Insights into the efficient charge separation over Nb_(2)O_(5)/2D-C_(3)N_(4) heterostructure for exceptional visible-light driven H_(2) evolution

Two-dimensional carbon nitride(2 D-C_(3) N_(4))nanosheets are promising materials in photocatalytic water splitting,but still suffer from easy agglomeration and fast photogene rated electron-hole pairs recombination.To tackle this issue,herein,a hierarchical Nb_(2) O_(5)/2 D-C_(3) N_(4) heterostructure is precisely constructed and the built-in electric field between Nb_(2)O_(5) and 2 D-C_(3) N_(4) can provide the driving force to separate/transfer the charge carriers efficiently.Moreover,the strongly Lewis acidic Nb_(2)O_(5) can adsorb TEOA molecules on its surface at locally high concentrations to facilitate the oxidation reaction kinetics under irradiation,resulting in efficient photogene rated electrons-holes separation and exceptional photocatalytic hydrogen evolution.As expected,the champion Nb_(2)O_(5)/2 D-C_(3)N_(4) heterostructure achieves an exceptional H2 evolution rate of 31.6 mmol g^(-1) h^(-1),which is 213.6 times and 4.3 times higher than that of pristine Nb_(2)O_(5) and2 D-C_(3)N_(4),respectively.Moreover,the champion heterostructure possesses a high apparent quantum efficiency(AQE)of 45.08%atλ=405 nm and superior cycling stability.Furthermore,a possible photocatalytic mechanism of the energy band alignment at the hetero-interface is proposed based on the systematical characterizations accompanied by density functional theory(DFT)calculations.This work paves the way for the precise construction of a high-quality heterostructured photocatalyst with efficient charge separation to boost hydrogen production.

Smart gas sensor arrays powered by artificial intelligence

Mobile robots behaving as humans should possess multifunctional flexible sensing systems including vision,hearing,touch,smell,and taste.A gas sensor array(GSA),also known as electronic nose,is a possible solution for a robotic olfactory system that can detect and discriminate a wide variety of gas molecules.Artificial intelligence(AI)applied to an electronic nose involves a diverse set of machine learning algorithms which can generate a smell print by analyzing the signal pattern from the GSA.A combination of GSA and AI algorithms can empower intelligent robots with great capabilities in many areas such as environmental monitoring,gas leakage detection,food and beverage production and storage,and especially disease diagnosis through detection of different types and concentrations of target gases with the advantages of portability,low-powerconsumption and ease-of-operation.It is exciting to envisage robots equipped with a"nose"acting as family doctor who will guard every family member's health and keep their home safe.In this review,we give a summary of the state-of the-art research progress in the fabrication techniques for GSAs and typical algorithms employed in artificial olfactory systems,exploring their potential applications in disease diagnosis,environmental monitoring,and explosive detection.We also discuss the key limitations of gas sensor units and their possible solutions.Finally,we present the outlook of GSAs over the horizon of smart homes and cities.

Machine learning in materials design:Algorithm and application

Traditional materials discovery is in ‘trial-and-error’ mode, leading to the issues of low-efficiency, high-cost, and unsustainability in materials design. Meanwhile, numerous experimental and computational trials accumulate enormous quantities of data with multi-dimensionality and complexity, which might bury critical ‘structure–properties’ rules yet unfortunately not well explored. Machine learning(ML), as a burgeoning approach in materials science, may dig out the hidden structure–properties relationship from materials bigdata, therefore, has recently garnered much attention in materials science. In this review, we try to shortly summarize recent research progress in this field, following the ML paradigm:(i) data acquisition →(ii) feature engineering →(iii) algorithm →(iv) ML model →(v) model evaluation →(vi) application. In section of application, we summarize recent work by following the ‘material science tetrahedron’:(i) structure and composition →(ii) property →(iii) synthesis →(iv) characterization, in order to reveal the quantitative structure–property relationship and provide inverse design countermeasures. In addition, the concurrent challenges encompassing data quality and quantity, model interpretability and generalizability, have also been discussed. This review intends to provide a preliminary overview of ML from basic algorithms to applications.