Pub Date : 2025-11-14DOI: 10.1016/j.coche.2025.101197
Yongxin Hu , Xingyang Li , Teng Zhou
The direct air capture (DAC) technology possesses transformative potential for achieving negative emissions. However, challenges such as massive energy consumption, low capture efficiency, and supply chain concerns have impeded their large-scale implementation. Process Systems Engineering (PSE) is expected to address these challenges and bridge existing gaps. This paper first conducts a bibliometric analysis of 1171 DAC-related research papers published between 2015 and 2025. We then classify recent representative DAC studies through the lens of PSE. Afterwards, we discuss the role of PSE methods and tools in material design, equipment retrofitting, process optimization, and system integration across molecular, unit, and process scales. Finally, we point out future research opportunities and challenges in cross-scale modeling and optimization, multisystem integration, and flexible design for varying DAC conditions.
{"title":"Direct air capture: recent progress in materials, equipment, and process engineering","authors":"Yongxin Hu , Xingyang Li , Teng Zhou","doi":"10.1016/j.coche.2025.101197","DOIUrl":"10.1016/j.coche.2025.101197","url":null,"abstract":"<div><div>The direct air capture (DAC) technology possesses transformative potential for achieving negative emissions. However, challenges such as massive energy consumption, low capture efficiency, and supply chain concerns have impeded their large-scale implementation. Process Systems Engineering (PSE) is expected to address these challenges and bridge existing gaps. This paper first conducts a bibliometric analysis of 1171 DAC-related research papers published between 2015 and 2025. We then classify recent representative DAC studies through the lens of PSE. Afterwards, we discuss the role of PSE methods and tools in material design, equipment retrofitting, process optimization, and system integration across molecular, unit, and process scales. Finally, we point out future research opportunities and challenges in cross-scale modeling and optimization, multisystem integration, and flexible design for varying DAC conditions.</div></div>","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"50 ","pages":"Article 101197"},"PeriodicalIF":6.8,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516573","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}
{"title":"Editorial overview: Microplastics and nanoplastics in the environment: progress and prospects","authors":"Nisha Singh , Damià Barceló , Kirpa Ram , Julien Gigault","doi":"10.1016/j.coche.2025.101196","DOIUrl":"10.1016/j.coche.2025.101196","url":null,"abstract":"","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"50 ","pages":"Article 101196"},"PeriodicalIF":6.8,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516445","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 : 2025-11-08DOI: 10.1016/j.coche.2025.101194
Laura Clarizia, Tejraj M Aminabhavi, Gunda Mohanakrishna, Nicolas Keller, Cui Y Toe
{"title":"Editorial overview: Solar photocatalytic and photoelectrochemical hydrogen evolution using novel and effective materials","authors":"Laura Clarizia, Tejraj M Aminabhavi, Gunda Mohanakrishna, Nicolas Keller, Cui Y Toe","doi":"10.1016/j.coche.2025.101194","DOIUrl":"10.1016/j.coche.2025.101194","url":null,"abstract":"","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"50 ","pages":"Article 101194"},"PeriodicalIF":6.8,"publicationDate":"2025-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145462594","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 : 2025-11-01DOI: 10.1016/j.coche.2025.101195
Roberto Mennitto , Richard Blom , Maurice Dörr , Marian Rosental , Nils Rettenmaier
Direct air capture (DAC) is a pivotal technology for achieving net-zero emissions, yet its scalability is constrained by energy intensity and material limitations. This work critically examines the current landscape of solid sorbents for DAC, focusing on their performance, durability, and environmental impact. Key sorbent classes — amine-functionalized materials, carbonates, zeolites, and metal-organic frameworks — are evaluated in terms of CO₂ uptake, energy requirements, and life cycle emissions. A novel exergetic efficiency metric is introduced, incorporating sorbent degradation to better reflect real-world performance. Structured supports such as laminates and monoliths are discussed for their role in enhancing mass transfer and reducing pressure drop, though often at increased cost and environmental burden. Life cycle assessment (LCA) results highlight that energy consumption dominates DAC’s carbon footprint, with sorbent-related impacts becoming significant only for short-lived or energy-intensive materials. Emerging materials like hydroxylated activated carbon, along with alternative processes such as moisture swing adsorption and electrochemical DAC, offer promising pathways to reduce energy demand and improve sustainability. The work underscores the need for integrated assessments that link sorbent properties, process design, and environmental metrics from early development stages. Future research should prioritise sorbent longevity, comprehensive kinetic data, and inclusion of support structures in LCA models to enable cost-effective and climate-positive DAC deployment.
{"title":"Solid sorbents for direct air capture: a technological and environmental perspective","authors":"Roberto Mennitto , Richard Blom , Maurice Dörr , Marian Rosental , Nils Rettenmaier","doi":"10.1016/j.coche.2025.101195","DOIUrl":"10.1016/j.coche.2025.101195","url":null,"abstract":"<div><div>Direct air capture (DAC) is a pivotal technology for achieving net-zero emissions, yet its scalability is constrained by energy intensity and material limitations. This work critically examines the current landscape of solid sorbents for DAC, focusing on their performance, durability, and environmental impact. Key sorbent classes — amine-functionalized materials, carbonates, zeolites, and metal-organic frameworks — are evaluated in terms of CO₂ uptake, energy requirements, and life cycle emissions. A novel exergetic efficiency metric is introduced, incorporating sorbent degradation to better reflect real-world performance. Structured supports such as laminates and monoliths are discussed for their role in enhancing mass transfer and reducing pressure drop, though often at increased cost and environmental burden. Life cycle assessment (LCA) results highlight that energy consumption dominates DAC’s carbon footprint, with sorbent-related impacts becoming significant only for short-lived or energy-intensive materials. Emerging materials like hydroxylated activated carbon, along with alternative processes such as moisture swing adsorption and electrochemical DAC, offer promising pathways to reduce energy demand and improve sustainability. The work underscores the need for integrated assessments that link sorbent properties, process design, and environmental metrics from early development stages. Future research should prioritise sorbent longevity, comprehensive kinetic data, and inclusion of support structures in LCA models to enable cost-effective and climate-positive DAC deployment.</div></div>","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"50 ","pages":"Article 101195"},"PeriodicalIF":6.8,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145412648","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 : 2025-10-30DOI: 10.1016/j.coche.2025.101193
Hamed Hoorijani, Yi Ouyang, Geraldine J Heynderickx, Kevin M Van Geem
Multiphase flow reactors are fundamental to industrial processes, but they remain challenging to model due to their inherently multiscale dynamics. While experiments and traditional physics-based models have advanced our understanding, their cost and complexity limit the study of large-scale systems and applications. Data-driven modeling has emerged as a promising alternative, enabling efficient prediction of transport–reaction phenomena across scales. This review categorizes state-of-the-art approaches into three main groups: reduced order models that simplify high-fidelity simulations, hybrid physics-data approaches that couple data models with physics-based simulations, and fully data-driven frameworks that leverage operator-learning and neural surrogates. Particular emphasis is placed on cross-scale learning for developing data models, as well as on emerging architectures such as PINN-based frameworks, neural operators, and transformer-inspired GPT models. Challenges in data availability, interpretability, and geometry transfer are discussed, along with future opportunities for reactor digitalization, adaptive control, and decarbonization through multiscale integration of data-driven models.
{"title":"Data across the scales: data-driven multiphase flow reactor modeling","authors":"Hamed Hoorijani, Yi Ouyang, Geraldine J Heynderickx, Kevin M Van Geem","doi":"10.1016/j.coche.2025.101193","DOIUrl":"10.1016/j.coche.2025.101193","url":null,"abstract":"<div><div>Multiphase flow reactors are fundamental to industrial processes, but they remain challenging to model due to their inherently multiscale dynamics. While experiments and traditional physics-based models have advanced our understanding, their cost and complexity limit the study of large-scale systems and applications. Data-driven modeling has emerged as a promising alternative, enabling efficient prediction of transport–reaction phenomena across scales. This review categorizes state-of-the-art approaches into three main groups: reduced order models that simplify high-fidelity simulations, hybrid physics-data approaches that couple data models with physics-based simulations, and fully data-driven frameworks that leverage operator-learning and neural surrogates. Particular emphasis is placed on cross-scale learning for developing data models, as well as on emerging architectures such as PINN-based frameworks, neural operators, and transformer-inspired GPT models. Challenges in data availability, interpretability, and geometry transfer are discussed, along with future opportunities for reactor digitalization, adaptive control, and decarbonization through multiscale integration of data-driven models.</div></div>","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"50 ","pages":"Article 101193"},"PeriodicalIF":6.8,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145412647","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 : 2025-10-16DOI: 10.1016/j.coche.2025.101192
Helga Kovacs
Noble metals (NMs) and rare earth elements (REEs) are becoming increasingly crucial in modern industry, particularly in green high-tech applications. As demand for these valuable metals continues to surge, their natural reserves are being depleted. Therefore, recovery of high-value metals from secondary minerals is essential for sustainable development. Phytomining has emerged as a sustainable approach for recovering NMs and REEs from alternative resources, offering a promising and sustainable solution for the production of these valuable metals. This study provides a glimpse of the overall phytoextraction-enrichment-extraction concept, with a particular focus on the final stage of extraction to reclaim NMs and REEs from bio-ores. Although phytomining has been effectively implemented for Ni across various scales, its application to NMs and REEs remains in its early stages. Within the phytoextraction-enrichment-extraction chain, the extraction phase plays a critical role in reclaiming these valuable elements. However, research on extracting NMs and REEs from biomass residues is currently scarce. This gap of knowledge likely arises from the novelty of the field, presenting both significant challenges and promising opportunities for further study. Moreover, existing extraction techniques have largely relied on pyrometallurgical and hydrometallurgical methods, both of which pose environmental concerns and entail high operational costs. Therefore, it is essential to investigate and advance eco-friendly, innovative techniques, with a particular focus on bio-metallurgy, to efficiently recover NMs and REEs from biomass ashes.
{"title":"Extraction of noble metals and rare earth elements using plants","authors":"Helga Kovacs","doi":"10.1016/j.coche.2025.101192","DOIUrl":"10.1016/j.coche.2025.101192","url":null,"abstract":"<div><div>Noble metals (NMs) and rare earth elements (REEs) are becoming increasingly crucial in modern industry, particularly in green high-tech applications. As demand for these valuable metals continues to surge, their natural reserves are being depleted. Therefore, recovery of high-value metals from secondary minerals is essential for sustainable development. Phytomining has emerged as a sustainable approach for recovering NMs and REEs from alternative resources, offering a promising and sustainable solution for the production of these valuable metals. This study provides a glimpse of the overall phytoextraction-enrichment-extraction concept, with a particular focus on the final stage of extraction to reclaim NMs and REEs from bio-ores. Although phytomining has been effectively implemented for Ni across various scales, its application to NMs and REEs remains in its early stages. Within the phytoextraction-enrichment-extraction chain, the extraction phase plays a critical role in reclaiming these valuable elements. However, research on extracting NMs and REEs from biomass residues is currently scarce. This gap of knowledge likely arises from the novelty of the field, presenting both significant challenges and promising opportunities for further study. Moreover, existing extraction techniques have largely relied on pyrometallurgical and hydrometallurgical methods, both of which pose environmental concerns and entail high operational costs. Therefore, it is essential to investigate and advance eco-friendly, innovative techniques, with a particular focus on bio-metallurgy, to efficiently recover NMs and REEs from biomass ashes.</div></div>","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"50 ","pages":"Article 101192"},"PeriodicalIF":6.8,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324347","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 : 2025-10-11DOI: 10.1016/j.coche.2025.101190
Tim M Nisbet, Alexander W van der Made
Direct air capture (DAC) is a crucial carbon dioxide removal (CDR) technology for achieving net-zero emissions by balancing atmospheric CO₂ release with removal. It serves two key roles: (a) when integrated with Carbon Capture and Storage (DAC-CCS), it enables permanent CO₂ removal to offset emissions from hard-to-abate sources like aviation; and (b) when combined with Carbon Capture and Utilization (DAC-CCU), it provides non-fossil CO₂ for producing defossilized fuels and zero-carbon chemicals. To fulfill these roles, DAC systems must be scalable and economically viable. While academic studies often focus on assessing sorbent performance under a limited range of weather conditions and for limited periods, we advocate that industrial scale deployment demands DAC systems with additional key features such as low pressure drop, high reliability for long periods (years) in a wide range of weather conditions (temperature, relative humidity), resistance to fouling from particulates in air, and without loss of performance by reingestion of CO2 depleted air. These key features are more commonly addressed in patent literature by companies nearing commercialization rather than in academic publications. Moreover, DAC technologies must be capital-efficient, and use low-cost, recyclable sorbents.
{"title":"Direct air capture of CO2: an industrial perspective","authors":"Tim M Nisbet, Alexander W van der Made","doi":"10.1016/j.coche.2025.101190","DOIUrl":"10.1016/j.coche.2025.101190","url":null,"abstract":"<div><div>Direct air capture (DAC) is a crucial carbon dioxide removal (CDR) technology for achieving net-zero emissions by balancing atmospheric CO₂ release with removal. It serves two key roles: (a) when integrated with Carbon Capture and Storage (DAC-CCS), it enables permanent CO₂ removal to offset emissions from hard-to-abate sources like aviation; and (b) when combined with Carbon Capture and Utilization (DAC-CCU), it provides non-fossil CO₂ for producing defossilized fuels and zero-carbon chemicals. To fulfill these roles, DAC systems must be scalable and economically viable. While academic studies often focus on assessing sorbent performance under a limited range of weather conditions and for limited periods, we advocate that industrial scale deployment demands DAC systems with additional key features such as low pressure drop, high reliability for long periods (years) in a wide range of weather conditions (temperature, relative humidity), resistance to fouling from particulates in air, and without loss of performance by reingestion of CO2 depleted air. These key features are more commonly addressed in patent literature by companies nearing commercialization rather than in academic publications. Moreover, DAC technologies must be capital-efficient, and use low-cost, recyclable sorbents.</div></div>","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"50 ","pages":"Article 101190"},"PeriodicalIF":6.8,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145263086","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 : 2025-10-04DOI: 10.1016/j.coche.2025.101189
Jacob W Toney , Aaron G Garrison , Weiliang Luo , Roland G St. Michel , Sukrit Mukhopadhyay , Heather J Kulik
Machine learning (ML) approaches enable screening of the vast chemical space of transition metal complexes (TMCs) at faster speeds than either experimental approaches or ab initio calculations, but their quality is highly dependent on the reference data used. Existing TMC datasets often leverage experimental structures, which biases methods trained on this data away from reactive configurations. Calculating properties of these TMCs also introduces challenges of spin and oxidation state assignment. Recent work on generating hypothetical TMCs with realistic connectivity and geometry has demonstrated promise to extend datasets beyond experimental structures, especially when combined with ML approaches to identify complexes with desirable properties. Experimental measurements would be ideal to train and/or test these models but are often scarce for TMCs, especially for those that are catalytically active. Thus, properties calculated with electronic structure theory are a popular alternative choice for training ML models. However, TMCs are challenging for many conventional electronic structure methods, and few benchmark datasets exist to assess which methods are most reliable and cost-effective. Many of the recommended methods are computationally demanding, leading to the use of neural network potentials as surrogate models for large-scale screening. By utilizing emerging tools for TMC structure generation and suitable electronic structure methods, increasingly high-quality datasets will be curated to enhance the predictive power of ML approaches to discover novel TMCs, including in the development of neural network potentials. By more accurately predicting TMC properties, promising and practical candidates for catalysis, photosensitizers, molecular devices, and medicine will be identified.
{"title":"Exploring beyond experiment: generating high-quality datasets of transition metal complexes with quantum chemistry and machine learning","authors":"Jacob W Toney , Aaron G Garrison , Weiliang Luo , Roland G St. Michel , Sukrit Mukhopadhyay , Heather J Kulik","doi":"10.1016/j.coche.2025.101189","DOIUrl":"10.1016/j.coche.2025.101189","url":null,"abstract":"<div><div>Machine learning (ML) approaches enable screening of the vast chemical space of transition metal complexes (TMCs) at faster speeds than either experimental approaches or <em>ab initio</em> calculations, but their quality is highly dependent on the reference data used. Existing TMC datasets often leverage experimental structures, which biases methods trained on this data away from reactive configurations. Calculating properties of these TMCs also introduces challenges of spin and oxidation state assignment. Recent work on generating hypothetical TMCs with realistic connectivity and geometry has demonstrated promise to extend datasets beyond experimental structures, especially when combined with ML approaches to identify complexes with desirable properties. Experimental measurements would be ideal to train and/or test these models but are often scarce for TMCs, especially for those that are catalytically active. Thus, properties calculated with electronic structure theory are a popular alternative choice for training ML models. However, TMCs are challenging for many conventional electronic structure methods, and few benchmark datasets exist to assess which methods are most reliable and cost-effective. Many of the recommended methods are computationally demanding, leading to the use of neural network potentials as surrogate models for large-scale screening. By utilizing emerging tools for TMC structure generation and suitable electronic structure methods, increasingly high-quality datasets will be curated to enhance the predictive power of ML approaches to discover novel TMCs, including in the development of neural network potentials. By more accurately predicting TMC properties, promising and practical candidates for catalysis, photosensitizers, molecular devices, and medicine will be identified.</div></div>","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"50 ","pages":"Article 101189"},"PeriodicalIF":6.8,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145217613","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 : 2025-09-19DOI: 10.1016/j.coche.2025.101181
Maoyuan Liao , Leilei Xiang , Yu Wang , Yuhao Fu , Jean D Harindintwali , Xin Jiang , Martin Elsner , Matthias C Rillig , Fang Wang
The co-contamination of agricultural soils by microplastics (MPs), antibiotics, and antibiotic resistance genes (ARGs) is an emerging environmental concern with significant ecological and public health implications. This review explores the sources, interactions, and consequences of MPs and antibiotics/ARGs co-occurrence in soil systems. Agricultural practices, such as manure application, wastewater irrigation, and sewage sludge amendment, are primary contributors to this co-contamination. MPs not only serve as physical vectors but also actively interact with antibiotics and ARGs through processes like adsorption, aging, and biofilm formation, enhancing the emergence and dissemination of resistance genes. These interactions disrupt soil physicochemical properties and microbial communities, impairing soil health and reducing crop productivity. Furthermore, the accumulation of MPs and ARGs in edible plants raises concerns about human exposure through the food chain. Emerging evidence links such exposure to health risks, including metabolic, cardiovascular, neurological, and gastrointestinal disorders. Understanding the mechanisms underlying this co-contamination is critical for informing risk assessments and guiding mitigation strategies to protect soil ecosystems and public health.
{"title":"Microplastics and antibiotics in agricultural soil: mechanisms and implications of co-contamination","authors":"Maoyuan Liao , Leilei Xiang , Yu Wang , Yuhao Fu , Jean D Harindintwali , Xin Jiang , Martin Elsner , Matthias C Rillig , Fang Wang","doi":"10.1016/j.coche.2025.101181","DOIUrl":"10.1016/j.coche.2025.101181","url":null,"abstract":"<div><div>The co-contamination of agricultural soils by microplastics (MPs), antibiotics, and antibiotic resistance genes (ARGs) is an emerging environmental concern with significant ecological and public health implications. This review explores the sources, interactions, and consequences of MPs and antibiotics/ARGs co-occurrence in soil systems. Agricultural practices, such as manure application, wastewater irrigation, and sewage sludge amendment, are primary contributors to this co-contamination. MPs not only serve as physical vectors but also actively interact with antibiotics and ARGs through processes like adsorption, aging, and biofilm formation, enhancing the emergence and dissemination of resistance genes. These interactions disrupt soil physicochemical properties and microbial communities, impairing soil health and reducing crop productivity. Furthermore, the accumulation of MPs and ARGs in edible plants raises concerns about human exposure through the food chain. Emerging evidence links such exposure to health risks, including metabolic, cardiovascular, neurological, and gastrointestinal disorders. Understanding the mechanisms underlying this co-contamination is critical for informing risk assessments and guiding mitigation strategies to protect soil ecosystems and public health.</div></div>","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"50 ","pages":"Article 101181"},"PeriodicalIF":6.8,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099647","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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