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Because of the unique optical properties of gold nanomaterials, the preparation of gold nanomaterials with excellent chirality has received extensive attention. In order to develop a simple fabrication method for three-dimensional chiral Au nanostructures with a size of several hundred nanometers, chiral gold nanoparticles were developed to transfer chirality of a peptide to gold nanoparticles. In this study, the controlled synthesis of asymmetric gold nanopolyhedrons was achieved. The asymmetric gold nanopolyhedrons prepared via peptide-directed growth can exhibit strong circular dichroism (∼±50 mdeg) couplets in the visible range (500–600 nm). Also, the morphology of chiral Au nanododecahedrons-peptide particles showed distorted and asymmetric properties. In order to prove that the size and spatial structure of gold nanopolyhedrons have an influence on their chiral optical properties, Au nanotrioctahedron-peptide particles were prepared by using Au nanotrioctahedrons with different morphologies. Au nanotrioctahedron-peptide particles also exhibited circular dichromatic couplets in the visible region.

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Hydrogen is considered a promising clean energy vector with the features of high energy capacity and zero-carbon emission. Water splitting is an environment-friendly and effective route for producing high-purity hydrogen, which contains two important half-cell reactions, namely, the anodic oxygen evolution reaction (OER) and the cathodic hydrogen evolution reaction (HER). At the heart of water splitting is high-performance electrocatalysts that efficiently improve the rate and selectivity of key chemical reactions. Recently, perovskite oxides have emerged as promising candidates for efficient water splitting electrocatalysts owing to their low cost, high electrochemical stability, and compositional and structural flexibility allowing for the achievement of high intrinsic electrocatalytic activity. In this review, we summarize the present research progress in the design, development, and application of perovskite oxides for electrocatalytic water splitting. The emphasis is on the innovative synthesis strategies and a deeper understanding of structure–activity relationships through a combination of systematic characterization and theoretical research. Finally, the main challenges and prospects for the further development of more efficient electrocatalysts based on perovskite oxides are proposed. It is expected to give guidance for the development of novel non-noble metal catalysts in electrochemical water splitting.

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Microorganisms play an important role in natural material and elemental cycles. Many common and general biology research techniques rely on microorganisms. Machine learning has been gradually integrated with multiple fields of study. Machine learning, including deep learning, aims to use mathematical insights to optimize variational functions to aid microbiology using various types of available data to help humans organize and apply collective knowledge of various research objects in a systematic and scaled manner. Classification and prediction have become the main achievements in the development of microbial community research in the direction of computational biology. This review summarizes the application and development of machine learning and deep learning in the field of microbiology and shows and compares the advantages and disadvantages of different algorithm tools in four fields: microbiome and taxonomy, microbial ecology, pathogen and epidemiology, and drug discovery.

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