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Abstract Cu is the most promising metal catalyst for CO₂electroreduction (CO₂RR) to multi-carbon products, yet the structure sensitivity of the reaction and the stability versus restructuring of the catalyst surface under reaction conditions remain controversial. Here, atomic scale simulations of surface energies and reaction pathway kinetics supported by experimental evidence unveil that CO₂RR does not take place on perfect planar Cu(111) and Cu(100) surfaces but rather on steps or kinks. These planar surfaces tend to restructure in reaction conditions to the active stepped surfaces, with the strong binding of CO on defective sites acting as a thermodynamic driving force. Notably, we identify that the square motifs adjacent to defects, not the defects themselves, as the active sites for CO₂RR via synergistic effect. We evaluate these mechanisms against experiments of CO₂RR on ultra-high vacuum-prepared ultraclean Cu surfaces, uncovering the crucial role of step-edge orientation in steering selectivity. Overall, our study refines the structural sensitivity of CO₂RR on Cu at the atomic level, highlights the self-activation mechanism and elucidates the origin of in situ restructuring of Cu surfaces during the reaction. 

Apache Spark arguably is the most prominent Big Data processing framework tackling the scalability challenge of a wide variety of modern workloads. A key to its success is caching critical data in memory, thereby eliminating wasteful computations of regenerating intermediate results. While critical to performance, caching is not automated. Instead, developers have to manually handle such a data management task via APIs, a process that is error-prone and labor-intensive, yet may still yield sub-optimal performance due to execution complexities. Existing optimizations rely on expensive profiling steps and/or application-specific cost models to enable a postmortem analysis and a manual modification to existing applications. This paper presents CACHEIT, built to take the guesswork off the users while running applications as-is. CACHEIT analyzes the program’s workflow, extracting important features such as dependencies and access patterns, using them as an oracle to detect high-value data candidates and guide the caching decisions at run time. CACHEIT liberates users from low-level memory management requirements, allowing them to focus on the business logic instead. CACHEIT is application-agnostic and requires no profiling or a cost model. A thorough evaluation with a broad range of Spark applications on real-world datasets shows that CACHEIT is effective in maintaining satisfactory performance, incurring only marginal slowdown compared to the manually well-tuned counterparts 

Calling context is crucial for improving the precision of program analyses in various use cases (clients), such as profiling, debugging, optimization, and security checking. Often the calling context is encoded using a numerical value. We have observed that many clients benefit not only from a deterministic but also globally distinguishable value across runs to simplify bookkeeping and guarantee complete uniqueness. However, existing work only guarantees determinism, not global distinguishability. Clients need to develop auxiliary helpers, which incurs considerable overhead to distinguish encoded values among all calling contexts. In this paper, we propose Deterministic Distinguishable Calling Context Encoding () that can enable both properties of calling context encoding natively. The key idea of is leveraging the static call graph and encoding each calling context as the running call path count. Thereby, a mapping is established statically and can be readily used by the clients. Our experiments with two client tools show that has a comparable overhead compared to two state-of-the-art encoding schemes, PCCE and PCC, and further avoids the expensive overheads of collision detection, up to 2.1× and 50%, for Splash-3 and SPEC CPU 2017, respectively. 

Thanks to recent advances in high-bandwidth, low-latency interconnects, running a data-intensive application with a remote memory pool is now a feasibility. When developing a data-intensive application, a managed language such as Java is often the developer’s choice due to convenience of the runtime such as automatic memory management. However, the memory management cost increases significantly in far memory due to remote memory accesses. Our insight is that data hotness (i.e., access frequency of objects) is the key to reducing the memory management cost and improving efficiency in far memory. In this paper, we present an ongoing work designing Polar, an enhanced runtime system that is hotness-aware, and optimized for far memory. In Polar, the garbage collector is augmented to identify cold (infrequently accessed) objects and relocate them to remote memory pools. By placing objects at memory locations based on their access frequency, Polar minimizes the number of remote accesses, ensures low access latency for the application, and thus improves overall performance. 

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. 

Abstract Heart failure, a leading global health challenge, affects over 23 million people worldwide, with heart transplantation being the gold standard for end-stage disease. However, the scarcity of viable donor hearts presents a significant barrier, with only one-third of available grafts used due to stringent selection criteria. Machine perfusion technologies, particularly normothermic machine perfusion (NMP), offer promise in improving graft preservation and assessment, yet their full potential for predicting transplantability remains underexplored. This study investigates three assessment methods to enhance human heart evaluation during NMP, focusing on mitochondrial function, left ventricular (LV) performance, and inflammatory markers. First, resonance Raman spectroscopy (RRS) is employed to assess mitochondrial redox state as a proxy for metabolic competency, offering a non-invasive and dynamic evaluation of mitochondrial function during ex vivo preservation. Second, LV function is quantified using intraventricular balloons, providing critical insights into graft viability and performance. Third, inflammatory markers and endothelial activation are assessed from perfusate to predict post-transplant outcomes. These methods were tested on human donor hearts declined for transplantation, preserved via static cold storage (SCS) and subsequently assessed with NMP in Langendorff mode. The results demonstrate that these parameters can be easily integrated into existing clinical perfusion workflows and hold potential for improving heart transplantation outcomes by enhancing graft selection and optimizing donor heart use. Future studies will further validate these biomarkers across different preservation techniques and evaluate their clinical applicability. 

The indirect exchange interaction between local magnetic moments via surface electrons has been long predicted to bolster the surface ferromagnetism in magnetic topological insulators (MTIs), which facilitates the quantum anomalous Hall effect. This unconventional effect is critical to determining the operating temperatures of future topotronic devices. However, the experimental confirmation of this mechanism remains elusive, especially in intrinsic MTIs. Here, we combine time-resolved photoemission spectroscopy with time-resolved magneto-optical Kerr effect measurements to elucidate the unique electromagnetism at the surface of an intrinsic MTI MnBi₂Te₄. Theoretical modeling based on 2D Ruderman-Kittel-Kasuya-Yosida interactions captures the initial quenching of a surface-rooted exchange gap within a factor of two but overestimates the bulk demagnetization by one order of magnitude. This mechanism directly explains the sizable gap in the quasi-2D electronic state and the nonzero residual magnetization in even-layer MnBi₂Te₄. Furthermore, it leads to efficient light-induced demagnetization comparable to state-of-the-art magnetophotonic crystals, promising an effective manipulation of magnetism and topological orders for future topotronics. 

Historical systematic exclusionary tactics based on race have forced people of certain demographic groups to congregate in specific urban areas. Aside from the ethical aspects of such segregation, these policies have implications for the allocation of urban resources including public transportation, healthcare, and education within the cities. The initial step towards addressing these issues involves conducting an audit to assess the status of equitable resource allocation. However, due to privacy and confidentiality concerns, individual-level data containing demographic information cannot be made publicly available. By leveraging publicly available aggregated demographic statistics data, we introduce PopSim, a system for generating semi-synthetic individual-level population data with demographic information. We use PopSim to generate multiple benchmark datasets for the city of Chicago and conduct extensive statistical evaluations to validate those. We further use our datasets for several case studies that showcase the application of our system for auditing equitable allocation of city resources. 

Abstract Enzymes that oxidize aromatic substrates have shown utility in a range of cell-based technologies including live cell proximity labeling (PL) and electron microscopy (EM), but are associated with drawbacks such as the need for toxic H₂O₂. Here, we explore laccases as a novel enzyme class for PL and EM in mammalian cells. LaccID, generated via 11 rounds of directed evolution from an ancestral fungal laccase, catalyzes the one-electron oxidation of diverse aromatic substrates using O₂instead of toxic H₂O₂, and exhibits activity selective to the surface plasma membrane of both living and fixed cells. We show that LaccID can be used with mass spectrometry-based proteomics to map the changing surface composition of T cells that engage with tumor cells via antigen-specific T cell receptors. In addition, we use LaccID as a genetically-encodable tag for EM visualization of cell surface features in mammalian cell culture and in the fly brain. Our study paves the way for future cell-based applications of LaccID.