Development and validation of a multi-modality fusion deep learning model for differentiating glioblastoma from solitary brain metastases. / åŒºåˆ†èƒ¶è´¨æ¯ç»†èƒžç˜¤å’Œå•å‘æ€§è„‘è½¬ç§»ç˜¤çš„å¤šæ¨¡æ€èžåˆæ·±åº¦å­¦ä¹ æ¨¡åž‹çš„å¼€å‘å’ŒéªŒè¯.

OBJECTIVES: Glioblastoma (GBM) and brain metastases (BMs) are the two most common malignant brain tumors in adults. Magnetic resonance imaging (MRI) is a commonly used method for screening and evaluating the prognosis of brain tumors, but the specificity and sensitivity of conventional MRI sequences in differential diagnosis of GBM and BMs are limited. In recent years, deep neural network has shown great potential in the realization of diagnostic classification and the establishment of clinical decision support system. This study aims to apply the radiomics features extracted by deep learning techniques to explore the feasibility of accurate preoperative classification for newly diagnosed GBM and solitary brain metastases (SBMs), and to further explore the impact of multimodality data fusion on classification tasks. METHODS: Standard protocol cranial MRI sequence data from 135 newly diagnosed GBM patients and 73 patients with SBMs confirmed by histopathologic or clinical diagnosis were retrospectively analyzed. First, structural T1-weight, T1C-weight, and T2-weight were selected as 3 inputs to the entire model, regions of interest (ROIs) were manually delineated on the registered three modal MR images, and multimodality radiomics features were obtained, dimensions were reduced using a random forest (RF)-based feature selection method, and the importance of each feature was further analyzed. Secondly, we used the method of contrast disentangled to find the shared features and complementary features between different modal features. Finally, the response of each sample to GBM and SBMs was predicted by fusing 2 features from different modalities. RESULTS: The radiomics features using machine learning and the multi-modal fusion method had a good discriminatory ability for GBM and SBMs. Furthermore, compared with single-modal data, the multimodal fusion models using machine learning algorithms such as support vector machine (SVM), Logistic regression, RF, adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT) achieved significant improvements, with area under the curve (AUC) values of 0.974, 0.978, 0.943, 0.938, and 0.947, respectively; our comparative disentangled multi-modal MR fusion method performs well, and the results of AUC, accuracy (ACC), sensitivity (SEN) and specificity(SPE) in the test set were 0.985, 0.984, 0.900, and 0.990, respectively. Compared with other multi-modal fusion methods, AUC, ACC, and SEN in this study all achieved the best performance. In the ablation experiment to verify the effects of each module component in this study, AUC, ACC, and SEN increased by 1.6%, 10.9% and 15.0%, respectively after 3 loss functions were used simultaneously. CONCLUSIONS: A deep learning-based contrast disentangled multi-modal MR radiomics feature fusion technique helps to improve GBM and SBMs classification accuracy.

Dual consistency regularization with subjective logic for semi-supervised medical image segmentation.

Semi-supervised learning plays a vital role in computer vision tasks, particularly in medical image analysis. It significantly reduces the time and cost involved in labeling data. Current methods primarily focus on consistency regularization and the generation of pseudo labels. However, due to the model's poor awareness of unlabeled data, aforementioned methods may misguide the model. To alleviate this problem, we propose a dual consistency regularization with subjective logic for semi-supervised medical image segmentation. Specifically, we introduce subjective logic into our semi-supervised medical image segmentation task to estimate uncertainty, and based on the consistency hypothesis, we construct dual consistency regularization under weak and strong perturbations to guide the model's learning from unlabeled data. To evaluate the performance of the proposed method, we performed experiments on three widely used datasets: ACDC, LA, and Pancreas. Experiments show that the proposed method achieved improvement compared with other state-of-the-art (SOTA) methods.

Linkage conversions in single-crystalline covalent organic frameworks.

Single-crystal X-ray diffraction is a powerful characterization technique that enables the determination of atomic arrangements in crystalline materials. Growing or retaining large single crystals amenable to it has, however, remained challenging with covalent organic frameworks (COFs), especially suffering from post-synthetic modifications. Here we show the synthesis of a flexible COF with interpenetrated qtz topology by polymerization of tetra(phenyl)bimesityl-based tetraaldehyde and tetraamine building blocks. The material is shown to be flexible through its large, anisotropic positive thermal expansion along the c axis (αc = +491 × 10-6 K-1), as well as through a structural transformation on the removal of solvent molecules from its pores. The as-synthesized and desolvated materials undergo single-crystal-to-single-crystal transformation by reduction and oxidation of its imine linkages to amine and amide ones, respectively. These redox-induced linkage conversions endow the resulting COFs with improved stability towards strong acid; loading of phosphoric acid leads to anhydrous proton conductivity up to ca. 6.0 × 10-2 S cm-1.

Dual-Proton Conductor for Fuel Cells with Flexible Operational Temperature.

The properties of proton conductors determine the operating temperature range of fuel cells. Typically, phosphoric acid (PA) proton conductors exhibit excellent proton conductivity owing to their high proton dissociation and self-diffusion abilities. However, at low temperatures or high current densities, water-induced PA loss causes rapid degradation of cell performance. Maintaining efficient and stable proton conductivity within a flexible temperature range can significantly reduce the start-up temperature of PA-doped proton exchange membrane fuel cells. In this study, a dual-proton conductor composed of an organic phosphonic acid (ethylenediamine tetramethylene phosphonic acid, EDTMPA) and an inorganic PA is developed for proton exchange membranes. The proposed dual-proton conductor can operate within a flexible temperature range of 80-160 °C, benefiting from the strong interaction between EDTMPA and PA, and the enhanced proton dissociation. Fuel cells with the EDTMPA-PA dual-proton conductor showed excellent cell stability at 80 °C. In particular, under the high current density of 1.5 A cm-2 at 160 °C, the voltage decay rate of the fuel cell with the dual-proton conductor is one-thousandth of that of the fuel cell with PA-only proton conductor, indicating excellent stability.

Machine Learning Study on Microwave-Assisted Batch Preparation and Oxygen Reduction Performance of Fe-N-C Catalysts.

The Fe-N-C catalyst represents one of the most promising candidates for replacing platinum-based catalysts toward the oxygen reduction reaction. The pivotal factor in the successful integration of Fe-N-C catalysts within applications is the attainment of a large-scale production capability. Microwave-assisted pyrolysis offers various advantages, including enhanced energy and time efficiency, uniform heating, and high yield in single-batch processes. These characteristics render it exceptionally suitable for the mass production of catalysts. Through a synergistic approach involving machine learning techniques and microscopic characterization, we discerned performance trends and underlying mechanisms within batch-synthesized Fe-N-C catalysts under microwave-assisted preparation conditions. Machine learning analysis revealed that the precursor mass exerts the most substantial influence on product performance. Furthermore, microscopic characterization unveiled that these influencing factors impact catalyst performance by modulating the degree of agglomeration. Our research introduces an efficacious machine learning model for prognosticating performance and dissecting the influencing factors pertinent to Fe-N-C catalyst synthesis within a microwave system.

CT-based radiomics for predicting radio-chemotherapy response and overall survival in nonsurgical esophageal carcinoma.

Background: To predict treatment response and 2 years overall survival (OS) of radio-chemotherapy in patients with esophageal cancer (EC) by radiomics based on the computed tomography (CT) images. Methods: This study retrospectively collected 171 nonsurgical EC patients treated with radio-chemotherapy from Jan 2010 to Jan 2019. 80 patients were randomly divided into training (n=64) and validation (n=16) cohorts to predict the radiochemotherapy response. The models predicting treatment response were established by Lasso and logistic regression. A total of 156 patients were allocated into the training cohort (n=110), validation cohort (n=23) and test set (n=23) to predict 2-year OS. The Lasso Cox model and Cox proportional hazards model established the models predicting 2-year OS. Results: To predict the radiochemotherapy response, WFK as a radiomics feature, and clinical stages and clinical M stages (cM) as clinical features were selected to construct the clinical-radiomics model, achieving 0.78 and 0.75 AUC (area under the curve) in the training and validation sets, respectively. Furthermore, radiomics features called WFI and WGI combined with clinical features (smoking index, pathological types, cM) were the optimal predictors to predict 2-year OS. The AUC values of the clinical-radiomics model were 0.71 and 0.70 in the training set and validation set, respectively. Conclusions: This study demonstrated that planning CT-based radiomics showed the predictability of the radiochemotherapy response and 2-year OS in nonsurgical esophageal carcinoma. The predictive results prior to treatment have the potential to assist physicians in choosing the optimal therapeutic strategy to prolong overall survival.

Prognosis Forecast of Re-Irradiation for Recurrent Nasopharyngeal Carcinoma Based on Deep Learning Multi-Modal Information Fusion.

Radiation therapy is the primary treatment for recurrent nasopharyngeal carcinoma. However, it may induce necrosis of the nasopharynx, leading to severe complications such as bleeding and headache. Therefore, forecasting necrosis of the nasopharynx and initiating timely clinical intervention has important implications for reducing complications caused by re-irradiation. This research informs clinical decision-making by making predictions on re-irradiation of recurrent nasopharyngeal carcinoma using deep learning multi-modal information fusion between multi-sequence nuclear magnetic resonance imaging and plan dose. Specifically, we assume that the hidden variables of model data can be divided into two categories: task-consistency and task-inconsistency. The task-consistency variables are characteristic variables contributing to target tasks, while the task-inconsistency variables are not apparently helpful. These modal characteristics are adaptively fused when the relevant tasks are expressed through the construction of supervised classification loss and self-supervised reconstruction loss. The cooperation of supervised classification loss and self-supervised reconstruction loss simultaneously reserves the information of characteristic space and controls potential interference simultaneously. Finally, multi-modal fusion effectively fuses information through an adaptive linking module. We evaluated this method on a multi-center dataset. and found the prediction based on multi-modal features fusion outperformed predictions based on single-modal, partial modal fusion or traditional machine learning methods.

Titanium Nitride Nanorods Array-Decorated Graphite Felt as Highly Efficient Negative Electrode for Iron-Chromium Redox Flow Battery.

Iron-chromium redox flow batteries have attracted widespread attention because of their low cost. However, the performance of these batteries is still lower than that of vanadium redox flow batteries due to the poor electrochemical activity of Cr3+ /Cr2+ redox couples on graphite felt electrodes. Herein, binder-free TiN nanorods array-decorated 3D graphite felt composite electrode-is demonstrated. The dendrite-like TiN nanorods array increases the specific surface area of the electrode. The nitrogen and oxygen elements on the surface provide more adsorption sites and electrochemically active sites for Cr3+ /Cr2+ . The contact resistance of the composite electrode is effectively reduced and its homogeneity and stability are improved by avoiding the use of a binder and mixing process. A battery prepared using the TiN nanorods array-decorated 3D graphite felt electrode has enabled the maximum power density to be 427 mW·cm-2 , which is 74.0% higher than a battery assembled with TiN nanoparticles bonded to graphite felt. At a current density of 80 mA·cm-2 , the TiN nanorods battery exhibits the highest coulombic efficiency of 93.0%, voltage efficiency of 90.4%, and energy efficiency of 84.1%. Moreover, the battery efficiency and composite electrode structure remains stable during a redox flow battery cycle test.

Durable High-Temperature Proton Exchange Membrane Fuel Cells Enabled by the Working-Temperature-Matching Palladium-Hydrogen Buffer Layer.

The durability degradation during stack-operating conditions seriously deteriorates the lifetime and performance of the fuel cell. To alleviate the rapid potential rise and performance degradation, an anode design is proposed to match the working temperature of high-temperature proton exchange membrane fuel cells (HT-PEMFCs) with the release temperature of hydrogen from palladium. The result is significantly enhanced hydrogen oxidation reaction (HOR) activity of Pd and superior performance of the Pd anode. Furthermore, Pd as hydrogen buffer and oxygen absorbent layer in the anode can provide additional in situ hydrogen and absorb infiltrated oxygen during local fuel starvation to maintain HOR and suppress reverse-current degradation. Compared with the traditional Pt/C anode, the Pd/C also greatly improved HT-PEMFCs durability during start-up/shut-down and current mutation. The storage/release of hydrogen provides innovative guidance for improving the durability of PEMFCs.

Automatic delineation of organ at risk in cervical cancer radiotherapy based on ensemble learning. / åŸºäºŽé›†æˆå­¦ä¹ çš„å®«é¢ˆç™Œæ”¾å°„æ²»ç–—å±åŠå™¨å®˜çš„è‡ªåŠ¨å‹¾ç”».

OBJECTIVES: The automatic delineation of organs at risk (OARs) can help doctors make radiotherapy plans efficiently and accurately, and effectively improve the accuracy of radiotherapy and the therapeutic effect. Therefore, this study aims to propose an automatic delineation method for OARs in cervical cancer scenarios of both after-loading and external irradiation. At the same time, the similarity of OARs structure between different scenes is used to improve the segmentation accuracy of OARs in difficult segmentations. METHODS: Our ensemble model adopted the strategy of ensemble learning. The model obtained from the pre-training based on the after-loading and external irradiation was introduced into the integrated model as a feature extraction module. The data in different scenes were trained alternately, and the personalized features of the OARs within the model and the common features of the OARs between scenes were introduced. Computer tomography (CT) images for 84 cases of after-loading and 46 cases of external irradiation were collected as the train data set. Five-fold cross-validation was adopted to split training sets and test sets. The five-fold average dice similarity coefficient (DSC) served as the figure-of-merit in evaluating the segmentation model. RESULTS: The DSCs of the OARs (the rectum and bladder in the after-loading images and the bladder in the external irradiation images) were higher than 0.7. Compared with using an independent residual U-net (convolutional networks for biomedical image segmentation) model [residual U-net (Res-Unet)] delineate OARs, the proposed model can effectively improve the segmentation performance of difficult OARs (the sigmoid in the after-loading CT images and the rectum in the external irradiation images), and the DSCs were increased by more than 3%. CONCLUSIONS: Comparing to the dedicated models, our ensemble model achieves the comparable result in segmentation of OARs for different treatment options in cervical cancer radiotherapy, which may be shorten time for doctors to sketch OARs and improve doctor's work efficiency.

Advancements of Polyvinylpyrrolidone-Based Polymer Electrolyte Membranes for Electrochemical Energy Conversion and Storage Devices.

Polymer electrolyte membranes (PEMs) play vital roles in electrochemical energy conversion and storage devices, such as polymer electrolyte membrane fuel cell (PEMFC), redox flow battery, and water electrolysis. As the crucial component of these devices, PEMs need to possess high ion conductivity and electronic insulation, remarkable mechanical and chemical stability, and outstanding isolation function for the materials on both sides of the cathode and anode. Polyvinylpyrrolidone has received widespread attention in the research of PEMs owing to its tertiary amine basic groups and exceptional hydrophilic properties. This review focuses on the application status of polyvinylpyrrolidone-based PEMs in PEMFC, vanadium redox flow battery, and alkaline water electrolysis, and describes in detail the key scientific problems in these fields, providing constructive suggestions and guidance for the application of polyvinylpyrrolidone-based PEMs in electrochemical energy conversion and storage devices.

A Low-Temperature Carbon Encapsulation Strategy for Stable and Poisoning-Tolerant Electrocatalysts.

Carbon encapsulation is an effective strategy for enhancing the durability of Pt-based electrocatalysts for the oxygen reduction reaction (ORR). However, high-temperature treatment is not only energy-intensive but also unavoidably leads to possible aggregation. Herein, a low-temperature polymeric carbon encapsulation strategy (≈150 °C) is reported to encase Pt nanoparticles in thin and amorphous carbonaceous layers. Benefiting from the physical confinement effect and enhanced antioxidant property induced by the surface carbon species, significantly improved stabilities can be achieved for polymeric carbon species encapsulated Pt nanoparticles (Pt@C/C). Particularly, a better antipoisoning capability toward CO, SOx , and POx is observed in the case of Pt@C/C. To minimize the thickness of the catalyst layer and reduce the mass transfer resistance, the high mass loading Pt@C/C (40 wt%) is prepared and applied to high-temperature polymer electrolyte membrane fuel cells (HT-PEMFCs). At 160 °C, a peak power density of 662 mW cm-2 is achieved with 40% Pt@C/C cathode in H2 -O2 HT-PEMFCs, which is superior to that with 40% Pt/C cathode. The facile strategy provides guidance for the synthesis of highly durable carbon encapsulated noble metal electrocatalysts toward ORR.

Novel Inorganic Integrated Membrane Electrodes for Membrane Capacitive Deionization.

In capacitive deionization (CDI), coion repulsion and Faradaic reactions during charging reduce the charge efficiency (CE), thus limiting the salt adsorption capacity (SAC) and energy efficiency. To overcome these issues, membrane CDI (MCDI) based on the enhanced permselectivity of the anode and cathode is proposed using the ion-exchange polymer as the independent membrane or coating. To develop a novel and cost-effective MCDI system, we fabricated an integrated membrane electrode using a thin layer of the inorganic ion-exchange material coated on the activated carbon (AC) electrode, which effectively improves the ion selectivity. Montmorillonite (MT, Al2O9Si3) and hydrotalcite (HT, Mg6Al2(CO3)(OH)16·4H2O) were selected as the main active anion- and cation-exchange materials, respectively, for the cathode and anode. The HT-MT MCDI system employing HT-AC and MT-AC electrodes obtained a CE of 90.5% and an SAC of 15.8 mg g-1 after 100 consecutive cycles (50 h); these values were considerably higher than those of the traditional CDI system employing pristine AC electrodes (initially, a CE of 55% and an SAC of 10.2 mg g-1, which attenuated continuously to zero, and even "inverted work" occurs after 50 h, i.e., desorption during charging and adsorption during discharging). The HT-MT MCDI system showed moderate tolerance to organic matters during desalination and retained 84% SAC and 89% CE after 70 cycles in 50-200 mg L-1 sodium alginate. This study demonstrates a simple and cost-effective method for fabricating high-CE electrodes for desalination with great application potential.

Unidirectional electron injection and accelerated proton transport in bacteriorhodopsin based Bio-p-n junctions.

Hampered by the absence of evidence and theoretical model of biological semiconductors, the unidirectional electron transport via the p-n junction between functional proteins and abiotic materials remains a challenge for bioelectronics. Bacteriorhodopsin (bR), a representative transmembrane protein, has demonstrated exceptional optoelectronic effects in bR/semiconductor hybrid materials and offers a possible pathway for addressing this challenge. In the present work, for the first time, bR is proved to be an n-type semiconductor with an indirect electron transition. Through the photo-electrochemical method used for studying the p-n junction effect in the bR and p-type semiconductor combined electrodes, we reached several important conclusions: The self-corrosion of bR integrated Cu2O electrodes is delayed for about 36 times; The photocurrent of bR integrated CuSCN electrodes is enhanced by about 400%, which is attributed to the directional migration of electrons via the p-n junction. Furthermore, the ultrafast kinetics we have explored, shows that the injection of electrons shortens the lifetime of the intermediate state O640 from 37.3 µs to 20.1 µs, what means that the protons transport rate accompanying the bR photocycle process is accelerated. Therefore, we believe that the concept of the bio-p-n junction and the mechanism of electron coupled proton transport, which are discussed herein, will promote useful research on bioelectronic applications for bR and its homologs.

The Effect of Functional Groups on the Electrocatalytic Activity of Carbon Nanotubes with Different Wall Numbers toward Carbohydrazide Oxidation Reaction.

Carbohydrazide could be applied as fuels for fuel cells to avoid the toxicity of hydrazine. Carbon nanotubes (CNTs) exhibit an improved electrocatalytic activity toward carbohydrazide oxidation reaction than other carbon materials like carbon black or multi-layer graphene, however, the role of defects and functional groups of CNTs for the reaction is not clear. In this study, the electrocatalytic properties of CNTs toward the carbohydrazide oxidation reaction is investigated, which demonstrate that the content of carboxyl groups is crucial to the catalytic activity. The one with higher carboxyl groups exhibits an improved catalytic activity toward carbohydrazide oxidation reaction. Furthermore, density functional theory calculation reveals that the carboxyl groups could increase the adsorption of carbohydrazide molecules on CNTs, which benefits their catalytic activity.

Advancement toward Polymer Electrolyte Membrane Fuel Cells at Elevated Temperatures.

Elevation of operational temperatures of polymer electrolyte membrane fuel cells (PEMFCs) has been demonstrated with phosphoric acid-doped polybenzimidazole (PA/PBI) membranes. The technical perspective of the technology is simplified construction and operation with possible integration with, e.g., methanol reformers. Toward this target, significant efforts have been made to develop acid-base polymer membranes, inorganic proton conductors, and organic-inorganic composite materials. This report is devoted to updating the recent progress of the development particularly of acid-doped PBI, phosphate-based solid inorganic proton conductors, and their composite electrolytes. Long-term stability of PBI membranes has been well documented, however, at typical temperatures of 160°C. Inorganic proton-conducting materials, e.g., alkali metal dihydrogen phosphates, heteropolyacids, tetravalent metal pyrophosphates, and phosphosilicates, exhibit significant proton conductivity at temperatures of up to 300°C but have so far found limited applications in the form of thin films. Composite membranes of PBI and phosphates, particularly in situ formed phosphosilicates in the polymer matrix, showed exceptionally stable conductivity at temperatures well above 200°C. Fuel cell tests at up to 260°C are reported operational with good tolerance of up to 16% CO in hydrogen, fast kinetics for direct methanol oxidation, and feasibility of nonprecious metal catalysts. The prospect and future exploration of new proton conductors based on phosphate immobilization and fuel cell technologies at temperatures above 200°C are discussed.

Intrinsic Effect of Carbon Supports on the Activity and Stability of Precious Metal Based Catalysts for Electrocatalytic Alcohol Oxidation in Fuel Cells: A Review.

Electrocatalyst supports, in particular carbonaceous materials, play critical roles in the electrocatalytic activity and stability of precious metal group (PMG)-based catalysts such as Pt, Pd, and Au for the electrochemical alcohol oxidation reaction (AOR) of fuels such as methanol and ethanol in polymer electrolyte membrane fuel cells (PEMFCs). Carbonaceous supports such as high surface area carbon provide electronic contact throughout the catalyst layer, isolate PMG nanoparticles (NPs) to maintain high electrochemical surface area, and provide hydrophobic properties to avoid flooding of the catalyst layer by liquid water produced. Compared to high surface area carbon, PMG catalysts supported on 1D and 2D carbon materials such as graphene and carbon nanotubes show enhanced activity and durability due to the intrinsic effect of the underlying carbonaceous supports on the electronic states of PMG NPs. The modification of the electronic environment, in particular the d-band centers of PMG NPs, weakens the adsorption of AOR intermediates, facilitates breaking of the C-C bonds, and thus enhances the electrocatalytic activity of PMG catalysts. The doping of heteroatoms further facilitates the electrocatalytic activity for the AOR through the structural, bifunctional, and electronic effects, in addition to the enhanced dispersion of PMG NPs in the carbon support. The prospects for the development of effective PMG-based catalysts for high-performance alcohol-fuel-based PEMFCs is discussed.

Enhanced electro-oxidation/peroxone (in situ) process with a Ti-based nickel-antimony doped tin oxide anode for phenol degradation.

Recently, a novel proof-of-concept oxygen reduction reaction (ORR) based electro-oxidation (EO) process has been developed, which was accomplished by integrating anodic electrochemical oxidation coupled with an in situ electro-peroxone process, by harnessing the anodic by-product O3 reacted with ORR cathode generated H2O2. To further enhance EO coupled in situ electro-peroxone, a nickel and antimony doped tin oxide anodic catalyst layer, namely NATO, was fabricated on Ti mesh to improve anodic oxidation and reinforce the generation of O3, thus promoting in situ Electro-peroxone. As a result, O3 generation rate was enhanced by 12.6%. Complete phenol, as a model organic compound, and 95% of TOC removal were achieved, respectively, during ORR-EO. Through kinetics and instrument analysis, results show that the amount of intermediates accumulated during phenol degradation was much less in this Ti/NATO based ORR-EO system than in a traditional EO system. Moreover, 35.7% of the energy consumption was saved for ORR-EO, owing to its reduced applied voltage and the enhanced in situ electro-peroxone process.

Unconventional p-d Hybridization Interaction in PtGa Ultrathin Nanowires Boosts Oxygen Reduction Electrocatalysis.

Alloying 3d transition metals with Pt has been discovered as an effective strategy to boost the catalytic activity in oxygen reduction reaction (ORR), which, however, often raises the insufficient catalyst durability issue due to rapid leaching of the 3d metal elements. To overcome this issue and realize enhancements in both the activity and the durability properties, here we report a new catalytic structure based on PtGa ultrathin alloy nanowires (NWs), which feature an unconventional strong p-d hybridization interaction. Relative to commercial Pt catalyst, the optimum Pt4.31Ga NWs catalyst exhibited 10.5- and 12.1-fold enhancement in the ORR mass activity and specific activity, respectively. Particularly, the Pt4.31Ga NWs catalyst showed only 15.8% loss in the mass activity after 30 000 cycles of durability test, as compared to a big decrease of 79.6% for the commercial Pt catalyst. Our mechanistic studies find a strong p-d hybridization interaction between Ga and Pt that accounts for the improved ORR performance via synergistically optimizing the surface electronic structure, enhancing the oxidation resistance of Pt, and suppressing the leaching of lattice Ga. We believe this work provides new perspectives to design active and durable electrocatalysts toward ORR.

Unique Ni Crystalline Core/Ni Phosphide Amorphous Shell Heterostructured Electrocatalyst for Hydrazine Oxidation Reaction of Fuel Cells.

It is highly attractive but challenging to develop transition-metal electrocatalysts for direct hydrazine fuel cells (DHzFCs). In this work, a nickel crystalline core@nickel phosphide amorphous shell heterostructured electrocatalyst supported by active carbon (Ni@NiP/C) is developed. Ni@NiP/C with a P/Ni molar ratio of 3:100, Ni@NiP3.0/C, exhibits outstanding catalytic activity for the hydrazine oxidation reaction (HzOR) in alkaline solution, achieving a much better catalytic activity (2675.1 A gNi-1@0.25 V vs RHE) and high stability, as compared to Ni nanoparticles supported on carbon (Ni/C) and Pt/C catalysts. The results indicate that formation of the NiP amorphous shell effectively inhibits the passivation of the Ni core active sites and enhances the adsorption of hydrazine on Ni by improving the adsorption energy, leading to high electrochemical activity and stability of the Ni@NiP3.0/C catalysts for HzOR. The density functional theory calculation confirms the structural and electrocatalytic effect of the core-shell heterostructure on the stability and activity of Ni active sites for HzOR. The unique crystalline core/amorphous shell-structured Ni@NiP/C demonstrates promising potential as an effective electrocatalyst for DHzFCs.