Green Chemical Engineering最新文献

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IF 9.1

Green Chemical Engineering Pub Date : 2025-03-10 DOI: 10.1016/S2666-9528(25)00006-8

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Outside Back Cover

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Green Chemical Engineering Pub Date : 2025-03-10 DOI: 10.1016/S2666-9528(25)00015-9

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Artificial intelligence for chemical engineering

IF 9.1

Green Chemical Engineering Pub Date : 2025-01-02 DOI: 10.1016/j.gce.2025.01.001

Zhen Song , Weifeng Shen , Zhiwen Qi , José María Ponce Ortega

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IF 9.1

Green Chemical Engineering Pub Date : 2024-11-21 DOI: 10.1016/S2666-9528(24)00070-0

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Green Chemical Engineering Pub Date : 2024-11-21 DOI: 10.1016/S2666-9528(24)00079-7

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Physics-informed machine learning to predict solvatochromic parameters of designer solvents with case studies in CO2 and lignin dissolution 通过物理信息机器学习预测设计溶剂的溶解变色参数--二氧化碳和木质素溶解案例研究

IF 9.1

Green Chemical Engineering Pub Date : 2024-11-17 DOI: 10.1016/j.gce.2024.11.003

Mood Mohan , Nikhitha Gugulothu , Sreelekha Guggilam , T. Rajitha Rajeshwar , Michelle K. Kidder , Jeremy C. Smith

{"title":"Physics-informed machine learning to predict solvatochromic parameters of designer solvents with case studies in CO2 and lignin dissolution","authors":"Mood Mohan , Nikhitha Gugulothu , Sreelekha Guggilam , T. Rajitha Rajeshwar , Michelle K. Kidder , Jeremy C. Smith","doi":"10.1016/j.gce.2024.11.003","DOIUrl":"10.1016/j.gce.2024.11.003","url":null,"abstract":"<div><div>The polarity of solvents plays a critical role in various research applications, particularly in their solubilities. Polarity is conveniently characterized by the Kamlet-Taft parameters that is, the hydrogen bonding acidity (<em>α</em>), the basicity (<em>β</em>), and the polarizability (<em>π∗</em>). Obtaining Kamlet-Taft parameters is very important for designer solvents, namely ionic liquids (ILs) and deep eutectic solvents (DESs). However, given the unlimited theoretical number of combinations of ionic pairs in ILs and hydrogen-bond donor/acceptor pairs in DESs, experimental determination of their Kamlet-Taft parameters is impractical. To address this, the present study developed two different machine learning (ML) algorithms to predict Kamlet-Taft parameters for designer solvents using quantum chemically derived input features. The ML models developed in the present study showed accurate predictions with high determination coefficient (R<sup>2</sup>) and low root mean square error (RMSE) values. Further, in the context of present interest in the circular bioeconomy, the relationship between the basicities and acidities of designer solvents and their ability to dissolve lignin and carbon dioxide (CO<sub>2</sub>) is discussed. Our method thus guides the design of effective solvents with optimal Kamlet-Taft parameter values dissolving and converting biomass and CO<sub>2</sub> into valuable chemicals.</div></div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 2","pages":"Pages 275-287"},"PeriodicalIF":9.1,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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COSMO-RS screening of organic mixtures for membrane extraction of aromatic amines: TOPO-based mixtures as promising solvents

IF 9.1

Green Chemical Engineering Pub Date : 2024-10-15 DOI: 10.1016/j.gce.2024.10.003

Gilles Van Eygen , Catherine Echezuria , Anita Buekenhoudt , João A.P. Coutinho , Bart Van der Bruggen , Patricia Luis

{"title":"COSMO-RS screening of organic mixtures for membrane extraction of aromatic amines: TOPO-based mixtures as promising solvents","authors":"Gilles Van Eygen , Catherine Echezuria , Anita Buekenhoudt , João A.P. Coutinho , Bart Van der Bruggen , Patricia Luis","doi":"10.1016/j.gce.2024.10.003","DOIUrl":"10.1016/j.gce.2024.10.003","url":null,"abstract":"<div><div>Aromatic amines are crucial in pharmaceuticals, but their synthesis is challenging due to unfavorable reaction equilibria and the use of costly, environmentally unfriendly methods. This study presents a membrane extraction (ME) process for <em>in situ</em> product removal (ISPR) of aromatic amines. Using a supported liquid membrane (SLM), <span><math><mrow><mi>α</mi></mrow></math></span>-methylbenzylamine (MBA) and 1-methyl-3-phenylpropylamine (MPPA) were separated from isopropyl amine (IPA). Conductor-like Screening Model for Real Solvents (COSMO-RS) was employed to screen over 200 organic mixtures, identifying twelve mixtures based on trioctylphosphine oxide (TOPO), lidocaine, and menthol as solvent candidates, due to their hydrophobicity. These mixtures were analysed for critical solvent properties including density, viscosity, hydrophobicity, and H-bonding interactions. ME tests showed TOPO-thymol had the highest solvent residual and selectivity. Moreover, TOPO-thymol demonstrated solute fluxes of 9.0±3.0 g/(m<sup>2</sup> h) for MBA, 16.5±5.4 g/(m<sup>2</sup> h) for MPPA, and 0.7±0.3 g/(m<sup>2</sup> h) for IPA, with selectivity values of 12.4±0.8 for MBA/IPA and 22.8±1.4 for MPPA/IPA. Compared to undecane, which had lower selectivity values of 6.9±0.8 for MBA/IPA and 10.1±1.3 for MPPA/IPA, TOPO-thymol showed superior selectivity, indicating its promise as an extractant for ME applications.</div></div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 2","pages":"Pages 263-274"},"PeriodicalIF":9.1,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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OFC: Outside Front Cover OFC：封面外侧

IF 9.1

Green Chemical Engineering Pub Date : 2024-09-03 DOI: 10.1016/S2666-9528(24)00045-1

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IF 9.1

Green Chemical Engineering Pub Date : 2024-09-03 DOI: 10.1016/S2666-9528(24)00053-0

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Advanced data-driven techniques in AI for predicting lithium-ion battery remaining useful life: a comprehensive review

IF 9.1

Green Chemical Engineering Pub Date : 2024-09-03 DOI: 10.1016/j.gce.2024.09.001

Sijing Wang , Ruoyu Zhou , Yijia Ren , Meiyuan Jiao , Honglai Liu , Cheng Lian

{"title":"Advanced data-driven techniques in AI for predicting lithium-ion battery remaining useful life: a comprehensive review","authors":"Sijing Wang , Ruoyu Zhou , Yijia Ren , Meiyuan Jiao , Honglai Liu , Cheng Lian","doi":"10.1016/j.gce.2024.09.001","DOIUrl":"10.1016/j.gce.2024.09.001","url":null,"abstract":"<div><div>As artificial intelligence (AI) technology evolves, data-driven approaches are gaining attention in predicting lithium-ion battery's remaining useful life (RUL). Indeed, accurate RUL prediction is challenging, primarily because of the complex nature of the work and dynamic shifts in model parameters. To address these challenges, this article comprehensively explores five significant publicly accessible lithium-ion battery datasets, encompassing diverse usage conditions and battery types, offering researchers a rich repository of experimental data. In particular, we not only provide detailed information and access addresses for each dataset, but also present, four innovative methods for battery aging health factor extraction. These methods, based on advanced AI techniques, are able to effectively identify and quantify key indicators of battery performance degradation, thereby enhancing the precision and dependability of RUL prediction. Additionally, the article identifies major challenges faced by current predictive techniques, including data quality, model generalization capabilities, and computational cost, highlighting the need for research focused on dataset diversity, multiple algorithm fusion, and hybrid physical-data-driven models to enhance prediction accuracy. We believe that this review will help researchers gain a comprehensive understanding of RUL estimation methods and promote the development of AI in battery.</div></div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 2","pages":"Pages 139-153"},"PeriodicalIF":9.1,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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