Ultra-Small High-Entropy Alloy as Multi-Functional Catalyst for Ammonia Based Fuel Cells.

Ammonia fuel cells using carbon-neutral ammonia as fuel are regarded as a fast, furious, and flexible next-generation carbon-free energy conversion technology, but it is limited by the kinetically sluggish ammonia oxidation reaction (AOR), oxygen reduction reaction (ORR), and hydrogen evolution reaction (HER). Platinum can efficiently drive these three types of reactions, but its scale-up application is limited by its susceptibility to poisoning and high cost. In order to reduce the cost and alleviate poisoning, incorporating Pt with various metals proves to be an efficient and feasible strategy. Herein, PtFeCoNiIr/C trifunctional high-entropy alloy (HEA) catalysts are prepared with uniform mixing and ultra-small size of 2 ± 0.5 nm by Joule heating method. PtFeCoNiIr/C exhibits efficient performance in AOR (Jpeak = 139.8 A g-1 PGM), ORR (E1/2 = 0.87 V), and HER (E10 = 20.3 mV), outperforming the benchmark Pt/C, and no loss in HER performance at 100 mA cm-2 for 200 h. The almost unchanged E1/2 in the anti-poisoning test indicates its promising application in real fuel cells powered by ammonia. This work opens up a new path for the development of multi-functional electrocatalysts and also makes a big leap toward the exploration of cost-effective device configurations for novel fuel cells.

Lattice Oxygen Activation through Deep Oxidation of Co4N by Jahn-Teller-Active Dopants for Improved Electrocatalytic Oxygen Evolution.

Triggering the lattice oxygen oxidation mechanism is crucial for improving oxygen evolution reaction (OER) performance, because it could bypass the scaling relation limitation associated with the conventional adsorbate evolution mechanism through the direct formation of oxygen-oxygen bond. High-valence transition metal sites are favorable for activating the lattice oxygen, but the deep oxidation of pre-catalysts suffers from a high thermodynamic barrier. Here, taking advantage of the Jahn-Teller (J-T) distortion induced structural instability, we incorporate high-spin Mn3+ ( t 2 g 3 e g 1 ${{t}_{2g}^{3}{e}_{g}^{1}}$ ) dopant into Co4N. Mn dopants enable a surface structural transformation from Co4N to CoOOH, and finally to CoO2, as observed by various in situ spectroscopic investigations. Furthermore, the reconstructed surface on Mn-doped Co4N triggers the lattice oxygen activation, as evidenced experimentally by pH-dependent OER, tetramethylammonium cation adsorption and online electrochemical mass spectrometry measurements of 18O-labelled catalysts. In general, this work not only offers the introducing J-T effect approach to regulate the structural transition, but also provides an understanding about the influence of the catalyst's electronic configuration on determining the reaction route, which may inspire the design of more efficient catalysts with activated lattice oxygen.

The Construction of a Lattice Image and Dislocation Analysis in High-Resolution Characterizations Based on Diffraction Extinctions.

This paper introduces a method for high-resolution lattice image reconstruction and dislocation analysis based on diffraction extinction. The approach primarily involves locating extinction spots in the Fourier transform spectrum (reciprocal space) and constructing corresponding diffraction wave functions. By the coherent combination of diffraction and transmission waves, the lattice image of the extinction planes is reconstructed. This lattice image is then used for dislocation localization, enabling the observation and analysis of crystal planes that exhibit electron diffraction extinction effects and atomic jump arrangements during high-resolution transmission electron microscopy (HRTEM) characterization. Furthermore, due to the method's effectiveness in localizing dislocations, it offers a unique advantage when analyzing high-resolution images with relatively poor quality. The feasibility of this method is theoretically demonstrated in this paper. Additionally, the method was successfully applied to observed edge dislocations, such as 1/6[211-], 1/6[2-11-], and 1/2[01-1], which are not easily observable in conventional HRTEM characterization processes, in electro-deposited Cu thin films. The Burgers vectors were determined. Moreover, this paper also attempted to observe screw dislocations that are challenging to observe in high-resolution transmission electron microscopy. By shifting a pair of diffraction extinction spots and superimposing the reconstructed images before and after the shift, screw dislocations with a Burgers vector of 1/2[011-] were successfully observed in electro-deposited Cu thin films.

Interfaces Engineering of Ultrafine Ni@Ni2P/C Core-Shell Heterostructure for High Yield Hydrogen Peroxide Electrosynthesis.

Developing cost-effective and sustainable catalysts with exceptional activity and selectivity is essential for the practical implementation of on-site H2O2 electrosynthesis, yet it remains a formidable challenge. Metal phosphide core-shell heterostructures anchored in carbon nanosheets (denoted as Ni@Ni2P/C NSs) are designed and synthesized via carbonization and phosphidation of the 2D Ni-BDC precursor. This core-shell nanostructure provides more accessible active sites and enhanced durability, while the 2D carbon nanosheet substrate prevents heterostructure aggregation and facilitates mass transfer. Theoretical calculations further reveal that the Ni/Ni2P heterostructure-induced optimization of geometric and electronic structures enables the favored adsorption of OOH* intermediate. All these features endow the Ni@Ni2P/C NSs with remarkable performance in 2e ORR for H2O2 synthesis, achieving a top yield rate of 95.6 mg L-1 h-1 with both selectivity and Faradaic efficiency exceeding 90% under a wide range of applied potentials. Furthermore, when utilized as the anode of an assembled gas diffusion electrode (GDE) device, the Ni@Ni2P/C NSs achieve in situ H2O2 production with excellent long-term durability (>32 h). Evidently, this work provides a unique insight into the origin of 2e ORR and proposes optimization of H2O2 production through nano-interface manipulation.

[A study of cognitive impairment quantitative assessment method based on gait characteristics].

Alzheimer's disease (AD) is a common and serious form of elderly dementia, but early detection and treatment of mild cognitive impairment can help slow down the progression of dementia. Recent studies have shown that there is a relationship between overall cognitive function and motor function and gait abnormalities. We recruited 302 cases from the Rehabilitation Hospital Affiliated to National Rehabilitation Aids Research Center and included 193 of them according to the screening criteria, including 137 patients with MCI and 56 healthy controls (HC). The gait parameters of the participants were collected during performing single-task (free walking) and dual-task (counting backwards from 100) using a wearable device. By taking gait parameters such as gait cycle, kinematics parameters, time-space parameters as the focus of the study, using recursive feature elimination (RFE) to select important features, and taking the subject's MoCA score as the response variable, a machine learning model based on quantitative evaluation of cognitive level of gait features was established. The results showed that temporal and spatial parameters of toe-off and heel strike had important clinical significance as markers to evaluate cognitive level, indicating important clinical application value in preventing or delaying the occurrence of AD in the future.

Strategies for Advanced Supercapacitors Based on 2D Transition Metal Dichalcogenides: From Material Design to Device Setup.

Recently, the development of new materials and devices has become the main research focus in the field of energy. Supercapacitors (SCs) have attracted significant attention due to their high power density, fast charge/discharge rate, and excellent cycling stability. With a lamellar structure, 2D transition metal dichalcogenides (2D TMDs) emerge as electrode materials for SCs. Although many 2D TMDs with excellent energy storage capability have been reported, further optimization of electrode materials and devices is still needed for competitive electrochemical performance. Previous reviews have focused on the performance of 2D TMDs as electrode materials in SCs, especially on their modification. Herein, the effects of element doping, morphology, structure and phase, composite, hybrid configuration, and electrolyte are emphatically discussed on the overall performance of 2D TMDs-based SCs from the perspective of device optimization. Finally, the opportunities and challenges of 2D TMDs-based SCs in the field are highlighted, and personal perspectives on methods and ideas for high-performance energy storage devices are provided.

A novel temporal generative adversarial network for electrocardiography anomaly detection.

Cardiac abnormality detection from Electrocardiogram (ECG) signals is a common task for cardiologists. To facilitate efficient and objective detection, automated ECG classification by using deep learning based methods have been developed in recent years. Despite their impressive performance, these methods perform poorly when presented with cardiac abnormalities that are not well represented, or absent, in the training data. To this end, we propose a novel one-class classification based ECG anomaly detection generative adversarial network (GAN). Specifically, we embedded a Bi-directional Long-Short Term Memory (Bi-LSTM) layer into a GAN architecture and used a mini-batch discrimination training strategy in the discriminator to synthesis ECG signals. Our method generates samples to match the data distribution from normal signals of healthy group so that a generalised anomaly detector can be built reliably. The experimental results demonstrate our method outperforms several state-of-the-art semi-supervised learning based ECG anomaly detection algorithms and robustly detects the unknown anomaly class in the MIT-BIH arrhythmia database. Experiments show that our method achieves the accuracy of 95.5% and AUC of 95.9% which outperforms the most competitive baseline by 0.7% and 1.7% respectively. Our method may prove to be a helpful diagnostic method for helping cardiologists identify arrhythmias.

Tuning the Mn Dopant To Boost the Hydrogen Evolution Performance of CoP Nanowire Arrays.

Because of its advantages such as abundant resources, low cost, simple synthesis, and high electrochemical stability, cobalt phosphide (CoP) is considered as a promising candidate for electrocatalytic hydrogen evolution reaction. Through element doping, the morphology and electronic structure of the catalyst can be tuned, resulting in both the increase of the active site number and the improvement of the intrinsic activity of each site. Herein, we designed and fabricated Mn-doped CoP nanowires with a length of 3 µm, a diameter of 50 nm, and the pores between the grains of 10 nm. As a highly efficient electrocatalyst for alkaline hydrogen evolution, the Mn10-doped CoP/NF (doping amount is about 10 atom %) electrode presented overpotentials of 60 mV @ 10 mA cm-2 and 112 mV @ 100 mA cm-2, improved by 35 and 23%, respectively, compared with CoP/NF. Characterizations indicate that Mn doping increases the electrochemical active area, reduces the impedance, and tunes the electronic structure of the material. Density functional theory calculations also revealed that an appropriate amount of Mn dopant at a suitable location can both react as an active site itself and boost the activity of the surrounding Co sites, delivering favorable H* adsorption and rapid reaction kinetics. This result may not only promote the development of hydrogen evolution reaction catalysts but also encourage explorations of the relationship between the property and fine doping structure.

Semicrystalline SrTiO3 -Decorated Anatase TiO2 Nanopie as Heterostructure for Efficient Photocatalytic Hydrogen Evolution.

The coupling of TiO2 and SrTiO3 through elaborate bandgap engineering can provide synergies for highly efficient photocatalysts. To further improve the separation between photogenerated electrons and holes, a nano-heterostructured combination of semicrystalline SrTiO3 (S-SrTiO3 ) and anatase TiO2 nanoparticles is designed, and an optimized interface is achieved between uniformly grown S-SrTiO3 and metal organic framework (MOF)-derived anatase TiO2 through a controlled hydrothermal process. Besides tuning of the bandgap and broadening of the absorption spectral range, S-SrTiO3 particles alleviate charge carrier recombination benefiting from the coupling of the semicrystalline SrTiO3 around the interface. Additionally, highly dispersed S-SrTiO3 on TiO2 provides a good spatial distribution of active sites and the abundant carbon remained from MOF may reduce charge transport resistance. Moreover, the rapid transfer within the nano-heterostructure promotes the separation of the photogenerated charge carriers. With the above predominant architecture, when used as a photocatalyst, the as-synthesized S-SrTiO3 /TiO2 heterostructure exhibits exceptionally high photocatalytic performance of 13 005 µmol h-1 g-1 for H2 production, exceeding most oxide-based photocatalysts reported. This study might provide mechanistic insights into a new perspective for the design and preparation of photocatalysts with novel structure and enhanced catalysis activity.

A Few-Shot Learning-Based Siamese Capsule Network for Intrusion Detection with Imbalanced Training Data.

Network intrusion detection remains one of the major challenges in cybersecurity. In recent years, many machine-learning-based methods have been designed to capture the dynamic and complex intrusion patterns to improve the performance of intrusion detection systems. However, two issues, including imbalanced training data and new unknown attacks, still hinder the development of a reliable network intrusion detection system. In this paper, we propose a novel few-shot learning-based Siamese capsule network to tackle the scarcity of abnormal network traffic training data and enhance the detection of unknown attacks. In specific, the well-designed deep learning network excels at capturing dynamic relationships across traffic features. In addition, an unsupervised subtype sampling scheme is seamlessly integrated with the Siamese network to improve the detection of network intrusion attacks under the circumstance of imbalanced training data. Experimental results have demonstrated that the metric learning framework is more suitable to extract subtle and distinctive features to identify both known and unknown attacks after the sampling scheme compared to other supervised learning methods. Compared to the state-of-the-art methods, our proposed method achieves superior performance to effectively detect both types of attacks.

Improved Functional Causal Likelihood-Based Causal Discovery Method for Diabetes Risk Factors.

Diabetes mellitus is a disease that has reached epidemic proportions globally in recent years. Consequently, the prevention and treatment of diabetes have become key social challenges. Most of the research on diabetes risk factors has focused on correlation analysis with little investigation into the causality of these risk factors. However, understanding the causality is also essential to preventing the disease. In this study, a causal discovery method for diabetes risk factors was developed based on an improved functional causal likelihood (IFCL) model. Firstly, the issue of excessive redundant and false edges in functional causal likelihood structures was resolved through the construction of an IFCL model using an adjustment threshold value. On this basis, an IFCL-based causal discovery algorithm was designed, and a simulation experiment was performed with the developed algorithm. The experimental results revealed that the causal structure generated using a dataset with a sample size of 2000 provided more information than that produced using a dataset with a sample size of 768. In addition, the causal structures obtained with the developed algorithm had fewer redundant and false edges. The following six causal relationships were identified: insulin→plasma glucose concentration, plasma glucose concentration→body mass index (BMI), triceps skin fold thickness→BMI and age, diastolic blood pressure→BMI, and number of times pregnant→age. Furthermore, the reasonableness of these causal relationships was investigated. The algorithm developed in this study enables the discovery of causal relationships among various diabetes risk factors and can serve as a reference for future causality studies on diabetes risk factors.

Highly sensitive non-enzymatic hydrogen peroxide monitoring platform based on nanoporous gold via a modified solid-phase reaction method.

In this work, nanoporous gold (NPG) fabricated using a modified solid-phase reaction method was developed as an electrocatalyst for the nonenzymatic detection of hydrogen peroxide (H2O2). The NPG morphology and structure were characterized by scanning electron microscopy and high-resolution transmission electron microscopy. The fabricated NPG exhibited a nanoporous framework with numerous structural defects. The NPG-based amperometric H2O2 sensor had a good selectivity, reproducibility, and low detection limit (0.3 µM) under near physiological conditions (pH = 7.4). The sensitivities of this sensor over concentration ranges of 0.002-5 mM and 5-37.5 mM were 159 and 64 µA mM-1 cm-2, respectively. These results indicate that the developed NPG is a promising material for the electrochemical sensing of H2O2.

Predicting human body composition using a modified adaptive genetic algorithm with a novel selection operator.

BACKGROUND: Changes to human body composition reflect changes in health status to some extent. It has been recognized that these changes occur earlier than diseases. This means that a reasonable prediction of body composition helps to improve model users' health. To overcome the low accuracy and poor adaptability of existing human body composition prediction models and obtain higher efficiency, we proposed a novel method for predicting human body composition which uses a modified adaptive genetic algorithm (MAGA). METHODS: Firstly, since there are many parameters for a human body composition model, and these parameters are associated, we designed a new parameter selection approach by combining the improved RReliefF method with the mRMR method. Following this, selected parameters were used to establish a model that fits body composition. Secondly, in order to accurately calculate the weight of parameters in this model, we proposed a solution which used a modified adaptive genetic algorithm, taking advantage of both roulette and optimum reservation strategies. This solution also has an improved selection operator. Thirdly, taking the percentage of body fat (PBF) as an example of body composition, we conducted experiments to compare performance between our algorithm and other algorithms. RESULTS: Through our simulations, we demonstrated that the adaptability of the proposed model is 0.9921, the mean relative error is 0.05%, the mean square error is 1.3 and the correlation coefficient is 0.982. When compared with the indexes of other models, our model has the highest adaptability and the smallest error. Additionally, the suggested model, which has a training time of 28.58s and a running time of 2.84s, is faster than some models. CONCLUSION: The PBF prediction model established by MAGA has high accuracy, stronger generalization ability and higher efficiency, which could provide a new method for human composition prediction.

When hollow multishelled structures (HoMSs) meet metal-organic frameworks (MOFs).

Hollow multishelled structures (HoMSs) have distinguished advantages, such as a large effective surface area, an optimized mass transport route, and a high loading capacity, but the fabrication of HoMSs has been a big challenge. In 2009, we developed a universal and facile method for HoMS fabrication, i.e., the sequential templating approach (STA). Progress in the synthetic methodology has enabled the study of HoMSs to develop and has made it a research hotspot in materials science. To date, HoMSs have shown their advantages in a wide range of applications, including catalysis, energy conversion and storage, drug delivery, etc. Based on the understanding in this field, we recently revealed the unique temporal-spatial ordering properties of HoMSs. Furthermore, we have been wondering if the structure of a HoMS can be modulated at the molecular level. Encouragingly, metal-organic frameworks (MOFs) are star materials with clearly defined molecular structures. The compositions, geometries, functionalities and topologies of MOFs have been well tuned by rational design. Integrating the unique properties of MOFs and HoMS could realize the systemic design of materials from the molecular to the micro-level, which would provide a series of advantages for various applications, such as developing high performance catalysts for cascade and/or selective catalysis, combining the reaction and separation process for multiple reactions, releasing drugs in a certain environment for smart medicine, and so on. We believe it is time to summarize the recent progress in the integration of MOFs and HoMSs, including HoMSs coated with MOFs, MOF-derived HoMSs, and MOFs with a hollow multishelled structure, and we also put forward our personal outlook in relation to the future opportunities and challenges in this emerging yet promising research field.

High Phase-Purity 1T-MoS2 Ultrathin Nanosheets by a Spatially Confined Template.

The crystal phase plays an important role in controlling the properties of a nanomaterial; however, it is a great challenge to obtain a nanomaterial with high purity of the metastable phase. For instance, the large-scale synthesis of the metallic phase MoS2 (1T-MoS2 ) is important for enhancing electrocatalytic reaction, but it can only be obtained under harsh conditions. Herein, a spatially confined template method is proposed to synthesize high phase-purity MoS2 with a 1T content of 83 %. Moreover, both the confined space and the structure of template will affect the purity of 1T-MoS2 ; in this case, this approach was extended to other similar spatially confined templates to obtain the high-purity material. The obtained ultrathin nanosheets exhibit good electrocatalytic activity and excellent stability in the hydrogen evolution reaction.

Beyond dealloying: development of nanoporous gold via metal-induced crystallization and its electrochemical properties.

Nanoporous metals (NPMs) possess a number of intriguing properties that result in NPMs being an important family of nanomaterials for many advanced applications. However, the methods of preparing NPMs are relatively complicated and have many limitations, which have hindered the commercial application of NPMs thus far. By introducing metal-induced crystallization, a solid-phase reaction method for preparing NPMs was developed in this study, which is highly efficient and environmentally friendly. The microstructure of the prepared nanoporous gold (NPG) was characterized on an atomic scale by scanning electron microscopy and high-resolution transmission electron microscopy. The results confirmed that the solid-phase reaction method is an effective alternative means of preparing highly pure NPG. The results of electrochemical tests demonstrated that thus-prepared NPG possesses higher electrocatalytic activity than other types of gold electrodes toward oxygen reduction in alkaline media. The combination of a simple preparation process and higher activity suggests that the developed method may promote the future use of NPG in new energy applications, such as fuel cells and metal-air batteries.

Lanthanide-Doped Photoluminescence Hollow Structures: Recent Advances and Applications.

Lanthanide-doped nanomaterials have attracted significant attention for their preeminent properties and widespread applications. Due to the unique characteristic, the lanthanide-doped photoluminescence materials with hollow structures may provide advantages including enhanced light harvesting, intensified electric field density, improved luminescent property, and larger drug loading capacity. Herein, the synthesis, properties, and applications of lanthanide-doped photoluminescence hollow structures (LPHSs) are comprehensively reviewed. First, different strategies for the engineered synthesis of LPHSs are described in detail, which contain hard, soft, self-templating methods and other techniques. Thereafter, the relationship between their structure features and photoluminescence properties is discussed. Then, niche applications including biomedicines, bioimaging, therapy, and energy storage/conversion are focused on and superiorities of LPHSs for these applications are particularly highlighted. Finally, keen insights into the challenges and personal prospects for the future development of the LPHSs are provided.

Preparation of a nanoporous active tungsten foil by two-step anodizing and deoxidized annealing for hydrogen evolution reaction.

Nanoporous metals made by anodizing represent a class of active materials with unique structural properties. In this work, nanoporous active W foils (NPAWFs) are prepared via two-step anodizing and deoxidized annealing in hydrogen atmosphere. During the course of this research, the anodizing and annealing conditions have been optimized systematically. The morphology, composition and catalytic property of as-prepared NPAWFs have been characterized by field-emission scanning electron microscope, energy dispersive spectrometer, x-ray diffraction and electrochemical measurements. The final results show that a reduction in the anodizing voltage from 60 V for 60 min to 40 V for 60 min causes better growth of nanoporous structure, and the deoxidation at 700 °C for 3 h can remove oxygen in the nanoporous layer while retaining the nanoporous structure and activity. Compared with non-treated W foil, the NPAWF exhibits superior hydrogen evolution reaction (HER) activity with a low onset overpotential of 199 mV and Tafel slope of 84 mV dec-1 due to its nanoporous structure and large specific surface area. Additionally, the NPAWF shows outstanding long-term stability in acidic media, indicating it is a promising transition metal HER electrocatalyst and can also be used as a high active matrix material.

Hollow Micro/Nanostructured Ceria-Based Materials: Synthetic Strategies and Versatile Applications.

Hollow micro/nanostructured CeO2 -based materials (HMNCMs) have triggered intensive attention as a result of their unique structural traits, which arise from their hollowness and the fascinating physicochemical properties of CeO2 . This attention has led to widespread applications with improved performance. Herein, a comprehensive overview of methodologies applied for the synthesis of various hollow structures, such as hollow spheres, nanotubes, nanoboxes, and multishelled hollow spheres, is provided. The synthetic strategies toward CeO2 hollow structures are classified into three major categories: 1) well-established template-assisted (hard-, soft-, and in situ template) methods; 2) newly emerging self-template approaches, including selective etching, Ostwald ripening, the Kirkendall effect, galvanic replacement, etc.; 3) bottom-up self-organized formation synthesis (namely, oriented attachment and self-deformation). Their underlying mechanisms are concisely described and discussed in detail, the differences and similarities of which are compared transversely and longitudinally. Niche applications of HMNCMs in a wide range of fields including catalysis, energy conversion and storage, sensors, absorbents, photoluminescence, and biomedicines are reviewed. Finally, an outlook of future opportunities and challenges in the synthesis and application of CeO2 -based hollow structures is also presented.

Triple-Shelled Manganese-Cobalt Oxide Hollow Dodecahedra with Highly Enhanced Performance for Rechargeable Alkaline Batteries.

Precisely carving of multi-shelled manganese-cobalt oxide hollow dodecahedra (Co/Mn-HD) with shell number up to three is achieved by a controlled calcination of the Mn-doped zeolitic imidazolate framework ZIF-67 precursor (Co/Mn-ZIF). The unique multi-shelled and polycrystalline structure not only provides a very large electrochemically active surface area (EASA), but also enhances the structural stability of the material. The residual C and N in the final structures might aid stability and increase their conductivity. When used in alkaline rechargeable battery, the triple-shelled Co/Mn-HD exhibits high electrochemical performance, reversible capacity (331.94 mAh g-1 at 1 Ag-1 ), rate performance (88 % of the capacity can be retained with a 20-fold increase in current density), and cycling stability (96 % retention over 2000 cycles).