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In this Article, we explore how the chemical pressure (CP) features of an intermetallic phase may provide opportunities to couple perturbations in electron count with the stabilization of the underlying geometrical structure. AuCu3‐type LnGa3 (Ln = lanthanide or group 3 metal) phases contain octahedral cavities of negative CP held open by overly compressed Ln–Ga interactions, leading to a series of transition metal‐stuffed derivatives. We present new additions to this family with the synthesis and crystal structures of Dy4T1−xGa12 with (T, x) = (Ag, 0.29) and (Ir, 0.15), adopting Y4PdGa12‐type superstructures of the AuCu3‐type. Density Functional Theory (DFT)‐CP calculations, when adjusted to avoid dipolar CP features, affirm that T atom incorporation provides a mechanism for the relief of packing tensions, while electronic density of states distributions illustrate that the T atoms serve largely as electron or hole donors to the band structure, as needed for them to attain d10 configurations. The maximum obtainable value for x may be limited by a mismatch between the Fermi energy and pseudogap, in line with the balance of factors envisioned by the frustrated and allowed structural transitions principle. Trends in resistivity measurements on T = Ir, Pd, and Ag compounds are interpretable in terms of the varying degrees of disorder arising from x< 1.0. 

Street View Images (SVI) are a common source of valuable data for researchers. Researchers have used SVI data for estimating pedestrian volumes, demographic surveillance, and to better understand built and natural environments in cityscapes. However, the most common source of publicly available SVI data is Google Street View. Google Street View images are collected infrequently, making temporal analysis challenging, especially in low population density areas. Our main contribution is the development of an open-source data pipeline for processing 360-degree video recorded from a car-mounted camera. The video data is used to generate SVIs, which then can be used as an input for longitudinal analysis. We demonstrate the use of the pipeline by collecting an SVI dataset over a 38-month longitudinal survey of Seattle, WA, USA during the COVID-19 pandemic. The output of our pipeline is validated through statistical analyses of pedestrian traffic in the images. We confirm known results in the literature and provide new insights into outdoor pedestrian traffic patterns. This study demonstrates the feasibility and value of collecting and using SVI for research purposes beyond what is possible with currently available SVI data. Our methods and dataset represent a first of its kind longitudinal collection and application of SVI data for research purposes. Limitations and future improvements to the data pipeline and case study are also discussed. 

Materials discovery lays the foundation for many technological advancements. The prediction and discovery of new materials are not simple tasks. Here, we outline some basic principles of solid-state chemistry, which might help to advance both, and discuss pitfalls and challenges in materials discovery. Using the recent work of Szymanski et al. [Nature 624, 86 (2023)], which reported the autonomous discovery of 43 novel materials, as an example, we discuss problems that can arise in unsupervised materials discovery and hope that by addressing these, autonomous materials discovery can be brought closer to reality. We discuss all 43 synthetic products and point out four common shortfalls in the analysis. These errors unfortunately lead to the conclusion that no new materials have been discovered in that work. We conclude that there are two important points of improvement that require future work from the community, as follows. (i) Automated Rietveld analysis of powder x-ray diffraction data is not yet reliable. Future improvement of such, and the development of a reliable artificial-intelligence-based tool for Rietveld fitting, would be very helpful, not only for autonomous materials discovery but also for the community in general. (ii) We find that disorder in materials is often neglected in predictions. The predicted compounds investigated herein have all their elemental components located on distinct crystallographic positions but in reality, elements can share crystallographic sites, resulting in higher-symmetry space groups and—very often—known alloys or solid solutions. This error might be related to the difficulty of modeling disorder in a computationally economical way and needs to be addressed both by computational and experimental material scientists. We find that two thirds of the claimed successful materials in Szymanski et al. are likely to be known compositionally disordered versions of the predicted ordered compounds. We highlight important issues in materials discovery, computational chemistry, and autonomous interpretation of x-ray diffraction. We discuss concepts of materials discovery from an experimentalist point of view, which we hope will be helpful for the community to further advance this important new aspect of our field. 

Abstract Advances in mass spectrometry (MS) have enabled high-throughput analysis of proteomes in biological systems. The state-of-the-art MS data analysis relies on database search algorithms to quantify proteins by identifying peptide–spectrum matches (PSMs), which convert mass spectra to peptide sequences. Different database search algorithms use distinct search strategies and thus may identify unique PSMs. However, no existing approaches can aggregate all user-specified database search algorithms with a guaranteed increase in the number of identified peptides and a control on the false discovery rate (FDR). To fill in this gap, we proposed a statistical framework, Aggregation of Peptide Identification Results (APIR), that is universally compatible with all database search algorithms. Notably, under an FDR threshold, APIR is guaranteed to identify at least as many, if not more, peptides as individual database search algorithms do. Evaluation of APIR on a complex proteomics standard dataset showed that APIR outpowers individual database search algorithms and empirically controls the FDR. Real data studies showed that APIR can identify disease-related proteins and post-translational modifications missed by some individual database search algorithms. The APIR framework is easily extendable to aggregating discoveries made by multiple algorithms in other high-throughput biomedical data analysis, e.g., differential gene expression analysis on RNA sequencing data. The APIR R package is available at https://github.com/yiling0210/APIR. 

Microresonator frequency combs and their design versatility have revolutionized research areas from data communication to exoplanet searches. While microcombs in the 1550 nm band are well documented, there is interest in using microcombs in other bands. Here, we demonstrate the formation and spectral control of normal-dispersion dark soliton microcombs at 1064 nm. We generate 200 GHz repetition rate microcombs by inducing a photonic bandgap of the microresonator mode for the pump laser with a photonic crystal. We perform the experiments with normal-dispersion microresonators made from Ta2O5 and explore unique soliton pulse shapes and operating behaviors. By adjusting the resonator dispersion through its nanostructured geometry, we demonstrate control over the spectral bandwidth of these combs, and we employ numerical modeling to understand their existence range. Our results highlight how photonic design enables microcomb spectra tailoring across wide wavelength ranges, offering potential in bioimaging, spectroscopy, and photonic-atomic quantum technologies.