Current Opinion in Chemical Engineering最新文献

{"title":"Customized membranes: needs and opportunities for moving beyond conventional interfacial polymerization for desalination membranes","authors":"Mert Can Hacıfazlıoğlu ,&nbsp;Salman Ahmadipouya ,&nbsp;Deniz Ipekci ,&nbsp;Ying Li ,&nbsp;Manish Kumar ,&nbsp;Jamie Warner ,&nbsp;Yuepeng Zhang ,&nbsp;Jeffrey R. McCutcheon","doi":"10.1016/j.coche.2025.101151","DOIUrl":"10.1016/j.coche.2025.101151","url":null,"abstract":"<div><div>Reverse osmosis (RO) has constituted most of the installed desalination capacity in recent decades. Commercial membranes offer excellent selectivity and reasonable productivity. These membranes, however, suffer from several weaknesses that stem from the use of interfacial polymerization as a means of manufacturing. The inability to control thickness, adjust easily to new chemistries, and avoid surface roughness that enhances foulilng propensity are a few of the weaknesses to conventional membrane fabrication. Numerous materials have been proposed as alternatives to polyamide for RO in recent decades. However, in spite of numerous publications on these new materials, it is remarkable to see how <em>none</em> has even come close to succeeding in replacing conventional RO membrane materials in a commercial setting. This is largely because many of these new materials are incompatible with existing membrane manufacturing approaches such as interfacial polymerization. We must be able to process new materials into thin, defect-free films on conventional supports. This is a significant hurdle for new material adoption in membranes today. New manufacturing methods are needed to address the inherent weaknesses of interfacial polymerization for polyamide and the general processing of newly discovered materials into thin film composite membranes for RO and nanofiltration platforms.</div></div>","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"49 ","pages":"Article 101151"},"PeriodicalIF":8.0,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262063","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":"Erratum to “Photogenerated charge carriers in photocatalytic materials for solar hydrogen evolution” [Curr Opin Chem Eng 47 (2025) 1087]","authors":"Nguyên Hoàng Ly ,&nbsp;Lalitha Gnanasekaran ,&nbsp;Tejraj M Aminabhavi ,&nbsp;Yasser Vasseghian ,&nbsp;Sang-Woo Joo","doi":"10.1016/j.coche.2025.101149","DOIUrl":"10.1016/j.coche.2025.101149","url":null,"abstract":"","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"49 ","pages":"Article 101149"},"PeriodicalIF":8.0,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144230984","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":"Kinetic modeling for radical polymerization and depolymerization","authors":"Yue Fang,&nbsp;Hanyu Gao","doi":"10.1016/j.coche.2025.101152","DOIUrl":"10.1016/j.coche.2025.101152","url":null,"abstract":"<div><div>Radical polymerization and its reverse process, radical depolymerization, are central to modern polymer manufacturing and recycling strategies. This review highlights recent advances in understanding the mechanisms and modeling techniques. Enhanced deterministic and stochastic models have successfully described the complexities of radical polymerization processes. Parallel breakthroughs in radical depolymerization kinetics, particularly through end-group-assisted unzipping and visible-light activation, have facilitated efficient monomer recovery under milder reaction conditions. Additionally, advanced modeling leveraging quantum chemistry and machine learning and experimental validations has significantly boosted predictive accuracy. By integrating state-of-the-art kinetic modeling with sustainable design principles, researchers are progressively establishing foundations for closed-loop polymer lifecycles.</div></div>","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"49 ","pages":"Article 101152"},"PeriodicalIF":8.0,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221319","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":"Next-generation nanobioengineered materials for micro- and nano-plastic detection","authors":"Kshitij RB Singh ,&nbsp;Jay Singh ,&nbsp;Shyam S. Pandey","doi":"10.1016/j.coche.2025.101102","DOIUrl":"10.1016/j.coche.2025.101102","url":null,"abstract":"<div><div>Micro- and nano-plastics (MNPs) have garnered global attention as pervasive and emerging contaminants due to their potential risks to humans and the environment. Their toxicity, bioaccumulation, and oxidative stress disrupt ecosystems, demanding an urgent need for risk monitoring. A thorough understanding of the extent of the problem and the need for an amicable solution utilizing nanobioengineered materials is highly desired owing to their unique properties, such as tailored surface chemistry, specificity, and high sensitivity. These properties allow them to interact with the contaminants at the molecular level, making them suitable for MNP detection. Moreover, they have the potential to overcome challenges, such as the complex environmental matrices, data reproducibility, and inefficient sampling faced by pre-existing techniques, making them a promising tool for detecting MNPs. This review presents the importance of next-generation nanobioengineered materials for developing biosensors for MNP detection, and efforts have also been directed to enrich the awareness of the researchers working in this domain by providing innovative solutions to challenges faced by pre-existing techniques. Additionally, utilizing these materials in biosensing devices helps to attain the Sustainable Development Goals of the United Nations by bridging Nano-biotechnology and environmental science, fostering future research, and shaping policies to combat MNP pollution.</div></div>","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"48 ","pages":"Article 101102"},"PeriodicalIF":8.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144240724","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":"Plastic carbon in the ocean","authors":"Shiye Zhao ,&nbsp;Lixin Zhu","doi":"10.1016/j.coche.2025.101101","DOIUrl":"10.1016/j.coche.2025.101101","url":null,"abstract":"<div><div>The annual influx of ∼11 million metric tons of plastic debris into the ocean poses a significant and growing threat to the marine environment globally. Additionally, plastic debris serves as a source of allochthonous carbon to marine ecosystems — a factor that has only drawn scientific attention recently. Herein, we synthesize recent evidence about this new form of plastic carbon in the ocean by addressing it as three components: particulate organic carbon of plastic (<em>pPOC</em>), dissolved organic carbon leaching from plastic (<em>pDOC</em>), and biogenic organic carbon of plastic-attached biofilm (<em>pBOC</em>). Current estimates of <em>pPOC</em> and <em>pDOC</em> account for only a modest fraction of natural carbon pool in the ocean, but their portions are expected to increase. <em>pDOC</em> is highly heterogenous, varying by polymer types, and has been shown to influence seawater biogeochemistry as well as the structure and function of microbial communities. Furthermore, biofilm biomass colonizing on plastic debris can utilize the <em>pP</em>OC and <em>pDOC</em> as carbon sources. Current evidences proved the incorporation of plastic carbon into microbial biomass, which consequently affects the carbon and nitrogen cycling. Given these emerging insights, we further suggest specific research questions aimed at stimulating research on the nature, dynamics, and role of plastic carbon in the ocean.</div></div>","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"48 ","pages":"Article 101101"},"PeriodicalIF":8.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144240723","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":"Industrial Agentic AI and generative modeling in complex systems","authors":"Mohammad Reza Boskabadi ,&nbsp;Yudong Cao ,&nbsp;Behnam Khadem ,&nbsp;William Clements ,&nbsp;Z. Nevin Gerek ,&nbsp;Eric Reuthe ,&nbsp;Abhishek Sivaram ,&nbsp;Christopher J Savoie ,&nbsp;Seyed Soheil Mansouri","doi":"10.1016/j.coche.2025.101150","DOIUrl":"10.1016/j.coche.2025.101150","url":null,"abstract":"<div><div>Manufacturing, consumer, transportation, and supply chain processes present significant challenges in monitoring, control, and design due to their inherently nonlinear nature and the difficulty of measuring critical variables in real time. The convergence of major innovations from the computer science field has the potential to revolutionize the engineering and control of complex industrial systems. Digital twinning and process simulation have been a staple of computers in process engineering for decades now. However, the advent of advanced sensor systems and big data integration, combined with generative AI and agentified AI (classic and quantum) systems, allows for much more granular and autonomous process control and real-time optimization of complex systems. Advanced process modeling, Agentic AI, and generative AI models have emerged as powerful tools to address the challenges of complex nonlinear systems. We propose here an integrated systems feedback and control architecture (SIC: Sense, Infer, Control) that leverages complementary process knowledge for enhanced real-time monitoring and decision-making, fully integrated into control system functions and the accompanying sensors. In this paper, we explore this integration of generative models in agentic AI ensembles into industrial processes through the lens of four recent industrial case studies: (1) the real-time optimization of motorsports strategy, (2) the development of indirect (soft) sensors for sustainable large-scale manufacturing operations, (3) the creation of sensor data-driven personalized health and cosmetic chemical formulations, and (4) the design of biomanufacturing systems using quantum and classic Agentic AI. These examples demonstrate how agentic and generative models, combined with full-scale process simulation and digital twinning, effectively augment process control, enabling advanced solutions for process optimization, quality improvement, and sustainable operations. The proposed SIC systems architecture serves to enhance process control automation by capturing complex nonlinear patterns and leveraging easily measurable variables. Generative models bridge gaps in process understanding, sensor technologies, control, and monitoring, offering actionable insights for efficient and informed decision-making across diverse industrial applications.</div></div>","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"48 ","pages":"Article 101150"},"PeriodicalIF":8.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212636","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":"Innovations in chemical process control: challenges and opportunities","authors":"Fernando V Lima ,&nbsp;Yuhe Tian ,&nbsp;Helen E Durand ,&nbsp;Joel A Paulson ,&nbsp;Lorenz T Biegler","doi":"10.1016/j.coche.2025.101148","DOIUrl":"10.1016/j.coche.2025.101148","url":null,"abstract":"<div><div>This paper provides a current perspective on innovations in chemical process control based on Mid-Atlantic Process Control Academy meetings held at Carnegie Mellon University (in 2019), Ohio State University (in 2023), and West Virginia University (in 2024), with the next one scheduled at Wayne State University (in 2025). These meetings were introduced in 2019 with the main objectives of discussing the current directions on model predictive control (MPC) as well as new breakthroughs in the process systems engineering community associated with process control. Topics addressed in this paper in the context of these meetings include process operability and flexibility, quantum computing, Bayesian optimization, and nonlinear and economic model predictive control. For each topic, recent theory, applications, and software infrastructure developments are discussed, and current challenges and opportunities for future research directions are outlined.</div></div>","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"48 ","pages":"Article 101148"},"PeriodicalIF":8.0,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144168686","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":"Accurate model needs for desalination systems","authors":"Wajeha Tauqir ,&nbsp;Pengfei Xu ,&nbsp;George M Bollas ,&nbsp;Matthew D Stuber","doi":"10.1016/j.coche.2025.101147","DOIUrl":"10.1016/j.coche.2025.101147","url":null,"abstract":"<div><div>Modeling serves as the nexus connecting design, control, and optimization in desalination process systems while also providing insights into the interplay between process-level and property-level phenomena. Modeling desalination processes presents challenges due to the complex thermophysical properties and nonideality of multielectrolyte solutions, especially at high concentrations. In this mini-review, we examine the current state of several widely used process modeling tools, their features, and the adaptability to modeling state-of-the-art desalination process systems. We also discuss thermodynamic models of electrolyte solutions and their ability to accurately predict the thermodynamic properties of aqueous multielectrolyte solutions. We conclude that refining and tailoring fundamental thermodynamic models to address the complexities of high-concentration regimes is essential for the design of advanced desalination systems and achieving improvements in energetic and economic efficiencies.</div></div>","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"48 ","pages":"Article 101147"},"PeriodicalIF":8.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107810","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":"Radical-chemistry-driven polymer synthesis, modification, and recycling: trends in modeling to upgrade our knowledge and process design","authors":"Dagmar R D’hooge","doi":"10.1016/j.coche.2025.101144","DOIUrl":"10.1016/j.coche.2025.101144","url":null,"abstract":"<div><div>Polymer synthesis, modification, and recycling are important polymer reaction engineering (PRE) processes that rely in many cases on radical chemistry. The optimal settings and innovation depend strongly on the characterization degree, which is complicated by the many chain lengths, compositions, and topologies. To grasp macromolecular variations, we need to bridge experimental and modeling methods, the latter the focus of the present work. Emphasis is on (i) faster kinetic Monte Carlo simulations; (ii) the striving for universal solvers; (iii) protocols for parameter determination; (iv) modeling outputs for structure-property relationships; and (v) optimization via artificial intelligence and machine learning methods.</div></div>","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"48 ","pages":"Article 101144"},"PeriodicalIF":8.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144088925","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":"Oxidation of alcohols in photocatalytic hydrogen production: from sacrifice to valorization","authors":"Patricia Garcia-Muñoz ,&nbsp;Fernando Fresno","doi":"10.1016/j.coche.2025.101146","DOIUrl":"10.1016/j.coche.2025.101146","url":null,"abstract":"<div><div>Photocatalytic hydrogen production, even if it should ideally be performed from pure water (i.e. <em>water splitting</em>), benefits from the presence of an <em>easily</em> oxidized reagent, either inorganic (classically sulfite/sulfide) or organic (classically methanol) that scavenges photoproduced holes and alleviates the process form the kinetically hindered, multi-electron process of water oxidation to molecular oxygen. Even if pioneering works of the photocatalytic reaction between alcohols and water examined the outcome of the oxidation branch of the reaction, the use of these reagents passed through a period in which reporting only hydrogen evolution became common practice, assuming total oxidation and taking the consumption of the organic as a <em>sacrifice</em> for hydrogen production. However, in more recent years, the oxidation outcome of the reaction has regained attention, mainly because of the interest in coupling photocatalysis with biomass utilization. Thus, the <em>valorization</em> of biomass-derived alcohol hole scavengers has become an interesting topic in photocatalysis research. Here, we highlight some recent works on this topic, selecting those that have received more attention in the last 2–5 years: polyol (glycerol, glucose) valorization, transformations of furfuryl alcohol and 5-hydroxymethyl furfural, and C-C coupling reactions starting from alcohols. In our opinion, these represent promising niches for the application of photocatalytic processes.</div></div>","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"49 ","pages":"Article 101146"},"PeriodicalIF":8.0,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943484","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}