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Diffusion of native defects such as vacancies and their interactions with impurities are fundamental to semiconductor crystal growth, device processing, and design. However, the transient equilibration of native defects is difficult to directly measure. We used (AlxGa1−x)2O3/Ga2O3 superlattices (SLs) to detect and analyze transient diffusion of cation vacancies during annealing in O2 at 1000–1100 °C. Using a novel finite difference scheme for diffusion with time- and space-varying diffusion constants, we determined diffusion constants for Al, Fe, and cation vacancies, including the vacancy concentration dependence for Al. In the case of SLs grown on Sn-doped β-Ga2O3 (010) substrates, gradients observed in the extent of Al diffusion indicate a supersaturation of vacancies in the substrates that transiently diffuse through the SLs coupled strongly to Sn and thus slowed compared to undoped cases. In the case of SLs grown on (010) Fe-doped substrates, the Al diffusion is uniform through the SLs, indicating a depth-uniform concentration of vacancies. We find no evidence for the introduction of VGa from the free surface at rates sufficient to affect Al diffusion at at. % concentrations, establishing an upper bound on surface injection. In addition, we show that unintentional impurities in Sn-doped Ga2O3 such as Fe, Ni, Mn, Cu, and Li also diffuse toward the surface and accumulate. Many of these likely have fast interstitial diffusion modes capable of destabilizing devices, thus suggesting that impurities may require further reduction. This work provides a method to measure transients in diffusion-mediating native defects otherwise hidden in common processes such as ion implantation, etching, and film growth. 

Knowing the object grabbed by a hand can offer essential contextual information for interaction between the human and the physical world. This paper presents a novel system, ViObject, for passive object recognition that uses accelerometer and gyroscope sensor data from commodity smartwatches to identify untagged everyday objects. The system relies on the vibrations caused by grabbing objects and does not require additional hardware or human effort. ViObject's ability to recognize objects passively can have important implications for a wide range of applications, from smart home automation to healthcare and assistive technologies. In this paper, we present the design and implementation of ViObject, to address challenges such as motion interference, different object-touching positions, different grasp speeds/pressure, and model customization to new users and new objects. We evaluate the system's performance using a dataset of 20 objects from 20 participants and show that ViObject achieves an average accuracy of 86.4%. We also customize models for new users and new objects, achieving an average accuracy of 90.1%. Overall, ViObject demonstrates a novel technology concept of passive object recognition using commodity smartwatches and opens up new avenues for research and innovation in this area. 

Abstract CO₂electroreduction (CO₂R) operating in acidic media circumvents the problems of carbonate formation and CO₂crossover in neutral/alkaline electrolyzers. Alkali cations have been universally recognized as indispensable components for acidic CO₂R, while they cause the inevitable issue of salt precipitation. It is therefore desirable to realize alkali‐cation‐free CO₂R in pure acid. However, without alkali cations, stabilizing *CO₂intermediates by catalyst itself at the acidic interface poses as a challenge. Herein, we first demonstrate that a carbon nanotube‐supported molecularly dispersed cobalt phthalocyanine (CoPc@CNT) catalyst provides the Co single‐atom active site with energetically localizeddstates to strengthen the adsorbate‐surface interactions, which stabilizes *CO₂intermediates at the acidic interface (pH=1). As a result, we realize CO₂conversion to CO in pure acid with a faradaic efficiency of 60 % at pH=2 in flow cell. Furthermore, CO₂is successfully converted in cation exchanged membrane‐based electrode assembly with a faradaic efficiency of 73 %. For CoPc@CNT, acidic conditions also promote the intrinsic activity of CO₂R compared to alkaline conditions, since the potential‐limiting step, *CO₂to *COOH, is pH‐dependent. This work provides a new understanding for the stabilization of reaction intermediates and facilitates the designs of catalysts and devices for acidic CO₂R. 

Abstract The effect of tropical cyclone (TC) size on TC-induced sea surface temperature (SST) cooling and subsequent TC intensification is an intriguing issue without much exploration. Via compositing satellite-observed SST over the western North Pacific during 2004–19, this study systematically examined the effect of storm size on the magnitude, spatial extension, and temporal evolution of TC-induced SST anomalies (SSTA). Consequential influence on TC intensification is also explored. Among the various TC wind radii, SSTA are found to be most sensitive to the 34-kt wind radius (R34) (1 kt ≈ 0.51 m s⁻¹). Generally, large TCs generate stronger and more widespread SSTA than small TCs (for category 1–2 TCs, R34: ∼270 vs 160 km; SSTA: −1.7° vs −0.9°C). Despite the same effect on prolonging residence time of TC winds, the effect of doubling R34 on SSTA is more profound than halving translation speed, due to more wind energy input into the upper ocean. Also differing from translation speed, storm size has a rather modest effect on the rightward shift and timing of maximum cooling. This study further demonstrates that storm size regulates TC intensification through an oceanic pathway: large TCs tend to induce stronger SST cooling and are exposed to the cooling for a longer time, both of which reduce the ocean’s enthalpy supply and thereby diminish TC intensification. For larger TCs experiencing stronger SST cooling, the probability of rapid intensification is half of smaller TCs. The presented results suggest that accurately specifying storm size should lead to improved cooling effect estimation and TC intensity prediction. 

Abstract Tropical Cyclones (TCs) are devastating natural disasters. Analyzing four decades of global TC data, here we find that among all global TC-active basins, the South China Sea (SCS) stands out as particularly difficult ocean for TCs to intensify, despite favorable atmosphere and ocean conditions. Over the SCS, TC intensification rate and its probability for a rapid intensification (intensification by ≥ 15.4 m s⁻¹day⁻¹) are only 1/2 and 1/3, respectively, of those for the rest of the world ocean. Originating from complex interplays between astronomic tides and the SCS topography, gigantic ocean internal tides interact with TC-generated oceanic near-inertial waves and induce a strong ocean cooling effect, suppressing the TC intensification. Inclusion of this interaction between internal tides and TC in operational weather prediction systems is expected to improve forecast of TC intensity in the SCS and in other regions where strong internal tides are present. 

Abstract Neural communication networks form the fundamental basis for brain function. These communication networks are enabled by emitted ligands such as neurotransmitters, which activate receptor complexes to facilitate communication. Thus, neural communication is fundamentally dependent on the transcriptome. Here we develop NeuronChat, a method and package for the inference, visualization and analysis of neural-specific communication networks among pre-defined cell groups using single-cell expression data. We incorporate a manually curated molecular interaction database of neural signaling for both human and mouse, and benchmark NeuronChat on several published datasets to validate its ability in predicting neural connectivity. Then, we apply NeuronChat to three different neural tissue datasets to illustrate its functionalities in identifying interneural communication networks, revealing conserved or context-specific interactions across different biological contexts, and predicting communication pattern changes in diseased brains with autism spectrum disorder. Finally, we demonstrate NeuronChat can utilize spatial transcriptomics data to infer and visualize neural-specific cell-cell communication.