Antioxidant and anti-Alzheimer's disease activities of 1,8-cineole and its cyclodextrin inclusion complex.

1,8-Cineole is a bicyclic monoterpene widely distributed in the essential oils of various medicinal plants, and it exhibits significant anti-inflammatory and antioxidant activities. We aimed to investigate the therapeutic effect of 1,8-cineole on anti-Alzheimer's disease by using transgenic Caenorhabditis elegans models. Our studies demonstrated that 1,8-cineole significantly relieved Aß1-42-induced paralysis and exhibited remarkable antioxidant and anti-Aß1-42 aggregation activities in transgenic nematodes CL4176, CL2006 and CL2355. We developed a 1,8-cineole/cyclodextrin inclusion complex, displaying enhanced anti-paralysis, anti-Aß aggregation and antioxidant activities compared to 1,8-cineole. In addition, we found 1,8-cineole treatment activated the SKN-1/Nrf-2 pathway and induced autophagy in nematodes. Our results demonstrated the antioxidant and anti-Alzheimer's disease activities of 1,8-cineole, which provide a potential therapeutic approach for Alzheimer's disease.

Nano-Enhanced Phase Reinforced Magnesium Matrix Composites: A Review of the Matrix, Reinforcement, Interface Design, Properties and Potential Applications.

Magnesium matrix composites are essential lightweight metal matrix composites, following aluminum matrix composites, with outstanding application prospects in automotive, aerospace lightweight and biomedical materials because of their high specific strength, low density and specific stiffness, good casting performance and rich resources. However, the inherent low plasticity and poor fatigue resistance of magnesium hamper its further application to a certain extent. Many researchers have tried many strengthening methods to improve the properties of magnesium alloys, while the relationship between wear resistance and plasticity still needs to be further improved. The nanoparticles added exhibit a good strengthening effect, especially the ceramic nanoparticles. Nanoparticle-reinforced magnesium matrix composites not only exhibit a high impact toughness, but also maintain the high strength and wear resistance of ceramic materials, effectively balancing the restriction between the strength and toughness. Therefore, this work aims to provide a review of the state of the art of research on the matrix, reinforcement, design, properties and potential applications of nano-reinforced phase-reinforced magnesium matrix composites (especially ceramic nanoparticle-reinforced ones). The conventional and potential matrices for the fabrication of magnesium matrix composites are introduced. The classification and influence of ceramic reinforcements are assessed, and the factors influencing interface bonding strength between reinforcements and matrix, regulation and design, performance and application are analyzed. Finally, the scope of future research in this field is discussed.

A Low-Frequency Fiber Bragg Grating Acceleration Sensor Based on Spring Support and Symmetric Compensation Structure with Flexible Hinges.

To measure vibration signals, a low-frequency fiber Bragg grating (FBG) acceleration sensor featuring a flexible hinge with a spring support and symmetric compensation structure has been designed. Based on the mechanical model of the sensor's structure, the expressions for sensitivity and resonant frequency of the sensor are derived. The structural parameters of the sensor are optimized, and a simulation analysis is conducted using ANSYS 19.2 software. According to the results of simulation analysis and size optimization, the sensor prototype is constructed. Subsequently, its amplitude-frequency response, sensitivity, and temperature characteristics are investigated through vibration experiments. The experimental results show that the resonant frequency of the sensor is 73 Hz, the operating frequency range is 0~60 Hz, and the sensitivity measures 24.24 pm/g. This design meets the requirements for measuring vibration signals at low frequencies.

Chirality-Driven Orbital Angular Momentum and Circular Dichroism in CoSi.

Chiral crystals and molecules were recently predicted to form an intriguing platform for unconventional orbital physics. Here, we report the observation of chirality-driven orbital textures in the bulk electronic structure of CoSi, a prototype member of the cubic B20 family of chiral crystals. Using circular dichroism in soft x-ray angle-resolved photoemission, we demonstrate the formation of a bulk orbital-angular-momentum texture and monopolelike orbital-momentum locking that depends on crystal handedness. We introduce the intrinsic chiral circular dichroism, icCD, as a differential photoemission observable and a natural probe of chiral electron states. Our findings render chiral crystals promising for spin-orbitronics applications.

Discovery of Potential Drug Targeting Key Genes in Alzheimer's Disease: Insights from Transcriptome Analysis and Molecular Docking.

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder that presents a significant global health challenge. To explore drugs targeting key genes in AD, R software was used to analyze the data of single nuclei transcriptome from human cerebral frontal cortex in AD, and the differentially expressed genes (DEGs) were screened. Then the gene ontology (GO) analysis, Kyoto gene and genome encyclopedia (KEGG) pathway enrichment and protein-protein interaction (PPI) network were analyzed. The hub genes were calculated by Cytoscape software. Molecular docking and molecular dynamics simulation were used to evaluate and visualize the binding between candidate drugs and key genes. A total of 564 DEGs were screened, and the hub genes were ISG15, STAT1, MX1, IFIT3, IFIT2, RSAD2, IFIT1, IFI44, IFI44L and DDX58. Enrichment terms mainly included response to virus, IFN-γ signaling pathway and virus infection. Diclofenac had good binding effect with IFI44 and IFI44L. Potential drugs may act on key gene targets and then regulate biological pathways such as virus response and IFN-γ-mediated signal pathway, so as to achieve anti-virus, improve immune balance and reduce inflammatory response, and thus play a role in anti-AD.

Improving mental arithmetic ability of primary school students with schema teaching method: An experimental study.

Skillful utilization of mental arithmetic can significantly improve students' mathematical computation ability. However, it was observed that primary school students often resort to reiterating the process of written arithmetic in their minds during mental arithmetic, which is not conducive to their numerical ability improvement. This paper devises a set of graphic teaching aids for primary school students' mental arithmetic improvement based on mental arithmetic strategies, schema theory, and working memory. To validate the effectiveness of schema teaching in enhancing mental arithmetic ability among primary school students, a controlled experiment was conducted with two groups of third-grade students randomly selected from a primary school in Jingshan City. The results, obtained through descriptive statistical analysis and the multitrait-multimethod approach (MTMM), indicated that the experimental group (n = 52) demonstrated significant improvements in speed, accuracy, and stability in mental addition and subtraction after a 14-day instruction period in schema teaching. This study offers a potent mental arithmetic teaching strategy for elementary mathematics education, which can lead to a comprehensive enhancement of students' mental calculation abilities. It also holds promise for inspiring innovative teaching methodologies in primary and secondary mathematics education in the future.

Nitrate-dependent anaerobic methane oxidation coupled to Fe(III) reduction as a source of ammonium and nitrous oxide.

'Candidatus Methanoperedens nitroreducens' is an archaeal methanotroph with global importance that links carbon and nitrogen cycles and great potential for sustainable operation of wastewater treatment. It has been reported to mediate the anaerobic oxidation of methane through a reverse methanogenesis pathway while reducing nitrate to nitrite. Here, we demonstrate that 'Ca. M. nitroreducens' reduces ferric iron forming ammonium (23.1 %) and nitrous oxide (N2O, 46.5 %) from nitrate. These results are supported with the upregulation of genes coding for proteins responsible for dissimilatory nitrate reduction to ammonium (nrfA), N2O formation (norV, cyt P460), and multiple multiheme c-type cytochromes for ferric iron reduction. Concomitantly, an increase in the N2O-reducing SJA-28 lineage and a decrease in the nitrite-reducing 'Candidatus Methylomirabilis oxyfera' are consistent with the changes in 'Ca. M. nitroreducens' end products. These findings demonstrate the highly flexible physiology of 'Ca. M. nitroreducens' in anaerobic ecosystems with diverse electron acceptor conditions, and further reveals its roles in linking methane oxidation to global biogeochemical cycles. 'Ca. M. nitroreducens' could significantly affect the bioavailability of nitrogen sources as well as the emission of greenhouse gas in natural ecosystems and wastewater treatment plants.

Different oxygen affinities of methanotrophs and Comammox Nitrospira inform an electrically induced symbiosis for nitrogen loss.

Aerobic methanotrophs establish a symbiotic association with denitrifiers to facilitate the process of aerobic methane oxidation coupled with denitrification (AME-D). However, the symbiosis has been frequently observed in hypoxic conditions continuing to pose an enigma. The present study has firstly characterized an electrically induced symbiosis primarily governed by Methylosarcina and Hyphomicrobium for the AME-D process in a hypoxic niche caused by Comammox Nitrospira. The kinetic analysis revealed that Comammox Nitrospira exhibited a higher apparent oxygen affinity compared to Methylosarcina. While the coexistence of comammox and AME-D resulted in an increase in methane oxidation and nitrogen loss rates, from 0.82 ± 0.10 to 1.72 ± 0.09 mmol CH4 d-1 and from 0.59 ± 0.04 to 1.30 ± 0.15 mmol N2 d-1, respectively. Furthermore, the constructed microbial fuel cells demonstrated a pronounced dependence of the biocurrents on AME-D due to oxygen competition, suggesting the involvement of direct interspecies electron transfer in the AME-D process under hypoxic conditions. Metagenomic and metatranscriptomic analysis revealed that Methylosarcina efficiently oxidized methane to formaldehyde, subsequently generating abundant NAD(P)H for nitrate reduction by Hyphomicrobium through the dissimilatory RuMP pathway, leading to CO2 production. This study challenges the conventional understanding of survival mechanism employed by AME-D symbionts, thereby contributing to the characterization responsible for limiting methane emissions and promoting nitrogen removal in hypoxic regions.

Design, Synthesis, and Biological Evaluation of Novel Cinnamide Derivatives as Neuroprotective Agents for the Treatment of Cerebral Ischemia.

BACKGROUND: Ischemic stroke, the most common type of cerebrovascular accident, is a major cause of severe disability among adults worldwide. Although there has been progress in interventions for ischemic stroke in the past decades, there is no effective treatment to prevent brain damage in acute ischemic stroke. Therefore, it is urgent to develop novel neuroprotective agents with a wide therapeutic time window to provide a better prognosis for ischemic stroke patients. OBJECTIVE: The current study aimed to synthesize novel derivatives with substituent cinnamide scaffolds, evaluate biological activity, and obtain neuroprotective agents. METHODS: The target compounds were synthesized using classical methods of medicinal chemistry. The neuroprotective effects in vitro against Glu-induced neurotoxicity injury were evaluated in PC12 cells by MTT assay. The cell apoptosis was analyzed by flow cytometer. The proteins were detected by western blotting. The neuroprotective activities in vivo were determined in two in vivo models of global and focal cerebral ischemia. RESULTS: Among the title compounds, 9t, 9u, 9y, and 9z exhibited good neuroprotection in vivo and in vitro, which were selected and further studied to determine their mechanism of action. 9t, 9u, 9y and 9z protected PC12 cells against glutamate-induced apoptosis in a dose-dependent manner via caspase-3 pathway. Moreover, the four compounds significantly reduced brain infarct area and exhibited excellent neuroprotective activities in the in vivo MCAO model. CONCLUSION: Compounds 9t, 9u, 9y, and 9z, as potent neuroprotective agents with anti- neurotoxicity activity in vitro and anticerebral infarction efficacy in vivo, might serve as a useful molecular tool for further physiology and pathophysiology function studies, leading to potential clinical therapeutic agents for ischemic injury.

Structural characteristics of amygdala subregions in type 2 diabetes mellitus.

Type 2 diabetes mellitus (T2DM) patients often suffer from depressive symptoms, which seriously affect cooperation in treatment and nursing. The amygdala plays a significant role in depression. This study aims to explore the microstructural alterations of the amygdala in T2DM and to investigate the relationship between the alterations and depressive symptoms. Fifty T2DM and 50 healthy controls were included. Firstly, the volumes of subcortical regions and subregions of amygdala were calculated by FreeSurfer. Covariance analysis (ANCOVA) was conducted between the two groups with covariates of age, sex, and estimated total intracranial volume to explore the differences in volume of subcortical regions and subregions of amygdala. Furthermore, the structural covariance within the amygdala subregions was performed. Moreover, we investigate the correlation between depressive symptoms and the volume of subcortical regions and amygdala subregions in T2DM. We observed a reduction in the volume of the bilateral cortico-amygdaloid transition area, left basal nucleus, bilateral accessory basal nucleus, left anterior amygdaloid area of amygdala, the left thalamus and left hippocampus in T2DM. T2DM patients showed decreased structural covariance connectivity between left paralaminar nucleus and the right central nucleus. Moreover, there was a negative correlation between self-rating depression scale scores and the volume of the bilateral cortico-amygdaloid transition area in T2DM. This study reveals extensive structural alterations in the amygdala subregions of T2DM patients. The reduction in the volume of the bilateral cortico-amygdaloid transition area may be a promising imaging marker for early recognition of depressive symptoms in T2DM.

Substrate-Independent Superhydrophobic Coating Capable of Photothermal-Induced Repairability for Multiple Damages Fabricated via Simple Blade Coating.

Repairable superhydrophobic surfaces have promising application potential in many fields. However, so far, it is still a challenge to develop a superhydrophobic surface with repairability for multiple types of damage through a simple method. In this paper, a repairable superhydrophobic coating was obtained on various substrates by blade-coating mixtures of polydimethylsiloxane (PDMS), polyvinylidene fluoride (PVDF), and multiwalled carbon nanotubes (MWCNTs) modified with dopamine (PDA) and octadecylamine (ODA). The obtained coating has a good liquid-repellent property with a water contact angle above 150° and a water sliding angle of ∼6° and possesses an excellent absorbance (∼97%) in the wavelength range of 250-2500 nm. Due to its high absorbance, the coating displays an outstanding photothermal effect with a temperature rise of ∼65 °C under irradiation by 1.0 kW/m2 of simulated sunlight. Furthermore, after being degraded by multiple stimuli, including plasma treatment, acid/alkali/oil immersion, sand impact, and the icing-thawing cycle, the coating can recover superhydrophobicity via sunlight irradiation, demonstrating the good photothermal-induced repairability of the coating. It can be expected that the good water-repellent property, photothermal effect, and repairability give this coating a promising prospect in practical applications.

Application of intelligent tongue image analysis in Conjunction with microbiomes in the diagnosis of MAFLD.

Background: Metabolic associated fatty liver disease (MAFLD) is a widespread liver disease that can lead to liver fibrosis and cirrhosis. Therefore, it is essential to develop early diagnosic and screening methods. Methods: We performed a cross-sectional observational study. In this study, based on data from 92 patients with MAFLD and 74 healthy individuals, we observed the characteristics of tongue images, tongue coating and intestinal flora. A generative adversarial network was used to extract tongue image features, and 16S rRNA sequencing was performed using the tongue coating and intestinal flora. We then applied tongue image analysis technology combined with microbiome technology to obtain an MAFLD early screening model with higher accuracy. In addition, we compared different modelling methods, including Extreme Gradient Boosting (XGBoost), random forest, neural networks(MLP), stochastic gradient descent(SGD), and support vector machine(SVM). Results: The results show that tongue-coating Streptococcus and Rothia, intestinal Blautia, and Streptococcus are potential biomarkers for MAFLD. The diagnostic model jointly incorporating tongue image features, basic information (gender, age, BMI), and tongue coating marker flora (Streptococcus, Rothia), can have an accuracy of 96.39%, higher than the accuracy value except for bacteria. Conclusion: Combining computer-intelligent tongue diagnosis with microbiome technology enhances MAFLD diagnostic accuracy and provides a convenient early screening reference.

Comparison of psoas major activation during standing hip flexion between chronic low back pain and healthy populations.

BACKGROUND: The psoas major (PM) has been identified as a potential contributor to chronic low back pain (LBP). However, few studies have investigated the effects of upright functional movement on PM activation in cLBP individuals. OBJECTIVE: This cross-sectional study aims to compare PM muscle activation characteristics in chronic LBP (cLBP) and healthy subjects during the transition from quiet double-leg standing to standing hip flexion. METHODS: Ultrasound Imaging was used to assess PM thickness at the lumbar vertebral level of L4-5 in 12 healthy and 12 cLBP participants. The changes in thickness between the test positions were utilized as a proxy for PM activation. RESULTS: The cLBP group exhibited greater thickness changes on the non-dominant side PM during contralateral hip flexion but not ipsilateral hip flexion (p= 0.369) compared to their healthy counterparts (p= 0.011; cLBP: resting 27.85 mm, activated 34.63 mm; healthy: resting 29.51 mm, activated 29.00 mm). There were no significant differences in dominant side PM thickness changes between the two groups during either contralateral or ipsilateral hip flexion (p= 0.306 and p= 0.077). CONCLUSION: Our findings suggest a potential overactivation of the PM in the cLBP population. This insight may aid in the development of tailored rehabilitation programs.

Temporomandibular disorder symptoms and their association with quality of life of dental patients in Malaysia.

OBJECTIVE: To assess the prevalence of temporomandibular disorders (TMD) and their effects on quality of life (QoL) of dental patients. METHODS: A survey consisting of two validated questionnaires was distributed to dental patients registered at the University of Malaya Faculty of Dentistry. The Fonseca Anamnestic Index (FAI) evaluates the prevalence and severity of TMD, while the Oral Health Impact Profile - Temporomandibular Disorder (OHIP-TMD) appraises the effects of TMD on oral health-related QoL. RESULTS: Out of 342 patients (aged 16 to 50 years, 45% male and 55% female) enrolled in the survey, 50.9% had varying degrees of TMD. All 7 domains of OHIP-TMD showed a statistically significant correlation with TMD severity. CONCLUSION: TMD seems to be prevalent among Malaysian dental patients. Not only does TMD affect the QoL of an individual, but the more severe the degree of reported symptoms, the poorer their perceived oral health QoL.

Metabolic Heterogeneity and Potential Immunotherapeutic Responses Revealed by Single-Cell Transcriptomics of Breast Cancer.

BACKGROUND: Breast cancer (BC) exhibits remarkable heterogeneity. However, the transcriptomic heterogeneity of BC at the single-cell level has not been fully elucidated. METHODS: We acquired BC samples from 14 patients. Single-cell RNA sequencing (scRNA-seq), bioinformatic analyses, along with immunohistochemistry (IHC) and immunofluorescence (IF) assays were carried out. RESULTS: According to the scRNA-seq results, 10 different cell types were identified. We found that Cancer-Associated Fibroblasts (CAFs) exhibited distinct biological functions and may promote resistance to therapy. Metabolic analysis of tumor cells revealed heterogeneity in glycolysis, gluconeogenesis, and fatty acid synthetase reprogramming, which led to chemotherapy resistance. Furthermore, patients with multiple metastases and progression were predicted to benefit from immunotherapy based on a heterogeneity analysis of T cells and tumor cells. CONCLUSIONS: Our findings provide a comprehensive understanding of the heterogeneity of BC, provide comprehensive insight into the correlation between cancer metabolism and chemotherapy resistance, and enable the prediction of immunotherapy responses based on T-cell heterogeneity.

Two-Level Spatio-Temporal Feature Fused Two-Stream Network for Micro-Expression Recognition.

Micro-expressions, which are spontaneous and difficult to suppress, reveal a person's true emotions. They are characterized by short duration and low intensity, making the task of micro-expression recognition challenging in the field of emotion computing. In recent years, deep learning-based feature extraction and fusion techniques have been widely used for micro-expression recognition, particularly methods based on Vision Transformer that have gained popularity. However, the Vision Transformer-based architecture used in micro-expression recognition involves a significant amount of invalid computation. Additionally, in the traditional two-stream architecture, although separate streams are combined through late fusion, only the output features from the deepest level of the network are utilized for classification, thus limiting the network's ability to capture subtle details due to the lack of fine-grained information. To address these issues, we propose a new two-level spatio-temporal feature fused with a two-stream architecture. This architecture includes a spatial encoder (modified ResNet) for learning texture features of the face, a temporal encoder (Swin Transformer) for learning facial muscle motor features, a feature fusion algorithm for integrating multi-level spatio-temporal features, a classification head, and a weighted average operator for temporal aggregation. The two-stream architecture has the advantage of extracting richer features compared to the single-stream architecture, leading to improved performance. The shifted window scheme of Swin Transformer restricts self-attention computation to non-overlapping local windows and allows cross-window connections, significantly improving the performance and reducing the computation compared to Vision Transformer. Moreover, the modified ResNet is computationally less intensive. Our proposed feature fusion algorithm leverages the similarity in output feature shapes at each stage of the two streams, enabling the effective fusion of multi-level spatio-temporal features. This algorithm results in an improvement of approximately 4% in both the F1 score and the UAR. Comprehensive evaluations conducted on three widely used spontaneous micro-expression datasets (SMIC-HS, CASME II, and SAMM) consistently demonstrate the superiority of our approach over comparative methods. Notably, our approach achieves a UAR exceeding 0.905 on CASME II, making it one of the few frameworks in the published micro-expression recognition literature to achieve such high performance.

NIR Light-Fuse Drug-Free Photothermal Armor-Piercing Microcapsule for Femoral Vein Thrombosis Therapy.

Acute thrombosis and its complications are leading global causes of disability and death. Existing thrombolytic drugs, such as alteplase and urokinase (UK), carry a significant bleeding risk during clinical treatments. Thus, the development of a novel thrombolysis strategy is of utmost urgency. Based on the previous work, the hollow structure of microcapsules (MC) is fabricated. Subsequently, armor-piercing MC, known as Fucoidan/S-Nitrosoglutathione/Melanin@MC (FGM@MC) is obtained, using a layer-by-layer (LBL) self-assembly method. Utilizing near-infrared (NIR) light as a trigger, the FGM@MC demonstrated photothermal thrombolysis at the site of thrombus due to its stable and outstanding photothermal properties. Simultaneously, photothermal stimulation leads to the release of a significant amount of nitric oxide from the FGM@MC, resulting in cavitation effects for mechanical thrombolysis. In vivo experiments confirmed the stable release of nitric oxide under NIR light irradiation. Treatment of femoral vein thrombosis in rats revealed that the thrombolytic effectiveness of FGM@MC+NIR (53.71%) is comparable to that of UK (59.70%). Notably, FGM@MC does not interfere with the coagulation function of rats and exhibits a favorable safety profile. In conclusion, this study demonstrates that the drug-free armor-piercing microcapsule has significant potential in the treatment of thrombosis, offering a safe and effective alternative to traditional thrombolytic therapies.

Image Understands Point Cloud: Weakly Supervised 3D Semantic Segmentation via Association Learning.

Weakly supervised point cloud semantic segmentation methods that require 1% or fewer labels with the aim of realizing almost the same performance as fully supervised approaches have recently attracted extensive research attention. A typical solution in this framework is to use self-training or pseudo-labeling to mine the supervision from the point cloud itself while ignoring the critical information from images. In fact, cameras widely exist in LiDAR scenarios, and this complementary information seems to be highly important for 3D applications. In this paper, we propose a novel cross-modality weakly supervised method for 3D segmentation that incorporates complementary information from unlabeled images. We design a dual-branch network equipped with an active labeling strategy to maximize the power of tiny parts of labels and to directly realize 2D-to-3D knowledge transfer. Afterward, we establish a cross-modal self-training framework, which iterates between parameter updating and pseudolabel estimation. In the training phase, we propose cross-modal association learning to mine complementary supervision from images by reinforcing the cycle consistency between 3D points and 2D superpixels. In the pseudolabel estimation phase, a pseudolabel self-rectification mechanism is derived to filter noisy labels, thus providing more accurate labels for the networks to be fully trained. The extensive experimental results demonstrate that our method even outperforms the state-of-the-art fully supervised competitors with less than 1% actively selected annotations.

CSFwinformer: Cross-Space-Frequency Window Transformer for Mirror Detection.

Mirror detection is a challenging task since mirrors do not possess a consistent visual appearance. Even the Segment Anything Model (SAM), which boasts superior zero-shot performance, cannot accurately detect the position of mirrors. Existing methods determine the position of the mirror under hypothetical conditions, such as the correspondence between objects inside and outside the mirror, and the semantic association between the mirror and surrounding objects. However, these assumptions do not apply to all scenarios. For instance, there may be no corresponding real objects to the reflected objects in the scene, or it may be challenging to extract meaningful semantic associations in complex scenes. On the other hand, humans can easily recognize mirrors through the specular texture caused by materials. To mine mirror features in more general scenes, we propose a Cross-Space-Frequency Window Transformer (CSFwinformer) to extract spatial and frequency features for texture analysis. Specifically, we design a Spatial-Frequency Window Alignment module (SFWA) to calculate spatial-frequency feature affinities and learn the difference between mirror and non-mirror textures. We then propose a Dilated Window Attention (DWA) to extract global features to complement the limitation of window alignment. Besides, we propose a Cross-Modality Context Contrast module (CMCC) to fuse cross-modality features and global features, which enables information flow between different windows to take full advantage of cross-modality information. Extensive experiments show that our method performs favorably against state-of-the-art methods on three mirror detection benchmarks and significantly improved SAM performance on mirror detection. The code is available at https://github.com/wangsen99/CSFwinformer.

Self-Limited Formation of Cobalt Nanoparticles for Spontaneous Hydrogen Production through Hydrazine Electrooxidation.

Hydrogen (H2 ) has emerged as a highly promising energy carrier owing to its remarkable energy density and carbon emission-free properties. However, the widespread application of H2 fuel has been limited by the difficulty of storage. In this work, spontaneous electrochemical hydrogen production is demonstrated using hydrazine (N2 H4 ) as a liquid hydrogen storage medium and enabled by a highly active Co catalyst for hydrazine electrooxidation reaction (HzOR). The HzOR electrocatalyst is developed by a self-limited growth of Co nanoparticles from a Co-based zeolitic imidazolate framework (ZIF), exhibiting abundant defective surface atoms as active sites for HzOR. Notably, these self-limited Co nanoparticles exhibit remarkable HzOR activity with a negative working potential of -0.1 V (at 10 mA cm-2 ) in 0.1 m N2 H4 /1 m KOH electrolyte. Density functional theory (DFT) calculations are employed to validate the superior performance of low-coordinated Co active sites in facilitating HzOR. By taking advantage of the potential difference between HzOR and the hydrogen evolution reaction (HER), a novel HzOR||HER electrochemical system is developed to spontaneously produce H2 without external energy input. Overall, the work offers valuable guidance for developing active HzOR catalyst. The novel HzOR||HER electrochemical system represents a promising and innovative solution for energy-efficient hydrogen production.