A LaCl3-based lithium superionic conductor compatible with lithium metal.

Inorganic superionic conductors possess high ionic conductivity and excellent thermal stability but their poor interfacial compatibility with lithium metal electrodes precludes application in all-solid-state lithium metal batteries1,2. Here we report a LaCl3-based lithium superionic conductor possessing excellent interfacial compatibility with lithium metal electrodes. In contrast to a Li3MCl6 (M = Y, In, Sc and Ho) electrolyte lattice3-6, the UCl3-type LaCl3 lattice has large, one-dimensional channels for rapid Li+ conduction, interconnected by La vacancies via Ta doping and resulting in a three-dimensional Li+ migration network. The optimized Li0.388Ta0.238La0.475Cl3 electrolyte exhibits Li+ conductivity of 3.02 mS cm-1 at 30 °C and a low activation energy of 0.197 eV. It also generates a gradient interfacial passivation layer to stabilize the Li metal electrode for long-term cycling of a Li-Li symmetric cell (1 mAh cm-2) for more than 5,000 h. When directly coupled with an uncoated LiNi0.5Co0.2Mn0.3O2 cathode and bare Li metal anode, the Li0.388Ta0.238La0.475Cl3 electrolyte enables a solid battery to run for more than 100 cycles with a cutoff voltage of 4.35 V and areal capacity of more than 1 mAh cm-2. We also demonstrate rapid Li+ conduction in lanthanide metal chlorides (LnCl3; Ln = La, Ce, Nd, Sm and Gd), suggesting that the LnCl3 solid electrolyte system could provide further developments in conductivity and utility.

1T'-transition metal dichalcogenide monolayers stabilized on 4H-Au nanowires for ultrasensitive SERS detection.

Unconventional 1T'-phase transition metal dichalcogenides (TMDs) have aroused tremendous research interest due to their unique phase-dependent physicochemical properties and applications. However, due to the metastable nature of 1T'-TMDs, the controlled synthesis of 1T'-TMD monolayers (MLs) with high phase purity and stability still remains a challenge. Here we report that 4H-Au nanowires (NWs), when used as templates, can induce the quasi-epitaxial growth of high-phase-purity and stable 1T'-TMD MLs, including WS2, WSe2, MoS2 and MoSe2, via a facile and rapid wet-chemical method. The as-synthesized 4H-Au@1T'-TMD core-shell NWs can be used for ultrasensitive surface-enhanced Raman scattering (SERS) detection. For instance, the 4H-Au@1T'-WS2 NWs have achieved attomole-level SERS detections of Rhodamine 6G and a variety of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike proteins. This work provides insights into the preparation of high-phase-purity and stable 1T'-TMD MLs on metal substrates or templates, showing great potential in various promising applications.

The Reconstruction of Pt(001) Surface and the Shell-Like Reconstruction of the Vicinal Pt(001) Surfaces Revealed by Neural Network Potential.

In this work, a highly accurate neural network potential (NNP) is presented, named PtNNP, and the exploration of the reconstruction of the Pt(001) surface and its vicinal surfaces with it. Contrary to the most accepted understanding of the Pt(001) surface reconstruction, the study reveals that the main driving force behind Pt(001) quasi-hexagonal reconstruction is not the surface stress relaxation but the increased coordination number of the surface atoms resulting in stronger intralayer binding in the reconstructed surface layer. In agreement with experimental observations, the optimized supercell size of the reconstructed Pt(001) surface contains (5 × 20) unit cells. Surprisingly, the reconstruction of the vicinal Pt(001) surfaces leads to a smooth shell-like surface layer covering the whole surface and diminishing sharp step edges.

Doping Ability Modulated by Interlayer Coupling in AA' and AB Stacked Bilayer V-WS2 Grown with Chemical Vapor Deposition.

Doping in transition metal dichalcogenide (TMD) has received extensive attention for its prospect in the application of photoelectric devices. Currently researchers focus on the doping ability and doping distribution in monolayer TMD and have obtained a series of achievements. Bilayer TMD has more excellent properties compared with monolayer TMD. Moreover, bilayer TMD with different stacking structures presents varying performance due to the difference in interlayer coupling. Herein, this work focuses on doping ability of dopants in different bilayer stacking structures that has not been studied yet. Results of this work show that the doping ability of V atoms in bilayer AA' and AB stacked WS2 is different, and the doping concentration of V atoms in AB stacked WS2 is higher than in AA' stacked WS2. Moreover, dopants from top and bottom layer can be distinguished by scanning transmission electron microscopy (STEM) image. Density functional theory (DFT) calculation further confirms the doping rule. This study reveals the mechanism of the different doping ability caused by stacking structures in bilayer TMD and lays a foundation for further preparation of controllable-doping bilayer TMD materials.

Out-of-Plane Single-Copper-Site Catalysts for Room-Temperature Benzene Oxidation.

Crafting single-atom catalysts (SACs) that possess "just right" modulated electronic and geometric structures, granting accessible active sites for direct room-temperature benzene oxidation is a coveted objective. However, achieving this goal remains a formidable challenge. Here, we introduce an innovative in situ phosphorus-immitting strategy using a new phosphorus source (phosphorus nitride, P3N5) to construct the phosphorus-rich copper (Cu) SACs, designated as Cu/NPC. These catalysts feature locally protruding metal sites on a nitrogen (N)-phosphorus (P)-carbon (C) support (NPC). Rigorous analyses, including X-ray absorption spectroscopy (XAS) and X-ray photoelectron spectroscopy (XPS), validate the coordinated bonding of nitrogen and phosphorus with atomically dispersed Cu sites on NPC. Crucially, systematic first-principles calculations, coupled with the climbing image nudged-elastic-band (CI-NEB) method, provide a comprehensive understanding of the structure-property-activity relationship of the distorted Cu-N2P2 centers in Cu/NPC for selective oxidation of benzene to phenol production. Interestingly, Cu/NPC has shown more energetically favorable C-H bond activation compared to the benchmark Cu/NC SACs in the direct oxidation of benzene, resulting in outstanding benzene conversion (50.3 %) and phenol selectivity (99.3 %) at room temperature. Furthermore, Cu/NPC achieves a remarkable turnover frequency of 263 h-1 and mass-specific activity of 35.2 mmol g-1 h-1, surpassing the state-of-the-art benzene-to-phenol conversion catalysts to date.

Concentration Phase Separation of Substitution-Doped Atoms in TMDCs Monolayer.

The density and spatial distribution of substituted dopants affect the transition metal dichalcogenides (TMDCs) materials properties. Previous studies have demonstrated that the density of dopants in TMDCs increases with the amount of doping, and the phenomenon of doping concentration difference between the nucleation center and the edge is observed, but the spatial distribution law of doping atoms has not been carefully studied. Here, it is demonstrated that the spatial distribution of dopants changes at high doping concentrations. The spontaneous formation of an interface with a steep doping concentration change is named concentration phase separation (CPS). The difference in the spatial distribution of dopants on both sides of the interface can be identified by an optical microscope. This is consistent with the results of spectral analysis and microstructure characterization of scanning transmission electron microscope. According to the calculation results of density functional theory, the chemical potential has two relatively stable energies as the doping concentration increases, which leads to the spontaneous formation of CPS. Understanding the abnormal phenomena is important for the design of TMDCs devices. This work has great significance in the establishment and improvement of the doping theory and the design of the doping process for 2D materials.

Controlled synthesis of van der Waals CoS2for improved p-type transistor contact.

Two-dimensional (2D) van der Waals (vdW) p-type semiconductors have shown attractive application prospects as atomically thin channels in electronic devices. However, the high Schottky hole barrier of p-type semiconductor-metal contacts induced by Fermi-level pinning is hardly removed. Herein, we prepare a vdW 1T-CoS2nanosheet as the contact electrode of a WSe2field-effect transistor (FET), which shows a considerably high on/off ratio > 107and a hole mobility of ∼114.5 cm2V-1s-1. The CoS2nanosheets exhibit metallic conductivity with thickness dependence, which surpasses most 2D transition metal dichalcogenide metals or semimetals. The excellent FET performance of the CoS2-contacted WSe2FET device can be attributed to the high work function of CoS2, which lowers the Schottky hole barrier. Our work provides an effective method for growing vdW CoS2and opens up more possibilities for the application of 2D p-type semiconductors in electronic devices.

Overexpression of NDR1 leads to pathogen resistance at elevated temperatures.

Abiotic and biotic environments influence a myriad of plant-related processes, including growth, development, and the establishment and maintenance of interaction(s) with microbes. In the case of the latter, elevated temperature has been shown to be a key factor that underpins host resistance and pathogen virulence. In this study, we elucidate a role for Arabidopsis NON-RACE-SPECIFIC DISEASE RESISTANCE1 (NDR1) by exploiting effector-triggered immunity to define the regulation of plant host immunity in response to both pathogen infection and elevated temperature. We generated time-series RNA sequencing data of WT Col-0, an NDR1 overexpression line, and ndr1 and ics1-2 mutant plants under elevated temperature. Not surprisingly, the NDR1-overexpression line showed genotype-specific gene expression changes related to defense response and immune system function. The results described herein support a role for NDR1 in maintaining cell signaling during simultaneous exposure to elevated temperature and avirulent pathogen stressors.

Deep learning-based endoscopic anatomy classification: an accelerated approach for data preparation and model validation.

BACKGROUND: Photodocumentation during endoscopy procedures is one of the indicators for endoscopy performance quality; however, this indicator is difficult to measure and audit in the endoscopy unit. Emerging artificial intelligence technology may solve this problem, which requires a large amount of material for model development. We developed a deep learning-based endoscopic anatomy classification system through convolutional neural networks with an accelerated data preparation approach. PATIENTS AND METHODS: We retrospectively collected 8,041 images from esophagogastroduodenoscopy (EGD) procedures and labeled them using two experts for nine anatomical locations of the upper gastrointestinal tract. A base model for EGD image multiclass classification was first developed, and an additional 6,091 images were enrolled and classified by the base model. A total of 5,963 images were manually confirmed and added to develop the subsequent enhanced model. Additional internal and external endoscopy image datasets were used to test the model performance. RESULTS: The base model achieved total accuracy of 96.29%. For the enhanced model, the total accuracy was 96.64%. The overall accuracy improved with the enhanced model compared with the base model for the internal test dataset without narrowband images (93.05% vs. 91.25%, p < 0.01) or with narrowband images (92.74% vs. 90.46%, p < 0.01). The total accuracy was 92.56% of the enhanced model on the external test dataset. CONCLUSIONS: We constructed a deep learning-based model with an accelerated approach that can be used for quality control in endoscopy units. The model was also validated with both internal and external datasets with high accuracy.

Development and validation of a deep learning-based algorithm for colonoscopy quality assessment.

BACKGROUND: Quality indicators should be assessed and monitored to improve colonoscopy quality in clinical practice. Endoscopists must enter relevant information in the endoscopy reporting system to facilitate data collection, which may be inaccurate. The current study aimed to develop a full deep learning-based algorithm to identify and analyze intra-procedural colonoscopy quality indicators based on endoscopy images obtained during the procedure. METHODS: A deep learning system for classifying colonoscopy images for quality assurance purposes was developed and its performance was assessed with an independent dataset. The system was utilized to analyze captured images and results were compared with those of real-world reports. RESULTS: In total, 10,417 images from the hospital endoscopy database and 3157 from Hyper-Kvasir open dataset were utilized to develop the quality assurance algorithm. The overall accuracy of the algorithm was 96.72% and that of the independent test dataset was 94.71%. Moreover, 761 real-world reports and colonoscopy images were analyzed. The accuracy of electronic reports about cecal intubation rate was 99.34% and that of the algorithm was 98.95%. The agreement rate for the assessment of polypectomy rates using the electronic reports and the algorithm was 0.87 (95% confidence interval 0.83-0.90). A good correlation was found between the withdrawal time calculated using the algorithm and that entered by the physician (correlation coefficient r = 0.959, p < 0.0001). CONCLUSION: We proposed a novel deep learning-based algorithm that used colonoscopy images for quality assurance purposes. This model can be used to automatically assess intra-procedural colonoscopy quality indicators in clinical practice.