Search for: All records

A topological superconductor, characterized by either a chiral order parameter or a topological surface state in proximity to bulk superconductivity, is foundational to topological quantum computing. A key open challenge is whether electron-electron interactions can tune such topological superconducting phase. Here, we provide experimental signatures of a unique topological superconducting phase in competition with electronic correlations in 10-unit-cell thick FeTexSe1-x films grown on SrTiO3 substrates. When the Te content x exceeds 0.7, we observe a topological transition marked by the emergence of a superconducting surface state. Near the FeTe limit, the system undergoes another transition where the surface state disappears, and superconductivity is suppressed. Theory suggests that electron-electron interactions in the odd-parity xy- band drives this second topological transition. The flattening and eventual decoherence of dxy-derived bands track the superconducting dome, linking correlation effects directly to superconducting coherent transport. Our work establishes many-body electronic correlations as a sensitive knob for tuning topology and superconductivity, offering a pathway to engineer new topological phases in correlated materials. 

ArticleCathodic Corrosion-Induced Structural Evolution of CuNi Electrocatalysts for Enhanced CO2 ReductionWenjin Sun 1,†, Bokki Min 2,†, Maoyu Wang 3, Xue Han 4, Qiang Gao 1, Sooyeon Hwang 5, Hua Zhou 3, and Huiyuan Zhu 1,2,*1 Department of Chemistry, University of Virginia, Charlottesville, VA 22904, USA2 Department of Chemical Engineering, University of Virginia, Charlottesville, VA 22904, USA3 Advanced Photon Source, Argonne National Laboratory, Lemont, IL 60439, USA4 Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA5 Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, NY 11973, USA* Correspondence: kkx8js@virginia.com† These authors contributed equally to this work.Received: 22 October 2024; Revised: 25 November 2024; Accepted: 27 November 2024; Published: 4 December 2024 Abstract: The electrochemical CO2 reduction reaction (CO2RR) has attracted significant attention as a promising strategy for storing intermittent energy in chemical bonds while sustainably producing value-added chemicals and fuels. Copper-based bimetallic catalysts are particularly appealing for CO2RR due to their unique ability to generate multi-carbon products. While substantial effort has been devoted to developing new catalysts, the evolution of bimetallic systems under operational conditions remains underexplored. In this work, we synthesized a series of CuxNi1−x nanoparticles and investigated their structural evolution during CO2RR. Due to the higher oxophilicity of Ni compared to Cu, the particles tend to become Ni-enriched at the surface upon air exposure, promoting the competing hydrogen evolution reaction (HER). At negative activation potentials, cathodic corrosion has been observed in CuxNi1−x nanoparticles, leading to the significant Ni loss and the formation of irregularly shaped Cu nanoparticles with increased defects. This structural evolution, driven by cathodic corrosion, shifts the electrolysis from HER toward CO2 reduction, significantly enhancing the Faradaic efficiency of multi-carbon products (C2+). 

Fine-grained urban flow inference (FUFI), which involves inferring fine-grained flow maps from their coarse-grained counterparts, is of tremendous interest in the realm of sustainable urban traffic services. To address the FUFI, existing solutions mainly concentrate on investigating spatial dependencies, introducing external factors, reducing excessive memory costs, etc., -- while rarely considering the catastrophic forgetting (CF) problem. Motivated by recent operator learning, we present an Urban Neural Operator solution with Incremental learning (UNOI), primarily seeking to learn grained-invariant solutions for FUFI in addition to addressing CF. Specifically, we devise an urban neural operator (UNO) in UNOI that learns mappings between approximation spaces by treating the different-grained flows as continuous functions, allowing a more flexible capture of spatial correlations. Furthermore, the phenomenon of CF behind time-related flows could hinder the capture of flow dynamics. Thus, UNOI mitigates CF concerns as well as privacy issues by placing UNO blocks in two incremental settings, i.e., flow-related and task-related. Experimental results on large-scale real-world datasets demonstrate the superiority of our proposed solution against the baselines.