Ethanol represents a promising liquid energy source for fuel cells. The development of direct ethanol fuel cells (DEFCs) is however challenged by the lack of efficient electrocatalysts for the complete oxidation of ethanol to CO 2 . Here we report the investigation of ethanol electro-oxidation on monodisperse and homogeneous Pt 3 Sn alloy nanoparticles. Electrochemical studies were conducted comparatively on the Pt 3 Sn nanoparticles supported on carbon (Pt 3 Sn/C), a commercial Pt/C catalyst, as well as KOH-treated Pt 3 Sn/C with the surface tin species removed. Our studies reveal the dual role of Sn in the EOR electrocatalysis on Pt 3 Sn/C: the surface Sn, likely in the form of tin oxides, enhances the oxidation of *CH x intermediate to *CO; the subsurface metallic Sn weakens the binding of *CO and facilitates its oxidative removal. A synergy of these two roles, plus the presence of Pt surface sites capable of cleaving the C−C bond, gives rise to the enhanced complete oxidation of ethanol.

Machine learning (ML) and deep learning (DL) are a subset of artificial intelligence (AI) that can automatically learn from data and can perform tasks such as predictions and decisionmaking. Interdisciplinary studies combining ML/DL with chemical health and safety have demonstrated their unparalleled advantages in identifying trend and prediction assistance, which can greatly save manpower, material resources, and financial resources. In this Review, commonly used ML/DL tools and concepts as well as popular ML/DL algorithms are introduced and discussed. More than 100 papers have been categorized and summarized to present the current development of ML/DL-based research in the area of chemical health and safety. In addition, the limitation of current studies and prospects of ML/DL-based study are also discussed. This Review can serve as useful guidance for researchers who are interested in implementing ML/DL into chemical health and safety research and for readers who try to learn more information about novel ML/DL techniques and applications.

Ethylene glycol is a potential feedstock for direct alcohol fuel cells (DAFCs). The electro‐oxidation of ethylene glycol is, however, challenged by poor selectivity toward the complete‐oxidation product, CO2. Herein, we report an electrocatalytic study of ethylene glycol oxidation on monodisperse and homogeneous Pt3Sn alloy nanoparticles. The catalytic activity and selectivity toward CO2 are evaluated under potentiostatic conditions by using gas chromatography‐mass spectrometry. A comparative study is performed on the alloy catalyst with surface tin (oxide) species removed through an alkaline treatment. By comparing the electrocatalytic performances of the pristine and treated Pt3Sn catalysts, as well as commercial Pt/C, we are able to reveal the distinct roles of surface tin oxide and subsurface metallic tin species in the bimetallic electrocatalyst for complete oxidation of ethylene glycol: the former enhances the cleavage of C−C bond and the latter facilitates oxidative removal of the *CO intermediate.

The indubitable rise of metal−organic framework (MOF) technology has opened the potential for commercialization as alternative materials with a versatile number of applications that range from catalysis to greenhouse gas capture. However, there are several factors that constrain the direct scale-up of MOFs from laboratory to industrial plant given the insufficient knowledge about the overall safety in synthesis processes. This article focuses on the study of MOF thermal stability, from concept to prediction, and the factors that influence such stability. The core of this work is a thermal stability prediction model for MOFs. This model can be applied to existing and new MOF structures, and it will allow for an estimation of the thermal stability temperature range of MOFs. This work contributes to the overall advancement of MOF technology and the efforts for its commercial use at industrial scale, combining both experimental data and computational techniques.