Pub Date : 2025-09-01Epub Date: 2024-10-28DOI: 10.1016/j.gce.2024.10.006
Qun Huang, Zhibing Zhang
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.
{"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":"2025-09-01","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}
Pub Date : 2025-09-01Epub Date: 2025-05-23DOI: 10.1016/S2666-9528(25)00032-9
{"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":"2025-09-01","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}
Waste H2SO4 from industrial isobutane alkylation, a hazardous thick liquid with a high concentration of acid soluble oil (ASO) impurities, poses challenges in the regeneration process. Herein, an innovative low-temperature carbonization process was proposed to convert waste H2SO4 into the regenerated concentrated H2SO4 and sulfonated activated carbon materials (SACMs) under mild reaction conditions. The optimal reaction temperature is identified at 423.15 K with the highest total organic carbon (TOC) removal of 90.57%. The high-purity regenerated H2SO4 with a concentration of 95% as a catalyst for isobutane alkylation exhibits excellent catalytic performance with 94.54 research octane number (RON) of the alkylate. SACMs, characterized as a novel porous carbon material with plentiful hydroxyl, carboxylic acid, and sulfonic acid functional groups, demonstrate an efficient catalytic activity in the dimerization of lactic acid to produce lactide with a yield of 46.95%. Hopefully, the novel recovery process provides a promising application to optimize the regeneration process of waste H2SO4 from industrial isobutane alkylation.
{"title":"Efficient removal and reusage of acid soluble oil in waste H2SO4 of isobutane alkylation by low-temperature carbonization process","authors":"Zhihong Ma, Weizhong Zheng, Kexin Yan, Qiaoling Zhang, Weizhen Sun, Ling Zhao","doi":"10.1016/j.gce.2024.08.007","DOIUrl":"10.1016/j.gce.2024.08.007","url":null,"abstract":"<div><div>Waste H<sub>2</sub>SO<sub>4</sub> from industrial isobutane alkylation, a hazardous thick liquid with a high concentration of acid soluble oil (ASO) impurities, poses challenges in the regeneration process. Herein, an innovative low-temperature carbonization process was proposed to convert waste H<sub>2</sub>SO<sub>4</sub> into the regenerated concentrated H<sub>2</sub>SO<sub>4</sub> and sulfonated activated carbon materials (SACMs) under mild reaction conditions. The optimal reaction temperature is identified at 423.15 K with the highest total organic carbon (TOC) removal of 90.57%. The high-purity regenerated H<sub>2</sub>SO<sub>4</sub> with a concentration of 95% as a catalyst for isobutane alkylation exhibits excellent catalytic performance with 94.54 research octane number (RON) of the alkylate. SACMs, characterized as a novel porous carbon material with plentiful hydroxyl, carboxylic acid, and sulfonic acid functional groups, demonstrate an efficient catalytic activity in the dimerization of lactic acid to produce lactide with a yield of 46.95%. Hopefully, the novel recovery process provides a promising application to optimize the regeneration process of waste H<sub>2</sub>SO<sub>4</sub> from industrial isobutane alkylation.</div></div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 3","pages":"Pages 380-387"},"PeriodicalIF":9.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116102","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}
Pub Date : 2025-09-01Epub Date: 2024-09-07DOI: 10.1016/j.gce.2024.09.003
Chaowu Wang , Jie Wang , Qibo Zhang
Developing efficient and environmentally friendly metal recovery technologies from secondary resources is crucial for enhancing resource utilization and promoting environmental sustainability. However, metals with similar physicochemical properties pose significant challenges in the recovery process, particularly for nickel and cobalt. Herein, we present a coordination-regulated approach utilizing water-, temperature-, and pH-codrived to achieve sequential precipitation recovery of nickel and cobalt from waste choline chloride/ethylene glycol (Ethaline) electrolyte containing Ni(II) and Co(II) ions. By carefully adjusting water content, temperature, and pH, we can control the speciation of Ni(II) ([NiCl(H2O)2(EG)2]+) and Co(II) ([CoCl2(H2O)2(EG)2]0) ions in the Ethaline-based electrolyte, thereby facilitating nickel preferential precipitation. Additionally, further introducing water into the Co(II)-rich phase promotes the formation of [CoCl(H2O)3(EG)2]+ complex ions, leading to efficient separation of cobalt. When oxalic acid is used as a precipitant, the recovery efficiencies for nickel and cobalt reach 96.3% and 97.5%, respectively, with purities of 97.8% and 98.5%. Importantly, distilling the water-containing solvent allows for regeneration of Ethaline with a yield rate as high as 97.1%, while maintaining its structural stability. This proposed strategy offers a promising pathway for sustainable metal recovery from spent Ethaline electrolytes containing metal ions while enabling solvent regeneration.
{"title":"Synergistic coordination-regulated separation of nickel and cobalt from spent Ni(II) and Co(II) bearing choline chloride/ethylene glycol electrolyte: theoretical and experimental investigations","authors":"Chaowu Wang , Jie Wang , Qibo Zhang","doi":"10.1016/j.gce.2024.09.003","DOIUrl":"10.1016/j.gce.2024.09.003","url":null,"abstract":"<div><div>Developing efficient and environmentally friendly metal recovery technologies from secondary resources is crucial for enhancing resource utilization and promoting environmental sustainability. However, metals with similar physicochemical properties pose significant challenges in the recovery process, particularly for nickel and cobalt. Herein, we present a coordination-regulated approach utilizing water-, temperature-, and pH-codrived to achieve sequential precipitation recovery of nickel and cobalt from waste choline chloride/ethylene glycol (Ethaline) electrolyte containing Ni(II) and Co(II) ions. By carefully adjusting water content, temperature, and pH, we can control the speciation of Ni(II) ([NiCl(H<sub>2</sub>O)<sub>2</sub>(EG)<sub>2</sub>]<sup>+</sup>) and Co(II) ([CoCl<sub>2</sub>(H<sub>2</sub>O)<sub>2</sub>(EG)<sub>2</sub>]<sup>0</sup>) ions in the Ethaline-based electrolyte, thereby facilitating nickel preferential precipitation. Additionally, further introducing water into the Co(II)-rich phase promotes the formation of [CoCl(H<sub>2</sub>O)<sub>3</sub>(EG)<sub>2</sub>]<sup>+</sup> complex ions, leading to efficient separation of cobalt. When oxalic acid is used as a precipitant, the recovery efficiencies for nickel and cobalt reach 96.3% and 97.5%, respectively, with purities of 97.8% and 98.5%. Importantly, distilling the water-containing solvent allows for regeneration of Ethaline with a yield rate as high as 97.1%, while maintaining its structural stability. This proposed strategy offers a promising pathway for sustainable metal recovery from spent Ethaline electrolytes containing metal ions while enabling solvent regeneration.</div></div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 3","pages":"Pages 398-409"},"PeriodicalIF":9.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116106","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}
Pub Date : 2025-09-01Epub Date: 2024-07-23DOI: 10.1016/j.gce.2024.07.004
Jianchun Chu , Maogang He , Georgios M. Kontogeorgis , Xiangyang Liu , Xiaodong Liang
Absorption refrigeration is a highly effective method for utilizing renewable energy, as it can be driven by low-grade heat sources such as industrial waste heat, solar energy, and geothermal energy. The development of new working pairs, particularly hydrofluorocarbon/hydrofluoroolefin refrigerants combined with ionic liquids, has been pivotal in enhancing the cooling efficiency of absorption refrigeration systems. These systems rely on the solubility difference between the generator and absorber, making solubility a crucial factor in determining their efficiency. In this context, we have established an advanced solubility estimation model. This model employs the Attention E(n)-equivariant Graph Neural Network (AEGNN) applied to disconnected graphs, enabling comprehensive learning from both topological and Euclidean structural information. Our atomic-scale model demonstrates significantly higher accuracy than traditional group contribution methods, with an average absolute deviation of 0.003 mol/mol from experimental data. Moreover, it encompasses a much broader range of working pairs. Through extensive screening, we have identified working pairs with high estimated solubility differences. Compared to the high-efficiency working pair identified in the literature, the best-screened working pairs exhibit an improvement in solubility differences by more than 0.3 mol/mol under common operating conditions.
{"title":"Screening HFC/HFO and ionic liquid for absorption refrigeration at the atomic scale by the prediction model of machine learning","authors":"Jianchun Chu , Maogang He , Georgios M. Kontogeorgis , Xiangyang Liu , Xiaodong Liang","doi":"10.1016/j.gce.2024.07.004","DOIUrl":"10.1016/j.gce.2024.07.004","url":null,"abstract":"<div><div>Absorption refrigeration is a highly effective method for utilizing renewable energy, as it can be driven by low-grade heat sources such as industrial waste heat, solar energy, and geothermal energy. The development of new working pairs, particularly hydrofluorocarbon/hydrofluoroolefin refrigerants combined with ionic liquids, has been pivotal in enhancing the cooling efficiency of absorption refrigeration systems. These systems rely on the solubility difference between the generator and absorber, making solubility a crucial factor in determining their efficiency. In this context, we have established an advanced solubility estimation model. This model employs the Attention E(n)-equivariant Graph Neural Network (AEGNN) applied to disconnected graphs, enabling comprehensive learning from both topological and Euclidean structural information. Our atomic-scale model demonstrates significantly higher accuracy than traditional group contribution methods, with an average absolute deviation of 0.003 mol/mol from experimental data. Moreover, it encompasses a much broader range of working pairs. Through extensive screening, we have identified working pairs with high estimated solubility differences. Compared to the high-efficiency working pair identified in the literature, the best-screened working pairs exhibit an improvement in solubility differences by more than 0.3 mol/mol under common operating conditions.</div></div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 3","pages":"Pages 357-364"},"PeriodicalIF":9.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141843949","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}
Pub Date : 2025-09-01Epub Date: 2024-07-04DOI: 10.1016/j.gce.2024.07.001
Ifunanya R. Akaniro , Gaihong Wang , Peixin Wang , Ruilong Zhang , Wenhua Xue , Jian Ye , Jonathan W.C. Wong , Jun Zhao
The use of biochar for organic pollutants adsorption has emerged as a key component in wastewater remediation research. In this study, biochar prepared from digestate was subjected to nitric acid functionalization to enhance its adsorption capacity for organic dye mixtures of methylene blue and methyl red in synthetic wastewater. Based on experimental evidence, modified biochar BC750_NM, with a micro-mesoporous structure and a specific surface area of ∼454.15 m2/g had the best adsorption performance at optimum conditions. This adsorbent exhibited both selective and simultaneous dye adsorption upon pH control, mainly attributable to a multi-interaction process in the medium. Notably, the adsorption of both methylene blue and methyl red approached 90% under acidic pH, while methylene blue was preferentially adsorbed over methyl red at alkaline pH to attain an excellent adsorption rate of 100% for methylene blue. Our approach not only yields a valuable resource for mitigating water pollution but also offers a sustainable solution for digestate management, showcasing the potential for innovative techniques to produce synergistic environmental solutions.
{"title":"pH-tuneable simultaneous and selective dye wastewater remediation with digestate-derived biochar: adsorption behaviour, mechanistic insights and potential application","authors":"Ifunanya R. Akaniro , Gaihong Wang , Peixin Wang , Ruilong Zhang , Wenhua Xue , Jian Ye , Jonathan W.C. Wong , Jun Zhao","doi":"10.1016/j.gce.2024.07.001","DOIUrl":"10.1016/j.gce.2024.07.001","url":null,"abstract":"<div><div>The use of biochar for organic pollutants adsorption has emerged as a key component in wastewater remediation research. In this study, biochar prepared from digestate was subjected to nitric acid functionalization to enhance its adsorption capacity for organic dye mixtures of methylene blue and methyl red in synthetic wastewater. Based on experimental evidence, modified biochar BC750_NM, with a micro-mesoporous structure and a specific surface area of ∼454.15 m<sup>2</sup>/g had the best adsorption performance at optimum conditions. This adsorbent exhibited both selective and simultaneous dye adsorption upon pH control, mainly attributable to a multi-interaction process in the medium. Notably, the adsorption of both methylene blue and methyl red approached 90% under acidic pH, while methylene blue was preferentially adsorbed over methyl red at alkaline pH to attain an excellent adsorption rate of 100% for methylene blue. Our approach not only yields a valuable resource for mitigating water pollution but also offers a sustainable solution for digestate management, showcasing the potential for innovative techniques to produce synergistic environmental solutions.</div></div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 3","pages":"Pages 344-356"},"PeriodicalIF":9.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141704406","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}
Pub Date : 2025-09-01Epub Date: 2024-08-05DOI: 10.1016/j.gce.2024.08.002
Peng Jiang , Lin Li , Han Lin , Tuo Ji , Liwen Mu , Yuanhui Ji , Xiaohua Lu , Jiahua Zhu
The higher heating value (HHV) of biomass is a crucial property for design calculations and numerical simulations in bioenergy utilization. However, existing models for HHV prediction faced challenges in terms of predictive accuracy and generalization capability across various solid waste types, especially for those with high ash content. This work proposed a novel HHV prediction model based on its reduction degree (DR) and ash content (Cash). First, ultimate analysis of biomass was applied to establish the calculation method of DR; then, the correlation between DR, Cash, and HHV was analyzed using the Pearson Correlation Coefficient; subsequently, the HHV = f (DR, Cash) model was developed using regression analysis. Furthermore, the accuracy was compared to previous literature in terms of correlation coefficient (R2), root mean square error (RMSE), and mean absolute error (MAE). Results revealed that this model provided attractive accuracy with R2 = 0.854, RMSE = 0.900, and MAE = 0.773 within a wide range of ash content from 0 to 83.32 wt%. Even higher accuracy was achieved with this model in predicting the HHV of coal, biochar, and bio-oil, with R2 of 0.961, 0.989, and 0.939, respectively. Conclusively, this work proposed the use of DR for HHV estimation, which was not only a simple and accurate approach but also widely applicable to various fuels.
生物质较高的热值（HHV）是生物能源利用设计计算和数值模拟的重要特性。然而，现有的HHV预测模型在各种固体废物类型，特别是高灰分固体废物的预测精度和泛化能力方面面临挑战。本文提出了一种基于还原度（DR）和灰分（Cash）的HHV预测模型。首先，采用生物量的极限分析，建立DR的计算方法；利用Pearson相关系数分析DR、Cash与HHV的相关性；随后，利用回归分析建立HHV = f （DR, Cash）模型。此外，在相关系数（R2）、均方根误差（RMSE）和平均绝对误差（MAE）方面与以往文献的准确性进行比较。结果表明，该模型在灰分0 ~ 83.32 wt%范围内具有较好的预测精度，R2 = 0.854, RMSE = 0.900, MAE = 0.773。该模型对煤、生物炭和生物油的HHV预测精度更高，R2分别为0.961、0.989和0.939。最后，本工作提出了将DR用于HHV估算的方法，这不仅是一种简单准确的方法，而且广泛适用于各种燃料。
{"title":"Establishing a generalized model for accurate prediction of higher heating values of substances with large ash fractions","authors":"Peng Jiang , Lin Li , Han Lin , Tuo Ji , Liwen Mu , Yuanhui Ji , Xiaohua Lu , Jiahua Zhu","doi":"10.1016/j.gce.2024.08.002","DOIUrl":"10.1016/j.gce.2024.08.002","url":null,"abstract":"<div><div>The higher heating value (HHV) of biomass is a crucial property for design calculations and numerical simulations in bioenergy utilization. However, existing models for HHV prediction faced challenges in terms of predictive accuracy and generalization capability across various solid waste types, especially for those with high ash content. This work proposed a novel HHV prediction model based on its reduction degree (<em>D</em><sub>R</sub>) and ash content (<em>C</em><sub>ash</sub>). First, ultimate analysis of biomass was applied to establish the calculation method of <em>D</em><sub>R</sub>; then, the correlation between <em>D</em><sub>R</sub>, <em>C</em><sub>ash</sub>, and HHV was analyzed using the Pearson Correlation Coefficient; subsequently, the HHV = <em>f</em> (<em>D</em><sub>R</sub><em>, C</em><sub>ash</sub>) model was developed using regression analysis. Furthermore, the accuracy was compared to previous literature in terms of correlation coefficient (<em>R</em><sup>2</sup>), root mean square error (RMSE), and mean absolute error (MAE). Results revealed that this model provided attractive accuracy with <em>R</em><sup>2</sup> = 0.854, RMSE = 0.900, and MAE = 0.773 within a wide range of ash content from 0 to 83.32 wt%. Even higher accuracy was achieved with this model in predicting the HHV of coal, biochar, and bio-oil, with <em>R</em><sup>2</sup> of 0.961, 0.989, and 0.939, respectively. Conclusively, this work proposed the use of <em>D</em><sub>R</sub> for HHV estimation, which was not only a simple and accurate approach but also widely applicable to various fuels.</div></div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 3","pages":"Pages 372-379"},"PeriodicalIF":9.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116101","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}
Microhelices with unique structures can achieve numerous functions mimicking micromotors and thus attract increasing attention from various fields especially in biomedical engineering. However, fabrication of biocompatible microhelices with controllable structures and favorable mechanical strengths still remains challenging. Here, we report a facile and efficient method for controllable fabrication of biocompatible microhelices with designable structures and enough mechanical strengths based on the liquid rope coiling effect due to phase viscosity differences. A two-stage microfluidic system is designed to generate all-aqueous-phase core-sheath microhelical flows without crosslinking, and the microhelical flows are subsequently utilized as templates for fabricating microhelices via UV-initiated curing of core flow segments. To demonstrate the efficiency of the proposed strategy, biocompatible photocurable polyethylene glycol diacrylate (PEGDA) solution, high-viscosity sodium alginate (NaAlg) solution and low-viscosity deionized water are employed as the inner, middle and outer phases respectively. The pitch (170 ∼ 500 μm), diameter (36 ∼ 87 μm), amplitude (0.52 ∼ 1.4 mm) and length (1.8 ∼ 5.6 mm) of PEGDA microhelices are precisely controlled by adjusting device dimensions, fluid viscosities, flowrates, and UV-irradiation time periods. The fabricated PEGDA microhelices exhibit outstanding mechanical strength. Furthermore, magnetic microhelices are successfully fabricated by incorporating Fe 3 O 4 nanoparticles into PEGDA solutions, which exhibit excellent motion performance in a magnetic field. The proposed strategy provides an efficient method for controllable fabrication of biocompatible microhelices with designable structures and functions for potential applications in various fields. • Biocompatible microhelices with designable structures are fabricated with helical flows as templates. • All-aqueous-phase core-sheath microhelical flows are generated with the liquid rope coiling effect. • The liquid rope coiling effect for forming microhelical flows is chiefly caused by phase viscosity difference. • Functional microhelices exhibit excellent mechanical strengths and magnetic-field-driven motion performances.
{"title":"Controllable fabrication of biocompatible microhelices based on the liquid rope coiling effect due to the phase viscosity difference","authors":"Younan Xia, Xinglong Zhou, Dawei Pan, Wei Wang, Rui Xie, Zhuang Liu, Xiao‐Jie Ju, Liang‐Yin Chu","doi":"10.1016/j.gce.2025.07.003","DOIUrl":"https://doi.org/10.1016/j.gce.2025.07.003","url":null,"abstract":"Microhelices with unique structures can achieve numerous functions mimicking micromotors and thus attract increasing attention from various fields especially in biomedical engineering. However, fabrication of biocompatible microhelices with controllable structures and favorable mechanical strengths still remains challenging. Here, we report a facile and efficient method for controllable fabrication of biocompatible microhelices with designable structures and enough mechanical strengths based on the liquid rope coiling effect due to phase viscosity differences. A two-stage microfluidic system is designed to generate all-aqueous-phase core-sheath microhelical flows without crosslinking, and the microhelical flows are subsequently utilized as templates for fabricating microhelices via UV-initiated curing of core flow segments. To demonstrate the efficiency of the proposed strategy, biocompatible photocurable polyethylene glycol diacrylate (PEGDA) solution, high-viscosity sodium alginate (NaAlg) solution and low-viscosity deionized water are employed as the inner, middle and outer phases respectively. The pitch (170 ∼ 500 μm), diameter (36 ∼ 87 μm), amplitude (0.52 ∼ 1.4 mm) and length (1.8 ∼ 5.6 mm) of PEGDA microhelices are precisely controlled by adjusting device dimensions, fluid viscosities, flowrates, and UV-irradiation time periods. The fabricated PEGDA microhelices exhibit outstanding mechanical strength. Furthermore, magnetic microhelices are successfully fabricated by incorporating Fe 3 O 4 nanoparticles into PEGDA solutions, which exhibit excellent motion performance in a magnetic field. The proposed strategy provides an efficient method for controllable fabrication of biocompatible microhelices with designable structures and functions for potential applications in various fields. • Biocompatible microhelices with designable structures are fabricated with helical flows as templates. • All-aqueous-phase core-sheath microhelical flows are generated with the liquid rope coiling effect. • The liquid rope coiling effect for forming microhelical flows is chiefly caused by phase viscosity difference. • Functional microhelices exhibit excellent mechanical strengths and magnetic-field-driven motion performances.","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147331198","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}
Pub Date : 2025-06-01Epub Date: 2024-08-11DOI: 10.1016/j.gce.2024.08.003
Yixiong Lin , Zhengqi Wu , Shiqi You , Chen Yang , Qinglian Wang , Wang Yin , Ting Qiu
Constrained by the substantial computational time required for numerical simulation, a deep learning technique is applied to investigate fluid flow and heat transfer processes in metal foam with a hierarchical pore structure. This work adopted a 3D convolutional neural network (CNN) combining U-Net architecture to predict velocity and temperature distributions, alongside corresponding permeability and overall heat transfer coefficient. This approach demonstrates excellent capability in intricate image segmentation. The training sets were acquired by lattice Boltzmann method (LBM) simulations. The CNN model, trained on a substantial amount of data, demonstrates remarkable precision, exhibiting mean relative errors of 0.57% for permeability prediction and 2.27% for overall heat transfer coefficient prediction. Moreover, in CNN prediction, a broader range of structure parameters and boundary conditions beyond those in the training set was used to evaluate the practicability of the trained CNN model. In contrast to numerical simulation, the CNN model economizes approximately 95.41% and 99.57% of computational time for velocity and temperature distribution prediction, respectively, providing a novel approach for exploring transport processes in metal foam with hierarchical pore structure.
{"title":"Deep learning-based prediction of velocity and temperature distributions in metal foam with hierarchical pore structure","authors":"Yixiong Lin , Zhengqi Wu , Shiqi You , Chen Yang , Qinglian Wang , Wang Yin , Ting Qiu","doi":"10.1016/j.gce.2024.08.003","DOIUrl":"10.1016/j.gce.2024.08.003","url":null,"abstract":"<div><div>Constrained by the substantial computational time required for numerical simulation, a deep learning technique is applied to investigate fluid flow and heat transfer processes in metal foam with a hierarchical pore structure. This work adopted a 3D convolutional neural network (CNN) combining U-Net architecture to predict velocity and temperature distributions, alongside corresponding permeability and overall heat transfer coefficient. This approach demonstrates excellent capability in intricate image segmentation. The training sets were acquired by lattice Boltzmann method (LBM) simulations. The CNN model, trained on a substantial amount of data, demonstrates remarkable precision, exhibiting mean relative errors of 0.57% for permeability prediction and 2.27% for overall heat transfer coefficient prediction. Moreover, in CNN prediction, a broader range of structure parameters and boundary conditions beyond those in the training set was used to evaluate the practicability of the trained CNN model. In contrast to numerical simulation, the CNN model economizes approximately 95.41% and 99.57% of computational time for velocity and temperature distribution prediction, respectively, providing a novel approach for exploring transport processes in metal foam with hierarchical pore structure.</div></div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 2","pages":"Pages 209-222"},"PeriodicalIF":9.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594168","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}
Pub Date : 2025-06-01Epub Date: 2025-01-02DOI: 10.1016/j.gce.2025.01.001
Zhen Song , Weifeng Shen , Zhiwen Qi , José María Ponce Ortega
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