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The broad employment of water electrolysis for hydrogen (H₂) production is restricted by its large voltage requirement and low energy conversion efficiency because of the sluggish oxygen evolution reaction (OER). Herein, we report a strategy to replace OER with a thermodynamically more favorable reaction, the partial oxidation of formaldehyde to formate under alkaline conditions, using a Cu₃Ag₇electrocatalyst. Such a strategy not only produces more valuable anodic product than O₂but also releases H₂at the anode with a small voltage input. Density functional theory studies indicate the H₂C(OH)O intermediate from formaldehyde hydration can be better stabilized on Cu₃Ag₇than on Cu or Ag, leading to a lower C-H cleavage barrier. A two-electrode electrolyzer employing an electrocatalyst of Cu₃Ag₇(+)||Ni₃N/Ni(–) can produce H₂at both anode and cathode simultaneously with an apparent 200% Faradaic efficiency, reaching a current density of 500 mA/cm²with a cell voltage of only 0.60 V.

Acid–base chemistry has immense importance for explaining and predicting the chemical products formed by an acid and a base when mixed together. However, the traditional chemistry theories used to describe acid–base reactions do not take into account the effect arising from the quantum mechanical nature of the acidic hydrogen shuttling potential and its dependence on the acid base distance. Here, infrared and NMR spectroscopies, in combination with first principles simulations, are performed to demonstrate that quantum mechanical effects, including electronic and nuclear quantum effects, play an essential role in defining the acid–base chemistry when 1-methylimidazole and acetic acid are mixed together. In particular, it is observed that the acid and the base interact to form a complex containing a strong hydrogen bond, in which the acidic hydrogen atom is neither close to the acid nor to the base, but delocalized between them. In addition, the delocalization of the acidic hydrogen atom in the complex leads to characteristic IR and NMR signatures. The presence of a hydrogen delocalized state in this simple system challenges the conventional knowledge of acid–base chemistry and opens up new avenues for designing materials in which specific properties produced by the hydrogen delocalized state can be harvested.

Short hydrogen bonds (SHBs), whose donor and acceptor heteroatoms lie within 2.7 Å, exhibit prominent quantum mechanical characters and are connected to a wide range of essential biomolecular processes. However, exact determination of the geometry and functional roles of SHBs requires a protein to be at atomic resolution. In this work, we analyze 1260 high-resolution peptide and protein structures from the Protein Data Bank and develop a boosting based machine learning model to predict the formation of SHBs between amino acids. This model, which we name as machine learning assisted prediction of short hydrogen bonds (MAPSHB), takes into account 21 structural, chemical and sequence features and their interaction effects and effectively categorizes each hydrogen bond in a protein to a short or normal hydrogen bond. The MAPSHB model reveals that the type of the donor amino acid plays a major role in determining the class of a hydrogen bond and that the side chain Tyr-Asp pair demonstrates a significant probability of forming a SHB. Combining electronic structure calculations and energy decomposition analysis, we elucidate how the interplay of competing intermolecular interactions stabilizes the Tyr-Asp SHBs more than other commonly observed combinations of amino acid side chains. The MAPSHB model,more »which is freely available on our web server, allows one to accurately and efficiently predict the presence of SHBs given a protein structure with moderate or low resolution and will facilitate the experimental and computational refinement of protein structures.

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While vat photopolymerization has many advantages over soft lithography in fabricating microfluidic devices, including efficiency and shape complexity, it has difficulty achieving well-controlled micrometer-sized (smaller than 100 μm) channels in the layer building direction. The considerable light penetration depth of transparent resin leads to over-curing that inevitably cures the residual resin inside flow channels, causing clogs. In this paper, a 3D printing process — in-situ transfer vat photopolymerization is reported to solve this critical over-curing issue in fabricating microfluidic devices. We demonstrate microchannels with highZ-resolution (within 10 μm level) and high accuracy (within 2 μm level) using a general method with no requirements on liquid resins such as reduced transparency nor leads to a reduced fabrication speed. Compared with all other vat photopolymerization-based techniques specialized for microfluidic channel fabrication, our universal approach is compatible with commonly used 405 nm light sources and commercial photocurable resins. The process has been verified by multifunctional devices, including 3D serpentine microfluidic channels, microfluidic valves, and particle sorting devices. This work solves a critical barrier in 3D printing microfluidic channels using the high-speed vat photopolymerization process and broadens the material options. It also significantly advances vat photopolymerization’s use in applications requiring small gaps with high accuracy in theZ-direction.

We study the task of long-form opinion text generation, which faces at least two distinct challenges. First, existing neural generation models fall short of coherence, thus requiring efficient content planning. Second, diverse types of information are needed to guide the generator to cover both subjective and objective content. To this end, we propose DYPLOC, a generation framework that conducts dynamic planning of content while generating the output based on a novel design of mixed language models. To enrich the generation with diverse content, we further propose to use large pre-trained models to predict relevant concepts and to generate claims. We experiment with two challenging tasks on newly collected datasets: (1) argument generation with Reddit ChangeMyView, and (2) writing articles using New York Times{'} Opinion section. Automatic evaluation shows that our model significantly outperforms competitive comparisons. Human judges further confirm that our generations are more coherent with richer content.