Dinuclear Research Articles

Computational Studies of Molybdenum-Containing Metal–Sulfur and Metal–Hydride Clusters

The development of transition metal clusters is an active area of research in inorganic chemistry, as they can be used as catalysts to perform chemically or biologically relevant reactions. Computational chemistry, employing density functional theory (DFT), plays a key role in rationalizing the electronic structure and properties of transition metal clusters. This article reviews recent quantum chemical studies of Mo3S4M clusters (M = Fe, Co, Ni), their CO- or N2-bound variants, and metal–hydride clusters. The ground state of the cluster systems was computed, and properties such as metal–metal bonding, orbital interactions, fluxional behavior of ligands, spectroscopy, and reaction mechanisms were rationalized and compared with available experimental results. Our research findings evidence that computational studies employing quantum chemical methods can guide experimental researchers to develop novel transition metal clusters for potential applications in catalysis.

Abstract 4138334: Identification and Immunological Characterization of Arrhythmia-Related Molecular Clusters in Ischemic Cardiomyopathy

Ischemic cardiomyopathy, a severe cardiac condition resulting from prolonged myocardial ischemia, is characterized by ventricular dilation, dysfunction, and an increased risk of life-threatening arrhythmias. Arrhythmia, a common complication in ischemic cardiomyopathy, is associated with poor clinical outcomes. Recent studies have indicated that the molecular heterogeneity of ischemic cardiomyopathy may contribute to the development of arrhythmias. Therefore, our study aimed to explore arrhythmia-related molecular clusters in ischemic cardiomyopathy and establish a prediction model for risk stratification. Based on gene expression profiles of ischemic cardiomyopathy patients, we analyzed the dysregulation of arrhythmia-associated genes and the associated immune characteristics (Figure 1A-C). Using unsupervised clustering analysis, we identified distinct arrhythmia-related molecular clusters in ischemic cardiomyopathy patients (Figure 1D-F). Cluster-specific differentially expressed genes were further identified using weighted gene co-expression network analysis (WGCNA). Functional enrichment analysis revealed significant differences in biological processes and pathways between the clusters (Figure 1G-H). Subsequently, we developed a prediction model using a random forest algorithm, which showed superior performance in distinguishing patients with different arrhythmia risks (Figure 1I-J). This forest model demonstrated excellent performance in one external validation datasets with AUC values of 0.891. In conclusion, our study provides novel insights into the molecular basis of arrhythmias in ischemic cardiomyopathy and establishes a prediction model with potential clinical utility for risk stratification and targeted therapies. Further investigation into the specific mechanisms underlying the identified molecular clusters may reveal new therapeutic targets for the management of arrhythmias in ischemic cardiomyopathy patients.

Dynamics of clustering rates in the rhode Island HIV-1 epidemic.

: Characterizing HIV clustering rates and their trends over time can improve understanding a local epidemic and enhance its control. Leveraging an academic-public health partnership in Rhode-Island, we explored longitudinal dynamics of statewide clustering rates among key populations from 1991 to 2023. Partial HIV-1 pol sequences were grouped by year of HIV-1 diagnosis. Molecular clusters were identified in cumulative annual phylogenies. Overall clustering rates, and clustering rates of newly-diagnosed and prevalent infections, and of specific socio-demographic characteristics of key populations over time were determined. Mann-Kendall statistics were used to estimate clustering rate trends and relationships among groups. By the end of 2023, 2,630 individuals with sequences represented the statewide epidemic in Rhode Island. Overall clustering rates increased from 7% in 1991 to 46% in 2023, correlating with cumulative sequence increase. Clustering rates of newly-diagnosed and prevalent infections significantly increased over time, higher in newly-diagnosed individuals since the early 2000 s. Increases were also observed among groups defined by gender, age, transmission risks, race, mental illness, HIV-1 subtypes, and country of birth, with some crossovers and divergence patterns over time. Exploring dynamics of HIV clustering rates over three decades in a statewide HIV-1 epidemic expanded its characterization and provided insight into its evolving changes. These dynamics may indicate a gradual shift towards a more concentrated and localized HIV-1 epidemic, highlighting important opportunities for targeted interventions to effectively prevent new HIV transmissions.

Integrated analysis of single-cell and bulk RNA-sequencing identifies a metastasis-related gene signature for predicting prognosis in lung adenocarcinoma.

Metastasis has been documented as an independent and significant prognostic feature of lung adenocarcinoma (LUAD) patients. However, the underlying genetic and molecular mechanisms responsible for LUAD metastasis and their prognostic significance are not exactly defined. The single-cell transcriptomic profiles of primary and metastatic LUAD samples were integrated as a whole dataset. Enrichment analysis and pseudotime trajectory analysis were performed to illustrate the cellular origins and changes during the metastatic process. The LUAD metastasis-related genes (LMRGs) molecular cluster and signature was constructed through unsupervised consensus clustering and ten machine-learning algorithms in The Cancer Genome Atlas (TCGA) LUAD cohort using ten machine-learning algorithms. Validation of the signature was conducted using four independent cohorts from the Gene Expression Omnibus (GEO) database. Kaplan-Meier, ROC, univariate and multivariate Cox-regression analyses were performed to test the stability of the signature. The gene CCT6A was subjected to knockdown, followed by validation through western blot analysis, flow cytometry, wound healing and transwell-migration assays to determine its potential significance. First, the signaling pathway networks remodeling and metabolic reprogramming were demonstrated to be involved in the metastasis of malignant LUAD cells, which facilitate their extravasation and adaptation to other organs. Furthermore, distinct subtypes of malignant LUAD cells exhibit tissue-specific patterns. Then, two distinct molecular patterns of LMRGs were established, which showed diverse prognoses. A LUAD metastasis-related gene signature (LMRGS) was constructed via a multiple machine-learning-based integrative procedure, which possesses distinctly superior accuracy than most common clinical features and 69 published prognostic signatures. The patients stratified by the signature into high-risk group had a significantly poorer prognosis compared to those in the low-risk group, and this was well validated across different clinical subgroups. In addition, the risk score calculated by LMRGS remained an independent prognostic parameter in both univariate and multivariate Cox regression. Notably, knockdown of CCT6A gene promoted cell apoptosis and decelerated the cell migration obviously. LMRGS could serve as a novel and promising tool to improve clinical outcomes for individual LUAD patients.

Gas Phase Reactions of Pristine and Single-Atom-Doped Copper and Silver Clusters: Probing Size-Dependent Stability and Novel Superatoms.

Gas phase reactions have been a subject of research interest, enabling reliable strategies to explore the stability and reactivity of metal clusters as well as to probe novel superatoms that form the building blocks to assemble new materials with tailored properties. Coinage metal clusters have attracted great research attention due to their simple electronic shell structures and rich photochemical and catalytic properties at relatively low cost. This perspective focuses on the recent progress made in studying the gas phase reactions of undamaged and single-atom-doped Cun±,0 and Agn±,0 clusters with O2, CO, and NO molecules. It covers various aspects, such as reaction mechanisms, relationships between structure and activity, control of reactivity by changing cluster size and composition, and the identification of novel superatoms (Cu18-, Ag13-, Ag17-, and Ag15O+). Lastly, we provide a detailed account of the obstacles and prospective avenues for future research in order to establish a connection between these findings and nanocluster systems that have practical applications.

Iron-molybdenum cofactor synthesis by a thermophilic nitrogenase devoid of the scaffold NifEN

The maturation and installation of the active site metal cluster (FeMo-co, Fe7S9CMo-R-homocitrate) in Mo-dependent nitrogenase requires the protein product of the nifB gene for production of the FeS cluster precursor (NifB-co, [Fe8S9C]) and the action of the maturase complex composed of the protein products from the nifE and nifN genes. However, some putative diazotrophic bacteria, like Roseiflexus sp. RS-1, lack the nifEN genes, suggesting an alternative pathway for maturation of FeMo-co that does not require NifEN. In this study, the Roseiflexus NifH, NifB, and apo-NifDK proteins produced in Escherichia coli are shown to be sufficient for FeMo-co maturation and insertion into the NifDK protein to achieve active nitrogenase. The E. coli expressed NifDKRS contained P-clusters but was devoid of FeMo-co (referred to as apo-NifDKRS). Apo-NifDKRS could be activated for N2 reduction by addition of preformed FeMo-co. Further, it was found that apo-NifDKRS plus E. coli produced NifBRS and NifHRS were sufficient to yield active NifDKRS when incubated with the necessary substrates (homocitrate, molybdate, and S-adenosylmethionine [SAM]), demonstrating that these proteins can replace the need for NifEN in maturation of Mo-nitrogenase. The E. coli produced NifHRS and NifBRS proteins were independently shown to be functional. The reconstituted NifDKRS demonstrated reduction of N2, protons, and acetylene in ratios observed for Azotobacter vinelandii NifDK. These findings reveal a distinct NifEN-independent pathway for nitrogenase activation involving NifHRS, NifBRS, and apo-NifDKRS.

Evidence of Quasi-Na Metallic Clusters in Sodium Ion Batteries through In Situ X-Ray Diffraction.

Carbonaceous materials have been considered the most promising anode in sodium-ion batteries (SIBs) due to their low cost, good electrical conductivity, and structural stability. The main challenge of carbonaceous anodes prior to their commercialization is low initial coulomb efficiencies, derived from a lack of an efficient technique to reveal a fundamental comprehension of sodium storage mechanisms. Here, the direct observation of quasi-Na metallic clusters in carbonaceous anodes during cycling through in situ XRD is reported. By means of such a technique, a strong self-adsorption behavior forming quasi-Na metallic clusters is detected within a rationally designed highly defective ultrathin carbon nanosheets (HDCS) anode. Such a self-adsorption and crystalline system transformation mechanism in HDCS brings capacity retention about 100% after 1000 cycles at 1 A g-1. This work provides a new principle for designing high-performance carbon anodes for SIBs.

Exploring nonlinear correlations among transition metal nanocluster properties using deep learning: a comparative analysis with LOO-CV method and cosine similarity

A novel approach is introduced for the rapid and accurate correlation analysis of nonlinear properties in Transition Metal (TM) clusters utilizing the Deep Leave-One-Out Cross-Validation technique. This investigation demonstrates that the Deep Neural Network (DNN)-based approach offers a more efficient predictive method for various properties of fourth-row TM nanoclusters compared to conventional Density Functional Theory methods, which are computationally intensive and time-consuming. The feature space, also known as descriptors, is established based on a broad spectrum of electronic and physical characteristics. Leveraging the similarities among these clusters, the DNN-based model is employed to explore the correlations among TM cluster properties. The proposed method, in conjunction with cosine similarity, achieves remarkable accuracy up to 10-9 for predicting total energy, lowest vibrational mode, binding energy, and HOMO-LUMO energy gap of TM2, TM3, and TM4nanoclusters. By analyzing correlation errors, the most closely coupled TM clusters are identified. Notably, Mn and Ni clusters exhibit the highest and lowest levels of energy coupling with other TMs, respectively. Generally, energy prediction for TM2, TM3, and TM4clusters exhibit similar trends, while an alternating behavior is observed for vibrational modes and binding energies. Furthermore, Ti, V, and Co demonstrate the highest binding energy correlations with TM2, TM3, and TM4sets, respectively. Regarding energy gap predictions, Ni exhibits the strongest correlation in the smallest TM2clusters, while Cr shows the highest dependence in TM3and TM4sets. Lastly, Zn displays the largest error in HOMO-LUMO energy gap across all sets, indicating distinctive independent energy gap characteristics.

Integrated strategy of mass spectrometry imaging and LC/MS-based GNPS for spatial characterization of alkaloids from Menispermi Rhizoma and study on potential anti-inflammatory mechanism by network pharmacology and molecular docking

Menispermi Rhizoma (MR) is the dried rhizome of Menispermum dauricum DC, which has been used to treat sore throat, enteritis, and rheumatic arthralgia. These therapeutic effects are attributed to its alkaloid ingredient. However, the chemical composition, anti-inflammatory mechanisms and the spatiotemporal distribution of the bioactive ingredients in MR have seldom been investigated. This study aims to clarify the anti-inflammation mechanism, material basis, and their spatial distribution of MR. Here, a LC/MS-based Global Natural Products Social Molecular Networking (GNPS) strategy was used to rapidly exhibit alkaloid molecular clusters that improve annotation accuracy and discover more unknown compounds. Then, a high-sensitive air flow-assisted ionization mass spectrometry imaging (AFAI-MSI) method was developed to visualize the spatial distributions alkaloids in different botanical parts of MR. The anti-inflammation mechanism was investigated based on network pharmacology and verified by molecular docking experiment. Finally, a total of 106 alkaloids including 24 aporphines, 23 monobenzylisoquinolines, 20 morphinanes, 20 proberberines, 12 bisbenzylisoquinolines, and 7 amides were identified in MR as well as 24 alkaloids were visualized in the medullary ray, cortex and epidermis regions. Moreover, the targets with a higher degree in the PPI network were TNF, GAPDH, AKT1, ALB, STAT3. GO and KEGG analysis revealed that MR in anti-inflammatory mechanism mainly involved plasma membrane, ATP binding, cytoplasm, identical protein binding and ATP binding. The signaling pathways mainly included NOD-like receptor signaling pathway, PI3K-Akt signaling pathway, AGE-RAGE signaling pathway in diabetic complications. The molecular docking results indicated that stepharanine-2-O-glucoside, bianfugedine, bianfugecine, dehydrostephanine had excellent affinity with SRC, MMP9 AKT1, STAT3 and TNF. This study comprehensively characterized the alkaloid ingredients and spatial distribution of MR, and revealed potential mechanism of MR in inflammation, which provides a reference for development and application of MR and other Traditional Chinese medicines (TCMs).

Proximal and classic epithelioid sarcomas are distinct molecular entities defined by MYC/GATA3 and SOX17/endothelial markers, respectively

Epithelioid sarcoma (ES) is a rare tumor hallmarked by the loss of INI1/SMARCB1 expression. Apart from this alteration, little is known about the biology of ES. Despite recent advances in treatment, the prognosis of ES remains unsatisfactory. To elucidate the molecular underpinnings of ES, and to identify diagnostic biomarkers and potential therapeutic vulnerabilities, we performed an integrated omics profiling (RNA sequencing and methylation array) of 24 primary, untreated ESs. Transcriptome and methylome analysis identified two distinct molecular clusters that essentially corresponded to the morphologic variants of ES, classic ES (C-ES) and the more aggressive proximal ES (P-ES). The P-ES group was characterized by hyperactivation of GATA3 and MYC pathways, with extensive epigenetic rewiring associated with EZH2 overexpression. Both DNA methylation and gene expression analysis indicated a striking similarity with the “MYC subgroup” of ATRT, another SMARCB1-deficient tumor, implying a shared molecular background and potential therapeutic vulnerabilities. Conversely, the C-ES group exhibited an endothelial-like molecular profile, with expression of vascular genes and elevated pro-angiogenic SOX17 signaling. Immunohistochemistry validated the overexpression of the chromatin regulators GATA3 (9/12 vs. 0/16) and EZH2 (7/7 vs. 2/6) in P-ESs, and of the vascular factors SOX17 (8/8 vs. 1/10) and N-cadherin (5/9 vs 0/10) in C-ESs. Therefore, these molecules emerge as potential diagnostic tools to fill the gap represented by the lack of ES subtype-specific biomarkers. In summary, our study shows that P-ES and C-ES represent distinct molecular entities defined by MYC/GATA3 and SOX17/endothelial molecular traits, respectively. Besides providing insights into the biology of ES, our study pinpoints subtype-specific biomarkers and potential therapeutic vulnerabilities.

Preparation of chiral metal organic framework modified silica monolithic capillary column and its application in enantioseparations of chiral drugs

Metal organic frameworks (MOFs) are crystalline compounds composed of metal ions (or metal clusters) and organic ligands. Chiral MOFs have been successfully utilized as novel materials for the separation of chiral enantiomers by chromatography, demonstrating excellent chiral separation performance. In this study, a chiral MOF-modified silica monolithic capillary column was used for pressurized capillary electrochromatography. First, a chiral MOF (Co-glycyl-L-glutamic acid, Co-L-GG) was synthesized. This MOF was then used to prepare a chiral capillary monolithic column via a one-step in situ polymerization method. The optimal conditions for preparing the chiral capillary monolithic column were determined as follows: Co-L-GG amount, 5 mg; polyethylene glycol amount, 0.96 mg; tetramethoxysilane dosage, 3.6 mL; trimethoxymethylsilane dosage, 0.4 mL. Next, the effects of the separation conditions on the separation of chiral drugs were investigated. Under the conditions of an applied voltage of -20 kV and a mobile phase consisting of acetonitrile and 20 mmol/L disodium hydrogen phosphate (80∶20, v/v), six chiral drugs were separated within 3 min, with baseline separation achieved for amlodipine, fluvastatin, and tryptophan. Moreover, the prepared chiral capillary monolithic column exhibited good reproducibility and stability. Finally, molecular docking studies were conducted using AutoDock to explore the chiral recognition mechanism, and the results were analyzed using Discovery Studio. The results indicated that larger differences in binding free energy between Co-L-GG and the enantiomers of the chiral drugs were correlated with higher enantioselectivity factors. However, this correlation did not necessarily lead to an increase in resolution. Co-L-GG, which is enriched with primary amines, secondary amines, and carbonyl groups, demonstrated enantiomeric recognition capability. In conclusion, this study demonstrates that chiral MOFs can be effectively used as chiral functional monomers to prepare chiral monolithic capillary columns, highlighting their significant potential for the separation and analysis of chiral compounds. The comprehensive exploration of the synthesis, characterization, and applications of these MOFs will help provide valuable insights into the development of advanced separation technologies.

Synergistic effects of metal single atoms and clusters on graphene-supported electrocatalysts: insights into the mechanism of oxygen reduction reaction

Synergistic effects of metal single atoms and clusters on graphene-supported electrocatalysts: insights into the mechanism of oxygen reduction reaction

Signature analysis of high-throughput transcriptomics screening data for mechanistic inference and chemical grouping.

High-throughput transcriptomics (HTTr) uses gene expression profiling to characterize the biological activity of chemicals in in vitro cell-based test systems. As an extension of a previous study testing 44 chemicals, HTTr was used to screen an additional 1,751 unique chemicals from the EPA's ToxCast collection in MCF7 cells using 8 concentrations and an exposure duration of 6 h. We hypothesized that concentration-response modeling of signature scores could be used to identify putative molecular targets and cluster chemicals with similar bioactivity. Clustering and enrichment analyses were conducted based on signature catalog annotations and ToxPrint chemotypes to facilitate molecular target prediction and grouping of chemicals with similar bioactivity profiles. Enrichment analysis based on signature catalog annotation identified known mechanisms of action (MeOAs) associated with well-studied chemicals and generated putative MeOAs for other active chemicals. Chemicals with predicted MeOAs included those targeting estrogen receptor (ER), glucocorticoid receptor (GR), retinoic acid receptor (RAR), the NRF2/KEAP/ARE pathway, AP-1 activation, and others. Using reference chemicals for ER modulation, the study demonstrated that HTTr in MCF7 cells was able to stratify chemicals in terms of agonist potency, distinguish ER agonists from antagonists, and cluster chemicals with similar activities as predicted by the ToxCast ER Pathway model. Uniform manifold approximation and projection (UMAP) embedding of signature-level results identified novel ER modulators with no ToxCast ER Pathway model predictions. Finally, UMAP combined with ToxPrint chemotype enrichment was used to explore the biological activity of structurally related chemicals. The study demonstrates that HTTr can be used to inform chemical risk assessment by determining in vitro points of departure, predicting chemicals' MeOA and grouping chemicals with similar bioactivity profiles.

Modeling and implementation of a position-sensitive fiber optic spark sensor with spectral radiation converter

Through numerical modeling of the optical part of the developed position-sensitive fiber-optic spark sensor with a spectral radiation converter, it was demonstrated that the sensor’s angular resolution in the range of ±18° is 3°. The spectral characteristics of the fluorescent glass plate with silver molecular clusters, which was used as the radiation converter in both numerical modeling and the experimental prototype, were investigated. Based on the developed optical sensor design, an experimental prototype was assembled. Testing of the prototype showed that it is capable of etecting sparks at a distance of 1000mm within an angular range of ±18° with an angular resolution of 3°. The results obtained from modeling and prototype testing are consistent and can be utilized in the development of sensors and emergency systems.

Cationic Group 13 and 14 Element Clusters.

Anionic and neutral clusters dominate the cluster chemistry of group 13 and 14 elements, many of which have become classic textbook examples of main group element clusters. However, facilitated by the development of unreactive, weakly coordinating anions, the number of known group 13 and 14 cationic cluster compounds has risen rapidly in recent years. Hence, this review aims to give an overview over this research field, which arouses increasing interest owing to the often unusual structures of the cationic clusters, as well as their application in bond activation chemistry. Challenges of the cluster formation are discussed and suitable starting materials are presented, as well as syntheses, structures and the rich follow-up chemistry of (also mixed) group 13 and 14 cluster cations.

Epitaxial Growth of Silver Clusters from Ag57 to Ag72 via Laminating Multiple Different Anion Templates.

The established capability of anion templates in precisely manipulating the size, geometry, and function of metal clusters is well acknowledged. However, the development of a systematic methodology for orchestrating the assembly of silver clusters, particularly those encompassing multiple distinct types of anion templates, remains elusive due to the formidable synthetic challenge. In this work, we report two novel silver clusters, Ag57 and Ag72, using two and three different anion templates, respectively. Ag57 features a gyroscope-like monovalent cation with an Ag3 triangle core sandwiched by one [SiW9O34]10- and a triad of Cl- anion templates. By intentionally introducing the third anion template, SO42-, the structure is expanded to the unprecedented Ag72 (with 15 silver atoms epitaxially grown on top of Ag57) resembling a tumbler, inside of which two Ag3 layers are laminated by one [SiW9O34]10-, seven Cl- and one SO42- anion templates in parallel with respect to longitudinal orientation. It is noteworthy that Ag72 exhibits remarkable structural complexity and represents a pioneering achievement as the first silver cluster incorporating three distinct types of anion templates. In addition, Ag72 demonstrates a significant advantage over Ag57, particular in terms of applications such as luminescent thermometers and remote laser ignition.

Epitaxial Growth of Silver Clusters from Ag57 to Ag72 via Laminating Multiple Different Anion Templates

The established capability of anion templates in precisely manipulating the size, geometry, and function of metal clusters is well acknowledged. However, the development of a systematic methodology for orchestrating the assembly of silver clusters, particularly those encompassing multiple distinct types of anion templates, remains elusive due to the formidable synthetic challenge. In this work, we report two novel silver clusters, Ag57 and Ag72, using two and three different anion templates, respectively. Ag57 features a gyroscope‐like monovalent cation with an Ag3 triangle core sandwiched by one [SiW9O34]10− and a triad of Cl− anion templates. By intentionally introducing the third anion template, SO42−, the structure is expanded to the unprecedented Ag72 (with 15 silver atoms epitaxially grown on top of Ag57) resembling a tumbler, inside of which two Ag3 layers are laminated by one [SiW9O34]10−, seven Cl− and one SO42− anion templates in parallel with respect to longitudinal orientation. It is noteworthy that Ag72 exhibits remarkable structural complexity and represents a pioneering achievement as the first silver cluster incorporating three distinct types of anion templates. In addition, Ag72 demonstrates a significant advantage over Ag57, particular in terms of applications such as luminescent thermometers and remote laser ignition.

Polyoxometalate‐Based Single‐Atom Catalyst with Precise Structure and Extremely Exposed Active Site for Efficient H2 Evolution

AbstractSingle‐atom catalysts with precise structure and extremely high catalytic efficiency remain a fervent focus in the fields of materials chemistry and catalytic science. Herein, a nickel‐substituted polyoxometalate (POM) {NiSb6O4(H2O)3[β‐Ni(hmta)SbW8O31]3}15− (NiPOM) with one extremely exposed nickel site [NiO3(H2O)3] was synthesized using the conventional aqueous method. The uniform dispersion of single nickel center with well‐defined structure was facilely achieved by anchoring nanosized NiPOM on graphene oxide (GO). The resulting NiPOM/GO can couple with CdS photoabsorber for the construction of low‐cost and ultra‐efficient hydrogen evolution system. The H2 yield can reach to 2753.27 mmol gPOM−1 h−1, which represents a record value among all the POM‐based photocatalytic systems. Remarkablely, an extremely high hydrogen yield of 3647.28 mmol gPOM−1 h−1 was achieved with simultaneous photooxidation of commercial waste plastic, representing the first POM‐based photocatalytic system for H2 evolution and waste plastic conversion. This work highlights a straightforward strategy for constructing extremely exposed single‐metal site with precise microenvironment by facilely manipulating nanosized molecular cluster to control individual atom.

Polyoxometalate-Based Single-Atom Catalyst with Precise Structure and Extremely Exposed Active Site for Efficient H2 Evolution.

Single-atom catalysts with precise structure and extremely high catalytic efficiency remain a fervent focus in the fields of materials chemistry and catalytic science. Herein, a nickel-substituted polyoxometalate (POM) {NiSb6O4(H2O)3[β-Ni(hmta)SbW8O31]3}15- (NiPOM) with one extremely exposed nickel site [NiO3(H2O)3] was synthesized using the conventional aqueous method. The uniform dispersion of single nickel center with well-defined structure was facilely achieved by anchoring nanosized NiPOM on graphene oxide (GO). The resulting NiPOM/GO can couple with CdS photoabsorber for the construction of low-cost and ultra-efficient hydrogen evolution system. The H2 yield can reach to 2753.27 mmol gPOM -1 h-1, which represents a record value among all the POM-based photocatalytic systems. Remarkablely, an extremely high hydrogen yield of 3647.28 mmol gPOM -1 h-1 was achieved with simultaneous photooxidation of commercial waste plastic, representing the first POM-based photocatalytic system for H2 evolution and waste plastic conversion. This work highlights a straightforward strategy for constructing extremely exposed single-metal site with precise microenvironment by facilely manipulating nanosized molecular cluster to control individual atom.

Non-targeted screening and toxicity study of safety risk substances in facial skincare products: Molecular networking and computational toxicology strategy

BackgroundRisk substances in cosmetics have long been associated with adverse reactions. However, as risk substances become more concealed and diversified, conventional targeted analysis methods are no longer sufficient to meet regulatory requirements. ObjectiveTo construct a rapid and effective non-targeted screening method for the identification of risk substances, and to provide a high-throughput method for toxicity assessment. MethodsMolecular networking was utilized for the non-targeted screening of risk substances in facial skincare products, and the toxicity of these risk substances was evaluated through molecular docking and quantitative structure-activity relationship (QSAR) models. ResultsThrough molecular networking, we identified seven known prohibited ingredients, six of which were confirmed using standard substances. In addition, 17 potential risk substances were discovered within molecular clusters containing prohibited ingredients, including antibiotics, antihistamines, and phthalates, etc. Notably, chloramphenicol base and N-desmethyl chlorpheniramine exhibited stronger binding affinity to keratin 5/14 than chloramphenicol and chlorpheniramine through molecular docking, respectively. Additionally, toxicity prediction results indicated that the carcinogenicity of antibiotics presented gender differences in mice and rats, and two chlorpheniramine derivatives also showed carcinogenicity in mice. Moreover, of the 24 compounds, 11 showed skin sensitization, while 14 caused skin irritation. Furthermore, half of these compounds demonstrated potential developmental toxicity, and only 4-nitrobenzenethiol was found to be mutagenic. ConclusionIn this study, we developed a visualization strategy for non-targeted screening of risk substances and a high-throughput method for initial toxicity assessment of risk substances.