The issue of opacity within data-driven artificial intelligence(AI)algorithms has become an impediment to these algorithms’extensive utilization,especially within sensitive domains concerning health,safety,and high p...The issue of opacity within data-driven artificial intelligence(AI)algorithms has become an impediment to these algorithms’extensive utilization,especially within sensitive domains concerning health,safety,and high profitability,such as chemical engineering(CE).In order to promote reliable AI utilization in CE,this review discusses the concept of transparency within AI utilizations,which is defined based on both explainable AI(XAI)concepts and key features from within the CE field.This review also highlights the requirements of reliable AI from the aspects of causality(i.e.,the correlations between the predictions and inputs of an AI),explainability(i.e.,the operational rationales of the workflows),and informativeness(i.e.,the mechanistic insights of the investigating systems).Related techniques are evaluated together with state-of-the-art applications to highlight the significance of establishing reliable AI applications in CE.Furthermore,a comprehensive transparency analysis case study is provided as an example to enhance understanding.Overall,this work provides a thorough discussion of this subject matter in a way that—for the first time—is particularly geared toward chemical engineers in order to raise awareness of responsible AI utilization.With this vital missing link,AI is anticipated to serve as a novel and powerful tool that can tremendously aid chemical engineers in solving bottleneck challenges in CE.展开更多
Recent years have witnessed significant advances in utilizing machine learning-based techniques for thermal metamaterial-based structures and devices to attain favorable thermal transport behaviors.Among the various t...Recent years have witnessed significant advances in utilizing machine learning-based techniques for thermal metamaterial-based structures and devices to attain favorable thermal transport behaviors.Among the various thermal transport behaviors,achieving thermal transparency stands out as particularly desirable and intriguing.Our earlier work demonstrated the use of a thermal metamaterial-based periodic interparticle system as the underlying structure for manipulating thermal transport behavior and achieving thermal transparency.In this paper,we introduce an approach based on graph neural network to address the complex inverse design problem of determining the design parameters for a thermal metamaterial-based periodic interparticle system with the desired thermal transport behavior.Our work demonstrates that combining graph neural network modeling and inference is an effective approach for solving inverse design problems associated with attaining desirable thermal transport behaviors using thermal metamaterials.展开更多
In this article,a series of high refractive indices(1.50-1.53)thiol phenyl polysiloxane(TPS)were synthesized via hydrolytic sol-gel reaction.The Fourier transform infrared spectra(FT-IR)and nuclear magnetic resonance ...In this article,a series of high refractive indices(1.50-1.53)thiol phenyl polysiloxane(TPS)were synthesized via hydrolytic sol-gel reaction.The Fourier transform infrared spectra(FT-IR)and nuclear magnetic resonance spectra(NMR)results showed that TPS conformed to the predicted structures.Natural terpene linalool was exploited as photocrosslinker to fabricate UV-curing linalool-polysiloxane hybrid films(LPH)with TPS via photoinitiated thiol-ene reaction.LPH rapidly cured under UV irradiation at the intensity of 80 mW/cm^(2) in 30 s,exhibiting good UV-curing properties.The optical transmittance of LPH in the wavelength of 300-800 nm was over 90%,exhibiting good optical transparency.The water contact angle and water vapor permeability results showed that the introduction of phenyl groups enhance the hydrophobicity and water vapor barrier properties of LPH.The results indicated the potential of LPHs in the applications of optical functional coatings.展开更多
Fiber products for microwave kilns were prepared using alumina fibers with alumina contents of 72 mass%and 80 mass%,and calcined alumina powder(4-6μm)as the main raw materials,silica sol as the binder,and cationic st...Fiber products for microwave kilns were prepared using alumina fibers with alumina contents of 72 mass%and 80 mass%,and calcined alumina powder(4-6μm)as the main raw materials,silica sol as the binder,and cationic starch as the flocculant.Effects of different raw materials and their additions on the wave transparency of fiber products were researched.The results show that as the alumina fiber(72%)addition increases,the heating rate of the samples first decreases and then increases,and the corresponding wave transparency of the sample first increases and then decreases.When the alumina fibers addition is 40 mass%and the alumina powder addition is 30 mass%,the prepared microwave kiln lining material has a higher mullite content,which improves the wave transparency of the sample.The sample prepared from alumina fibers with an alumina content of 80%has a suitable glass-mullite phase ratio,performs lower overall dielectric constant and good wave transparency,and is a suitable lining material for microwave kilns.展开更多
文摘The issue of opacity within data-driven artificial intelligence(AI)algorithms has become an impediment to these algorithms’extensive utilization,especially within sensitive domains concerning health,safety,and high profitability,such as chemical engineering(CE).In order to promote reliable AI utilization in CE,this review discusses the concept of transparency within AI utilizations,which is defined based on both explainable AI(XAI)concepts and key features from within the CE field.This review also highlights the requirements of reliable AI from the aspects of causality(i.e.,the correlations between the predictions and inputs of an AI),explainability(i.e.,the operational rationales of the workflows),and informativeness(i.e.,the mechanistic insights of the investigating systems).Related techniques are evaluated together with state-of-the-art applications to highlight the significance of establishing reliable AI applications in CE.Furthermore,a comprehensive transparency analysis case study is provided as an example to enhance understanding.Overall,this work provides a thorough discussion of this subject matter in a way that—for the first time—is particularly geared toward chemical engineers in order to raise awareness of responsible AI utilization.With this vital missing link,AI is anticipated to serve as a novel and powerful tool that can tremendously aid chemical engineers in solving bottleneck challenges in CE.
基金funding from the National Natural Science Foundation of China (Grant Nos.12035004 and 12320101004)the Innovation Program of Shanghai Municipal Education Commission (Grant No.2023ZKZD06).
文摘Recent years have witnessed significant advances in utilizing machine learning-based techniques for thermal metamaterial-based structures and devices to attain favorable thermal transport behaviors.Among the various thermal transport behaviors,achieving thermal transparency stands out as particularly desirable and intriguing.Our earlier work demonstrated the use of a thermal metamaterial-based periodic interparticle system as the underlying structure for manipulating thermal transport behavior and achieving thermal transparency.In this paper,we introduce an approach based on graph neural network to address the complex inverse design problem of determining the design parameters for a thermal metamaterial-based periodic interparticle system with the desired thermal transport behavior.Our work demonstrates that combining graph neural network modeling and inference is an effective approach for solving inverse design problems associated with attaining desirable thermal transport behaviors using thermal metamaterials.
基金the financial funding of the Guangdong Province Applied Science and Technology R&D Special Fund Project:Key Technologies for Industrialization of Sulfur-Resistant and High Refractive-Index LED Packaging Silicone Materials(2016B090930010).
文摘In this article,a series of high refractive indices(1.50-1.53)thiol phenyl polysiloxane(TPS)were synthesized via hydrolytic sol-gel reaction.The Fourier transform infrared spectra(FT-IR)and nuclear magnetic resonance spectra(NMR)results showed that TPS conformed to the predicted structures.Natural terpene linalool was exploited as photocrosslinker to fabricate UV-curing linalool-polysiloxane hybrid films(LPH)with TPS via photoinitiated thiol-ene reaction.LPH rapidly cured under UV irradiation at the intensity of 80 mW/cm^(2) in 30 s,exhibiting good UV-curing properties.The optical transmittance of LPH in the wavelength of 300-800 nm was over 90%,exhibiting good optical transparency.The water contact angle and water vapor permeability results showed that the introduction of phenyl groups enhance the hydrophobicity and water vapor barrier properties of LPH.The results indicated the potential of LPHs in the applications of optical functional coatings.
文摘Fiber products for microwave kilns were prepared using alumina fibers with alumina contents of 72 mass%and 80 mass%,and calcined alumina powder(4-6μm)as the main raw materials,silica sol as the binder,and cationic starch as the flocculant.Effects of different raw materials and their additions on the wave transparency of fiber products were researched.The results show that as the alumina fiber(72%)addition increases,the heating rate of the samples first decreases and then increases,and the corresponding wave transparency of the sample first increases and then decreases.When the alumina fibers addition is 40 mass%and the alumina powder addition is 30 mass%,the prepared microwave kiln lining material has a higher mullite content,which improves the wave transparency of the sample.The sample prepared from alumina fibers with an alumina content of 80%has a suitable glass-mullite phase ratio,performs lower overall dielectric constant and good wave transparency,and is a suitable lining material for microwave kilns.
