AI tools. Finally, we discuss some challenges and opportunities as well as future prospects.
{"title":"Enzyme property prediction using artificial intelligence","authors":"Le Yuan , Saman Shafaei , Huimin Zhao","doi":"10.1016/j.coche.2025.101208","DOIUrl":"10.1016/j.coche.2025.101208","url":null,"abstract":"<div><div>Artificial intelligence (AI)-driven enzyme property prediction enables rapid discovery and engineering of enzymes for a wide range of biotechnological and therapeutic applications. Here, we first introduce the key components in AI model development, including enzyme datasets, protein representation methods, and model architectures. We then highlight a variety of AI tools developed for the prediction of enzyme properties and functional annotations, including enzyme structure, kinetic parameters, substrate specificity, thermostability, solubility, Enzyme Commission number, and Gene Ontology term. Moreover, we describe representative downstream applications enabled by these AI tools. Finally, we discuss some challenges and opportunities as well as future prospects.</div></div>","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"51 ","pages":"Article 101208"},"PeriodicalIF":6.8,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-19DOI: 10.1016/j.coche.2025.101207
Zhifei Yuliu, Ruofan Shi, Marianthi G Ierapetritou
Optimizing supply chains is essential for the efficient and successful operation of chemical engineering processes. Real-world supply chain performance is frequently impacted by uncertainty. This work reviews recent literature in supply chain design and planning under uncertainty, considering two or multiple stages. Progress in various applications such as biomass valorization, waste management, and pharmaceutical manufacturing is examined. Recently used uncertainty handling methods (scenario-based stochastic programming, distributionally robust optimization, fuzzy programming, and chance-constrained programming) and computational challenges are discussed. Key gaps identified for future research include the treatment of decision-dependent uncertainty and solution strategies for nonconvexity.
{"title":"Mathematical programming approaches to supply chain optimization under uncertainty: a review","authors":"Zhifei Yuliu, Ruofan Shi, Marianthi G Ierapetritou","doi":"10.1016/j.coche.2025.101207","DOIUrl":"10.1016/j.coche.2025.101207","url":null,"abstract":"<div><div>Optimizing supply chains is essential for the efficient and successful operation of chemical engineering processes. Real-world supply chain performance is frequently impacted by uncertainty. This work reviews recent literature in supply chain design and planning under uncertainty, considering two or multiple stages. Progress in various applications such as biomass valorization, waste management, and pharmaceutical manufacturing is examined. Recently used uncertainty handling methods (scenario-based stochastic programming, distributionally robust optimization, fuzzy programming, and chance-constrained programming) and computational challenges are discussed. Key gaps identified for future research include the treatment of decision-dependent uncertainty and solution strategies for nonconvexity.</div></div>","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"51 ","pages":"Article 101207"},"PeriodicalIF":6.8,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145787467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-13DOI: 10.1016/j.coche.2025.101206
Rafael Granados-Fernández , Miguel A. Rodriguez-Cano , Cristina Sáez, Justo Lobato, Manuel A. Rodrigo
Scaling up electrochemical technologies is key to industrial adoption. Mature processes like chlor-alkali and alumina electrolysis continue evolving to address environmental concerns. Recent commercial advances include water electrolysis and hydrogen fuel cells, though efficiency improvements are needed for profitability. Most emerging technologies face challenges in stability, reproducibility, and operability under real-world conditions. Environmental treatments remain limited to demonstration scale, while CO₂ electroreduction and organic electrosynthesis are still in early development. However, growing pressure to reduce emissions and replace fossil fuels is accelerating interest in e-fuels and electro-organic production. This review highlights recent progress and key barriers in scaling electrochemical technologies toward full-scale industrial implementation.
{"title":"Recent progress in scaling up promising electrochemical technologies","authors":"Rafael Granados-Fernández , Miguel A. Rodriguez-Cano , Cristina Sáez, Justo Lobato, Manuel A. Rodrigo","doi":"10.1016/j.coche.2025.101206","DOIUrl":"10.1016/j.coche.2025.101206","url":null,"abstract":"<div><div>Scaling up electrochemical technologies is key to industrial adoption. Mature processes like chlor-alkali and alumina electrolysis continue evolving to address environmental concerns. Recent commercial advances include water electrolysis and hydrogen fuel cells, though efficiency improvements are needed for profitability. Most emerging technologies face challenges in stability, reproducibility, and operability under real-world conditions. Environmental treatments remain limited to demonstration scale, while CO₂ electroreduction and organic electrosynthesis are still in early development. However, growing pressure to reduce emissions and replace fossil fuels is accelerating interest in e-fuels and electro-organic production. This review highlights recent progress and key barriers in scaling electrochemical technologies toward full-scale industrial implementation.</div></div>","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"51 ","pages":"Article 101206"},"PeriodicalIF":6.8,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145733692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-12DOI: 10.1016/j.coche.2025.101204
Jesús Serrano-Jiménez, Carlos Martín, Marina Pinzón, Paula Sánchez, Ana Raquel de la Osa
Hydrogen production via ammonia electrolysis is a promising alternative but still faces significant challenges hindering its practical deployment. Research has primarily focused on the development of anodic electrocatalysts, while limited studies address their integration into a functional laboratory-scale electrolyzer. Indeed, only two notable large-scale investigations have been reported. This review provides insight into the advances achieved in the past five years in the field of ammonia electrolysis, encompassing both small- and large-scale systems. First, the ongoing trends in the design of noble and non-noble electrocatalysts to maximize activity and stability have been analyzed. At the laboratory scale, the influence of crucial elements (porous transport layers and membranes) as well as the operational parameters (such as feed composition, temperature, etc.) is discussed, highlighting the most promising approaches to improve overall electrolyzer performance. At a larger scale, the main achievements are associated with system scale-up, particularly those related to stack design and material availability.
{"title":"Exploring the potential of ammonia electrolysis for hydrogen production: from lab-performance to stack architectures","authors":"Jesús Serrano-Jiménez, Carlos Martín, Marina Pinzón, Paula Sánchez, Ana Raquel de la Osa","doi":"10.1016/j.coche.2025.101204","DOIUrl":"10.1016/j.coche.2025.101204","url":null,"abstract":"<div><div>Hydrogen production via ammonia electrolysis is a promising alternative but still faces significant challenges hindering its practical deployment. Research has primarily focused on the development of anodic electrocatalysts, while limited studies address their integration into a functional laboratory-scale electrolyzer. Indeed, only two notable large-scale investigations have been reported. This review provides insight into the advances achieved in the past five years in the field of ammonia electrolysis, encompassing both small- and large-scale systems. First, the ongoing trends in the design of noble and non-noble electrocatalysts to maximize activity and stability have been analyzed. At the laboratory scale, the influence of crucial elements (porous transport layers and membranes) as well as the operational parameters (such as feed composition, temperature, etc.) is discussed, highlighting the most promising approaches to improve overall electrolyzer performance. At a larger scale, the main achievements are associated with system scale-up, particularly those related to stack design and material availability.</div></div>","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"51 ","pages":"Article 101204"},"PeriodicalIF":6.8,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145733691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Green hydrogen production via water electrolysis is a pivotal component of the transition to a carbon-neutral energy system. Among available technologies, alkaline water electrolysis (AWE) offers a scalable, cost-effective pathway that avoids reliance on critical raw materials such as precious metals. However, AWE systems must operate under increasingly demanding conditions such as frequent start-up and shut-down cycles driven by intermittent renewable power, which can be mitigated, however, at an increase in capital and operational costs. Furthermore, AWE systems for economic viability need to operate under high current densities. Despite this, most academic studies are still conducted at low current densities and room temperature, conditions far removed from industrial relevance. This review critically examines the limitations of such traditional testing approaches and highlights recent advances in evaluating catalyst activity and durability under industry-representative conditions: elevated temperatures (60–80°C), concentrated electrolytes (20–40 wt% KOH), and high current densities (≥1 A cm⁻²). We explore innovative laboratory-scale cell designs, three-electrode configurations for intrinsic activity screening, and custom single-cell setups that mimic commercial stacks. The importance of long-term stability testing, including accelerated stress tests simulating intermittent operation, is emphasized. Finally, the need for standardized protocols and interlaboratory validation is underscored as essential for bridging the gap between academic research and industrial deployment of robust, non-precious AWE electrodes.
通过水电解绿色制氢是向碳中性能源系统过渡的关键组成部分。在现有的技术中，碱性电解（AWE）提供了一种可扩展的、具有成本效益的途径，避免了对贵金属等关键原材料的依赖。然而，AWE系统必须在越来越苛刻的条件下运行，例如由间歇性可再生能源驱动的频繁启动和关闭周期，然而，这可以通过增加资本和运营成本来缓解。此外，AWE系统的经济可行性需要在高电流密度下运行。尽管如此，大多数学术研究仍然是在低电流密度和室温下进行的，这些条件与工业应用相距甚远。这篇综述严格审查了这种传统测试方法的局限性，并强调了在工业代表性条件下评估催化剂活性和耐久性的最新进展：高温（60-80°C），浓缩电解质（20-40 wt% KOH）和高电流密度（≥1 A cm⁻²）。我们探索创新的实验室规模的电池设计，用于内在活性筛选的三电极配置，以及模仿商业堆栈的定制单电池设置。强调了长期稳定性测试的重要性，包括模拟间歇操作的加速压力测试。最后，标准化协议和实验室间验证的需求被强调为弥合学术研究和工业部署之间的差距至关重要，坚固，非贵重AWE电极。
{"title":"Toward industrially relevant testing of activity and stability in alkaline electrolysis electrode materials","authors":"Madis Lüsi , Miha Hotko , Nik Maselj , Aleš Marsel , Nejc Hodnik","doi":"10.1016/j.coche.2025.101205","DOIUrl":"10.1016/j.coche.2025.101205","url":null,"abstract":"<div><div>Green hydrogen production via water electrolysis is a pivotal component of the transition to a carbon-neutral energy system. Among available technologies, alkaline water electrolysis (AWE) offers a scalable, cost-effective pathway that avoids reliance on critical raw materials such as precious metals. However, AWE systems must operate under increasingly demanding conditions such as frequent start-up and shut-down cycles driven by intermittent renewable power, which can be mitigated, however, at an increase in capital and operational costs. Furthermore, AWE systems for economic viability need to operate under high current densities. Despite this, most academic studies are still conducted at low current densities and room temperature, conditions far removed from industrial relevance. This review critically examines the limitations of such traditional testing approaches and highlights recent advances in evaluating catalyst activity and durability under industry-representative conditions: elevated temperatures (60–80°C), concentrated electrolytes (20–40 wt% KOH), and high current densities (≥1 A cm⁻²). We explore innovative laboratory-scale cell designs, three-electrode configurations for intrinsic activity screening, and custom single-cell setups that mimic commercial stacks. The importance of long-term stability testing, including accelerated stress tests simulating intermittent operation, is emphasized. Finally, the need for standardized protocols and interlaboratory validation is underscored as essential for bridging the gap between academic research and industrial deployment of robust, non-precious AWE electrodes.</div></div>","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"51 ","pages":"Article 101205"},"PeriodicalIF":6.8,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145733690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-08DOI: 10.1016/j.coche.2025.101202
Hannah E Holmes , Jinsu Kim , Matthew J Realff
Direct air capture (DAC) is a promising technology for removing carbon dioxide from the atmosphere. However, its widespread deployment is challenged by high energy requirements, water management, sorbent degradation, integration with variable renewable energy sources, and fluctuating climatic conditions. The design, operation, and control of solid adsorption-DAC systems is a complex problem that requires holistic engineering of the adsorbent material, adsorption system, DAC process, and upstream and downstream operations. In this review, we show how Process Systems Engineering (PSE) can address this multiscale system design challenge by highlighting recent PSE advancements in three areas: (i) process-informed sorbent selection, (ii) heat integration and water management, and (iii) technological viability assessments. We summarize the progress that PSE has made in connecting sorbent properties to system design and optimization, outlining the key metrics and workflow needed to advance from sorbent to comprehensive system evaluation. We highlight effective energy and resource management strategies, such as DAC integration with heat and power generation, the use of renewable electricity or underutilized sources from existing infrastructure, and combined heat and water integration. For viability assessments, we emphasize comprehensive approaches that integrate technoeconomic and life cycle assessments with sorbent degradation, geospatial analysis, and scaling predictions. We conclude with future PSE directions that will be important for scaling adsorption-DAC, including process strategies for variable energy and climate conditions, predictive sorbent degradation models, and optimized scheduling to balance energy and capital.
{"title":"Process systems engineering: a key enabler of adsorption-based direct air capture","authors":"Hannah E Holmes , Jinsu Kim , Matthew J Realff","doi":"10.1016/j.coche.2025.101202","DOIUrl":"10.1016/j.coche.2025.101202","url":null,"abstract":"<div><div>Direct air capture (DAC) is a promising technology for removing carbon dioxide from the atmosphere. However, its widespread deployment is challenged by high energy requirements, water management, sorbent degradation, integration with variable renewable energy sources, and fluctuating climatic conditions. The design, operation, and control of solid adsorption-DAC systems is a complex problem that requires holistic engineering of the adsorbent material, adsorption system, DAC process, and upstream and downstream operations. In this review, we show how Process Systems Engineering (PSE) can address this multiscale system design challenge by highlighting recent PSE advancements in three areas: (i) process-informed sorbent selection, (ii) heat integration and water management, and (iii) technological viability assessments. We summarize the progress that PSE has made in connecting sorbent properties to system design and optimization, outlining the key metrics and workflow needed to advance from sorbent to comprehensive system evaluation. We highlight effective energy and resource management strategies, such as DAC integration with heat and power generation, the use of renewable electricity or underutilized sources from existing infrastructure, and combined heat and water integration. For viability assessments, we emphasize comprehensive approaches that integrate technoeconomic and life cycle assessments with sorbent degradation, geospatial analysis, and scaling predictions. We conclude with future PSE directions that will be important for scaling adsorption-DAC, including process strategies for variable energy and climate conditions, predictive sorbent degradation models, and optimized scheduling to balance energy and capital.</div></div>","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"51 ","pages":"Article 101202"},"PeriodicalIF":6.8,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145733774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-05DOI: 10.1016/j.coche.2025.101203
Simon D Rihm , Aleksandar Kondinski , Markus Kraft
This paper investigates how digital chemistry technologies such as machine learning, knowledge engineering, and laboratory automation are revolutionizing materials discovery for pressing global challenges in energy and healthcare. We introduce a comprehensive technology framework that integrates advanced databases, artificial intelligence models, semantic ontologies, and robotic systems to address fundamental challenges in chemical research. The World Avatar platform serves as a central case study, demonstrating its unique ability to connect computational design with experimental execution through dynamic and interoperable workflows. Practical applications in reticular chemistry and automated laboratory systems showcase the platform’s capacity to enable autonomous discovery processes. Together, these technological advances are driving chemical research toward more scalable, reproducible, and intelligent materials development approaches.
{"title":"Product design, synthesis, and lab automation with The World Avatar","authors":"Simon D Rihm , Aleksandar Kondinski , Markus Kraft","doi":"10.1016/j.coche.2025.101203","DOIUrl":"10.1016/j.coche.2025.101203","url":null,"abstract":"<div><div>This paper investigates how digital chemistry technologies such as machine learning, knowledge engineering, and laboratory automation are revolutionizing materials discovery for pressing global challenges in energy and healthcare. We introduce a comprehensive technology framework that integrates advanced databases, artificial intelligence models, semantic ontologies, and robotic systems to address fundamental challenges in chemical research. The World Avatar platform serves as a central case study, demonstrating its unique ability to connect computational design with experimental execution through dynamic and interoperable workflows. Practical applications in reticular chemistry and automated laboratory systems showcase the platform’s capacity to enable autonomous discovery processes. Together, these technological advances are driving chemical research toward more scalable, reproducible, and intelligent materials development approaches.</div></div>","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"51 ","pages":"Article 101203"},"PeriodicalIF":6.8,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-03DOI: 10.1016/j.coche.2025.101201
Mattia Galanti, Rens Teunissen, Ivo Roghair, Martin van Sint Annaland
Accurate thermodynamic characterization of CO2 adsorption on solid amine-functionalized sorbents is essential for modeling and optimizing direct air capture (DAC) processes. This study presents a systematic compilation and comprehensive analysis of available adsorption isotherm data for Lewatit® VPOC 1065, one of the most studied benchmark sorbents for DAC applications. Six independent literature datasets were critically evaluated, revealing significant discrepancies in reported adsorption capacities and inconsistencies across the temperature and partial pressure ranges, particularly within the low-pressure regime relevant for DAC. Global fitting of a temperature-dependent Toth model was performed to investigate the capability of this widely used single-mechanism approach to capture experimental trends across the entire dataset. The Toth model demonstrated substantial limitations, particularly at low partial pressures, highlighting inadequacies in representing the complex adsorption behavior of the sorbent. Moreover, comparative analyses indicated that these limitations stem partially from inter-author variability, experimental uncertainties at ultra-low pressures, and potential unknown adsorption mechanisms, for example, a physisorption — chemisorption dual site mode. Based on these insights, future research directions were identified.
{"title":"Toward consistent thermodynamic modeling of CO2 adsorption on Lewatit VPOC 1065 under dry conditions: isotherm variability, data gaps, and model fitting","authors":"Mattia Galanti, Rens Teunissen, Ivo Roghair, Martin van Sint Annaland","doi":"10.1016/j.coche.2025.101201","DOIUrl":"10.1016/j.coche.2025.101201","url":null,"abstract":"<div><div>Accurate thermodynamic characterization of CO<sub>2</sub> adsorption on solid amine-functionalized sorbents is essential for modeling and optimizing direct air capture (DAC) processes. This study presents a systematic compilation and comprehensive analysis of available adsorption isotherm data for Lewatit® VPOC 1065, one of the most studied benchmark sorbents for DAC applications. Six independent literature datasets were critically evaluated, revealing significant discrepancies in reported adsorption capacities and inconsistencies across the temperature and partial pressure ranges, particularly within the low-pressure regime relevant for DAC. Global fitting of a temperature-dependent Toth model was performed to investigate the capability of this widely used single-mechanism approach to capture experimental trends across the entire dataset. The Toth model demonstrated substantial limitations, particularly at low partial pressures, highlighting inadequacies in representing the complex adsorption behavior of the sorbent. Moreover, comparative analyses indicated that these limitations stem partially from inter-author variability, experimental uncertainties at ultra-low pressures, and potential unknown adsorption mechanisms, for example, a physisorption — chemisorption dual site mode. Based on these insights, future research directions were identified.</div></div>","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"51 ","pages":"Article 101201"},"PeriodicalIF":6.8,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-11-29DOI: 10.1016/j.coche.2025.101200
Edgar Martín-Hernández , Borja Hernández , Aurora del Carmen Munguia-Lopez , Sidney Omelon
Novel computational techniques raised under the concept of artificial intelligence have vast applications in science and engineering. In this work, we review the most relevant frameworks and applications of artificial intelligence and machine learning oriented to the development of sustainable production and consumption systems using a bottom-up multi-scale approach. Firstly, we address frameworks for molecular and processing unit design and flowsheet design. Secondly, we assess methods proposed for the environmental and social assessment of superstructures. Finally, we also discuss the contributions and applications of artificial intelligence in the development of policies that support the shift of paradigm to the circular economy.
{"title":"Artificial intelligence and machine learning for process and policy design in the transition towards circular economy systems: advancements and opportunities","authors":"Edgar Martín-Hernández , Borja Hernández , Aurora del Carmen Munguia-Lopez , Sidney Omelon","doi":"10.1016/j.coche.2025.101200","DOIUrl":"10.1016/j.coche.2025.101200","url":null,"abstract":"<div><div>Novel computational techniques raised under the concept of artificial intelligence have vast applications in science and engineering. In this work, we review the most relevant frameworks and applications of artificial intelligence and machine learning oriented to the development of sustainable production and consumption systems using a bottom-up multi-scale approach. Firstly, we address frameworks for molecular and processing unit design and flowsheet design. Secondly, we assess methods proposed for the environmental and social assessment of superstructures. Finally, we also discuss the contributions and applications of artificial intelligence in the development of policies that support the shift of paradigm to the circular economy.</div></div>","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"51 ","pages":"Article 101200"},"PeriodicalIF":6.8,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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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