AI-driven materials databases are transforming research by integrating experimental and computational data to enhance discovery and optimization.Platforms such as Digital Catalysis Platform(DigCat)and Dynamic Database...AI-driven materials databases are transforming research by integrating experimental and computational data to enhance discovery and optimization.Platforms such as Digital Catalysis Platform(DigCat)and Dynamic Database of Solid-State Electrolyte(DDSE)demonstrate how machine learning and predictive modeling can improve catalyst and solid-state electrolyte development.These databases facilitate data standardization,high-throughput screening,and cross-disciplinary collaboration,addressing key challenges in materials informatics.As AI techniques advance,materials databases are expected to play an increasingly vital role in accelerating research and innovation.展开更多
Elemental composition is a key parameter in solid waste treatment and disposal. This study has proposed a method based on infrared spectroscopy and machine learning algorithms that can rapidly predict the elemental co...Elemental composition is a key parameter in solid waste treatment and disposal. This study has proposed a method based on infrared spectroscopy and machine learning algorithms that can rapidly predict the elemental composition (C, H, N, S) of solid waste. Both noise and moisture spectral interference that may occur in practical application are investigated. By comparing two feature selection methods and five machine learning algorithms, the most suitable models are selected. Moreover, the impacts of noise and moisture on the models are discussed, with paper, plastic, textiles, wood, and leather as examples of recyclable waste components. The results show that the combination of the feature selection and K-nearest neighbor (KNN) approaches exhibits the best prediction performance and generalization ability. Particularly, the coefficient of determination (R2) of the validation set, cross validation and test set are higher than 0.93, 0.89, and 0.97 for predicting the C, H, and N contents, respectively. Further, KNN is less sensitive to noise. Under moisture interference, the combination of feature selection and support vector regression or partial least-squares regression shows satisfactory results. Therefore, the elemental compositions of solid waste are quickly and accurately predicted under noise and moisture disturbances using infrared spectroscopy and machine learning algorithms.展开更多
Flexible devices provide advantages such as conformability,portability,and low cost.Paper-based electronics offers a number of advantages for many applications.It is lightweight,inexpensive,and biodegradable,making it...Flexible devices provide advantages such as conformability,portability,and low cost.Paper-based electronics offers a number of advantages for many applications.It is lightweight,inexpensive,and biodegradable,making it an ideal choice for disposable electronics.In this work,we propose a novel configuration of photodetectors using paper as flexible substrates and amorphous Ga_(2)O_(3)as the active materials,respectively.The photoresponse characteristics are investigated systematically.A decent responsivity yield and a specific detectivity of up to 66 mA/W and 3×10^(12)Jones were obtained at a low operating voltage of 10 V.The experiments also demonstrate that neither the twisting nor bending deformation can bring obvious performance degradation to the device.This work presents a candidate strategy for the application of conventional paper substrates to low-cost flexible solar-blind photodetectors,showing the potential of being integrated with other materials to create interactive flexible circuits.展开更多
文摘AI-driven materials databases are transforming research by integrating experimental and computational data to enhance discovery and optimization.Platforms such as Digital Catalysis Platform(DigCat)and Dynamic Database of Solid-State Electrolyte(DDSE)demonstrate how machine learning and predictive modeling can improve catalyst and solid-state electrolyte development.These databases facilitate data standardization,high-throughput screening,and cross-disciplinary collaboration,addressing key challenges in materials informatics.As AI techniques advance,materials databases are expected to play an increasingly vital role in accelerating research and innovation.
基金support from the National Key R&D Program of China(No.2020YFC1910100).
文摘Elemental composition is a key parameter in solid waste treatment and disposal. This study has proposed a method based on infrared spectroscopy and machine learning algorithms that can rapidly predict the elemental composition (C, H, N, S) of solid waste. Both noise and moisture spectral interference that may occur in practical application are investigated. By comparing two feature selection methods and five machine learning algorithms, the most suitable models are selected. Moreover, the impacts of noise and moisture on the models are discussed, with paper, plastic, textiles, wood, and leather as examples of recyclable waste components. The results show that the combination of the feature selection and K-nearest neighbor (KNN) approaches exhibits the best prediction performance and generalization ability. Particularly, the coefficient of determination (R2) of the validation set, cross validation and test set are higher than 0.93, 0.89, and 0.97 for predicting the C, H, and N contents, respectively. Further, KNN is less sensitive to noise. Under moisture interference, the combination of feature selection and support vector regression or partial least-squares regression shows satisfactory results. Therefore, the elemental compositions of solid waste are quickly and accurately predicted under noise and moisture disturbances using infrared spectroscopy and machine learning algorithms.
基金supported by the National Natural Science Foundation of China(Nos.12274243,11874230,52233014,51172208,and 12074044)the Fund of State Key Laboratory of Information Photonics and Optical Communications(No.IPOC2022ZT10)+1 种基金the Open Fund of IPOC(No.IPOC2022A02)the Macao Science and Technology Development Fund(FDCT Grants 0106/2020/A3 and 0031/2021/ITP).
文摘Flexible devices provide advantages such as conformability,portability,and low cost.Paper-based electronics offers a number of advantages for many applications.It is lightweight,inexpensive,and biodegradable,making it an ideal choice for disposable electronics.In this work,we propose a novel configuration of photodetectors using paper as flexible substrates and amorphous Ga_(2)O_(3)as the active materials,respectively.The photoresponse characteristics are investigated systematically.A decent responsivity yield and a specific detectivity of up to 66 mA/W and 3×10^(12)Jones were obtained at a low operating voltage of 10 V.The experiments also demonstrate that neither the twisting nor bending deformation can bring obvious performance degradation to the device.This work presents a candidate strategy for the application of conventional paper substrates to low-cost flexible solar-blind photodetectors,showing the potential of being integrated with other materials to create interactive flexible circuits.
