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  1. The electrochemical CO2 reduction reaction (ECO2RR) driven by renewable electricity holds promise to store intermittent energy in chemical bonds, while producing value-added chemicals and fuels sustainably. Unfortunately, it remains a grand challenge to simultaneously achieve a high faradaic efficiency (FE), a low overpotential, and a high current density of the ECO2RR. Herein, we report the synthesis of heterostructured Bi–Cu2S nanocrystals via a one-pot solution-phase method. The epitaxial growth of Cu2S on Bi leads to abundant interfacial sites and the resultant heterostructured Bi–Cu2S nanocrystals enable highly efficient ECO2RR with a largely reduced overpotential (240 mV lower than that of Bi), a near-unity FE (>98%) for formate production, and a high partial current density (2.4- and 5.2-fold higher JHCOO− than Cu2S and Bi at −1.0 V vs. reversible hydrogen electrode, RHE). Density functional theory (DFT) calculations show that the electron transfer from Bi to Cu2S at the interface leads to the preferential stabilization of the formate-evolution intermediate (*OCHO).
    Free, publicly-accessible full text available April 1, 2023
  2. Abstract

    The electrochemical nitrate reduction reaction (NO3RR) to ammonia is an essential step toward restoring the globally disrupted nitrogen cycle. In search of highly efficient electrocatalysts, tailoring catalytic sites with ligand and strain effects in random alloys is a common approach but remains limited due to the ubiquitous energy-scaling relations. With interpretable machine learning, we unravel a mechanism of breaking adsorption-energy scaling relations through the site-specific Pauli repulsion interactions of the metald-states with adsorbate frontier orbitals. The non-scaling behavior can be realized on (100)-type sites of ordered B2 intermetallics, in which the orbital overlap between the hollow *N and subsurface metal atoms is significant while the bridge-bidentate *NO3is not directly affected. Among those intermetallics predicted, we synthesize monodisperse ordered B2 CuPd nanocubes that demonstrate high performance for NO3RR to ammonia with a Faradaic efficiency of 92.5% at −0.5 VRHEand a yield rate of 6.25 mol h−1g−1at −0.6 VRHE. This study provides machine-learned design rules besides thed-band center metrics, paving the path toward data-driven discovery of catalytic materials beyond linear scaling limitations.

  3. Free, publicly-accessible full text available March 1, 2023
  4. Learning explicit and implicit patterns in human trajectories plays an important role in many Location-Based Social Networks (LBSNs) applications, such as trajectory classification (e.g., walking, driving, etc.), trajectory-user linking, friend recommendation, etc. A particular problem that has attracted much attention recently – and is the focus of our work – is the Trajectory-based Social Circle Inference (TSCI), aiming at inferring user social circles (mainly social friendship) based on motion trajectories and without any explicit social networked information. Existing approaches addressing TSCI lack satisfactory results due to the challenges related to data sparsity, accessibility and model efficiency. Motivated by the recent success of machine learning in trajectory mining, in this paper we formulate TSCI as a novel multi-label classification problem and develop a Recurrent Neural Network (RNN)-based framework called DeepTSCI to use human mobility patterns for inferring corresponding social circles. We propose three methods to learn the latent representations of trajectories, based on: (1) bidirectional Long Short-Term Memory (LSTM); (2) Autoencoder; and (3) Variational autoencoder. Experiments conducted on real-world datasets demonstrate that our proposed methods perform well and achieve significant improvement in terms of macro-R, macro-F1 and accuracy when compared to baselines.