Heterostructured Pt-PbS Nanobelt Achieves Remarkable Direct Formic Acid Oxidation Catalysis.

Developing efficient and CO-tolerant platinum (Pt)-based anodic catalysts is challenging for a direct formic acid fuel cell (DFAFC). Herein, we report heterostructured Pt-lead-sulfur (PtPbS)-based nanomaterials with gradual phase regulation as efficient formic acid oxidation reaction (FAOR) catalysts. The optimized Pt-PbS nanobelts (Pt-PbS NBs/C) display the mass and specific activities of 5.90 A mgPt-1 and 21.4 mA cm-2, 2.2/1.2, 1.5/1.1, and 36.9/79.3 times greater than those of PtPb-PbS NBs/C, Pt-PbSO4 NBs/C, and commercial Pt/C, respectively. Simultaneously, it exhibits a higher membrane electrode assembly (MEA) power density (183.5 mW cm-2) than commercial Pt/C (40.3 mW cm-2). This MEA stably operates at 0.4 V for 25 h, demonstrating a competitive potential of device application. The distinctive heterostructure endows the Pt-PbS NBs/C with optimized dehydrogenation steps and resisting the CO poisoning, thus presenting the remarkable FAOR performance. This work paves an effective avenue for creating high-performance anodic catalysts for fuel cells and beyond.

An all-metallic nanovesicle for hydrogen oxidation.

Vesicle, a microscopic unit that encloses a volume with an ultrathin wall, is ubiquitous in biomaterials. However, it remains a huge challenge to create its inorganic metal-based artificial counterparts. Here, inspired by the formation of biological vesicles, we proposed a novel biomimetic strategy of curling the ultrathin nanosheets into nanovesicles, which was driven by the interfacial strain. Trapped by the interfacial strain between the initially formed substrate Rh layer and subsequently formed RhRu overlayer, the nanosheet begins to deform in order to release a certain amount of strain. Density functional theory (DFT) calculations reveal that the Ru atoms make the curling of nanosheets more favorable in thermodynamics applications. Owing to the unique vesicular structure, the RhRu nanovesicles/C displays excellent hydrogen oxidation reaction (HOR) activity and stability, which has been proven by both experiments and DFT calculations. Specifically, the HOR mass activity of RhRu nanovesicles/C are 7.52 A mg(Rh+Ru)-1 at an overpotential of 50 mV at the rotating disk electrode (RDE) level; this is 24.19 times that of commercial Pt/C (0.31 mA mgPt-1). Moreover, the hydroxide exchange membrane fuel cell (HEMFC) with RhRu nanovesicles/C displays a peak power density of 1.62 W cm-2 in the H2-O2 condition, much better than that of commercial Pt/C (1.18 W cm-2). This work creates a new biomimetic strategy to synthesize inorganic nanomaterials, paving a pathway for designing catalytic reactors.

Ligand-Assisted Coupling Manipulation for Efficient and Stable FAPbI3 Colloidal Quantum Dot Solar Cells.

For emerging perovskite quantum dots (QDs), understanding the surface features and their impact on the materials and devices is becoming increasingly urgent. In this family, hybrid FAPbI3 QDs (FA: formamidium) exhibit higher ambient stability, near-infrared absorption and sufficient carrier lifetime. However, hybrid QDs suffer from difficulty in modulating surface ligand, which is essential for constructing conductive QD arrays for photovoltaics. Herein, assisted by an ionic liquid formamidine thiocyanate, we report a facile surface reconfiguration methodology to modulate surface and manipulate electronic coupling of FAPbI3 QDs, which is exploited to enhance charge transport for fabricating high-quality QD arrays and photovoltaic devices. Finally, a record-high efficiency approaching 15 % is achieved for FAPbI3 QD solar cells, and they retain over 80 % of the initial efficiency after aging in ambient environment (20-30 % humidity, 25 °C) for over 600 h.

First-principles materials simulation and design for alkali and alkaline metal ion batteries accelerated by machine learning.

The challenge of regeneration of batteries requires a performance improvement in the alkali/alkaline metal ion battery (AMIB) materials, whereas the traditional research paradigm fully based on experiments and theoretical simulations needs massive research and development investment. During the last decade, machine learning (ML) has made breakthroughs in many complex disciplines, which testifies to their high processing speed and ability to capture relationships. Inspired by these achievements, ML has also been introduced to bring a new paradigm for shortening the development of AMIB materials. In this Perspective, the focus will be on how this new ML technology solves the key problems of redox potentials, ionic conductivity and stability parameters in first-principles materials' simulation and design for AMIBs. It is found that ML not only accelerates the property prediction, but also gives physicochemical insights into AMIB materials' design. In addition, the final part of this paper summarizes current achievements and looks forward to the progress of a novel paradigm in direct/inverse design with the increasing number of databases, skills, and ML technologies for AMIBs.