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It is generally known that the incorporation of crystals in the glass matrix can enhance the ductility of metallic glasses (MGs), at the expense of reduced strength, and that the deformation of MGs, particularly during shear banding, can induce crystal formation/growth. Here, we show that these known trends for the interplay between crystals and deformation of MGs may hold true or become inverted depending on the size of the crystals relative to the shear bands. We performed molecular dynamics simulations of tensile tests on nanocrystal-bearing MGs. When the crystals are relatively small, they bolster the strength rather than the ductility of MGs, and the crystals within a shear band undergo redissolution as the shear band propagates. In contrast, larger crystals tend to enhance ductility at the cost of strength, and the crystal volume fraction increases during deformation. These insights offer a more comprehensive understanding of the intricate relationship between deformation and crystals/crystallization in MGs, useful for fine-tuning the structure and mechanical properties of both MGs and MG–crystal composites.

The dual-cations electrochemical system was considered to be a promising strategy to facilitate sluggish diffusion kinetics. Here the authors prepare zinc-based alloy anode with three-dimensional interface, thus to improve the interfacial stability, achieve high-performing battery system in the aqueous electrolytes containing dual cations.

Abstract Ferroptosis, a newly discovered irondependent form of regulated cell death caused by excessive accumulation of lipid peroxides, is linked to the development and treatment response of various types of cancer, including gastric cancer (GC). Noncoding RNAs (ncRNAs), as key regulators in cancer, have both oncogenic and tumor suppressive roles. However, studies on ferroptosis-related ncRNA networks in GC are still lacking. Here, we first identified 61 differentially expressed genes associated with ferroptosis in GC by computing and analyzing gene expression profile of tumor and normal tissues for GC. Then, upstream lncRNAs and miRNAs interacting with them were found through miRNet and miRBase databases, and hub lncRNAs and miRNAs were obtained through topological analysis. Finally, the ceRNA regulatory network linked to ferroptosis in GC was established, which includes two ferroptosis marker genes (TXNIP and TSC22D3), one driver gene (GABARAPL1), and one suppressor gene (CAV1). Kaplan-Meier survival analysis showed that changes in the expression of these genes were associated with the survival of GC patients. Furthermore, our study revealed that this ceRNA network may influence the progression of GC by regulating ferroptosis process. These results will help experimental researchers to design an experiment study to further explore the roles of this regulatory network in GC ferroptosis.

Abstract Lithium–sulfur (Li–S) batteries are promising candidates for next-generation energy storage systems owing to their high energy density and low cost. However, critical challenges including severe shuttling of lithium polysulfides (LiPSs) and sluggish redox kinetics limit the practical application of Li–S batteries. Carbon nitrides (C x N y ), represented by graphitic carbon nitride (g-C3N4), provide new opportunities for overcoming these challenges. With a graphene-like structure and high pyridinic-N content, g-C3N4 can effectively immobilize LiPSs and enhance the redox kinetics of S species. In addition, its structure and properties including electronic conductivity and catalytic activity can be regulated by simple methods that facilitate its application in Li–S batteries. Here, the recent progress of applying CxNy-based materials including the optimized g-C3N4, g-C3N4-based composites, and other novel C x N y materials is systematically reviewed in Li–S batteries, with a focus on the structure–activity relationship. The limitations of existing C x N y -based materials are identified, and the perspectives on the rational design of advanced C x N y -based materials are provided for high-performance Li–S batteries.

Efficient oxygen evolution relies on the development of promising catalysts. Herein, the authors demonstrate that cobalt tetrahedra, stabilized over the surface of YBCo4O7 material, can catalyze oxygen evolution reaction efficiently.

Abstract Background Alkaline earth metal ions are important protein binding ligands in human body, and it is of great significance to predict their binding residues. Results In this paper, Mg2+ and Ca2+ ligands are taken as the research objects. Based on the characteristic parameters of protein sequences, amino acids, physicochemical characteristics of amino acids and predicted structural information, deep neural network algorithm is used to predict the binding sites of proteins. By optimizing the hyper-parameters of the deep learning algorithm, the prediction results by the fivefold cross-validation are better than those of the Ionseq method. In addition, to further verify the performance of the proposed model, the undersampling data processing method is adopted, and the prediction results on independent test are better than those obtained by the support vector machine algorithm. Conclusions An efficient method for predicting Mg2+ and Ca2+ ligand binding sites was presented.

Abstract The solid–solid electrode–electrolyte interface represents an important component in solid‐state batteries (SSBs), as ionic diffusion, reaction, transformation, and restructuring could all take place. As these processes strongly influence the battery performance, studying the evolution of the solid–solid interfaces, particularly in situ during battery operation, can provide insights to establish the structure–property relationship for SSBs. Synchrotron X‐ray techniques, owing to their unique penetration power and diverse approaches, are suitable to investigate the buried interfaces and examine structural, compositional, and morphological changes. In this review, we will discuss various surface‐sensitive synchrotron‐based scattering, spectroscopy, and imaging methods for the in situ characterization of solid–solid interfaces and how this information can be correlated to the electrochemical properties of SSBs. The goal is to overview the advantages and disadvantages of each technique by highlighting representative examples, so that similar strategies can be applied by battery researchers and beyond to study similar solid‐solid interface systems.

Proteins need to interact with different ligands to perform their functions. Among the ligands, the metal ion is a major ligand. At present, the prediction of protein metal ion ligand binding residues is a challenge. In this study, we selected Zn2+, Cu2+, Fe2+, Fe3+, Co2+, Mn2+, Ca2+ and Mg2+ metal ion ligands from the BioLip database as the research objects. Based on the amino acids, the physicochemical properties and predicted structural information, we introduced the disorder value as the feature parameter. In addition, based on the component information, position weight matrix and information entropy, we introduced the propensity factor as prediction parameters. Then, we used the deep neural network algorithm for the prediction. Furtherly, we made an optimization for the hyper-parameters of the deep learning algorithm and obtained improved results than the previous IonSeq method.

Aqueous sodium-ion batteries (ASIBs) represent a promising battery technology for stationary energy storage, due to their attractive merits of low cost, high abundance, and inherent safety. Recently, a variety of advanced cathode, anode, and electrolyte materials have been developed for ASIBs, which not only enhance our fundamental understanding of the Na insertion mechanism, but also facilitate the research and development of practical ASIB systems. Among these electrode materials, iron-based materials are of particular importance because of the high abundance, low price, and low toxicity of Fe elements. However, to our knowledge, there are no review papers that specifically discuss the properties of Fe-based materials for ASIBs yet. In this review, we present the recent research progress on Fe-based cathode/anode materials, which include polyanionic compounds, Prussian blue, oxides, carbides, and selenides. We also discuss the research efforts to build Fe-based ASIB full cells. Lastly, we share our perspectives on the key challenges that need to be addressed and suggest alternative directions for aqueous Na-ion batteries. We hope this review paper can promote more research efforts on the development of low-cost and low-toxicity materials for aqueous battery applications.

Abstract Fe-based single-atomic site catalysts (SASCs), with the natural metalloproteases-like active site structure, have attracted widespread attention in biocatalysis and biosensing. Precisely, controlling the isolated single-atom Fe-N-C active site structure is crucial to improve the SASCs’ performance. In this work, we use a facile ion-imprinting method (IIM) to synthesize isolated Fe-N-C single-atomic site catalysts (IIM-Fe-SASC). With this method, the ion-imprinting process can precisely control ion at the atomic level and form numerous well-defined single-atomic Fe-N-C sites. The IIM-Fe-SASC shows better peroxidase-like activities than that of non-imprinted references. Due to its excellent properties, IIM-Fe-SASC is an ideal nanoprobe used in the colorimetric biosensing of hydrogen peroxide (H2O2). Using IIM-Fe-SASC as the nanoprobe, in situ detection of H2O2 generated from MDA-MB-231 cells has been successfully demonstrated with satisfactory sensitivity and specificity. This work opens a novel and easy route in designing advanced SASC and provides a sensitive tool for intracellular H2O2 detection.

Metal anode instability due to several intrinsic factors limits their widespread use in energy storage. Here, the authors report a 3D alloy anode via a universal alloy electrodeposition approach to overcome the anode instability issues and demonstrate a seawater-based aqueous battery.

Background The FAT atypical cadherin 1/2/3/4 (FAT1/2/3/4) has been linked to the occurrence and development of various cancers. However, the prognostic and immunological role of FAT1/2/3/4 in non-small cell lung cancer (NSCLC) has not been clarified. Methods The association of FAT1/2/3/4 mutations with tumor mutation burden (TMB), tumor immunity in the microenvironment, and response to ICIs in NSCLC was investigated. Whole-exome sequencing data of lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) samples from the Cancer Genome Atlas (TCGA), and an immunotherapy data set comprising mutation and survival data of 75 NSCLC patients were analyzed. Two independent pan-cancer cohorts with large samples were used to validate the prognostic value of FAT1/2/3/4 mutations in immunotherapy. Results A high mutation rate of FAT1/2/3/4 (57.3%, 603/1052) was observed in NSCLC patients. TMB was significantly higher in samples with mutated FAT1/2/3/4 compared to samples with wildtype FAT1/2/3/4 ( P < .05). FAT2 mutation was found to be an independent prognostic biomarker in LUAD. FAT1/2/3/4 were aberrantly expressed in LUAD and LUSC, and high FAT2 expression strongly correlated with high PD-L1 levels in LUAD. Moreover, LUAD patients with FAT1 mutations showed significantly high activated dendritic cells infiltration, whereas those with FAT2/3/4 mutations had high infiltration of CD8 + T-cells, M1 macrophages, activated memory CD4 + T-cells, and helper follicular T-cells. It was also observed that FAT1/2/4 mutations were significantly associated with better enhanced objective response and durable clinical benefit, whereas FAT1/2/3 mutations correlated with longer progression-free survival in ICI-treated NSCLC cohort. FAT1/4 mutations were related to better overall survival in pan-cancer patients treated with ICIs. Conclusions FAT family genes are potential prognostic and immunological biomarkers and correlate with response to ICIs in NSCLC.

Proton-coupled electron transfer (PCET) has been observed in chemical, energy, and biological transformation processes. Here we demonstrate that the rate of PCET and oxygen evolution reaction can be dramatically enhanced by tuning crystal orientation and the correlated proton diffusion.

The realization of many protein functions is inseparable from the interaction with ligands; in particular, the combination of protein and metal ion ligands performs an important biological function. Currently, it is a challenging work to identify the metal ion ligand-binding residues accurately by computational approaches. In this study, we proposed an improved method to predict the binding residues of 10 metal ion ligands (Zn2+, Cu2+, Fe2+, Fe3+, Co2+, Mn2+, Ca2+, Mg2+, Na+, and K+). Based on the basic feature parameters of amino acids, and physicochemical and predicted structural information, we added another two features of amino acid correlation information and binding residue propensity factors. With the optimized parameters, we used the GBM algorithm to predict metal ion ligand-binding residues. In the obtained results, the Sn and MCC values were over 10.17% and 0.297, respectively. Besides, the Sn and MCC values of transition metals were higher than 34.46% and 0.564, respectively. In order to test the validity of our model, another method (Random Forest) was also used in comparison. The better results of this work indicated that the proposed method would be a valuable tool to predict metal ion ligand-binding residues.

Abstract Background Proteins perform their functions by interacting with acid radical ions. Recently, it was a challenging work to precisely predict the binding residues of acid radical ion ligands in the research field of molecular drug design. Results In this study, we proposed an improved method to predict the acid radical ion binding residues by using K-nearest Neighbors classifier. Meanwhile, we constructed datasets of four acid radical ion ligand (NO2 −, CO3 2−, SO4 2−, PO4 3−) binding residues from BioLip database. Then, based on the optimal window length for each acid radical ion ligand, we refined composition information and position conservative information and extracted them as feature parameters for K-nearest Neighbors classifier. In the results of 5-fold cross-validation, the Matthew’s correlation coefficient was higher than 0.45, the values of accuracy, sensitivity and specificity were all higher than 69.2%, and the false positive rate was lower than 30.8%. Further, we also performed an independent test to test the practicability of the proposed method. In the obtained results, the sensitivity was higher than 40.9%, the values of accuracy and specificity were higher than 84.2%, the Matthew’s correlation coefficient was higher than 0.116, and the false positive rate was lower than 15.4%. Finally, we identified binding residues of the six metal ion ligands. In the predicted results, the values of accuracy, sensitivity and specificity were all higher than 77.6%, the Matthew’s correlation coefficient was higher than 0.6, and the false positive rate was lower than 19.6%. Conclusions Taken together, the good results of our prediction method added new insights in the prediction of the binding residues of acid radical ion ligands.

Abstract Background In many important life activities, the execution of protein function depends on the interaction between proteins and ligands. As an important protein binding ligand, the identification of the binding site of the ion ligands plays an important role in the study of the protein function. Results In this study, four acid radical ion ligands (NO2 −,CO3 2−,SO4 2−,PO4 3−) and ten metal ion ligands (Zn2+,Cu2+,Fe2+,Fe3+,Ca2+,Mg2+,Mn2+,Na+,K+,Co2+) are selected as the research object, and the Sequential minimal optimization (SMO) algorithm based on sequence information was proposed, better prediction results were obtained by 5-fold cross validation. Conclusions An efficient method for predicting ion ligand binding sites was presented.

Engineering atomically dispersed metal site catalysts with controlled local coordination environments and 3D nanostructures effectively improves the catalytic performance for the oxygen reduction reaction (ORR) and the carbon dioxide reduction reaction (CO2RR), which are critical for clean energy conversion and chemical production. Herein, an innovative approach for preparing core−shell nanostructured catalysts with different single‐metal sites in the core and the shell, respectively, is developed. In particular, as the shell precursors, covalent organic polymers with a thin layered structure that is polymerized in situ and coated on a metal‐doped ZIF‐derived carbon core are used, followed by a controlled thermal activation. The selective combination and construction of different metal sites increase active site density in the surface layers, promote structural robustness, facilitate mass/charge transfer, and yield a possible synergy of active sites in the core and the shell. The p‐FeNC(shell)@CoNC(core), consisting of a polymerized FeTPPCl‐derived carbon layer (p‐FeNC) on a Co‐doped ZIF‐derived carbon (CoNC), exhibits remarkable ORR activity and stability in acidic media along with encouraging durability in H2–air fuel cells. Likewise, a p‐FeNC(shell)@NiNC(core) catalyst demonstrates outstanding CO2RR activity and stability. Hence, integrating two appropriate single‐metal sites in core and shell precursors, respectively, can modulate morphological and catalytic properties for a possible synergy toward different electrocatalysis processes.

Carbon dioxide can be electrochemically reduced to form valuable chemical feedstocks, but efficiency of electrocatalysts should be improved. Here the authors report nanotube-derived bismuth for electrocatalytic reduction of carbon dioxide to formate, with performance that is enhanced by defects.

Abstract Nanoscale electrocatalysts have exhibited promising activity and stability, improving the kinetics of numerous electrochemical reactions in renewable energy systems such as electrolyzers, fuel cells, and metal-air batteries. Due to the size effect, nano particles with extreme small size have high surface areas, complicated morphology, and various surface terminations, which make them different from their bulk phases and often undergo restructuring during the reactions. These restructured materials are hard to probe by conventional ex-situ characterizations, thus leaving the true reaction centers and/or active sites difficult to determine. Nowadays, in situ techniques, particularly X-ray absorption spectroscopy (XAS), have become an important tool to obtain oxidation states, electronic structure, and local bonding environments, which are critical to investigate the electrocatalysts under real reaction conditions. In this review, we go over the basic principles of XAS and highlight recent applications of in situ XAS in studies of nanoscale electrocatalysts.

While water splitting offers a carbon-neutral means to store energy, water oxidation is sluggish and corrosive over earth-abundant electrocatalysts. Here, authors show single ruthenium atoms over cobalt-iron layered double hydroxides to be effective and stable oxygen evolution electrocatalysts.

Abstract The development of an efficient electrocatalyst toward the hydrogen evolution reaction (HER) is of significant importance in transforming renewable electricity to pure and clean hydrogen by water splitting. However, the construction of an active electrocatalyst with multiple sites that can promote the dissociation of water molecules still remains a great challenge. Herein, a partial‐single‐atom, partial‐nanoparticle composite consisting of nanosized ruthenium (Ru) nanoparticles (NPs) and individual Ru atoms as an energy‐efficient HER catalyst in alkaline medium is reported. The formation of this unique composite mainly results from the dispersion of Ru NPs to small‐size NPs and single atoms (SAs) on the Fe/N codoped carbon (Fe–N–C) substrate due to the thermodynamic stability. The optimal catalyst exhibits an outstanding HER activity with an ultralow overpotential (9 mV) at 10 mA cm−2 (η10), a high turnover frequency (8.9 H2 s−1 at 50 mV overpotential), and nearly 100% Faraday efficiency, outperforming the state‐of‐the‐art commercial Pt/C and other reported HER electrocatalysts in alkaline condition. Both experimental and theoretical calculations reveal that the coexistence of Ru NPs and SAs can improve the hydride coupling and water dissociation kinetics, thus synergistically enhancing alkaline hydrogen evolution performance.

The prediction of ion ligand–binding residues in protein sequences is a challenging work that contributes to understand the specific functions of proteins in life processes. In this article, we selected binding residues of 14 ion ligands as research objects, including four acid radical ion ligands and 10 metal ion ligands. Based on the amino acid sequence information, we selected the composition and position conservation information of amino acids, the predicted structural information, and physicochemical properties of amino acids as basic feature parameters. We then performed a statistical analysis and reclassification for dihedral angle and proposed new methods on the extraction of feature parameters. The methods mainly included applying information entropy on the extraction of polarization charge and hydrophilic–hydrophobic information of amino acids and using position weight matrices on the extraction of position conservation information. In the prediction model, we used the random forest algorithm and obtained better prediction results than previous works. With the independent test, the Matthew's correlation coefficient and accuracy of 10 metal ion ligand–binding residues were larger than 0.07 and 52%, respectively; the corresponding evaluation values of four acid radical ion ligand–binding residues were larger than 0.15 and 86%, respectively. Further, we classified and combined the phi and psi angles and optimized prediction model for each ion ligand–binding residue.

Many proteins realize their special functions by binding with specific metal ion ligands during a cell’s life cycle. The ability to correctly identify metal ion ligand-binding residues is valuable for the human health and the design of molecular drug. Precisely identifying these residues, however, remains challenging work. We have presented an improved computational approach for predicting the binding residues of 10 metal ion ligands (Zn2+, Cu2+, Fe2+, Fe3+, Co2+, Ca2+, Mg2+, Mn2+, Na+, and K+) by adding reclassified relative solvent accessibility (RSA). The best accuracy of fivefold cross-validation was higher than 77.9%, which was about 16% higher than the previous result on the same dataset. It was found that different reclassification of the RSA information can make different contributions to the identification of specific ligand binding residues. Our study has provided an additional understanding of the effect of the RSA on the identification of metal ion ligand binding residues.

Single-atom nanozymes (SANs) possess unique features of maximum atomic utilization and present highly assembled enzyme-like structure and remarkable enzyme-like activity. By introducing SANs into immunoassay, limitations of ELISA such as low stability of horseradish peroxidase (HRP) can be well addressed, thereby improving the performance of the immunoassays. In this work, we have developed novel Fe-N-C single-atom nanozymes (Fe-Nx SANs) derived from Fe-doped polypyrrole (PPy) nanotube and substituted the enzymes in ELISA kit for enhancing the detection sensitivity of amyloid beta 1-40. Results indicate that the Fe-Nx SANs contain high density of single-atom active sites and comparable enzyme-like properties as HRP, owing to the maximized utilization of Fe atoms and their abundant active sites, which could mimic natural metalloproteases structures. Further designed SAN-linked immunosorbent assay (SAN-LISA) demonstrates the ultralow limit of detection (LOD) of 0.88 pg/mL, much more sensitive than that of commercial ELISA (9.98 pg/mL). The results confirm that the Fe-Nx SANs can serve as a satisfactory replacement of enzyme labels, which show great potential as an ultrasensitive colorimetric immunoassay.

The catalytic conversion of carbon dioxide into value-added products requires an understanding of the active species present under working conditions. Here, the authors discover copper-containing complexes to reversibly transform during electrocatalysis into methane-producing copper nanoclusters.