Chem & Bio Engineering最新文献

Advanced Separation Materials and Processes

Chem & Bio Engineering Pub Date : 2025-02-27 DOI: 10.1021/cbe.5c0000910.1021/cbe.5c00009

Zongbi Bao*, and , Banglin Chen*,

引用次数: 0

Advanced Separation Materials and Processes. 先进的分离材料和工艺。

Chem & Bio Engineering Pub Date : 2025-02-27 DOI: 10.1021/cbe.5c00009

Zongbi Bao, Banglin Chen

引用次数: 0

Nucleic Acid Framework-Enabled Spatial Organization for Biological Applications

Chem & Bio Engineering Pub Date : 2024-12-30 DOI: 10.1021/cbe.4c0016410.1021/cbe.4c00164

Rui Zhang, Xiaolei Zuo* and Fangfei Yin*,

{"title":"Nucleic Acid Framework-Enabled Spatial Organization for Biological Applications","authors":"Rui Zhang, Xiaolei Zuo* and Fangfei Yin*, ","doi":"10.1021/cbe.4c0016410.1021/cbe.4c00164","DOIUrl":"https://doi.org/10.1021/cbe.4c00164https://doi.org/10.1021/cbe.4c00164","url":null,"abstract":"Nucleic acid frameworks (NAFs) are artificially prepared from natural nucleic acids with a precise size and structure. DNA origami exhibits controllable 2D lamellar structure and thus is easily used to construct 3D structures with different morphologies. Tetrahedral DNA nanostructures (TDNs) are prepared with four DNA strands that hybridize to each other with a tetrahedral structure. Here we summarize molecular spatial organization with DNA origami and TDNs as models for 2D- and 3D-recombinations, discuss NAF-based biomimicking of proteins and biomembranes, and introduce the identification probes, functional groups, and intercalators for biosensing, bioimaging, and nanomedicine therapy. NAFs are also extended to applications to guide the formation of inorganic nanoparticles with precise size and structure. Thus, the NAFs exhibit special organization, are easy to functionalize, and are becoming an important platform for interdisciplinary study and applications, such as nanotechnology, biochemistry, synthetic biology, and nanomedicine.","PeriodicalId":100230,"journal":{"name":"Chem & Bio Engineering","volume":"2 2","pages":"71–86 71–86"},"PeriodicalIF":0.0,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/cbe.4c00164","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143496258","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}

引用次数: 0

Nucleic Acid Framework-Enabled Spatial Organization for Biological Applications. 用于生物应用的核酸框架空间组织。

Chem & Bio Engineering Pub Date : 2024-12-30 eCollection Date: 2025-02-27 DOI: 10.1021/cbe.4c00164

Rui Zhang, Xiaolei Zuo, Fangfei Yin

引用次数: 0

Elucidating the Role of Water on Limonene Oxidation with H₂O₂ over γ-Al₂O₃. 阐明水在γ-Al2O3 与 H2O2 的柠檬烯氧化作用中的作用

Chem & Bio Engineering Pub Date : 2024-12-18 eCollection Date: 2025-02-27 DOI: 10.1021/cbe.4c00151

Hsi-Hsin Lin, Jedidiah Chukwusom, Hyunju Lee, Brent H Shanks

{"title":"Elucidating the Role of Water on Limonene Oxidation with H2O2 over γ-Al2O3.","authors":"Hsi-Hsin Lin, Jedidiah Chukwusom, Hyunju Lee, Brent H Shanks","doi":"10.1021/cbe.4c00151","DOIUrl":"10.1021/cbe.4c00151","url":null,"abstract":"Limonene oxide, which is produced from limonene epoxidation, is a valuable molecule that can be applied in flavor, fragrance, and renewable polymer applications. A catalytic reaction system using H2O2 with γ-Al2O3 and ethyl acetate (EtOAc) as the solvent has been explored as an effective system for this reaction. In these previous studies, a number of postulates have been proposed as to how water affects the reaction; therefore, the focus of this work is to elucidate the role of water in limonene epoxidation. While not impacting the selectivity to limonene oxide, the amount of water in the reaction system is shown to significantly impact the limonene reactivity. Furthermore, through both addition of excess water and removal of water with a Dean-Stark apparatus, the control of the H2O2/H2O ratio is demonstrated to be the primary factor controlling reactivity. In contrast, changes in limonene concentrations for a specific H2O2/H2O ratio are shown to have little impact on the reaction rate. This study shows that the competitive adsorption of H2O2 and water on the catalyst surface is key in explaining the water impact on the reaction performance.","PeriodicalId":100230,"journal":{"name":"Chem & Bio Engineering","volume":"2 2","pages":"123-131"},"PeriodicalIF":0.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11873845/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560536","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}

引用次数: 0

Elucidating the Role of Water on Limonene Oxidation with H2O2 over γ-Al2O3

Chem & Bio Engineering Pub Date : 2024-12-18 DOI: 10.1021/cbe.4c0015110.1021/cbe.4c00151

Hsi-Hsin Lin, Jedidiah Chukwusom, Hyunju Lee and Brent H. Shanks*,

{"title":"Elucidating the Role of Water on Limonene Oxidation with H2O2 over γ-Al2O3","authors":"Hsi-Hsin Lin, Jedidiah Chukwusom, Hyunju Lee and Brent H. Shanks*, ","doi":"10.1021/cbe.4c0015110.1021/cbe.4c00151","DOIUrl":"https://doi.org/10.1021/cbe.4c00151https://doi.org/10.1021/cbe.4c00151","url":null,"abstract":"Limonene oxide, which is produced from limonene epoxidation, is a valuable molecule that can be applied in flavor, fragrance, and renewable polymer applications. A catalytic reaction system using H2O2 with γ-Al2O3 and ethyl acetate (EtOAc) as the solvent has been explored as an effective system for this reaction. In these previous studies, a number of postulates have been proposed as to how water affects the reaction; therefore, the focus of this work is to elucidate the role of water in limonene epoxidation. While not impacting the selectivity to limonene oxide, the amount of water in the reaction system is shown to significantly impact the limonene reactivity. Furthermore, through both addition of excess water and removal of water with a Dean–Stark apparatus, the control of the H2O2/H2O ratio is demonstrated to be the primary factor controlling reactivity. In contrast, changes in limonene concentrations for a specific H2O2/H2O ratio are shown to have little impact on the reaction rate. This study shows that the competitive adsorption of H2O2 and water on the catalyst surface is key in explaining the water impact on the reaction performance.","PeriodicalId":100230,"journal":{"name":"Chem & Bio Engineering","volume":"2 2","pages":"123–131 123–131"},"PeriodicalIF":0.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/cbe.4c00151","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143496255","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}

引用次数: 0

Red Chemistry Principles Introduced to Advance the Sector of Green Chemical Processing.

Chem & Bio Engineering Pub Date : 2024-11-27 eCollection Date: 2025-01-23 DOI: 10.1021/cbe.4c00171

Siyabonga Khumalo

引用次数: 0

Red Chemistry Principles Introduced to Advance the Sector of Green Chemical Processing

Chem & Bio Engineering Pub Date : 2024-11-27 DOI: 10.1021/cbe.4c0017110.1021/cbe.4c00171

Siyabonga Khumalo*,

引用次数: 0

Advanced Temperature-Integrated Backpropagation Neural Network for Enhanced Prediction of Syngas Composition in Complex Organic Waste Gasification

Chem & Bio Engineering Pub Date : 2024-11-26 DOI: 10.1021/cbe.4c0014610.1021/cbe.4c00146

Mingyue Yan, Huiyang Bi, HuanXu Wang, Caicai Xu, Lihao Chen, Lei Zhang, Shuangwei Chen, Xuming Xu, Zhongjian Li, Yang Hou, Lecheng Lei and Bin Yang*,

{"title":"Advanced Temperature-Integrated Backpropagation Neural Network for Enhanced Prediction of Syngas Composition in Complex Organic Waste Gasification","authors":"Mingyue Yan, Huiyang Bi, HuanXu Wang, Caicai Xu, Lihao Chen, Lei Zhang, Shuangwei Chen, Xuming Xu, Zhongjian Li, Yang Hou, Lecheng Lei and Bin Yang*, ","doi":"10.1021/cbe.4c0014610.1021/cbe.4c00146","DOIUrl":"https://doi.org/10.1021/cbe.4c00146https://doi.org/10.1021/cbe.4c00146","url":null,"abstract":"Accurate prediction of syngas compositions in multicomponent organic waste gasification is challenging because of its intricate composition and abundant volatile matter, which contrasts with traditional coal gasification influenced mainly by oxygen–coal ratio. Through process analysis, we identified the furnace temperature as a crucial factor directly impacting gasification reactions. Herein, we developed a hybrid backpropagation neural network (BPNN) model integrating furnace temperature data obtained from a temperature soft-sensing model and utilizing principal component analysis (PCA) for dimensionality reduction. The resulting T-PCA-BPNN model demonstrated outstanding predictive performance, achieving R2 values of 0.95, 0.97, and 0.94 for CO2, CO, and H2, respectively. Compared to the base BPNN model, the total mean square error (MSE) and mean absolute error (MAE) decreased by 49.4% and 13.3%, respectively. Furthermore, the percentage of predictive errors within 1% (QR) surpassed 90%, underscoring the model’s practical applicability. Leveraging PCA and SHapley Additive exPlanations (SHAP) analysis, we established a syngas regulation strategy that controls critical parameters to identify postdimensionality reduction through practical operational adjustments. This data-driven model enhances syngas prediction, thereby facilitating improved process control and optimization in complex organic waste gasification.","PeriodicalId":100230,"journal":{"name":"Chem & Bio Engineering","volume":"2 2","pages":"110–122 110–122"},"PeriodicalIF":0.0,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/cbe.4c00146","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143496246","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}

引用次数: 0

Advanced Temperature-Integrated Backpropagation Neural Network for Enhanced Prediction of Syngas Composition in Complex Organic Waste Gasification.

Chem & Bio Engineering Pub Date : 2024-11-26 eCollection Date: 2025-02-27 DOI: 10.1021/cbe.4c00146

Mingyue Yan, Huiyang Bi, HuanXu Wang, Caicai Xu, Lihao Chen, Lei Zhang, Shuangwei Chen, Xuming Xu, Zhongjian Li, Yang Hou, Lecheng Lei, Bin Yang

{"title":"Advanced Temperature-Integrated Backpropagation Neural Network for Enhanced Prediction of Syngas Composition in Complex Organic Waste Gasification.","authors":"Mingyue Yan, Huiyang Bi, HuanXu Wang, Caicai Xu, Lihao Chen, Lei Zhang, Shuangwei Chen, Xuming Xu, Zhongjian Li, Yang Hou, Lecheng Lei, Bin Yang","doi":"10.1021/cbe.4c00146","DOIUrl":"10.1021/cbe.4c00146","url":null,"abstract":"Accurate prediction of syngas compositions in multicomponent organic waste gasification is challenging because of its intricate composition and abundant volatile matter, which contrasts with traditional coal gasification influenced mainly by oxygen-coal ratio. Through process analysis, we identified the furnace temperature as a crucial factor directly impacting gasification reactions. Herein, we developed a hybrid backpropagation neural network (BPNN) model integrating furnace temperature data obtained from a temperature soft-sensing model and utilizing principal component analysis (PCA) for dimensionality reduction. The resulting T-PCA-BPNN model demonstrated outstanding predictive performance, achieving R 2 values of 0.95, 0.97, and 0.94 for CO2, CO, and H2, respectively. Compared to the base BPNN model, the total mean square error (MSE) and mean absolute error (MAE) decreased by 49.4% and 13.3%, respectively. Furthermore, the percentage of predictive errors within 1% (QR) surpassed 90%, underscoring the model's practical applicability. Leveraging PCA and SHapley Additive exPlanations (SHAP) analysis, we established a syngas regulation strategy that controls critical parameters to identify postdimensionality reduction through practical operational adjustments. This data-driven model enhances syngas prediction, thereby facilitating improved process control and optimization in complex organic waste gasification.","PeriodicalId":100230,"journal":{"name":"Chem & Bio Engineering","volume":"2 2","pages":"110-122"},"PeriodicalIF":0.0,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11873848/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560534","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}

引用次数: 0