Activation of Ga Liquid Catalyst with Continuously Exposed Active Sites for Electrocatalytic C-N Coupling.

Environmentally friendly electrocatalytic coupling of CO2 and N2 for urea synthesis is a promising strategy. However, it is still facing problems such as low yield as well as low stability. Here, a new carbon-coated liquid alloy catalyst, Ga79Cu11Mo10@C is designed for efficient electrochemical urea synthesis by activating Ga active sites. During the N2 and CO2 co-reduction process, the yield of urea reaches 28.25 mmol h-1 g-1, which is the highest yield reported so far under the same conditions, the Faraday efficiency (FE) is also as high as 60.6 % at -0.4 V vs. RHE. In addition, the catalyst shows excellent stability under 100 h of testing. Comprehensive analyses showed that sequential exposure of a high density of active sites promoted the adsorption and activation of N2 and CO2 for efficient coupling reactions. This coupling reaction occurs through a thermodynamic spontaneous reaction between *N=N* and CO to form a C-N bond. The deformability of the liquid state facilitates catalyst recovery and enhances stability and resistance to poisoning. Moreover, the introduction of Cu and Mo stimulates the Ga active sites, which successfully synthesises the *NCON* intermediate. The reaction energy barrier of the third proton-coupled electron transfer process rate-determining step (RDS) *NHCONH→*NHCONH2 was lowered, ensuring the efficient synthesis of urea.

Electronic and Geometric Effects Endow PtRh Jagged Nanowires with Superior Ethanol Oxidation Catalysis.

Complete ethanol oxidation reaction (EOR) in C1 pathway with 12 transferred electrons is highly desirable yet challenging in direct ethanol fuel cells. Herein, PtRh jagged nanowires synthesized via a simple wet-chemical approach exhibit exceptional EOR mass activity of 1.63 A mgPt-1 and specific activity of 4.07 mA cm-2 , 3.62-fold and 4.28-folds increments relative to Pt/C, respectively. High proportions of 69.33% and 73.42% of initial activity are also retained after chronoamperometric test (80 000 s) and 1500 consecutive potential cycles, respectively. More importantly, it is found that PtRh jagged nanowires possess superb anti-CO poisoning capability. Combining X-ray absorption spectroscopy, X-ray photoelectron spectroscopy as well as density functional theory calculations unveil that the remarkable catalytic activity and CO tolerance stem from both the Rh-induced electronic effect and geometric effect (manifested by shortened Pt─Pt bond length and shrinkage of lattice constants), which facilitates EOR catalysis in C1 pathway and improves reaction kinetics by reducing energy barriers of rate-determining steps (such as *CO → *COOH). The C1 pathway efficiency of PtRh jagged nanowires is further verified by the high intensity of CO2 relative to CH3 COOH/CH3 CHO in infrared reflection absorption spectroscopy.

Large-Scale Information Retrieval and Correction of Noisy Pharmacogenomic Datasets through Residual Thresholded Deep Matrix Factorization.

Pharmacogenomics studies are attracting an increasing amount of interest from researchers in precision medicine. The advances in high-throughput experiments and multiplexed approaches allow the large-scale quantification of drug sensitivities in molecularly characterized cancer cell lines (CCLs), resulting in a number of open drug sensitivity datasets for drug biomarker discovery. However, a significant inconsistency in drug sensitivity values among these datasets has been noted. Such inconsistency indicates the presence of substantial noise, subsequently hindering downstream analyses. To address the noise in drug sensitivity data, we introduce a robust and scalable deep learning framework, Residual Thresholded Deep Matrix Factorization (RT-DMF). This method takes a single drug sensitivity data matrix as its sole input and outputs a corrected and imputed matrix. Deep Matrix Factorization (DMF) excels at uncovering subtle patterns, due to its minimal reliance on data structure assumptions. This attribute significantly boosts DMF's ability to identify complex hidden patterns among nuisance effects in the data, thereby facilitating the detection of signals that are therapeutically relevant. Furthermore, RT-DMF incorporates an iterative residual thresholding (RT) procedure, which plays a crucial role in retaining signals more likely to hold therapeutic importance. Validation using simulated datasets and real pharmacogenomics datasets demonstrates the effectiveness of our approach in correcting noise and imputing missing data in drug sensitivity datasets (open source package available at https://github.com/tomwhoooo/rtdmf).

·H effectively enhance electrocatalytic nitrogen fixation.

Nowadays, most reported ammonia (NH3) yields and Faradaic efficiency (FE) of electrocatalysts are very low in the field of electrocatalytic nitrogen reduction reactions (NRR). Here, we are reported ·H for the first time in the field of electrocatalytic NRR, which are generated by sulfite (SO32-) and H2O in electrolyte solutions upon exposure to UV light. The high NH3 yields can achieve 100.7 µg h-1 mgcat-1, while stability can achieve 64 h and the FE can achieve 27.1% at -0.3 V (vs. RHE) with UV irradiation. In situ Fourier transform infrared spectroscopy (FTIR), electron spin resonance (ESR), density functional theory (DFT) and 1H nuclear magnetic resonance (NMR) tests showed that the ∙H effectively lowered the reaction energy barrier at each step of the NRR process and inhibits the occurrence of competitive hydrogen evolution reaction (HER). This explores the path and provides ideas for the field of electrocatalysis involving water.

PtIrM (M = Ni, Co) jagged nanowires for efficient methanol oxidation electrocatalysis.

It remains a huge challenge to develop methanol oxidation electrocatalysts with remarkable catalytic activity and anti-CO poisoning capability. Herein, PtIrNi and PtIrCo jagged nanowires are successfully synthesized via a facile wet-chemical approach. Pt and Ir components are concentrated in the exterior and Ni is concentrated in the interior of PtIrNi jagged nanowires, while PtIrCo jagged nanowires feature the homogeneous distribution of constituent metals. The PtIrNi and PtIrCo jagged nanowires exhibit mass activities of 1.88 A/mgPt and 1.85 A/mgPt, respectively, 3.24 and 3.19 times higher than that of commercial Pt/C (0.58 A/mgPt). In-situ Fourier transform infrared spectroscopy indicates that CO2 was formed at a very low potential for both nanowires, in line with the high ratio of forward current density to backward current density for PtIrNi jagged nanowires (1.30) and PtIrCo jagged nanowires (1.46) relative to Pt/C (0.76). Also, the CO stripping and X-ray photoelectron spectroscopy results substantiate the remarkable CO tolerance of the jagged nanowires. Besides, the two jagged nanowires possess exceptional activities toward ethanol and ethylene glycol oxidation reactions. This work provides a novel line of thought in terms of rational design of alcohol oxidation electrocatalysts with distinctive nanostructures.

CTRL: Closed-Loop Transcription to an LDR via Minimaxing Rate Reduction.

This work proposes a new computational framework for learning a structured generative model for real-world datasets. In particular, we propose to learn a Closed-loop Transcriptionbetween a multi-class, multi-dimensional data distribution and a Linear discriminative representation (CTRL) in the feature space that consists of multiple independent multi-dimensional linear subspaces. In particular, we argue that the optimal encoding and decoding mappings sought can be formulated as a two-player minimax game between the encoder and decoderfor the learned representation. A natural utility function for this game is the so-called rate reduction, a simple information-theoretic measure for distances between mixtures of subspace-like Gaussians in the feature space. Our formulation draws inspiration from closed-loop error feedback from control systems and avoids expensive evaluating and minimizing of approximated distances between arbitrary distributions in either the data space or the feature space. To a large extent, this new formulation unifies the concepts and benefits of Auto-Encoding and GAN and naturally extends them to the settings of learning a both discriminative and generative representation for multi-class and multi-dimensional real-world data. Our extensive experiments on many benchmark imagery datasets demonstrate tremendous potential of this new closed-loop formulation: under fair comparison, visual quality of the learned decoder and classification performance of the encoder is competitive and arguably better than existing methods based on GAN, VAE, or a combination of both. Unlike existing generative models, the so-learned features of the multiple classes are structured instead of hidden: different classes are explicitly mapped onto corresponding independent principal subspaces in the feature space, and diverse visual attributes within each class are modeled by the independent principal components within each subspace.

Fast site-to-site electron transfer of high-entropy alloy nanocatalyst driving redox electrocatalysis.

Designing electrocatalysts with high-performance for both reduction and oxidation reactions faces severe challenges. Here, the uniform and ultrasmall (~3.4 nm) high-entropy alloys (HEAs) Pt18Ni26Fe15Co14Cu27 nanoparticles are synthesized by a simple low-temperature oil phase strategy at atmospheric pressure. The Pt18Ni26Fe15Co14Cu27/C catalyst exhibits excellent electrocatalytic performance for hydrogen evolution reaction (HER) and methanol oxidation reaction (MOR). The catalyst shows ultrasmall overpotential of 11 mV at the current density of 10 mA cm-2, excellent activity (10.96 A mg-1Pt at -0.07 V vs. reversible hydrogen electrode) and stability in the alkaline medium. Furthermore, it is also the efficient catalyst (15.04 A mg-1Pt) ever reported for MOR in alkaline solution. Periodic DFT calculations confirm the multi-active sites for both HER and MOR on the HEA surface as the key factor for both proton and intermediate transformation. Meanwhile, the construction of HEA surfaces supplies the fast site-to-site electron transfer for both reduction and oxidation processes.