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Abstract Van der Waals (vdW) superconductors remain superconducting down to the monolayer limit, enabling the exploration of emergent physical phenomena and functionality driven by reduced dimensionality. Here, we report the characterization of the kinetic inductance of atomically thin NbSe₂, a two-dimensional van der Waals superconductor, using superconducting coplanar waveguides and microwave measurement techniques familiar to circuit quantum electrodynamics (cQED). The kinetic inductance scales inversely with the number of NbSe₂layers, reaching 1.2 nH/□in the monolayer limit. Furthermore, the measured kinetic inductance exhibits a thickness-dependent crossover from clean- to dirty-limit behavior, with enhanced dirty-limit contributions emerging in the ultra-thin regime. These effects are likely driven by increased surface scattering, multi-band superconductivity, and geometric confinement. Additionally, the self-Kerr nonlinearity of the NbSe₂films ranges fromK/2π= −0.006 to −14.7 Hz/photon, indicating its strong potential in applications requiring compact, nearly linear, high-inductance superconducting quantum devices and detectors. The fabrication and characterization techniques demonstrated here are extensible to the investigation of other two-dimensional superconductors. 

We observe blockade of microwave photons in a Tavis-Cummings system comprising a superconducting cavity and up to $N=3$ transmon qubits. The effect is characterized with photon-number-resolving spectroscopy using an additional dispersively coupled transmon “witness” qubit to directly probe the cavity’s photon-number distribution. We first observe polariton formation with splitting proportional to $\sqrt{N}$ , confirming the Tavis-Cummings coupling, and subsequently obtain sub-Poissonian cavity photon statistics when the cavity is driven at polariton frequencies. 

Inbred mice used for biomedical research display an underdeveloped immune system compared with adult humans, which is attributed in part to the artificial laboratory environment. Despite representing a central component of adaptive immunity, the impact of the laboratory environment on the B cell compartment has not been investigated in detail. Here, we performed an in-depth examination of B cells following rewilding, the controlled release of inbred laboratory mice into an outdoor enclosure. In rewilded mice, we observed B cells in circulation with increased signs of maturation, alongside heightened germinal center responses within secondary lymphoid organs. Rewilding also expanded B cells in the gut, which was accompanied by elevated systemic levels of immunoglobulin G (IgG) and IgM antibodies reactive to the microbiota. Our findings indicate that exposing laboratory mice to a more natural environment enhances B cell development to better reflect the immune system of free-living mammals. 

Abstract Use of single‐atom catalysts (SACs) has become a popular strategy for tuning activity and selectivity toward specific pathways. However, conventional SAC synthesis methods require high temperatures and pressures, complicated procedures, and expensive equipment. Recently, underpotential deposition (UPD) has been investigated as a promising alternative, yielding high‐loading SAC electrodes under ambient conditions and within minutes. Yet only few studies have employed UPD to synthesize SACs, and all have been limited to UPD of Cu. In this work, a flexible UPD approach for synthesis of mono‐ and bi‐metallic Cu, Fe, Co, and Ni SACs directly on oxidized, commercially available carbon electrodes is reported. The UPD mechanism is investigated using in situ X‐ray absorption spectroscopy and, finally, the catalytic performance of a UPD‐synthesized Co SAC is assessed for electrochemical nitrate reduction to ammonia. The findings expand upon the usefulness and versatility of UPD for SAC synthesis, with hopes of enabling future research toward realization of fast, reliable, and fully electrified SAC synthesis processes. 

As students read textbooks, they often highlight the material they deem to be most important. We analyze students’ highlights to predict their subsequent performance on quiz questions. Past research in this area has encoded highlights in terms of where the highlights appear in the stream of text—a positional representation. In this work, we construct a semantic representation based on a state-of-the-art deep-learning sentence embedding technique (SBERT) that captures the content-based similarity between quiz questions and highlighted (as well as non-highlighted) sentences in the text. We construct regression models that include latent variables for student skill level and question difficulty and augment the models with highlighting features. We find that highlighting features reliably boost model performance. We conduct experiments that validate models on held-out questions, students, and student-questions and find strong generalization for the latter two but not for held-out questions. Surprisingly, highlighting features improve models for questions at all levels of the Bloom taxonomy, from straightforward recall questions to inferential synthesis/evaluation/creation questions.