Superiority of Dual‐Atom Catalysts in Electrocatalysis: One Step Further Than Single‐Atom Catalysts

Advanced Energy Materials · 2022 · 482 citations

• Computer Science
• Materials science
• Nanotechnology

Abstract In recent years, dual‐atom catalysts (DACs) have attracted extensive attention, as an extension of single‐atom catalysts (SACs). Compared with SACs, DACs have higher metal loading and more complex and flexible active sites, thus achieving better catalytic performance and providing more opportunities for electrocatalysis. This review introduces the research progress in recent years on how to design new DACs to enhance the performance of electrocatalysis. Firstly, the advantages of DACs in increasing metal loading are introduced. Then, the role of DACs in changing the adsorption condition of reactant molecules on metal atoms is discussed. Moreover, the ways in which DACs can reduce the reaction energy barrier of key steps and change the reaction path are explored. Catalytic applications in different electrocatalytic reactions, including the carbon dioxide reduction reaction, oxygen reduction reaction, oxygen evolution reaction, hydrogen evolution reaction, and nitrogen reduction reaction are followed. Finally, a brief summary is made and the key challenges and prospects of DACs are introduced.

Understanding the structure-performance relationship of active sites at atomic scale

Nano Research · 2022 · 527 citations

• Computer Science
• Nanotechnology
• Materials science

Metal-based atomically dispersed catalysts have attracted more attention because of their excellent catalytic performance and nearly 100% atom utilization. Therefore, it is very important to comprehensively and systematically understand the relationship between catalytic active sites and catalytic performance at atomic scale. Here, we discuss and summarize in detail the key and fundamental factors affecting the active site, and relate them to the catalytic performance. First, we describe the effectiveness of active site design by coordination effects. Then, the role of chemical bonds in the active sites in changing the reaction performance is discussed. In addition, for intermetallic compounds, we explore how the spacing of active atoms affects the catalytic behavior. Moreover, the importance of synergistic effect in catalyst design is further discussed. Finally, the key parameters affecting the catalytic performance at atomic scale are summarized, and the main challenges and development prospects of atomic catalysts in the future are put forward.

Ginseng polysaccharides alter the gut microbiota and kynurenine/tryptophan ratio, potentiating the antitumour effect of antiprogrammed cell death 1/programmed cell death ligand 1 (anti-PD-1/PD-L1) immunotherapy

Gut · 2021 · 496 citations

• Pharmacology
• Biology
• Immunology

OBJECTIVE: has been shown to possess immunomodulatory potential. In this study, we aimed to investigate whether the combination treatment of ginseng polysaccharides (GPs) and αPD-1 monoclonal antibody (mAb) could sensitise the response by modulating gut microbiota. DESIGN: Syngeneic mouse models were administered GPs and αPD-1 mAb, the sensitising antitumour effects of the combination therapy on gut microbiota were assessed by faecal microbiota transplantation (FMT) and 16S PacBio single-molecule real-time (SMRT) sequencing. To assess the immune-related metabolites, metabolomics analysis of the plasma samples was performed. RESULTS: was higher in responders to anti-PD-1 blockade than non-responders in the clinic. Furthermore, the combination therapy sensitised the response to PD-1 inhibitor in the mice receiving microbes by FMT from six non-responders by reshaping the gut microbiota from non-responders towards that of responders. CONCLUSION: Our results demonstrate that GPs combined with αPD-1 mAb may be a new strategy to sensitise non-small cell lung cancer patients to anti-PD-1 immunotherapy. The gut microbiota can be used as a novel biomarker to predict the response to anti-PD-1 immunotherapy.

Energy storage mechanisms in vacancy-ordered Wadsley–Roth layered niobates

Journal of Materials Chemistry A · 2021 · 18 citations

• Materials science
• Chemical physics
• Crystallography

Layered niobates with vacancy-ordered Wadsley–Roth structures were investigated as Li-ion battery anodes. Using operando and ex situ methods and DFT, we identify an intercalation mechanism dominated by Li-diffusion kinetics and layer evolution.

Towards Visually Explaining Variational Autoencoders

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 2020 · 266 citations

• Computer Science
• Computer Science
• Artificial Intelligence

Recent advances in Convolutional Neural Network (CNN) model interpretability have led to impressive progress in visualizing and understanding model predictions. In particular, gradient-based visual attention methods have driven much recent effort in using visual attention maps as a means for visual explanations. A key problem, however, is these methods are designed for classification and categorization tasks, and their extension to explaining generative models, e.g., variational autoencoders (VAE) is not trivial. In this work, we take a step towards bridging this crucial gap, proposing the first technique to visually explain VAEs by means of gradient-based attention. We present methods to generate visual attention from the learned latent space, and also demonstrate such attention explanations serve more than just explaining VAE predictions. We show how these attention maps can be used to localize anomalies in images, demonstrating state-of-the-art performance on the MVTec-AD dataset. We also show how they can be infused into model training, helping bootstrap the VAE into learning improved latent space disentanglement, demonstrated on the Dsprites dataset.

Statistical Foundations of Data Science

Chapman and Hall/CRC eBooks · 2020 · 198 citations

• Computer Science
• Computer Science
• Data science

Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.