iProps: A comprehensive software tool for protein classification and analysis with automatic machine learning capabilities and model interpretation capabilities.

Protein classification is a crucial field in bioinformatics. The development of a comprehensive tool that can perform feature evaluation, visualization, automated machine learning, and model interpretation would significantly advance research in protein classification. However, there is a significant gap in the literature regarding tools that integrate all these essential functionalities. This paper presents iProps, a novel Python-based software package, meticulously crafted to fulfill these multifaceted requirements. iProps is distinguished by its proficiency in feature extraction, evaluation, automated machine learning, and interpretation of classification models. Firstly, iProps fully leverages evolutionary information and amino acid reduction information to propose or extend several numerical protein features that are independent of sequence length, including SC-PSSM, ORDip, TRC, CTDC-E, CKSAAGP-E, and so forth; at the same time, it also implements the calculation of 17 other numerical features within the software. iProps also provides feature combination operations for the aforementioned features to generate more hybrid features, and has added data balancing sampling processing as well as built-in classifier settings, among other functionalities. Thus, It can discern the most effective protein class recognition feature from a multitude of candidates, utilizing three automated machine learning algorithms to identify the most optimal classifiers and parameter settings. Furthermore, iProps generates a detailed explanatory report that includes 23 informative graphs derived from three interpretable models. To assess the performance of iProps, a series of numerical experiments were conducted using two well-established datasets. The results demonstrated that our software achieved superior recognition performance in every case. Beyond its contributions to bioinformatics, iProps broadens its applicability by offering robust data analysis tools that are beneficial across various disciplines, capitalizing on its automated machine learning and model interpretation capabilities. As an open-source platform, iProps is readily accessible and features an intuitive user interface, ensuring ease of use for individuals, even those without a background in programming. The source code of the software is available for download at the following website: https://github.com/LigosQ/iProps and https://gitee.com/LigosQ/iProps.

Cuproptosis, the novel type of oxidation-induced cell death in thoracic cancers: can it enhance the success of immunotherapy?

Copper is an important metal micronutrient, required for the balanced growth and normal physiological functions of human organism. Copper-related toxicity and dysbalanced metabolism were associated with the disruption of intracellular respiration and the development of various diseases, including cancer. Notably, copper-induced cell death was defined as cuproptosis which was also observed in malignant cells, representing an attractive anti-cancer instrument. Excess of intracellular copper leads to the aggregation of lipoylation proteins and toxic stress, ultimately resulting in the activation of cell death. Differential expression of cuproptosis-related genes was detected in normal and malignant tissues. Cuproptosis-related genes were also linked to the regulation of oxidative stress, immune cell responses, and composition of tumor microenvironment. Activation of cuproptosis was associated with increased expression of redox-metabolism-regulating genes, such as ferredoxin 1 (FDX1), lipoic acid synthetase (LIAS), lipoyltransferase 1 (LIPT1), dihydrolipoamide dehydrogenase (DLD), drolipoamide S-acetyltransferase (DLAT), pyruvate dehydrogenase E1 subunit alpha 1 (PDHA1), and pyruvate dehydrogenase E1 subunit beta (PDHB)). Accordingly, copper-activated network was suggested as an attractive target in cancer therapy. Mechanisms of cuproptosis and regulation of cuproptosis-related genes in different cancers and tumor microenvironment are discussed in this study. The analysis of current findings indicates that therapeutic regulation of copper signaling, and activation of cuproptosis-related targets may provide an effective tool for the improvement of immunotherapy regimens.

Investigation of intra-fractionated range guided adaptive proton therapy (RGAPT): II. Range-shift compensated on-line treatment adaptation and verification.

We previously proposed range-guided adaptive proton therapy (RGAPT) that uses mid-range treatment beams as probing beams and intra-fractionated range measurements for online adaptation. In this work, we demonstrated experimental verification and reported the dosimetric accuracy for RGAPT. A STEEV phantom was used for the experiments, and a 3 × 3 × 3 cm3cube inside the phantom was assigned to be the treatment target. We simulated three online range shift scenarios: reference, overshoot, and undershoot, by placing upstream Lucite sheets, 4, 0, and 8 that corresponded to changes of 0, 6.8, and -6.8 mm, respectively, in water-equivalent path length. The reference treatment plan was to deliver single-field uniform target doses in pencil beam scanning mode and generated on the Eclipse treatment planning system. Different numbers of mid-range layers, including single, three, and five layers, were selected as probing beams to evaluate beam range (BR) measurement accuracy in positron emission tomography (PET). Online plans were modified to adapt to BR shifts and compensate for probing beam doses. In contrast, non-adaptive plans were also delivered and compared to adaptive plans by film measurements. The mid-range probing beams of three (5.55MU) and five layers (8.71MU) yielded accurate range shift measurements in 60 s of PET acquisition with uncertainty of 0.5 mm while the single-layer probing (1.65MU) was not sufficient for measurements. The adaptive plans achieved an average gamma (2%/2 mm) passing rate of 95%. In contrast, the non-adaptive plans only had an average passing rate of 69%. RGAPT planning and delivery are feasible and verified by the experiments. The probing beam delivery, range measurements, and adaptive planning and delivery added a small increase in treatment delivery workflow time but resulted in substantial dose improvement. The three-layer mid-range probing was most suitable considering the balance of high range measurement accuracy and the low number of probing beam layers.

Investigation of intra-fractionated range guided adaptive proton therapy: I. On-line PET imaging and range measurement.

Objective.Develop a prototype on-line positron emission tomography (PET) scanner and evaluate its capability of on-line imaging and intra-fractionated proton-induced radioactivity range measurement.Approach.Each detector consists of 32 × 32 array of 2 × 2 × 30 mm3Lutetium-Yttrium Oxyorthosilicate scintillators with single-scintillator-end readout through a 20 × 20 array of 3 × 3 mm2Silicon Photomultipliers. The PET can be configurated with a full-ring of 20 detectors for conventional PET imaging or a partial-ring of 18 detectors for on-line imaging and range measurement. All detector-level readout and processing electronics are attached to the backside of the system gantry and their output signals are transferred to a field-programable-gate-array based system electronics and data acquisition that can be placed 2 m away from the gantry. The PET imaging performance and radioactivity range measurement capability were evaluated by both the offline study that placed a radioactive source with known intensity and distribution within a phantom and the online study that irradiated a phantom with proton beams under different radiation and imaging conditions.Main results.The PET has 32 cm diameter and 6.5 cm axial length field-of-view (FOV), ∼2.3-5.0 mm spatial resolution within FOV, 3% sensitivity at the FOV center, 18%-30% energy resolution, and ∼9 ns coincidence time resolution. The offline study shows the PET can determine the shift of distal falloff edge position of a known radioactivity distribution with the accuracy of 0.3 ± 0.3 mm even without attenuation and scatter corrections, and online study shows the PET can measure the shift of proton-induced positron radioactive range with the accuracy of 0.6 ± 0.3 mm from the data acquired with a short-acquisition (60 s) and low-dose (5 MU) proton radiation to a human head phantom.Significance.This study demonstrated the capability of intra-fractionated PET imaging and radioactivity range measurement and will enable the investigation on the feasibility of intra-fractionated, range-shift compensated adaptive proton therapy.

A stacking-based algorithm for antifreeze protein identification using combined physicochemical, pseudo amino acid composition, and reduction property features.

Antifreeze proteins have wide applications in the medical and food industries. In this study, we propose a stacking-based classifier that can effectively identify antifreeze proteins. Initially, feature extraction was performed in three aspects: reduction properties, scalable pseudo amino acid composition, and physicochemical properties. A hybrid feature set comprised of the combined information from these three categories was obtained. Subsequently, we trained the training set based on LightGBM, XGBoost, and RandomForest algorithms, and the training outcomes were passed to the Logistic algorithm for matching, thereby establishing a stacking algorithm. The proposed algorithm was tested on the test set and an independent validation set. Experimental data indicates that the algorithm achieved a recognition accuracy of 98.3 %, and an accuracy of 98.5 % on the validation set. Lastly, we analyzed the reasons why numerical features achieved high recognition capabilities from multiple aspects. Data dimensionality reduction and the analysis from two-dimensional and three-dimensional views revealed separability between positive and negative samples, and the protein three-dimensional structure further demonstrated significant differences in related features between the two samples. Analysis of the classifier revealed that Hr*Hr, HrHr, and Sc-PseAAC_1, 188D(152,116,57,183) were among the seven most important numerical features affecting algorithm recognition. For Hr*Hr and HrHr, supportive sequence level evidence for the reduction dictionary was found in terms of conservation area analysis, multiple sequence alignment, and amino acid conservative substitution. Moreover, the importance of the reduction dictionary was recognized through a comparative analysis of importance before and after the reduction, realizing the effectiveness of the dictionary in improving feature importance. A decision tree model has been utilized to discern the distinctions between dipeptides associated with the physical and chemical properties of His(H), Iso(I), Leu(L), and Lys(K) and other dipeptides. We finally analyzed the other seven features of importance, and data analysis confirmed that hydrophobicity, secondary structure, charge properties, van der Waals forces, and solvent accessibility are also factors affecting the antifreeze capability of proteins.

Ultra-Low-Potential Methanol Oxidation on Single-Ir-Atom Catalyst.

Methanol oxidation plays a central role to implement sustainable energy economy, which is restricted by the sluggish reaction kinetics due to the multi-electron transfer process accompanied by numerous sequential intermediate. In this study, an efficient cascade methanol oxidation reaction is catalyzed by single-Ir-atom catalyst at ultra-low potential (<0.1 V) with the promotion of the thermal and electrochemical integration in a high temperature polymer electrolyte membrane electrolyzer. At the elevated temperature, the electron deficient Ir site with higher methanol affinity could spontaneous catalyze the CH3OH dehydrogenation to CO under the voltage, then the generated CO and H2 was electrochemically oxidized to CO2 and proton. However, the methanol cannot thermally decompose with the voltage absence, which confirm the indispensable of the coupling of thermal and electrochemical integration for the methanol oxidation. By assembling the methanol oxidation reaction with hydrogen evolution reaction with single-Ir-atom catalysts in the anode chamber, a max hydrogen production rate reaches 18 mol gIr -1 h-1, which is much greater than that of Ir nanoparticles and commercial Pt/C. This study also demonstrated the electrochemical methanol oxidation activity of the single atom catalysts, which broadens the renewable energy devices and the catalyst design by an integration concept.

Electrocatalysis Boosts the Methanol Thermocatalytic Dehydrogenation for High-Purity H2 and CO Production.

Hydrogen production from methanol represents an energy-sustainable way to produce ethanol, but it normally results in heavy CO2 emissions. The selective conversion of methanol into H2 and valuable chemical feedstocks offers a promising strategy; however, it is limited by the harsh operating conditions and low conversion efficiency. Herein, we realize efficient high-purity H2 and CO production from methanol by coupling the thermocatalytic methanol dehydrogenation with electrocatalytic hydrogen oxidation on a bifunctional Ru/C catalyst. Electrocatalysis enables the acceleration of C-H cleavage and reduces the partial pressure of hydrogen at the anode, which drives the chemical equilibrium and significantly enhances methanol dehydrogenation. Furthermore, a bilayer Ru/C + Pd/C electrode is designed to mitigate CO poisoning and facilitate hydrogen oxidation. As a result, a high yield of H2 (558.54 mmol h-1 g-1) with high purity (99.9%) was achieved by integrating an applied cell voltage of 0.4 V at 200 °C, superior to the conventional thermal and electrocatalytic processes, and CO is the main product at the anode. This work presents a new avenue for efficient H2 production together with valuable chemical synthesis from methanol.

A Novel Polymer-Encapsulated Multi-Imaging Modality Fiducial Marker with Positive Signal Contrast for Image-Guided Radiation Therapy.

BACKGROUND: Current fiducial markers (FMs) in external-beam radiotherapy (EBRT) for prostate cancer (PCa) cannot be positively visualized on magnetic resonance imaging (MRI) and create dose perturbation and significant imaging artifacts on computed tomography (CT) and MRI. We report our initial experience with clinical imaging of a novel multimodality FM, NOVA. METHODS: We tested Gold Anchor [G-FM], BiomarC [carbon, C-FM], and NOVA FMs in phantoms imaged with kilovoltage (kV) X-rays, transrectal ultrasound (TRUS), CT, and MRI. Artifacts of the FMs on CT were quantified by the relative streak artifacts level (rSAL) metric. Proton dose perturbations (PDPs) were measured with Gafchromic EBT3 film, with FMs oriented either perpendicular to or parallel with the beam axis. We also tested the performance of NOVA-FMs in a patient. RESULTS: NOVA-FMs were positively visualized on all 4 imaging modalities tested. The rSAL on CT was 0.750 ± 0.335 for 2-mm reconstructed slices. In F-tests, PDP was associated with marker type and depth of measurement (p < 10-6); at 5-mm depth, PDP was significantly greater for the G-FM (12.9%, p = 10-6) and C-FM (6.0%, p = 0.011) than NOVA (4.5%). EBRT planning with MRI/CT image co-registration and daily alignments using NOVA-FMs in a patient was feasible and reproducible. CONCLUSIONS: NOVA-FMs were positively visible and produced less PDP than G-FMs or C-FMs. NOVA-FMs facilitated MRI/CT fusion and identification of regions of interest.

A Fault Diagnosis Method for 5G Cellular Networks Based on Knowledge and Data Fusion.

As 5G networks become more complex and heterogeneous, the difficulty of network operation and maintenance forces mobile operators to find new strategies to stay competitive. However, most existing network fault diagnosis methods rely on manual testing and time stacking, which suffer from long optimization cycles and high resource consumption. Therefore, we herein propose a knowledge- and data-fusion-based fault diagnosis algorithm for 5G cellular networks from the perspective of big data and artificial intelligence. The algorithm uses a generative adversarial network (GAN) to expand the data set collected from real network scenarios to balance the number of samples under different network fault categories. In the process of fault diagnosis, a naive Bayesian model (NBM) combined with domain expert knowledge is firstly used to pre-diagnose the expanded data set and generate a topological association graph between the data with solid engineering significance and interpretability. Then, as the pre-diagnostic prior knowledge, the topological association graph is fed into the graph convolutional neural network (GCN) model simultaneously with the training data set for model training. We use a data set collected by Minimization of Drive Tests under real network scenarios in Lu'an City, Anhui Province, in August 2019. The simulation results demonstrate that the algorithm outperforms other traditional models in fault detection and diagnosis tasks, achieving an accuracy of 90.56% and a macro F1 score of 88.41%.

A Universal Approach for Sustainable Urea Synthesis via Intermediate Assembly at the Electrode/Electrolyte Interface.

Electrocatalytic C-N coupling process is indeed a sustainable alternative for direct urea synthesis and co-upgrading of carbon dioxide and nitrate wastes. However, the main challenge lies in the unactivated C-N coupling process. Here, we proposed a strategy of intermediate assembly with alkali metal cations to activate C-N coupling at the electrode/electrolyte interface. Urea synthesis activity follows the trend of Li+

Multi-UAV Data Collection and Path Planning Method for Large-Scale Terminal Access.

In the context of the relentless evolution of network and communication technologies, the need for enhanced communication content and quality continues to escalate. Addressing the demands of data collection from the abundance of terminals within Internet of Things (IoT) scenarios, this paper presents an advanced approach to multi-Unmanned Aerial Vehicle (UAV) data collection and path planning tailored for extensive terminal accessibility. This paper focuses on optimizing the complex interplay between task completion time and task volume equilibrium. To this end, a novel strategy is devised that integrates sensor area partitioning and flight trajectory planning for multiple UAVs, forming an optimization framework geared towards minimizing task completion duration. The core idea of this work involves designing an innovative k-means algorithm capable of balancing data quantities within each cluster, thereby achieving balanced sensor node partitioning based on data volume. Then, the UAV flight trajectory paths are discretely modeled, and a grouped, improved genetic algorithm is used to solve the Multiple Traveling Salesman Problem (MTSP). The algorithm introduces a 2-opt optimization operator to improve the computational efficiency of the genetic algorithm. Empirical validation through comprehensive simulations clearly underscores the efficacy of the proposed approach. In particular, the method demonstrates a remarkable capacity to rectify the historical issue of diverse task volumes among multiple UAVs, all the while significantly reducing task completion times. Moreover, its convergence rate substantially outperforms that of the conventional genetic algorithm, attesting to its computational efficiency. This paper contributes an innovative and efficient paradigm to improve the problem of data collection from IoT terminals through the use of multiple UAVs. As a result, it not only augments the efficiency and balance of task distribution but also showcases the potential of tailored algorithm solutions for realizing optimal outcomes in complex engineering scenarios.

Electrodeposition-Assisted Crystal Growth Regulation of PdBi Clusters on Carbon Cloths for Ethanol Oxidation.

Carbon-supported Pd-based clusters are one of the most promising anodic catalysts for ethanol oxidation reaction (EOR) due to their encouraging activity and practical applications. However, unclear growth mechanism of Pd-based clusters on the carbon-based materials has hindered their extensive applications. Herein, we first introduce multi-void spherical PdBi cluster/carbon cloth (PdBi/CC) composites by an electrodeposition routine. The growth mechanism of PdBi clusters on the CC supports has been systemically investigated by evaluating the selected samples and tuning their compositions, which involve the big difference in standard redox potential between Pd2+/Pd and Bi3+/Bi and easy adsorption of Bi3+ on the surface of Pd-rich seeds. Benefitting from the ensembles of many nanocrystal subunits, multi-void spherical PdBi clusters can present collective properties and novel functionalities. In addition, the outstanding characteristics of CC supports enable PdBi clusters with stable nanostructures. Thanks to the unique structure, Pd20Bi/CC catalysts manifest higher EOR activity and better stability compared to Pd/CC. Systematic characterizations and a series of CO poisoning tests further confirm that the dramatically enhanced EOR activity and stability can be attributed to the incorporation of Bi species and the strong coupling of the structure between PdBi clusters and CC supports.

Reconfigurable Digital Satellite-Borne Base Station Design and Virtual Function Fast Migration Algorithm.

A breakthrough in the technology for virtualizing satellite-borne networks and computing and storage resources can significantly increase the processing capacity and resource utilization efficiency of satellite-borne base stations in response to the development trend in multi-star and multi-system converged satellite internet iterative systems. The protocol processing function of traditional satellite communication systems is generally placed in the ground station system for processing, with poor flexibility and low efficiency. As a result, a reconfigurable digital satellite-borne base station architecture design is suggested, allowing for separation of the hardware and software of the satellite-borne base station and flexible programming and dynamic loading of the satellite-borne base station's functions by software. Meanwhile, a fast adaptive migration algorithm based on multi-dimensional environment awareness is proposed on top of the reconfigurable digital base station, and migration precomputation and real-time computation are added in order to realize rapid deployment of the digital base station system network. Simulation results demonstrate the effectiveness of the proposed algorithm in enhancing system stability and decreasing real-time calculation costs associated with system network migration under conditions of high dynamic changes for each network element in a star-loaded environment. In conclusion, a digital satellite-borne base station system that effectively addresses the issues of low flexibility and high dynamic changes of nodes in the resource-constrained satellite environment can be created by combining the adaptive migration algorithm and the reconfigurable digitized satellite-borne base station architecture.

Confinement Effects in Carbonized ZIF-Confined Hollow PtCo Nanospheres Enable the Methanol Oxidation Reaction.

Confinement effects in highly porous nanostructures can effectively adjust the selectivity and kinetics of electrochemical reactions, which can boost the methanol oxidation reaction (MOR). In this work, carbonized ZIF-8-confined hollow PtCo nanospheres (PtCo@carbonized ZIF-8) were fabricated using a facile strategy. A monodisperse confined region was successfully prepared, and the dispersion of the PtCo nanoparticles (NPs) could be precisely regulated, allowing for the effective tuning of the confined region. Thus, the precise regulation of the catalytic reaction was achieved. Importantly, hollow PtCo NPs were prepared using a method based on the Kirkendall effect, and their forming mechanism was systematically investigated. Because of the confinement effects of carbonized zeolitic imidazolate framework-8 (ZIF-8), the crystal and electronic structures of the PtCo NPs were able to be effectively tuned. Our electrochemical results show that PtCo@carbonized ZIF-8 composites manifest a higher mass activity (1.4 A mgPt-1) and better stability compared to commercial Pt/C.

Cellular Network Fault Diagnosis Method Based on a Graph Convolutional Neural Network.

The efficient and accurate diagnosis of faults in cellular networks is crucial for ensuring smooth and uninterrupted communication services. In this paper, we propose an improved 4G/5G network fault diagnosis with a few effective labeled samples. Our solution is a heterogeneous wireless network fault diagnosis algorithm based on Graph Convolutional Neural Network (GCN). First, the common failure types of 4G/5G networks are analyzed, and then the graph structure is constructed with the data in the network parameter, given data sets as nodes and similarities as edges. GCN is used to extract features from the graph data, complete the classification task for nodes, and finally predict the fault types of cells. A large number of experiments are carried out based on the real data set, which is achieved by driving tests. The results show that, compared with a variety of traditional algorithms, the proposed method can effectively improve the performance of network fault diagnosis with a small number of labeled samples.

Adipose METTL14-Elicited N6 -Methyladenosine Promotes Obesity, Insulin Resistance, and NAFLD Through Suppressing ß Adrenergic Signaling and Lipolysis.

White adipose tissue (WAT) lipolysis releases free fatty acids as a key energy substance to support metabolism in fasting, cold exposure, and exercise. Atgl, in concert with Cgi-58, catalyzes the first lipolytic reaction. The sympathetic nervous system (SNS) stimulates lipolysis via neurotransmitter norepinephrine that activates adipocyte ß adrenergic receptors (Adrb1-3). In obesity, adipose Adrb signaling and lipolysis are impaired, contributing to pathogenic WAT expansion; however, the underling mechanism remains poorly understood. Recent studies highlight importance of N6 -methyladenosine (m6A)-based RNA modification in health and disease. METTL14 heterodimerizes with METTL3 to form an RNA methyltransferase complex that installs m6A in transcripts. Here, this work shows that adipose Mettl3 and Mettl14 are influenced by fasting, refeeding, and insulin, and are upregulated in high fat diet (HFD) induced obesity. Adipose Adrb2, Adrb3, Atgl, and Cgi-58 transcript m6A contents are elevated in obesity. Mettl14 ablation decreases these transcripts' m6A contents and increases their translations and protein levels in adipocytes, thereby increasing Adrb signaling and lipolysis. Mice with adipocyte-specific deletion of Mettl14 are resistant to HFD-induced obesity, insulin resistance, glucose intolerance, and nonalcoholic fatty liver disease (NAFLD). These results unravel a METTL14/m6A/translation pathway governing Adrb signaling and lipolysis. METTL14/m6A-based epitranscriptomic reprogramming impairs adipose Adrb signaling and lipolysis, promoting obesity, NAFLD, and metabolic disease.

Peptide YY: A Paneth cell antimicrobial peptide that maintains Candida gut commensalism.

The mammalian gut secretes a family of multifunctional peptides that affect appetite, intestinal secretions, and motility whereas others regulate the microbiota. We have found that peptide YY (PYY1-36), but not endocrine PYY3-36, acts as an antimicrobial peptide (AMP) expressed by gut epithelial paneth cells (PC). PC-PYY is packaged into secretory granules and is secreted into and retained by surface mucus, which optimizes PC-PYY activity. Although PC-PYY shows some antibacterial activity, it displays selective antifungal activity against virulent Candida albicans hyphae-but not the yeast form. PC-PYY is a cationic molecule that interacts with the anionic surfaces of fungal hyphae to cause membrane disruption and transcriptional reprogramming that selects for the yeast phenotype. Hence, PC-PYY is an antifungal AMP that contributes to the maintenance of gut fungal commensalism.

Revealing the origin of activity in phthalocyanine-based dual-metal sites towards electrochemical nitric oxide reduction.

Coordinated ligands play crucial roles in tuning the electrochemical nitrate reduction performance of phthalocyanine (Pc)-based dual atom catalysts. With the assistance of axial O ligands, fast NO to NH3 conversion can be realized on O-Ni2-Pc and O-Cu2-Pc. A 2-N product, N2O, can be synthesized on Co2-Pc, Cr2-Pc, O-Co2-Pc, and O-Fe2-Pc through N-N coupling with high NO coverage. ΔENO can be identified as a valid descriptor to support rational M2-Pc design.

Oxygen-Bridged Copper-Iron Atomic Pair as Dual-Metal Active Sites for Boosting Electrocatalytic NO Reduction.

Electrocatalytic reduction of nitric oxide (NO) to ammonia (NH3 ) is a promising approach to NH3 synthesis. However, due to the lack of efficient electrocatalysts, the performance of electrocatalytic NO reduction reaction (NORR) is far from satisfactory. Herein, it is reported that an atomic copper-iron dual-site electrocatalyst bridged by an axial oxygen atom (OFeN6 Cu) is anchored on nitrogen-doped carbon (CuFe DS/NC) for NORR. The CuFe DS/NC can significantly enhance the electrocatalytic NH3 synthesis performance (Faraday efficiency, 90%; yield rate, 112.52 µmol cm-2  h-1 ) at -0.6 V versus RHE, which is dramatically higher than the corresponding Cu single-atom, Fe single-atom and all NORR single-atom catalysts in the literature so far. Moreover, an assembled proof-of-concept Zn-NO battery using CuFe DS/NC as the cathode outputs a power density of 2.30 mW cm-2 and an NH3 yield of 45.52 µg h-1  mgcat -1 . The theoretical calculation result indicates that bimetallic sites can promote electrocatalytic NORR by changing the rate-determining step and accelerating the protonation process. This work provides a flexible strategy for efficient sustainable NH3 synthesis.

Electrocatalytic Urea Synthesis with 63.5 % Faradaic Efficiency and 100 % N-Selectivity via One-step C-N coupling.

Electrocatalytic urea synthesis via coupling N2 and CO2 provides an effective route to mitigate energy crisis and close carbon footprint. However, the difficulty on breaking N≡N is the main reason that caused low efficiencies for both electrocatalytic NH3 and urea synthesis, which is the bottleneck restricting their industrial applications. Herein, a new mechanism to overcome the inert of the nitrogen molecule was proposed by elongating N≡N instead of breaking N≡N to realize one-step C-N coupling in the process for urea production. We constructed a Zn-Mn diatomic catalyst with axial chloride coordination, Zn-Mn sites display high tolerance to CO poisoning and the Faradaic efficiency can even be increased to 63.5 %, which is the highest value that has ever been reported. More importantly, negligible N≡N bond breakage effectively avoids the generation of ammonia as intermediates, therefore, the N-selectivity in the co-electrocatalytic system reaches100 % for urea synthesis. The previous cognition that electrocatalysts for urea synthesis must possess ammonia synthesis activity has been broken. Isotope-labelled measurements and Operando synchrotron-radiation Fourier transform infrared spectroscopy validate that activation of N-N triple bond and nitrogen fixation activity arise from the one-step C-N coupling process of CO species with adsorbed N2 molecules.