Isolation, characterization, and circulation sphere of a filovirus in fruit bats.

Bats are associated with the circulation of most mammalian filoviruses (FiVs), with pathogenic ones frequently causing deadly hemorrhagic fevers in Africa. Divergent FiVs have been uncovered in Chinese bats, raising concerns about their threat to public health. Here, we describe a long-term surveillance to track bat FiVs at orchards, eventually resulting in the identification and isolation of a FiV, Dehong virus (DEHV), from Rousettus leschenaultii bats. DEHV has a typical filovirus-like morphology with a wide spectrum of cell tropism. Its entry into cells depends on the engagement of Niemann-Pick C1, and its replication is inhibited by remdesivir. DEHV has the largest genome size of filoviruses, with phylogenetic analysis placing it between the genera Dianlovirus and Orthomarburgvirus, suggesting its classification as the prototype of a new genus within the family Filoviridae. The continuous detection of viral RNA in the serological survey, together with the wide host distribution, has revealed that the region covering southern Yunnan, China, and bordering areas is a natural circulation sphere for bat FiVs. These emphasize the need for a better understanding of the pathogenicity and potential risk of FiVs in the region.

Molecular mechanism of human ISG20L2 for the ITS1 cleavage in the processing of 18S precursor ribosomal RNA.

The exonuclease ISG20L2 has been initially characterized for its role in the mammalian 5.8S rRNA 3' end maturation, specifically in the cleavage of ITS2 of 12S precursor ribosomal RNA (pre-rRNA). Here, we show that human ISG20L2 is also involved in 18S pre-rRNA maturation through removing the ITS1 region, and contributes to ribosomal biogenesis and cell proliferation. Furthermore, we determined the crystal structure of the ISG20L2 nuclease domain at 2.9 Å resolution. It exhibits the typical αßα fold of the DEDD 3'-5' exonuclease with a catalytic pocket located in the hollow near the center. The catalytic residues Asp183, Glu185, Asp267, His322 and Asp327 constitute the DEDDh motif in ISG20L2. The active pocket represents conformational flexibility in the absence of an RNA substrate. Using structural superposition and mutagenesis assay, we mapped RNA substrate binding residues in ISG20L2. Finally, cellular assays revealed that ISG20L2 is aberrantly up-regulated in colon adenocarcinoma and promotes colon cancer cell proliferation through regulating ribosome biogenesis. Together, these results reveal that ISG20L2 is a new enzymatic member for 18S pre-rRNA maturation, provide insights into the mechanism of ISG20L2 underlying pre-rRNA processing, and suggest that ISG20L2 is a potential therapeutic target for colon adenocarcinoma.

HEAP: a task adaptive-based explainable deep learning framework for enhancer activity prediction.

Enhancers are crucial cis-regulatory elements that control gene expression in a cell-type-specific manner. Despite extensive genetic and computational studies, accurately predicting enhancer activity in different cell types remains a challenge, and the grammar of enhancers is still poorly understood. Here, we present HEAP (high-resolution enhancer activity prediction), an explainable deep learning framework for predicting enhancers and exploring enhancer grammar. The framework includes three modules that use grammar-based reasoning for enhancer prediction. The algorithm can incorporate DNA sequences and epigenetic modifications to obtain better accuracy. We use a novel two-step multi-task learning method, task adaptive parameter sharing (TAPS), to efficiently predict enhancers in different cell types. We first train a shared model with all cell-type datasets. Then we adapt to specific tasks by adding several task-specific subset layers. Experiments demonstrate that HEAP outperforms published methods and showcases the effectiveness of the TAPS, especially for those with limited training samples. Notably, the explainable framework HEAP utilizes post-hoc interpretation to provide insights into the prediction mechanisms from three perspectives: data, model architecture and algorithm, leading to a better understanding of model decisions and enhancer grammar. To the best of our knowledge, HEAP will be a valuable tool for insight into the complex mechanisms of enhancer activity.

Assessing parameter efficient methods for pre-trained language model in annotating scRNA-seq data.

Annotating cell types of single-cell RNA sequencing (scRNA-seq) data is crucial for studying cellular heterogeneity in the tumor microenvironment. Recently, large-scale pre-trained language models (PLMs) have achieved significant progress in cell-type annotation of scRNA-seq data. This approach effectively addresses previous methods' shortcomings in performance and generalization. However, fine-tuning PLMs for different downstream tasks demands considerable computational resources, rendering it impractical. Hence, a new research branch introduces parameter-efficient fine-tuning (PEFT). This involves optimizing a few parameters while leaving the majority unchanged, leading to substantial reductions in computational expenses. Here, we utilize scBERT, a large-scale pre-trained model, to explore the capabilities of three PEFT methods in scRNA-seq cell type annotation. Extensive benchmark studies across several datasets demonstrate the superior applicability of PEFT methods. Furthermore, downstream analysis using models obtained through PEFT showcases their utility in novel cell type discovery and model interpretability for potential marker genes. Our findings underscore the considerable potential of PEFT in PLM-based cell type annotation, presenting novel perspectives for the analysis of scRNA-seq data.

SUMOylation of Smad2 mediates TGF-ß-regulated endothelial-mesenchymal transition.

Endothelial-mesenchymal transition (EndoMT) is a complex biological process in which endothelial cells are transformed into mesenchymal cells, and dysregulated EndoMT causes a variety of pathological processes. Transforming growth factor beta (TGF-ß) signaling effectively induces the EndoMT process in endothelial cells, and Smad2 is the critical protein of the TGF-ß signaling pathway. However, whether small ubiquitin-like modifier modification (SUMOylation) is involved in EndoMT remains unclear. Here, we show that Smad2 is predominantly modified by SUMO1 at two major SUMOylation sites with PIAS2α as the primary E3 ligase, whereas SENP1 (sentrin/SUMO-specific protease 1) mediates the deSUMOylation of Smad2. In addition, we identified that SUMOylation significantly enhances the transcriptional activity and protein stability of Smad2, regulating the expression of downstream target genes. SUMOylation increases the phosphorylation of Smad2 and the formation of the Smad2-Smad4 complex, thus promoting the nuclear translocation of Smad2. Ultimately, the wildtype, but not SUMOylation site mutant Smad2 facilitated the EndoMT process. More importantly, TGF-ß enhances the nuclear translocation of Smad2 by enhancing its SUMOylation and promoting the EndoMT process. These results demonstrate that SUMOylation of Smad2 plays a critical role in the TGF-ß-mediated EndoMT process, providing a new theoretical basis for the treatment and potential drug targets of EndoMT-related clinical diseases.

Conservation genetics and potential geographic distribution modeling of Corybas taliensis, a small 'sky Island' orchid species in China.

BACKGROUND: Corybas taliensis is an endemic species of sky islands in China. Its habitat is fragile and unstable, and it is likely that the species is threatened. However, it is difficult to determine the conservation priority or unit without knowing the genetic background and the overall distribution of this species. In this study, we used double digest restriction-site associated DNA-sequencing (ddRAD-seq) to investigate the conservation genomics of C. taliensis. At the same time, we modeled the extent of suitable habitat for C. taliensis in present and future (2030 and 2090) habitat using the maximum-entropy (MaxEnt) model. RESULTS: The results suggested that the related C. fanjingshanensis belongs to C. taliensis and should not be considered a separate species. All the sampling locations were divided into three genetic groups: the Sichuan & Guizhou population (SG population), the Hengduan Mountains population (HD population) and Himalayan population (HM population), and we found that there was complex gene flow between the sampling locations of HD population. MT was distinct genetically from the other sampling locations due to the unique environment in Motuo. The genetic diversity (π, He) of C. taliensis was relatively high, but its contemporary effective population size (Ne) was small. C. taliensis might be currently affected by inbreeding depression, although its large population density may be able to reduce the effect of this. The predicted areas of suitable habitat currently found in higher mountains will not change significantly in the future, and these suitable habitats are predicted to spread to other higher mountains under future climate change. However, suitable habitat in relatively low altitude areas may disappear in the future. This suggests that C. taliensis will be caught in a 'summit trap' in low altitude areas, however, in contrast, the high altitude of the Himalaya and the Hengduan Mountains are predicted to act as 'biological refuges' for C. taliensis in the future. CONCLUSIONS: These results not only provide a new understanding of the genetic background and potential resource distribution of C. taliensis, but also lay the foundation for its conservation and management.

High-Areal Capacity, High-Rate Lithium Metal Anodes Enabled by Nitrogen-Doped Graphene Mesh.

Hosts hold great prospects for addressing the dendrite growth and volume expansion of the Li metal anode, but Li dendrites are still observable under the conditions of high deposition capacity and/or high current density. Herein, a nitrogen-doped graphene mesh (NGM) is developed, which possesses a conductive and lithiophilic scaffold for efficient Li deposition. The abundant nanopores in NGM can not only provide sufficient room for Li deposition, but also speed up Li ion transport to achieve a high-rate capability. Moreover, the evenly distributed N dopants on the NGM can guide the uniform nucleation of Li so that to inhibit dendrite growth. As a result, the composite NGM@Li anode shows satisfactory electrochemical performances for Li-S batteries, including a high capacity of 600 mAh g-1 after 300 cycles at 1 C and a rate capacity of 438 mAh g-1 at 3 C. This work provides a new avenue for the fabrication of graphene-based hosts with large areal capacity and high-rate capability for Li metal batteries.

A novel convolution attention model for predicting transcription factor binding sites by combination of sequence and shape.

The discovery of putative transcription factor binding sites (TFBSs) is important for understanding the underlying binding mechanism and cellular functions. Recently, many computational methods have been proposed to jointly account for DNA sequence and shape properties in TFBSs prediction. However, these methods fail to fully utilize the latent features derived from both sequence and shape profiles and have limitation in interpretability and knowledge discovery. To this end, we present a novel Deep Convolution Attention network combining Sequence and Shape, dubbed as D-SSCA, for precisely predicting putative TFBSs. Experiments conducted on 165 ENCODE ChIP-seq datasets reveal that D-SSCA significantly outperforms several state-of-the-art methods in predicting TFBSs, and justify the utility of channel attention module for feature refinements. Besides, the thorough analysis about the contribution of five shapes to TFBSs prediction demonstrates that shape features can improve the predictive power for transcription factors-DNA binding. Furthermore, D-SSCA can realize the cross-cell line prediction of TFBSs, indicating the occupancy of common interplay patterns concerning both sequence and shape across various cell lines. The source code of D-SSCA can be found at https://github.com/MoonLord0525/.

Multiple sequence alignment based on deep reinforcement learning with self-attention and positional encoding.

MOTIVATION: Multiple sequence alignment (MSA) is one of the hotspots of current research and is commonly used in sequence analysis scenarios. However, there is no lasting solution for MSA because it is a Nondeterministic Polynomially complete problem, and the existing methods still have room to improve the accuracy. RESULTS: We propose Deep reinforcement learning with Positional encoding and self-Attention for MSA, based on deep reinforcement learning, to enhance the accuracy of the alignment Specifically, inspired by the translation technique in natural language processing, we introduce self-attention and positional encoding to improve accuracy and reliability. Firstly, positional encoding encodes the position of the sequence to prevent the loss of nucleotide position information. Secondly, the self-attention model is used to extract the key features of the sequence. Then input the features into a multi-layer perceptron, which can calculate the insertion position of the gap according to the features. In addition, a novel reinforcement learning environment is designed to convert the classic progressive alignment into progressive column alignment, gradually generating each column's sub-alignment. Finally, merge the sub-alignment into the complete alignment. Extensive experiments based on several datasets validate our method's effectiveness for MSA, outperforming some state-of-the-art methods in terms of the Sum-of-pairs and Column scores. AVAILABILITY AND IMPLEMENTATION: The process is implemented in Python and available as open-source software from https://github.com/ZhangLab312/DPAMSA.

Service blockage on the downlink in large-scale satellite optical networks: a multi-downlink scheduling for routing selection.

Over years of space laser communication technology advances, satellite optical networks (SONs) have emerged as a pivotal component in 6 G networks. Satellite services are transmitted from the global view, undergoing transmission through SONs, and being downloaded to the targeted areas. However, the transmission capacity of satellites passing through the areas where users are concentrated may be insufficient to download services transmitted worldwide. This problem exists in various kinds of satellite networks and may cause a large amount of service congestion. In this paper, we propose a multi-downlink delivery routing selection (MDD-RS) strategy to study the total utilization of transmission capacity of SONs. We construct an integer linear programming (ILP) model to establish an optimal case study for minimal network capacity occupation. Also, we design an online option, MDD-RS heuristic algorithm, dynamically calculating path routes, considering bandwidth allocation and resource constraints. A comparative analysis against the conventional single-downlink scheme reveals superior performance of the MDD-RS heuristic algorithm, with a reduction in blocking probability of 0.129 and an improvement in bandwidth utilization of 0.032.

Local augmented graph neural network for multi-omics cancer prognosis prediction and analysis.

Cancer prognosis prediction and analysis can help patients understand expected life and help clinicians provide correct therapeutic guidance. Thanks to the development of sequencing technology, multi-omics data, and biological networks have been used for cancer prognosis prediction. Besides, graph neural networks can simultaneously consider multi-omics features and molecular interactions in biological networks, becoming mainstream in cancer prognosis prediction and analysis. However, the limited number of neighboring genes in biological networks restricts the accuracy of graph neural networks. To solve this problem, a local augmented graph convolutional network named LAGProg is proposed in this paper for cancer prognosis prediction and analysis. The process follows: first, given a patient's multi-omics data features and biological network, the corresponding augmented conditional variational autoencoder generates features. Then, the generated augmented features and the original features are fed into a cancer prognosis prediction model to complete the cancer prognosis prediction task. The conditional variational autoencoder consists of two parts: encoder-decoder. In the encoding phase, an encoder learns the conditional distribution of the multi-omics data. As a generative model, a decoder takes the conditional distribution and the original feature as inputs to generate the enhanced features. The cancer prognosis prediction model consists of a two-layer graph convolutional neural network and a Cox proportional risk network. The Cox proportional risk network consists of fully connected layers. Extensive experiments on 15 real-world datasets from TCGA demonstrated the effectiveness and efficiency of the proposed method in predicting cancer prognosis. LAGProg improved the C-index values by an average of 8.5% over the state-of-the-art graph neural network method. Moreover, we confirmed that the local augmentation technique could enhance the model's ability to represent multi-omics features, improve the model's robustness to missing multi-omics features, and prevent the model's over-smoothing during training. Finally, based on genes identified through differential expression analysis, we discovered 13 prognostic markers highly associated with breast cancer, among which ten genes have been proved by literature review.

Longitudinal analysis of quality of life in primary lung cancer patients with chlamydia pneumoniae infection: a time-to-deterioration model.

BACKGROUND: Chlamydia pneumoniae (Cpn) IgG and IgA has been strongly linked to lung cancer, but its impact on patients' quality of life remains unclear. Our objective was to investigate the relationship between pre-treatment Cpn IgG and IgA and time to deterioration (TTD) of the HRQoL in patients with primary lung cancer. METHODS: A prospective hospital-based study was conducted from June 2017 to December 2018, enrolling 82 patients with primary lung cancer admitted to the First Affiliated Hospital of Fujian Medical University for questionnaire surveys. Cpn IgG and IgA was detected by microimmunofluorescence method. HRQoL was assessed at baseline and during follow-up using the EORTC Quality of Life Questionnaire version 3.0 (EORTC QLQ-C30) and EORTC Quality of Life Questionnaire-Lung Cancer (EORTC QLQ-LC13). HRQoL scores were calculated using the QoLR package, and TTD events were determined (minimum clinically significant difference = 5 points). Cox regression analysis was used to evaluate the effect of Cpn IgG and IgA on HRQoL. RESULTS: We investigated the relationship between Cpn IgG and IgA and quality of life in patients with primary lung cancer. The study was found that 75.61% of cases were Cpn IgG + and 45.12% were Cpn IgA + . Cpn IgA + IgG + was 41.46%. For EORTC QLQ-C30, Physical function (PF) and Pain (PA) TTD events on the functional scale and Symptom scale were the most common during follow-up. After adjusting for gender and smoking status, Pre-treatment Cpn IgA + was found to signifcantly delay TTD of Physical functioning(HR = 0.539, 95% CI: 0.291-0.996, P = 0.048). In addition, Cpn IgG + before treatment significantly delayed TTD in Emotional functioning (HR = 0.310, 95% CI: 0.115-0.836, P = 0.021). For EORTC QLQ-LC13, deterioration of dyspnea (LC-DY) was the most common event. However, Cpn IgG and IgA before treatment had no effect on the TTD of EORTC QLQ-LC13 items. CONCLUSIONS: According to EORTC QLQ-C30 and EORTC QLQ-LC13, Cpn IgA delayed TTD in Physical functioning and Cpn IgG delayed TTD in Emotional functioning.

An Intelligent Cell-Derived Nanorobot Bridges Synergistic Crosstalk Between Sonodynamic Therapy and Cuproptosis to Promote Cancer Treatment.

Recent progress in cuproptosis sheds light on the development of treatment approaches for advancing sonodynamic therapy (SDT) due to its unique cell death mechanism. Herein, we elaborately developed an intelligent cell-derived nanorobot (SonoCu), composed of macrophage-membrane-camouflaged nanocarrier encapsulating copper-doped zeolitic imidazolate framework-8 (ZIF-8), perfluorocarbon, and sonosensitizer Ce6, for synergistically triggering cuproptosis-augmented SDT. SonoCu not only improved tumor accumulation and cancer-cell uptake through cell-membrane camouflaging but responded to ultrasound stimuli to enhance intratumor blood flow and oxygen supply, which consequently overcame treatment barriers and activated sonodynamic cuproptosis. Importantly, the SDT effectiveness could be further amplified by cuproptosis through multiple mechanisms, including reactive oxygen species accumulation, proteotoxic stress, and metabolic regulation, which synergistically sensitized cancer cell death. Particularly, SonoCu exhibited ultrasound-responsive cytotoxicity against cancer cells but not healthy cells, endowing it with good biosafety. Therefore, we present the first anticancer combination of SDT and cuproptosis, which may inspire studies pursuing a rational multimodal treatment strategy.

Genome-Wide Identification and Analysis of the EIN3/EIL Transcription Factor Gene Family in Doubled Haploid (DH) Poplar.

Ethylene (ET) is an important phytohormone that regulates plant growth, development and stress responses. The ethylene-insensitive3/ethylene-insensitive3-like (EIN3/EIL) transcription factor family, as a key regulator of the ET signal transduction pathway, plays an important role in regulating the expression of ET-responsive genes. Although studies of EIN3/EIL family members have been completed in many species, their role in doubled haploid (DH) poplar derived from another culture of diploid Populus simonii × P. nigra (donor tree, DT) remains ambiguous. In this study, a total of seven EIN3/EIL gene family members in the DH poplar genome were identified. Basic physical and chemical property analyses of these genes were performed, and these proteins were predicted to be localized to the nucleus. According to the phylogenetic relationship, EIN3/EIL genes were divided into two groups, and the genes in the same group had a similar gene structure and conserved motifs. The expression patterns of EIN3/EIL genes in the apical buds of different DH poplar plants were analyzed based on transcriptome data. At the same time, the expression patterns of PsnEIL1, PsnEIN3, PsnEIL4 and PsnEIL5 genes in different tissues of different DH plants were detected via RT-qPCR, including the apical buds, young leaves, functional leaves, xylem, cambium and roots. The findings presented above indicate notable variations in the expression levels of PsnEIL genes across various tissues of distinct DH plants. Finally, the PsnEIL1 gene was overexpressed in DT, and the transgenic plants showed a dwarf phenotype, indicating that the PsnEIL1 gene was involved in regulating the growth and development of poplar. In this study, the EIN3/EIL gene family of DH poplar was analyzed and functionally characterized, which provides a theoretical basis for the future exploration of the EIN3/EIL gene function.

Decoupling the effects of air temperature change on soil erosion in Northeast China.

Changes in the air temperature tend to indirectly affect soil erosion by influencing rainfall, vegetation growth, economic development, and agricultural activities. In this study, the partial least squares-structural equation model (PLS-SEM) was used to decouple the impacts of temperature change on soil erosion in Northeast China from 2001 to 2019, and the indirect effect of temperature change on the pathways of natural and socioeconomic factors was analyzed. The results showed that temperature increase in Northeast China caused an increase in soil erosion by increasing rainfall and promoting economic development. Under the pathway of natural factors, in spring, the promoting effect on soil erosion under the influence of temperature change on rainfall was greater than the inhibiting effect on soil erosion under by the influence of temperature change on vegetation. In summer, the opposite effect was observed. Under the pathway of natural factors, over time, the promoting effect of temperature increase on soil erosion increased by 22.7%. Under the pathway of socioeconomic factors, temperature change not only aggravated soil erosion by promoting economic development, but also indirectly increased investments in agriculture and water conservation by improving the economy, thus inhibiting soil erosion to a certain extent. Over time, the contribution of temperature change to soil erosion through socioeconomic pathway was reduced by 44.4%. When the pathway of natural factors is compared with that of socioeconomics factors, temperature change imposed a more notable effect on the change in soil erosion through the socioeconomic pathway, indicating that human activities are the driving factors with a greater effect on soil erosion. Based on this, reasonable human intervention is an important means to alleviate soil erosion aggravation caused by rising temperatures.

Response of plants and soils to inundation duration and construction of the plant‒soil association mode in the hydro‒fluctuation belt of the reservoir wetland.

Hydro-Fluctuation Belt (HFB), a periodically exposed bank area formed by changes in water level fluctuations, is critical for damaging the reservoir wetland landscape and ecological balance. Thus, it is important to explore the mechanism of hydrological conditions on the plant-soil system of the HFB for protection of the reservoir wetland and landscape restoration. Here, we investigated the response of plant community characteristics and soil environment of the HFB of Tonghui River National Wetland Park (China), is a typical reservoir wetland, to the duration of inundation, as well as the correlation between the distribution of dominant plants and soil pH, nutrient contents, and enzyme activity by linear regression and canonical correlation analyses. The results show that as the duration of inundation decreases, the vegetation within the HFB is successional from annual or biennial herbs to perennial herbs and shrubs, with dominant plant species prominent and uneven distribution of species. Soil nutrient contents and enzyme activities of HFB decreased with increasing inundation duration. Dominant species of HFB plant community are related to soil environment, with water content, pH, urease, and available potassium being principle soil environmental factors affecting their distribution. When HFB was inundated for 0-30 days, soil pH was strongly acidic, with available potassium content above 150 mg kg-1 and higher urease activity, distributed with Arundo donax L., Polygonum perfoliatum L., Alternanthera philoxeroides (Mart.) Griseb., and Daucus carota L. communities. When inundated for 30-80 days, soil pH was acidic, with lower available potassium content (50-150 mg kg-1) and urease activity, distributed with Beckmannia syzigachne (Steud.) Fern.+ Polygonum lapathifolium L., Polygonum lapathifolium L., Medicago lupulina L. + Dysphania ambrosioides L. and Leptochloa panicea (Retz.) Ohwi communities. Using the constructed HFB plant-soil correlation model, changes in the wetland soil environment can be quickly judged by the succession of plant dominant species, which provides a simpler method for the monitoring of the soil environment in the reservoir wetland, and is of great significance for the scientific management and reasonable protection of the reservoir-type wetland ecosystem.

AgIn5S8/g-C3N4 Composite Photocatalyst Coupled with Low-Temperature Plasma-Enhanced Degradation of Hydroxypropyl-Guar-Simulated Oilfield Wastewater.

The effective treatment and recovery of fracturing wastewater has always been one of the difficult problems to be solved in oilfield wastewater treatment. Accordingly, in this paper, photocatalytic-coupled low-temperature plasma technology was used to degrade the simulated wastewater containing hydroxypropyl guar, the main component of fracturing fluid. Results indicated that hydroxypropyl-guar wastewater could be degraded to a certain extent by either photocatalytic technology or plasma technology; the chemical oxygen demand and viscosity of the treated wastewater under two single-technique optimal conditions were 781 mg·L-1, 0.79 mPa·s-1 and 1296 mg·L-1, 1.01 mPa·s-1, respectively. Furthermore, the effective coupling of AgIn5S8/gC3N4 photocatalysis and dielectric-barrier discharge-low-temperature plasma not only enhanced the degradation degree of hydroxypropyl guar but also improved its degradation efficiency. Under the optimal conditions of coupling treatment, the hydroxypropyl-guar wastewater achieved the effect of a single treatment within 6 min, and the chemical oxygen demand and viscosity of the treated wastewater reduced to below 490 mg·L-1 and 0.65 mPa·s-1, respectively. In the process of coupled treatment, the AgIn5S8/gC3N4 could directly absorb the light and strong electric field generated by the system discharge and play an important role in the photocatalytic degradation, thus effectively improving the energy utilization rate of the discharge system and enhancing the degradation efficiency of hydroxypropyl guar.

The Enhanced Performance of NiCuOOH/NiCu(OH)2 Electrode Using Pre-Conversion Treatment for the Electrochemical Oxidation of Ammonia.

Electrochemical oxidation of ammonia is an attractive process for wastewater treatment, hydrogen production, and ammonia fuel cells. However, the sluggish kinetics of the anode reaction has limited its applications, leading to a high demand for novel electrocatalysts. Herein, the electrode with the in situ growth of NiCu(OH)2 was partially transformed into the NiCuOOH phase by a pre-treatment using highly oxidative solutions. As revealed by SEM, XPS, and electrochemical analysis, such a strategy maintained the 3D structure, while inducing more active sites before the in situ generation of oxyhydroxide sites during the electrochemical reaction. The optimized NiCuOOH-1 sample exhibited the current density of 6.06 mA cm-2 at 0.5 V, which is 1.67 times higher than that of NiCu(OH)2 (3.63 mA cm-2). Moreover, the sample with a higher crystalline degree of the NiCuOOH phase exhibited lower performance, demonstrating the importance of a moderate treatment condition. In addition, the NiCuOOH-1 sample presented low selectivity (<20%) towards NO2- and stable activity during the long-term operation. The findings of this study would provide valuable insights into the development of transition metal electrocatalysts for ammonia oxidation.

Mechanically Interlocked Polyrotaxane Networks with Collective Motions of Multiple Main-Chain Mechanical Bonds.

Type I main-chain polyrotaxanes (PRs) with multiple wheels threaded onto the axle are widely employed to design slide-ring materials. However, Type II main-chain PRs with axles threading into the macrocycles on the polymer backbones have rarely been studied, although they feature special topological structures and dynamic characteristics. Herein, we report the design and preparation of Type II main-chain PR-based mechanically interlocked networks (PRMINs), based on which the relationship between microscopic motion of mechanical bonds on the PRs and macroscopic mechanical performance of materials has been revealed. The representative PRMIN-2 exhibits a robust feature in tensile tests with high stretchability (1680%) and toughness (47.5 MJ/m3). Moreover, it also has good puncture performance with puncture energy of 22.0 mJ. Detailed rheological measurements and coarse-grained molecular dynamics (CGMD) simulation reveal that the embedded multiple [2]rotaxane mechanical bonds on the PR backbones of PRMINs could undergo a synergistic long-range sliding motion under external force, with the introduction of collective dangling chains into the network. As a result, the synchronized motions of coherent PR chains can be readily activated to accommodate network deformation and efficiently dissipate energy, thereby leading to enhanced mechanical performances of PRMINs.

Enhancing Reversibility and Stability of Mg Metal Anodes: High-Exposure (002) Facets and Nanosheet Arrays for Superior Mg Plating/Stripping.

Magnesium metal batteries (MMBs), recognized as promising contenders for post-lithium battery technologies, face challenges such as uneven magnesium (Mg) plating and stripping behaviors, leading to uncontrollable dendrite growth and irreversible structural damage. Herein, we have developed a Mg foil featuring prominently exposed (002) facets and an architecture of nanosheet arrays (termed (002)-Mg), created through a one-step acid etching method. Specifically, the prominent exposure of Mg (002) facets, known for their inherently low surface and adsorption energies with Mg atoms, not only facilitates smooth nucleation and dense deposition but also significantly mitigates side reactions on the Mg anode. Moreover, the nanosheet arrays on the surface evenly distribute the electric field and Mg ion flux, enhancing Mg ion transfer kinetics. As a result, the fabricated (002)-Mg electrodes exhibit unprecedented long-cycle performance, lasting over 6000 h (> 8 months) at a current density of 3 mA cm-2 for a capacity of 3 mAh cm-2. Furthermore, the corresponding pouch cells equipped with various electrolytes and cathodes demonstrate remarkable capacity and cycling stability, highlighting the superior electrochemical compatibility of the (002)-Mg electrode. This study provides new insights into the advancement of durable MMBs by modifying the crystal structure and morphology of Mg.