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Spintronics has emerged as a key technology for fast and nonvolatile memory with great CMOS compatibility. As the building blocks for these cutting-edge devices, magnetic materials require precise characterization of their critical properties, such as the effective anisotropy field (Hk,eff, related to magnetic stability) and damping (α, a key factor in device energy efficiency). Accurate measurements of these properties are essential for designing and fabricating high-performance spintronic devices. Among advanced metrology techniques, time-resolved magneto-optical Kerr effect (TR-MOKE) stands out for its superb temporal and spatial resolutions, surpassing traditional methods like ferromagnetic resonance. However, the full potential of TR-MOKE has not yet been fully fledged due to the lack of systematic optimization and robust operational guidelines. In this study, we address this gap by developing experimentally validated guidelines for optimizing TR-MOKE metrology across materials with perpendicular magnetic anisotropy and in-plane magnetic anisotropy. While Co20Fe60B20 thin films are used for experimental validation, this optimization framework can be readily extended to a variety of materials such as L10-FePd with easy-axis dispersion. Our work identifies the optimal ranges of the field angle to simultaneously achieve high signal amplitudes and improve measurement sensitivities to Hk,eff and α. By suppressing the influence of inhomogeneities and boosting sensitivity, our work significantly enhances TR-MOKE capability to extract magnetic properties with high accuracy and reliability. This optimization framework positions TR-MOKE as an indispensable tool for advancing spintronics, paving the way for energy-efficient and high-speed devices that will redefine the landscape of modern computing and memory technologies. 

Abstract The lungs of squamate reptiles (lizards and snakes) are highly diverse, exhibiting single chambers, multiple chambers, transitional forms with two to three chambers, along with a suite of other anatomical features, including finger-like epithelial projections into the body cavity known as diverticulae. During embryonic development of the simple, sac-like lungs of anoles, the epithelium is pushed through the openings of a pulmonary smooth muscle mesh by the forces of luminal fluid pressure. This process of stress ball morphogenesis generates the faveolar epithelium typical of squamate lungs. Here, we compared embryonic lung development in brown anoles, leopard geckos, and veiled chameleons to determine if stress ball morphogenesis is conserved across squamates and to understand the physical processes that generate transitional-chambered lungs with diverticulae. We found that epithelial protrusion through the holes in a pulmonary smooth muscle mesh is conserved across squamates. Surprisingly, however, we found that luminal inflation is not conserved. Instead, leopard geckos and veiled chameleons appear to generate their faveolae via epithelial folding downstream of epithelial proliferation. We also found experimental and computational evidence suggesting that the transitional chambers and diverticulae of veiled chameleon lungs develop via apical constriction, a process known to be crucial for airway branching in the bird lung. Thus, distinct morphogenetic mechanisms generate epithelial diversity in squamate lungs, which may underpin their species-specific physiological and ecological adaptations. 

Quaternary metal‐chalcogenides combining rare‐earth cations with late transition metal cations are attracting growing attention for their optical properties, such as for solar energy conversion or second harmonic generation. Synthetic explorations of theII₃‐I₂‐IV₂‐Ch₈family (II = Eu;I = Cu or Ag;IV = Si, Ch = S or Se) have yielded Eu₃Ag₂Si₂S₈(1) and Eu₃Cu_1.08(1)Si_2.42(1)Se₈(2). Their structures have been characterized by X‐ray diffraction to form in the noncentrosymmetric space groupI3dand to exhibit two distinct types of mixed‐site occupancies, for the Ag(I) cations in1and mixed Cu(I)/Si(IV) cations in2. In both, the cation disorder occurs to achieve charge balancing with the chalcogenide anions. A high yield of1can be achievedwith optical measurements showing indirect and direct band transitions of ≈2.2(1) and ≈2.4(1) eV, respectively. Its second harmonic generation response is found to be relatively strong, approximately 0.9 × AgGaS₂, confirming its noncentrosymmetric structure. Band structure calculations reveal the valence and conduction band edges stem predominantly from the filled Ag(I)/Cu(I)‐based states and empty Si(IV)‐based states, respectively, with additional contributions from the chalcogenide anions. Calculation results also show that cation disorder facilitates a reduction in the antibonding interactions between the Ag(I)/Cu(I)d‐based and chalcogenidep‐based states. 

Abstract The Household Pulse Survey (HPS), released by the US Census Bureau at the start of the coronavirus pandemic, gathers timely information about the societal and economic impacts of coronavirus. The first phase of the survey was launched in April 2020 and ran for 12 weeks. To track the immediate impact of the pandemic, individual respondents during this phase were re-sampled for up to three consecutive weeks. Motivated by expected job loss during the pandemic, using public-use microdata, this work proposes unit-level, model-based estimators that incorporate longitudinal dependence at both the response and domain level. In particular, using a pseudo-likelihood, we consider a Bayesian hierarchical unit-level, model-based approach for both Gaussian and binary response data under informative sampling. To facilitate construction of these model-based estimates, we develop an efficient Gibbs sampler. An empirical simulation study is conducted to compare the proposed approach to models that do not account for unit-level longitudinal correlation. Finally, using public-use HPS micro-data, we provide an analysis of ‘expected job loss’ that compares both design- and model-based estimators and demonstrates superior performance for the proposed model-based approaches. 

Molecular docking is a computational technique used to predict ligand binding potential, conformation, and location for a given receptor, and is regarded as an attractive method to use in drug design due to its relatively low computational and monetary cost. However, molecular docking programs tend not to be accessible to novice users. Most docking programs require at least a basic knowledge of command line and computer programming to install and configure the program. Additionally, tutorials for the most commonly used programs tend to be inflexible, requiring a specific molecule or set of molecules to be bound to a specific receptor, and need the installation and usage of other programs or websites to download and prepare structures. To increase general access to molecular docking, basil_dock utilizes a series of easy-to-use Jupyter notebooks that do not assume user familiarity with molecular docking procedures and concepts, requiring little command line usage and software installation. The series includes four notebooks that were created to reflect the different steps in the molecular docking process: (1) the preparation of ligand and protein files prior to docking, (2) the docking of ligands to a protein receptor, (3) analyzing the resulting data and determining how different functional groups in the ligand can affect protein-ligand binding, and (4) identifying essential locations for binding within the ligand and protein. The notebooks enable novice users flexibility and customization in exploring docking procedures and systems, as well as teaching users the basis behind molecular docking without having to leave the environment to obtain information and materials from other applications. The first version of basil_dock allows users to choose from receptors uploaded to the Protein Data Bank and to add additional ligands as desired. Users can then select between the Vina and Smina docking engines and change ligand functional groups to see how the substitution of atom groups affects binding affinity and ligand conformation. The data can then be analyzed to determine residues in the receptor and atom groups in the ligand that are likely to be integral to forming the ligand-protein complex and to discern which ligands are likely to be orally bioactive based on Lipinski’s Rule of Five. From this work, a package of python scripts has been created to streamline the generating, splitting, and writing of ligand files, greatly reducing the number of errors arising from attempting to split a comprehensive ligand file manually. Libraries used in basil_dock include Vina, Smina, RDKit, openbabel, and MDAnalysis. While the package has been designed based off the needs of basil_dock, it has been created to be extensible. Support for this project was provided by NSF 2142033 

This year, the National Science Foundation (NSF) is celebratingits 75th anniversary. NSF support was essential in the originaldevelopment of BASIL (Biochemistry Authentic Scientific InquiryLab). Ongoing NSF support over the past ten years has enabled the BASILcommunity to grow in numbers and in collaboration with other teacher/scholar teamswho are seeking to change undergraduate biochemistry education. At the same time,NSF support has also provided support for our most critical online resource, theRCSB Protein Data Bank, which has always provided us with the structures that westudy and, increasingly, is providing us with the tools that our students use to explorethese structures and predict their function.