{"title":"OFC: Outside Front Cover","authors":"","doi":"10.1016/S2666-9528(25)00023-8","DOIUrl":"10.1016/S2666-9528(25)00023-8","url":null,"abstract":"","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 3","pages":"Page OFC"},"PeriodicalIF":9.1,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Outside Back Cover","authors":"","doi":"10.1016/S2666-9528(25)00032-9","DOIUrl":"10.1016/S2666-9528(25)00032-9","url":null,"abstract":"","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 3","pages":"Page OBC"},"PeriodicalIF":9.1,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"OFC: Outside Front Cover","authors":"","doi":"10.1016/S2666-9528(25)00006-8","DOIUrl":"10.1016/S2666-9528(25)00006-8","url":null,"abstract":"","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 2","pages":"Page OFC"},"PeriodicalIF":9.1,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594170","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}
{"title":"Outside Back Cover","authors":"","doi":"10.1016/S2666-9528(25)00015-9","DOIUrl":"10.1016/S2666-9528(25)00015-9","url":null,"abstract":"","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 2","pages":"Page OBC"},"PeriodicalIF":9.1,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594167","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}
{"title":"Critical evaluation of feature importance assessment in FFNN-based models for predicting Kamlet-Taft parameters","authors":"Yoshiyasu Takefuji","doi":"10.1016/j.gce.2025.01.003","DOIUrl":"10.1016/j.gce.2025.01.003","url":null,"abstract":"<div><div>Mohan et al. developed a feed-forward neural network (FFNN) model to predict Kamlet-Taft parameters using quantum chemically derived features, achieving notable predictive accuracy. However, this study raises concerns about conflating prediction accuracy with feature importance accuracy, as high R<sup>2</sup> and low root mean square error (RMSE) do not guarantee valid feature importance assessments. The reliance on SHapley Additive exPlanations (SHAP) for feature evaluation is problematic due to model-specific biases that could misrepresent true associations. A broader understanding of data distribution, statistical relationships, and significance testing through p-values is essential to rectify this. This paper advocates for employing robust statistical methods, like Spearman's correlation, to effectively assess genuine associations and mitigate biases in feature importance analysis.</div></div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 3","pages":"Pages 289-290"},"PeriodicalIF":9.1,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhen Song , Weifeng Shen , Zhiwen Qi , José María Ponce Ortega
{"title":"Artificial intelligence for chemical engineering","authors":"Zhen Song , Weifeng Shen , Zhiwen Qi , José María Ponce Ortega","doi":"10.1016/j.gce.2025.01.001","DOIUrl":"10.1016/j.gce.2025.01.001","url":null,"abstract":"","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 2","pages":"Pages 137-138"},"PeriodicalIF":9.1,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594171","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}
{"title":"OFC: Outside Front Cover","authors":"","doi":"10.1016/S2666-9528(24)00070-0","DOIUrl":"10.1016/S2666-9528(24)00070-0","url":null,"abstract":"","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 1","pages":"Page OFC"},"PeriodicalIF":9.1,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704748","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}
{"title":"Outside Back Cover","authors":"","doi":"10.1016/S2666-9528(24)00079-7","DOIUrl":"10.1016/S2666-9528(24)00079-7","url":null,"abstract":"","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 1","pages":"Page OBC"},"PeriodicalIF":9.1,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704747","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}
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}
{"title":"Evaluation of gum arabic and gelatine coacervated microcapsule morphology and core oil encapsulation efficiency by combining the spreading coefficient and two component surface energy theory","authors":"Qun Huang, Zhibing Zhang","doi":"10.1016/j.gce.2024.10.006","DOIUrl":"10.1016/j.gce.2024.10.006","url":null,"abstract":"<div><div>Microcapsules containing various flavour/fragrance oils with different properties were fabricated using gelatine and gum arabic by complex coacervation. The surface properties (surface polarity and the spreading coefficients) of core oils were investigated in order to evaluate their effects on the capsule morphology and encapsulation efficiency based on a spreading coefficient and two component surface energy theory. Contact angles, interfacial tensions, and surface polarities were measured, and results were discussed with respect to the internal structure as well as encapsulation efficiency of different oil microcapsules. The thermodynamic spreading coefficients theory did not give an exactly accurate prediction of capsule morphology using high molecular weight biopolymer as the wall material in this work. Notwithstanding, the morphology predictions for different oil microcapsules are holistically consistent with the values of their encapsulation efficiency. Also, it has been found that the encapsulation efficiency increased with the decreasing surface polarity of the core oil holistically.</div></div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 3","pages":"Pages 420-429"},"PeriodicalIF":9.1,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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