A synergetic cocatalyst for conversion of carbon dioxide, sunlight, and water into methanol.

The conversion of CO2 into liquid fuels, using only sunlight and water, offers a promising path to carbon neutrality. An outstanding challenge is to achieve high efficiency and product selectivity. Here, we introduce a wireless photocatalytic architecture for conversion of CO2 and water into methanol and oxygen. The catalytic material consists of semiconducting nanowires decorated with core-shell nanoparticles, with a copper-rhodium core and a chromium oxide shell. The Rh/CrOOH interface provides a unidirectional channel for proton reduction, enabling hydrogen spillover at the core-shell interface. The vectorial transfer of protons, electrons, and hydrogen atoms allows for switching the mechanism of CO2 reduction from a proton-coupled electron transfer pathway in aqueous solution to hydrogenation of CO2 with a solar-to-methanol efficiency of 0.22%. The reported findings demonstrate a highly efficient, stable, and scalable wireless system for synthesis of methanol from CO2 that could provide a viable path toward carbon neutrality and environmental sustainability.

The CRL3gigaxonin ubiquitin ligase-USP15 pathway governs the destruction of neurofilament proteins.

Giant axonal neuropathy (GAN) is caused by mutations in the GAN gene encoding for gigaxonin (GIG), which functions as an adaptor of the CUL3-RBX1-GIG (CRL3GIG) E3 ubiquitin ligase complex. The pathological hallmark of GAN is characterized by the accumulation of densely packed neurofilaments (NFs) in the axons. However, there are fundamental knowledge gaps in our understanding of the molecular mechanisms by which the ubiquitin-proteasome system controls the homeostasis of NF proteins. Recently, the deubiquitylating enzyme USP15 was reported to play a crucial role in regulating ubiquitylation and proteasomal degradation of CRL4CRBN substrate proteins. Here, we report that the CRL3GIG-USP15 pathway governs the destruction of NF proteins NEFL and INA. We identified a specific degron called NEFLL12 degron for CRL3GIG. Notably, mutations in the C-terminal Kelch domain of GIG, represented by L309R, R545C, and C570Y, disrupted the binding of GIG to NEFL and INA, leading to the accumulation of these NF proteins. This accounts for the loss-of-function mutations in GAN patients. In addition to regulating NFs, CRL3GIG also controls actin filaments by directly targeting actin-filament-binding regulatory proteins TPM1, TPM2, TAGLN, and CNN2 for proteasomal degradation. Thus, our findings broadly impact the field by providing fundamental mechanistic insights into regulating extremely long-lived NF proteins NEFL and INA by the CRL3GIG-USP15 pathway and offering previously unexplored therapeutic opportunities to treat GAN patients and other neurodegenerative diseases by explicitly targeting downstream substrates of CRL3GIG.

scMultiGAN: cell-specific imputation for single-cell transcriptomes with multiple deep generative adversarial networks.

The emergence of single-cell RNA sequencing (scRNA-seq) technology has revolutionized the identification of cell types and the study of cellular states at a single-cell level. Despite its significant potential, scRNA-seq data analysis is plagued by the issue of missing values. Many existing imputation methods rely on simplistic data distribution assumptions while ignoring the intrinsic gene expression distribution specific to cells. This work presents a novel deep-learning model, named scMultiGAN, for scRNA-seq imputation, which utilizes multiple collaborative generative adversarial networks (GAN). Unlike traditional GAN-based imputation methods that generate missing values based on random noises, scMultiGAN employs a two-stage training process and utilizes multiple GANs to achieve cell-specific imputation. Experimental results show the efficacy of scMultiGAN in imputation accuracy, cell clustering, differential gene expression analysis and trajectory analysis, significantly outperforming existing state-of-the-art techniques. Additionally, scMultiGAN is scalable to large scRNA-seq datasets and consistently performs well across sequencing platforms. The scMultiGAN code is freely available at https://github.com/Galaxy8172/scMultiGAN.

Tunable green syngas generation from CO2 and H2O with sunlight as the only energy input.

The carbon-neutral synthesis of syngas from CO2 and H2O powered by solar energy holds grand promise for solving critical issues such as global warming and the energy crisis. Here we report photochemical reduction of CO2 with H2O into syngas using core/shell Au@Cr2O3 dual cocatalyst-decorated multistacked InGaN/GaN nanowires (NWs) with sunlight as the only energy input. First-principle density functional theory calculations revealed that Au and Cr2O3 are synergetic in deforming the linear CO2 molecule to a bent state with an O-C-O angle of 116.5°, thus significantly reducing the energy barrier of CO2RR compared with that over a single component of Au or Cr2O3. Hydrogen evolution reaction was promoted by the same cocatalyst simultaneously. By combining the cooperative catalytic properties of Au@Cr2O3 with the distinguished optoelectronic virtues of the multistacked InGaN NW semiconductor, the developed photocatalyst demonstrated high syngas activity of 1.08 mol/gcat/h with widely tunable H2/CO ratios between 1.6 and 9.2 under concentrated solar light illumination. Nearly stoichiometric oxygen was evolved from water splitting at a rate of 0.57 mol/gcat/h, and isotopic testing confirmed that syngas originated from CO2RR. The solar-to-syngas energy efficiency approached 0.89% during overall CO2 reduction coupled with water splitting. The work paves a way for carbon-neutral synthesis of syngas with the sole inputs of CO2, H2O, and solar light.

ScN/GaN(11Ì 00): A New Platform for the Epitaxy of Twin-Free Metal-Semiconductor Heterostructures.

We study the molecular beam epitaxy of rock-salt ScN on the wurtzite GaN(11̅00) surface. To this end, ScN is grown on freestanding GaN(11̅00) substrates and self-assembled GaN nanowires exhibiting (11̅00) sidewalls. On both substrates, ScN crystallizes twin-free thanks to a specific epitaxial relationship, namely ScN(110)[001]∥GaN(11̅00)[0001], providing a congruent, low-symmetry interface. The 13.1% uniaxial lattice mismatch occurring in this orientation mostly relaxes within the first few monolayers of growth by forming a near-coincidence site lattice, where 7 GaN planes coincide with 8 ScN planes, leaving the ScN surface nearly free of extended defects. Overgrowth of the ScN with GaN leads to a kinetic stabilization of the zinc blende phase, that rapidly develops wurtzite inclusions nucleating on {111} nanofacets, commonly observed during zinc blende GaN growth. Our ScN/GaN(11̅00) platform opens a new route for the epitaxy of twin-free metal-semiconductor heterostructures including closely lattice-matched GaN, ScN, HfN, and ZrN compounds.

Simultaneously Regulated Highly Polarized and Long-Lived Valley Excitons in WSe2/GaN Heterostructures.

Interlayer excitons, with prolonged lifetimes and tunability, hold potential for advanced optoelectronics. Previous research on the interlayer excitons has been dominated by two-dimensional heterostructures. Here, we construct WSe2/GaN composite heterostructures, in which the doping concentration of GaN and the twist angle of bilayer WSe2 are employed as two ingredients for the manipulation of exciton behaviors and polarizations. The exciton energies in monolayer WSe2/GaN can be regulated continuously by the doping levels of the GaN substrate, and a remarkable increase in the valley polarizations is achieved. Especially in a heterostructure with 4°-twisted bilayer WSe2, a maximum polarization of 38.9% with a long lifetime is achieved for the interlayer exciton. Theoretical calculations reveal that the large polarization and long lifetime are attributed to the high exciton binding energy and large spin flipping energy during depolarization in bilayer WSe2/GaN. This work introduces a distinctive member of the interlayer exciton with a high degree of polarization and a long lifetime.

Harmonizing three-dimensional MRI using pseudo-warping field guided GAN.

In pursuit of cultivating automated models for magnetic resonance imaging (MRI) to aid in diagnostics, an escalating demand for extensive, multisite, and heterogeneous brain imaging datasets has emerged. This potentially introduces biased outcomes when directly applied for subsequent analysis. Researchers have endeavored to address this issue by pursuing the harmonization of MRIs. However, most existing image-based harmonization methods for MRI are tailored for 2D slices, which may introduce inter-slice variations when they are combined into a 3D volume. In this study, we aim to resolve inconsistencies between slices by introducing a pseudo-warping field. This field is created randomly and utilized to transform a slice into an artificially warped subsequent slice. The objective of this pseudo-warping field is to ensure that generators can consistently harmonize adjacent slices to another domain, without being affected by the varying content present in different slices. Furthermore, we construct unsupervised spatial and recycle loss to enhance the spatial accuracy and slice-wise consistency across the 3D images. The results demonstrate that our model effectively mitigates inter-slice variations and successfully preserves the anatomical details of the images during the harmonization process. Compared to generative harmonization models that employ 3D operators, our model exhibits greater computational efficiency and flexibility.

Harmonic field extension for QSM with reduced spatial coverage using physics-informed generative adversarial network.

Quantitative susceptibility mapping (QSM) is frequently employed in investigating brain iron related to brain development and diseases within deep gray matter (DGM). Nonetheless, the acquisition of whole-brain QSM data is time-intensive. An alternative approach, focusing the QSM specifically on areas of interest such as the DGM by reducing the field-of-view (FOV), can significantly decrease scan times. However, severe susceptibility value underestimations have been reported during QSM reconstruction with a limited FOV, largely attributable to artifacts from incorrect background field removal in the boundary region. This presents a considerable barrier to the clinical use of QSM with small spatial coverages using conventional methods alone. To mitigate the propagation of these errors, we proposed a harmonic field extension method based on a physics-informed generative adversarial network. Both quantitative and qualitative results demonstrate that our method outperforms conventional methods and delivers results comparable to those obtained with full FOV. Furthermore, we demonstrate the versatility of our method by applying it to data acquired prospectively with limited FOV and to data from patients with Parkinson's disease. The method has shown significant improvements in local field results, with QSM outcomes. In a clear illustration of its feasibility and effectiveness in real clinical environments, our proposed method addresses the prevalent issue of susceptibility underestimation in QSM with small spatial coverage.

Enhancing GaN Nanowires Performance Through Partial Coverage with Oxide Shells.

Core-shell gallium nitride (GaN)-based nanowires offer noteworthy opportunities for innovation in high-frequency opto- and microelectronics. This work delves deeply into the physical properties of crystalline GaN nanowires with aluminum and hafnium oxide shells. Particular attention is paid to partial coverage of nanowires, resulting with exceptional properties. First, the crystal lattice relaxation is observed by X-ray diffraction, photoluminescence, and Raman spectroscopy measurements. A high potential of partial coverage for optoelectronic applications is revealed with photo- and cathodoluminescence spectra along with an exploration of their temperature dependency. Next, the study focuses on understanding the mechanisms behind the observed enhancement of the luminescence efficiency. It is confirmed that nanowires are effectively protected against photoadsorption using partial coatings. This research advances the frontiers of nanotechnology, investigating the benefits of partial coverage, and shedding light on its complex interaction with cores.

An Active and Robust Catalytic Architecture of NiCo/GaN Nanowires for Light-Driven Hydrogen Production from Methanol.

On-site hydrogen production from liquid organic hydrogen carriers e.g., methanol provides an emerging strategy for the safe storage and transportation of hydrogen. Herein, a catalytic architecture consisting of nickel-cobalt nanoclusters dispersed on gallium nitride nanowires supported by silicon for light-driven hydrogen production from methanol is reported. By correlative microscopic, spectroscopic characterizations, and density functional theory calculations, it is revealed that NiCo nanoclusters work in synergy with GaN nanowires to enable the achievement of a significantly reduced activation energy of methanol dehydrogenation by switching the potential-limiting step from *CHO → *CO to *CH3O → *CH2O. In combination with the marked photothermal effect, a high hydrogen rate of 5.62 mol·gcat-1·h-1 with a prominent turnover frequency of 43,460 h-1 is achieved at 5 Wcm-2 without additional energy input. Remarkably, the synergy between Co and Ni, in combination with the unique surface of GaN, renders the architecture with outstanding resistance to sintering and coking. The architecture thereby exhibits a high turnover number of >16,310,000 over 600 h. Outdoor testing validates the viability of the architecture for active and robust hydrogen evolution under natural concentrated sunlight. Overall, this work presents a promising architecture for on-site hydrogen production from CH3OH by virtually unlimited solar energy.

High Thermal Stability and Low Thermal Resistance of Large Area GaN/3C-SiC/Diamond Junctions for Practical Device Processes.

Thermal management is critical in contemporary electronic systems, and integrating diamond with semiconductors offers the most promising solution to improve heat dissipation. However, developing a technique that can fully exploit the high thermal conductivity of diamond, withstand high-temperature annealing processes, and enable mass production is a significant challenge. In this study, the successful transfer of AlGaN/GaN/3C-SiC layers grown on Si to a large-size diamond substrate is demonstrated, followed by the fabrication of GaN high electron mobility transistors (HEMTs) on the diamond. Notably, no exfoliation of 3C-SiC/diamond bonding interfaces is observed even after annealing at 1100 °C, which is essential for high-quality GaN crystal growth on the diamond. The thermal boundary conductance of the 3C-SiC-diamond interface reaches ≈55 MW m-2 K-1, which is efficient for device cooling. GaN HEMTs fabricated on the diamond substrate exhibit the highest maximum drain current and the lowest surface temperature compared to those on Si and SiC substrates. Furthermore, the device thermal resistance of GaN HEMTs on the diamond substrate is significantly reduced compared to those on SiC substrates. These results indicate that the GaN/3C-SiC on diamond technique has the potential to revolutionize the development of power and radio-frequency electronics with improved thermal management capabilities.

Any-nucleus distributed active programmable transmit coil.

PURPOSE: There are 118 known elements. Nearly all of them have NMR active isotopes and at least 39 different nuclei have biological relevance. Despite this, most of today's MRI is based on only one nucleus-1H. To facilitate imaging all potential nuclei, we present a single transmit coil able to excite arbitrary nuclei in human-scale MRI. THEORY AND METHODS: We present a completely new type of RF coil, the Any-nucleus Distributed Active Programmable Transmit Coil (ADAPT Coil), with fast switches integrated into the structure of the coil to allow it to operate at any relevant frequency. This coil eliminates the need for the expensive traditional RF amplifier by directly converting direct current (DC) power into RF magnetic fields with frequencies chosen by digital control signals sent to the switches. Semiconductor switch imperfections are overcome by segmenting the coil. RESULTS: Circuit simulations demonstrated the effectiveness of the ADAPT Coil approach, and a 9 cm diameter surface ADAPT Coil was implemented. Using the ADAPT Coil, 1H, 23Na, 2H, and 13C phantom images were acquired, and 1H and 23Na ex vivo images were acquired. To excite different nuclei, only digital control signals were changed, which can be programmed in real time. CONCLUSION: The ADAPT Coil presents a low-cost, scalable, and efficient method for exciting arbitrary nuclei in human-scale MRI. This coil concept provides further opportunities for scaling, programmability, lowering coil costs, lowering dead-time, streamlining multinuclear MRI workflows, and enabling the study of dozens of biologically relevant nuclei.

A review of self-supervised, generative, and few-shot deep learning methods for data-limited magnetic resonance imaging segmentation.

Magnetic resonance imaging (MRI) is a ubiquitous medical imaging technology with applications in disease diagnostics, intervention, and treatment planning. Accurate MRI segmentation is critical for diagnosing abnormalities, monitoring diseases, and deciding on a course of treatment. With the advent of advanced deep learning frameworks, fully automated and accurate MRI segmentation is advancing. Traditional supervised deep learning techniques have advanced tremendously, reaching clinical-level accuracy in the field of segmentation. However, these algorithms still require a large amount of annotated data, which is oftentimes unavailable or impractical. One way to circumvent this issue is to utilize algorithms that exploit a limited amount of labeled data. This paper aims to review such state-of-the-art algorithms that use a limited number of annotated samples. We explain the fundamental principles of self-supervised learning, generative models, few-shot learning, and semi-supervised learning and summarize their applications in cardiac, abdomen, and brain MRI segmentation. Throughout this review, we highlight algorithms that can be employed based on the quantity of annotated data available. We also present a comprehensive list of notable publicly available MRI segmentation datasets. To conclude, we discuss possible future directions of the field-including emerging algorithms, such as contrastive language-image pretraining, and potential combinations across the methods discussed-that can further increase the efficacy of image segmentation with limited labels.

Research Progress in Liquid Phase Growth of GaN Crystals.

As a wide band gap semiconductor, gallium nitride (GaN) has high breakdown voltage, excellent structural stability and mechanical properties, giving it unique advantages in applications such as high frequency, high power, and high temperature. As a result, it has broad application prospects in optoelectronics and microelectronics. However, the lack of high-quality, large-size GaN crystal substrates severely limit the improvement of electronic device performance. To solve this problem, liquid phase growth of GaN has attracted much attention because it can produce higher quality GaN crystals compared to traditional vapor phase growth methods. This review introduces two main methods of liquid phase growth of GaN: the flux method and ammonothermal method, as well as their advantages and challenges. It reviews the research history and recent advances of these two methods, including the effects of different solvents and mineralizers on the growth quality and performance of GaN crystals, as well as various technical improvements. This review aims to outline the principles, characteristics, and development trends of liquid phase growth of GaN, to provide more inspiration for future research on liquid phase growth, and to achieve further breakthroughs in its development and commercial application.

Effect of Fe Doping Profile on Current Collapse in GaN-based RF HEMTs.

Using computer-aided design (TCAD) simulation, the impact of the Fe doping profile, including concentration, decay rate, and depth of the doping region on current-collapse magnitude (▵CC) in 0.5-µm gated GaN-based high electron mobility transistors (HEMTs) is systematically investigated. Accurate simulation models are established and developed to facilitate the fabrication of electronics. It is elucidated that the intricate interplay between trapping and de-trapping of Fe-related traps at the gate-drain edge is responsible for current collapse. The concentration and decay rate of the doping region have a more significant impact on current collapse than the depth. Increased trap state density near two-dimensional electron gas (2DEG) channel caused by deep-level acceptors would boost ▵CC. However, a minor dynamic reduction in 2DEG density (nT) induces a relatively small ▵CC. By adjusting the concentration, decay rate, and depth of the doping region, ▵CC of GaN-based Radio Frequency (RF) HEMTs can be reduced by approximately 50.3 %. The optimized distribution of Fe doping discussed in this work helps to prepare GaN-based RF HEMTs with a limited current collapse effect.

Application and performance enhancement of FAIMS spectral data for deep learning analysis using generative adversarial network reinforcement.

When using High-field asymmetric ion mobility spectrometry (FAIMS) to process complex mixtures for deep learning analysis, there is a problem of poor recognition performance due to the lack of high-quality data and low sample diversity. In this paper, a Generative Adversarial Network (GAN) method is introduced to simulate and generate highly realistic and diverse spectral for expanding the dataset using real mixture spectral data of 15 classes collected by FAIMS. The mixed datasets were put into VGG and ResNeXt for testing respectively, and the experimental results proved that the best recognition effect was achieved when the ratio of real data to generated data was 1:4: where accuracy improved by 24.19 % and 6.43 %; precision improved by 23.71 % and 6.97 %; recall improved by 21.08 % and 7.09 %; and F1-score improved by 24.50 % and 8.23 %. The above results strongly demonstrate that GAN can effectively expand the data volume and increase the sample diversity without increasing the additional experimental cost, which significantly enhances the experimental effect of FAIMS spectral for the analysis of complex mixtures.

GANSamples-ac4C: Enhancing ac4C site prediction via generative adversarial networks and transfer learning.

RNA modification, N4-acetylcytidine (ac4C), is enzymatically catalyzed by N-acetyltransferase 10 (NAT10) and plays an essential role across tRNA, rRNA, and mRNA. It influences various cellular functions, including mRNA stability and rRNA biosynthesis. Wet-lab detection of ac4C modification sites is highly resource-intensive and costly. Therefore, various machine learning and deep learning techniques have been employed for computational detection of ac4C modification sites. The known ac4C modification sites are limited for training an accurate and stable prediction model. This study introduces GANSamples-ac4C, a novel framework that synergizes transfer learning and generative adversarial network (GAN) to generate synthetic RNA sequences to train a better ac4C modification site prediction model. Comparative analysis reveals that GANSamples-ac4C outperforms existing state-of-the-art methods in identifying ac4C sites. Moreover, our result underscores the potential of synthetic data in mitigating the issue of data scarcity for biological sequence prediction tasks. Another major advantage of GANSamples-ac4C is its interpretable decision logic. Multi-faceted interpretability analyses detect key regions in the ac4C sequences influencing the discriminating decision between positive and negative samples, a pronounced enrichment of G in this region, and ac4C-associated motifs. These findings may offer novel insights for ac4C research. The GANSamples-ac4C framework and its source code are publicly accessible at http://www.healthinformaticslab.org/supp/.

GaN atomic electric fields from a segmented STEM detector: Experiment and simulation.

Atomic electric fields in a thin GaN sample are measured with the centre-of-mass approach in 4D-scanning transmission electron microscopy (4D-STEM) using a 12-segmented STEM detector in a Spectra 300 microscope. The electric fields, charge density and potential are compared to simulations and an experimental measurement using a pixelated 4D-STEM detector. The segmented detector benefits from a high recording speed, which enables measurements at low radiation doses. However, there is measurement uncertainty due to the limited number of segments analysed in this study.

Enhancing resolution and contrast in fibre bundle-based fluorescence microscopy using generative adversarial network.

Fibre bundle (FB)-based endoscopes are indispensable in biology and medical science due to their minimally invasive nature. However, resolution and contrast for fluorescence imaging are limited due to characteristic features of the FBs, such as low numerical aperture (NA) and individual fibre core sizes. In this study, we improved the resolution and contrast of sample fluorescence images acquired using in-house fabricated high-NA FBs by utilising generative adversarial networks (GANs). In order to train our deep learning model, we built an FB-based multifocal structured illumination microscope (MSIM) based on a digital micromirror device (DMD) which improves the resolution and the contrast substantially compared to basic FB-based fluorescence microscopes. After network training, the GAN model, employing image-to-image translation techniques, effectively transformed wide-field images into high-resolution MSIM images without the need for any additional optical hardware. The results demonstrated that GAN-generated outputs significantly enhanced both contrast and resolution compared to the original wide-field images. These findings highlight the potential of GAN-based models trained using MSIM data to enhance resolution and contrast in wide-field imaging for fibre bundle-based fluorescence microscopy. Lay Description: Fibre bundle (FB) endoscopes are essential in biology and medicine but suffer from limited resolution and contrast for fluorescence imaging. Here we improved these limitations using high-NA FBs and generative adversarial networks (GANs). We trained a GAN model with data from an FB-based multifocal structured illumination microscope (MSIM) to enhance resolution and contrast without additional optical hardware. Results showed significant enhancement in contrast and resolution, showcasing the potential of GAN-based models for fibre bundle-based fluorescence microscopy.

Mg incorporation induced microstructural evolution of reactively sputtered GaN epitaxial films to Mg-doped GaN nanorods.

Mg-doped GaN films/nanorods were grown epitaxially onc-sapphire by reactive co-sputtering of GaAs and Mg at different N2percentages in Ar-N2sputtering atmosphere. Energy dispersive x-ray spectroscopy revealed that the Mg incorporation increases with increase of Mg area coverage of GaAs target, but does not depend on N2percentage. In comparison to undoped GaN films, Mg-doped GaN displayed substantial decrease of lateral conductivity and electron concentration with the initial incorporation of Mg, indicatingp-type doping, but revealed insulating behaviour at larger Mg content. Morphological investigations by scanning electron microscopy have shown that the films grown with 2%-4% Mg area coverages displayed substantially improved columnar structure, compared to undoped GaN films, along with rough and voided surface features at lower N2percentages. With increase of Mg area coverage to 6%, the growth of vertically aligned and well-separated nanorods, terminating with smooth hexagonal faces was observed in the range of 50%-75% N2in sputtering atmosphere. High-resolution x-ray diffraction studies confirmed the epitaxial character of Mg-doped GaN films and nanorods, which displayed completec-axis orientation of crystallites and a mosaic structure, aligned laterally with thec-sapphire lattice. The catalyst-free growth of self-assembled Mg-doped GaN nanorods is attributed to increase of surface energy anisotropy due to the incorporation of Mg. However, with further increase of Mg area coverage to 8%, the nanorods revealed lateral merger, suggesting enhanced radial growth at larger Mg content.