Boosted Light Alkane Deep Oxidation via Metal Bond Length Modulation-Induced C-C Bond Preferential Activation.

Light alkanes (LAs), typical VOCs existing in both stationary and mobile sources, pose significant environmental concerns. Although noble metal catalysts demonstrate strong C-H bond activation, their effectiveness in degrading LAs is hindered by inherent challenges, including poor chemical stability and water resistance. Here, from a new perspective, we propose a feasible strategy that adjusting the metal bond lengths within Pd clusters through partial substitution of smaller radius 3d transition metals (3dTMs) to prioritize the activation of low-energy C-C bonds within LAs. Benefiting from this, PdCo/CeO2 exhibits exceptional catalytic performance in propane degradation due to their high capacity for C-C cleavage stemming from the shorter Pd-Co length (2.51 Å) and lower coordination number (1.73), boosting the activation of α-H and ß-H of propane simultaneously and accelerating the mobility of postactivated oxygen species to prevent Pd center deep oxidation. The presence of 3dTMs on Pd clusters improves the redox and charge transfer ability of catalysts, resulting in an amplified generation of oxygen vacancies and facilitating the adsorption and activation of reactants. Mechanistic studies and DFT calculations suggest that the substitution of 3dTMs significantly accelerate C-C bond cleavage within C3 intermediates to generate the subsequent C2 and C1 intermediates, suppressing the generation of harmful byproducts.

Establishing biosynthetic pathway for the production of p-hydroxyacetophenone and its glucoside in Escherichia coli.

p-Hydroxyacetophenone (p-HAP) and its glucoside picein are plant-derived natural products that have been extensively used in chemical, pharmaceutical and cosmetic industries owing to their antioxidant, antibacterial and antiseptic activities. However, the natural biosynthetic pathways for p-HAP and picein have yet been resolved so far, limiting their biosynthesis in microorganisms. In this study, we design and construct a biosynthetic pathway for de novo production of p-HAP and picein from glucose in E. coli. First, screening and characterizing pathway enzymes enable us to successfully establish functional biosynthetic pathway for p-HAP production. Then, the rate-limiting step in the pathway caused by a reversible alcohol dehydrogenase is completely eliminated by modulating intracellular redox cofactors. Subsequent host strain engineering via systematic increase of precursor supplies enables production enhancement of p-HAP with a titer of 1445.3 mg/L under fed-batch conditions. Finally, a novel p-HAP glucosyltransferase capable of generating picein from p-HAP is identified and characterized from a series of glycosyltransferases. On this basis, de novo biosynthesis of picein from glucose is achieved with a titer of 210.7 mg/L under fed-batch conditions. This work not only demonstrates a microbial platform for p-HAP and picein synthesis, but also represents a generalizable pathway design strategy to produce value-added compounds.

Accurate Prediction of Soil Heavy Metal Pollution Using an Improved Machine Learning Method: A Case Study in the Pearl River Delta, China.

In traditional soil heavy metal (HM) pollution assessment, spatial interpolation analysis is often carried out on the limited sampling points in the study area to get the overall status of heavy metal pollution. Unfortunately, in many machine learning spatial information enhancement algorithms, the additional spatial information introduced fails to reflect the hierarchical heterogeneity of the study area. Therefore, we designed hierarchical regionalization labels based on three interpolation techniques (inverse distance weight, ordinary kriging, and trend surface interpolation) as new spatial covariates for a machine learning (ML) model. It was demonstrated that regional spatial information improved the prediction performance of the model (R2 > 0.7). On the basis of the prediction results, the status of HM pollution in the Pearl River Delta (PRD) region was evaluated: cadmium (Cd) and copper (Cu) were the most serious pollutants in the PRD (the point overstandard rates are 18.77% and 12.95%, respectively). The analysis of index importance and bivariate local indicators of spatial association (LISA) shows that the key factors affecting the spatial distribution of heavy metals are geographical and climatic conditions [namely, altitude, humidity index, and normalized vegetation difference index (NDVI)] and some industrial activities (such as metal processing, printing and dyeing, and electronics industry). This study develops a novel approach to improve existing spatial interpolation techniques, which will enable more precise and scientific soil environmental management.

Comparison and application of SOFM, fuzzy c-means and k-means clustering algorithms for natural soil environment regionalization in China.

Soil attributes and their environmental drivers exhibit different patterns in different geographical directions, along with distinct regional characteristics, which may have important effects on substance migration and transformation such as organic matter and soil elements or the environmental impacts of pollutants. Therefore, regional soil characteristics should be considered in the process of regionalization for environmental management. However, no comprehensive evaluation or systematic classification of the natural soil environment has been established for China. Here, we established an index system for natural soil environmental regionalization (NSER) by combining literature data obtained based on bibliometrics with the analytic hierarchy process (AHP). Based on the index system, we collected spatial distribution data for 14 indexes at the national scale. In addition, three clustering algorithms-self-organizing feature mapping (SOFM), fuzzy c-means (FCM) and k-means (KM)-were used to classify and define the natural soil environment. We imported four cluster validity indexes (CVI) to evaluate different models: Davies-Bouldin index (DB), Silhouette index (Sil) and Calinski-Harabasz index (CH) for FCM and KM, clustering quality index (CQI) for SOFM. Analysis and comparison of the results showed that when the number of clusters was 13, the FCM clustering algorithm achieved the optimal clustering results (DB = 1.16, Sil = 0.78, CH = 6.77 × 106), allowing the natural soil environment of China to be divided into 12 regions with distinct characteristics. Our study provides a set of comprehensive scientific research methods for regionalization research based on spatial data, it has important reference value for improving soil environmental management based on local conditions in China.

Machine learning based on the graph convolutional self-organizing map method increases the accuracy of pollution source identification: A case study of trace metal(loid)s in soils of Jiangmen City, south China.

Rapid economic development and industrialization may include environmentally harmful human activities that cause heavy-metal accumulation in soils, ultimately threatening the quality of the soil environment and human health. Therefore, accurate identification of pollution sources is an important weapon in efforts to control and prevent pollution. The self-organizing map (SOM) method is widely used in pollution source identification because of its capacity for visualization of high-dimensional data. The SOM ignores the graph structure relationship among chemical elements in soils; the SOM analysis of pollution sources has high uncertainty. Here, we propose a new analysis method, i.e., the graph convolutional self-organizing map (GCSOM), which uses a graph convolutional network (GCN) to extract the graph structure relationship among the chemical elements in soils, then performs data visualization using an SOM. We compared the performances of GCSOM and SOM, then assessed the pollution source characteristics of trace metal(loid)s (TMs, mostly heavy metals) in Jiangmen City using the GCSOM. Our experimental results showed that the GCSOM is superior to the SOM for identification of TM sources, while the TMs in the soil of Jiangmen originate from three main sources: agricultural activities (mainly in Taishan City, Jiangmen), traffic emissions (mainly in Xinhui and Pengjiang Districts), and industrial activities (mainly in Xinhui District). The risk assessment indicated that the risk of all TMs was within threshold.

Rationally Engineering a CuO/Pd@SiO2 Core-Shell Catalyst with Isolated Bifunctional Pd and Cu Active Sites for n-Butylamine Controllable Decomposition.

Volatile organic amines are a category of typical volatile organic compounds (VOCs) extensively presented in industrial exhausts causing serious harm to the atmospheric environment and human health. Monometallic Pd and Cu-based catalysts are commonly adopted for catalytic destruction of hazardous organic amines, but their applications are greatly limited by the inevitable production of toxic amide and NOx byproducts and inferior low-temperature activity. Here, a CuO/Pd@SiO2 core-shell-structured catalyst with diverse functionalized active sites was creatively developed, which realized the total decomposition of n-butylamine at 260 °C with a CO2 yield and N2 selectivity reaching up to 100% and 98.3%, respectively (obviously better than those of Pd@SiO2 and CuO/SiO2), owing to the synergy of isolated Pd and Cu sites in independent mineralization of n-butylamine and generation of N2, respectively. The formation of amide and short-chain aliphatic hydrocarbon intermediates via C-C bond cleavage tended to occur over Pd sites, while the C-N bond was prone to breakage over Cu sites, generating NH2· species and long free-N chain intermediates at low temperatures, avoiding the production of hazardous amide and NOx. The SiO2 channel collapse and H+ site production resulted in the formation of N2O via suppressing NH2· diffusion. This work provides critical guidance for a rational fabrication of catalysts with high activity and N2 selectivity for environmentally friendly destruction of nitrogen-containing VOCs.

Human health risk-based Generic Assessment Criteria for agricultural soil in Jiangsu and Zhejiang provinces, China.

Agricultural soil pollution in China poses a major threat to human health and food safety. There are no agricultural soil environmental standards based on human health in China, which prevents effective screening and assessment of risks. Jiangsu (JS) and Zhejiang (ZJ) provinces, located in the Yangtze River Delta (YRD) core region, have obvious differences in agricultural land conditions, which will result in differences in Generic Assessment Criteria (GAC). In this study, we derived and compared human health risk-based GAC using the Contaminated Land Exposure Assessment (CLEA) model for agricultural land scenarios in these two provinces. We found differences in the GAC between JS and ZJ due to differences in parameters. These differences are greatest for benzene, and cadmium (Cd). For Cd, the contribution of oral intake exceeds 90 %, and the vegetable consumption rate and mean daily intake (MDI) may be key parameters affecting GAC. For the volatile organic compound benzene, the inhalation of indoor vapor accounts for about 30 %, and the key parameters affecting the GAC for benzene may be the attenuation factor and soil organic matte (SOM). The derived GAC are generally lager (i.e., less stringent) than the GB15618-2018 and UK Suitable 4 Use Levels (S4ULs); however, the derived GAC for JS and ZJ were lower than the soil screening values (SSV) for residential land in China. This may be related to methods, land use types, and critical receptors. This work will contribute to the development of regional soil environmental standards in China.

Human health risk-based soil environmental criteria (SEC) for park soil in Beijing, China.

Urban parks are important places that allow urban residents to experience nature but are also associated with the risk of exposure to contaminated soil. Therefore, it is necessary to establish appropriate soil environment criteria (SEC) to manage park soil quality. Studies on the demographic characteristics and behavioral patterns of urban park visitors are helpful for the selection of sensitive receptors and the determination of parameters in the establishment of SEC. This study explored the park visitors' demographic characteristics and behavioral patterns, and applied the results to derive SEC. Eighty-six parks in Beijing were selected, and mobile phone data were obtained to analysis the demographic characteristics and residence time of the visitors. Kruskal-Wallis test, kernel density estimation and random forest model were used for data analysis. The CLEA model was used to derive SEC. The results showed that the demographic characteristics and behavioral patterns of visitors in different types of parks were quite different. Parks were mostly used by males and visitors aged 31-45. Most visitors stayed in the park for 1-2 h, and the distance from a given visitor's home to the park was the most important factor affecting stay time. Then, several parameters such as the parameters related to the receptors and occupation period were optimized, and the SEC of sensitive parks and non-sensitive parks were derived. Exposure frequency may be the main reason for the difference of SEC between the two types of parks. The SECs of sensitive parks were higher than the soil screening values (SSVs) for class 1 land in GB36600-2018, indicating that the current SSVs for some parks may be too conservative. This study provides a reference for the formulation and revision of soil environmental standards for park land, and suggests strengthening research on human behavioral patterns.

Characters and environmental driving factors of bacterial community in soil of Beijing urban parks.

In an era of unprecedented human influence, different human activities have different degrees of impact on specific bacteria, resulting in the regional biological homogenization of soil bacteria. However, the contribution of the impact that a large number of anthropogenic activities on bacteria remains unknown. Here, by high-throughput amplicon sequencing, we characterized the composition, diversity and influencing factors of soil microbes in Beijing urban parks at geographic space and park management aspect. It is the first time to quantify and compare the importance of the impact of up to 15 human activities on soil bacterial communities. The results show that the dominant bacterial phyla in Beijing urban parks were Actinobacteria, Proteobacteria, Acidobacteria and Chloroflexi. The environmental management of different park types, as well as the land use history and development conditions of different regions, had significant differences in soil bacterial community structure. Soil bacteria in urban parks were disturbed by direct human interference far more than natural causes. The most important factors were related to the number of tourists and residents, industrial production and land use patterns. These factors may also be related to the abundance of unknown bacteria in urban parks. This also directly shows that human activities have a non-negligible impact on soil bacteria. The ways in which different human activities brought by global urbanization and their impacting mechanisms are used should be the starting point of future research.

Construction and application of the knowledge graph method in management of soil pollution in contaminated sites: A case study in South China.

Contaminated sites are a main cause of urban soil problems and have led to increasing pollution and public risk in China as a result of the rapid growth of industrial and urban land use. Because land pollution involves extensive multi-source heterogeneous information, identifying the risk of urban soil pollution efficiently and predicting pollution-related events are important for urban environmental management. Knowledge graphs (KGs) have unique advantages in dealing with massive amounts of information. This study attempts to construct a KG of contaminated sites in South China to explore its feasibility and effectiveness in urban soil environmental management. The results demonstrate that KGs have a favorable effect in information retrieval, knowledge reasoning, and visualization. Studied cases in this article demonstrate that the KG model can achieve many functions, including the display of global information of polluted sites, and discovery of regional distribution of characteristic pollutants and main pollutants of specific industries, based on special query syntax. However, this approach is limited by some technical difficulties, such as knowledge mining of natural resources, which must be overcome in future studies to improve the operability of KG technologies.

Selenium (Se) plays a key role in the biological effects of some viruses: Implications for COVID-19.

Host nutrition is an important factor affecting disease progression. Selenium (Se) is an essential trace element for the human body with anti-inflammatory, antioxidant, and immune effects, and Se deficiency increases RNA-virus replication and virulent mutations, which lead to more severe tissue damage and symptoms. Low Se status in the host may be an important cause of health complications induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this article, we describe the metabolic mechanisms by which Se is involved in anti-inflammatory, antioxidant, and immune effects, and review the role and clinical effects of Se in viral infection. We then discuss the potential relationship between Se and coronavirus disease 2019 (COVID-19). The association between soil Se level and the incidence of COVID-19 was observed in different cities of Hubei Province. The incidence of COVID-19 was more than 10 times lower in Se-enriched cities (Enshi, Shiyan, and Xiangyang) than in Se-deficient cities (Suizhou and Xiaogan). Although the relationship between soil Se levels and the incidence of COVID-19 in Hubei still needs further study, these findings provide baseline information demonstrating the effect of Se levels on SARS-CoV-2, which could contribute to the prevention and management of COVID-19.

Status of arsenic accumulation in agricultural soils across China (1985-2016).

Based on 1677 published studies, 1648 sites across China collected from 1985 to 2016 were used to research the concentrations of arsenic in agricultural soils. In order to understand the status of arsenic pollution in agricultural soils in China over the past three decades, and to learn about the arsenic stocks in agricultural soils in various regions, and compared the relationship with annual arsenic emissions in China, and finally evaluated the potential ecological risks and human health risks. The median arsenic concentration in the surface agricultural soils of China was 10.40 mg Kg-1, and it ranged from 0.4 mg Kg-1 to 175.8 mg Kg-1. The inventory of arsenic in Chinese agricultural surface soils was estimated to be 3.71 × 106 t. In this study, the arsenic concentrations were found to be higher in Central, South, and Southwest China than those in other regions. The trend of arsenic pollution in agricultural soils has gradually increased over the past three decades. However, the growth rate of arsenic concentrations pollution in farmlands agricultural in China slowed during 2012-2016. The ecological risk index and geoaccumulation index revealed that arsenic in Chinese agricultural soil poses a low risk to the ecosystem. For human health assessment, the dietary pathway was the main pathway of exposure to arsenic in farmland soil of China. However, children's soil intake also contributed 34.48% to the exposure to arsenic, owing to their behavior. This study can provide a reference for the management of arsenic agricultural pollution in farmland soils in China.

Characteristics and environmental response of secondary minerals in AMD from Dabaoshan Mine, South China.

This article documents the new precipitates formed related to acid mine drainage (AMD) at Dabaoshan mine (South China). X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscope & Energy Spectrometer (SEM-EDS) have been used to detect minerals in AMD impoundment and downstream creeks. The occurrences, the mineralogical species and the micro-morphological characteristics of secondary minerals from different pH conditions has been carried out. Iron- hydroxysulfates and iron-oxyhydroxides are the main secondary minerals, and they occurred as both poorly and well-crystalline minerals. Jarosite nearly predominate as pseudocubic crystals at pH 2.5-4.0. Schwertmannite-rich sediments occurred at pH 3.82-4.5 as urchin-like, pin-cushion and as well as globular-like aggregates and show high concentrations of Mn, Cu, Pb and As due to adsorption and co-precipitation. Goethite formed mainly as botryoidal and flaky assemblages. Paragenesis of different types of schwertmannite indicate that pH condition is not the dominant factor controlling morphology but the main parameter for the variation of minerals species. Statistical analysis reveal obvious changing tendency in Zn, Cd and SO4 within pH. FTIR analysis show adsorption of Cu, Pb, Zn and As on secondary iron minerals. Water elements with high concentrations in the impoundment and the obvious decrease in downstream creak reflected an accumulation and evaporation in AMD impoundment and a dilution in downstream area respectively. These results indicate that secondary minerals associated with AMD can play an important role in attenuating toxic elements.

[Ultrasound assisted the enzymolysis of ginsenosides to prepare pare ginseng saponin Compound K].

To prepare ginseng saponin Compound K with ultrasound-assisted total zymolytic ginseng saponins. The conversion rate was taken as the index to detect the pre-treatment factors such as ultrasonic power and ultrasonic time, as well as the impact of enzymatic factors, such as pH value, temperature, concentration of substrate, dosage of enzyme and reaction time, on the conversion rate. The response surface method was used to optimize the preparation conditions. The enzymolytic products were identified with MS, 1H-NMR and 13C-NMR. The results showed that the optimum conditions of the ultrasound-assisted enzymolysis were 250 W for ultrasonic power, 15 min for ultrasonic time, 5.5 for enzymolytic pH, 50 degrees C for enzymolytic temperature, 36 h for enzymolytic time, 4:5 for enzymolytic dosage: substrate and 1.0 g x L(-1) for concentration of substrate. The relative molecular mass of reaction products was 622.4. Therefore, the nuclear magnetic map verified that the reaction product was rare ginseng saponin Compound K. Under the above conditions, based on the total zymolytic ginseng saponins, the conversion rate of rare ginseng saponin Compound K was 6.91% in proportion to the total of ginsenosides. The process features gentle reaction conditions, high conversion rate and simple and reliable process, which is suitable for industrial production.

Context-aware and local-aware fusion with transformer for medical image segmentation.

Objective. Convolutional neural networks (CNNs) have made significant progress in medical image segmentation tasks. However, for complex segmentation tasks, CNNs lack the ability to establish long-distance relationships, resulting in poor segmentation performance. The characteristics of intra-class diversity and inter-class similarity in images increase the difficulty of segmentation. Additionally, some focus areas exhibit a scattered distribution, making segmentation even more challenging.Approach. Therefore, this work proposed a new Transformer model, FTransConv, to address the issues of inter-class similarity, intra-class diversity, and scattered distribution in medical image segmentation tasks. To achieve this, three Transformer-CNN modules were designed to extract global and local information, and a full-scale squeeze-excitation module was proposed in the decoder using the idea of full-scale connections.Main results. Without any pre-training, this work verified the effectiveness of FTransConv on three public COVID-19 CT datasets and MoNuSeg. Experiments have shown that FTransConv, which has only 26.98M parameters, outperformed other state-of-the-art models, such as Swin-Unet, TransAttUnet, UCTransNet, LeViT-UNet, TransUNet, UTNet, and SAUNet++. This model achieved the best segmentation performance with a DSC of 83.22% in COVID-19 datasets and 79.47% in MoNuSeg.Significance. This work demonstrated that our method provides a promising solution for regions with high inter-class similarity, intra-class diversity and scatter distribution in image segmentation.

Insights into the characteristics of changes in dissolved organic matter fluorescence components on the natural attenuation process of toluene.

Natural attenuation (NA) is of great significance for the remediation of contaminated groundwater, and how to identify NA patterns of toluene in aquifers more quickly and effectively poses an urgent challenge. In this study, the NA of toluene in two typical soils was conducted by means of soil column experiment. Based on column experiments, dissolved organic matter (DOM) was rapidly identified using fluorescence spectroscopy, and the relationship between DOM and the NA of toluene was established through structural equation modeling analysis. The adsorption rates of toluene in clay and sandy soil were 39 % and 26 %, respectively. The adsorption capacity and total NA capacity of silty clay were large. The occurrence of fluorescence peaks of protein-like components and specific products indicated the occurrence of biodegradation. Arenimonas, Acidovorax and Brevundimonas were the main degrading bacteria identified in Column A, while Pseudomonas, Azotobacter and Mycobacterium were the main ones identified in Column B. The pH, ORP, and Fe(II) were the most important factors affecting the composition of microbial communities, which in turn affected the NA of toluene. These results provide a new way to quickly identify NA of toluene.

Unlocking the potential of surface modification with phosphate on ball milled zero-valent iron reactivity:Implications for radioactive metal ions removal.

Numerous investigations have illuminated the profound impact of phosphate on the adsorption of uranium, however, the effect of phosphate-mediated surface modification on the reactivity of zero-valent iron (ZVI) remained enigmatic. In this study, a phosphate-modified ZVI (P-ZVIbm) was prepared with a facile ball milling strategy, and compared with ZVIbm, the U(VI) removal amount (435.2 mg/g) and efficiency (3.52×10-3 g·mg-1·min-1) of P-ZVIbm were disclosed nearly 2.0 and 54 times larger than those of ZVIbm respectively. The identification of products revealed that the adsorption mechanism dominated the removal process for ZVIbm, while the reactive modified layer strengthened both the adsorption pattern and reduction performance on P-ZVIbm. DFT calculation result demonstrated that the binding configuration shifted from bidentate binuclear to multidentate configuration, further shortening the Fe-U atomic distance. More importantly, the electron transferred is more accessible through the surface phosphate layer, and selectively donated to U(VI), accounting for the elevated reduction performance of P-ZVIbm. This investigation explicitly underscores the critical role of ZVI's surface microenvironment in the domain of radioactive metal ion mitigation and introduces a novel methodology to amplify the sequestration of U(VI) from aqueous environments.

ICAM-1 may promote the loss of dopaminergic neurons by regulating inflammation in MPTP-induced Parkinson's disease mouse models.

Parkinson's disease (PD) is a chronic neurodegenerative disease with unclear pathogenesis that involves neuroinflammation and intestinal microbial dysbiosis. Intercellular adhesion molecule-1 (ICAM-1), an inflammatory marker, participates in neuroinflammation during dopaminergic neuronal damage. However, the explicit mechanisms of action of ICAM-1 in PD have not been elucidated. We established a subacute PD mouse model by the intraperitoneal injection of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and observed motor symptoms and gastrointestinal dysfunction in mice. Immunofluorescence was used to examine the survival of dopaminergic neurons, expression of microglial and astrocyte markers, and intestinal tight junction-associated proteins. Then, we use 16 S rRNA sequencing to identify alterations in the microbiota. Our findings revealed that ICAM-1-specific antibody (Ab) treatment relieved behavioural defects, gastrointestinal dysfunction, and dopaminergic neuronal death in MPTP-induced PD mice. Further mechanistic investigations indicated that ICAM-1Ab might suppress neuroinflammation by inhibiting the activation of astrocytes and microglia in the substantia nigra and relieving colon barrier impairment and intestinal inflammation. Furthermore, 16 S rRNA sequencing revealed that the relative abundances of bacterial Firmicutes, Clostridia, and Lachnospiraceae were elevated in the PD mice. However, ICAM-1Ab treatment ameliorated the MPTP-induced disorders in the intestinal microbiota. Collectively, we concluded that the suppressing ICAM-1 might lead to the a significant decrease of inflammation and restore the gut microbial community, thus ameliorating the damage of DA neurons.

Electrochemical Generation of Te Vacancy Pairs in PtTe for Efficient Hydrogen Evolution.

Two-dimensional (2D) van der Waals materials are increasingly seen as potential catalysts due to their unique structures and unmatched properties. However, achieving precise synthesis of these remarkable materials and regulating their atomic and electronic structures at the most fundamental level to enhance their catalytic performance remain a significant challenge. In this study, we synthesized single-crystal bulk PtTe crystals via chemical vapor transport and subsequently produced atomically thin, large PtTe nanosheets (NSs) through electrochemical cathode intercalation. These NSs are characterized by a significant presence of Te vacancy pairs, leading to undercoordinated Pt atoms on their basal planes. Experimental and theoretical studies together reveal that Te vacancy pairs effectively optimize and enhance the electronic properties (such as charge distribution, density of states near the Fermi level, and d-band center) of the resultant undercoordinated Pt atoms. This optimization results in a significantly higher percentage of dangling O-H water, a decreased energy barrier for water dissociation, and an increased binding affinity of these Pt atoms to active hydrogen intermediates. Consequently, PtTe NSs featuring exposed and undercoordinated Pt atoms demonstrate outstanding electrocatalytic activity in hydrogen evolution reactions, significantly surpassing the performance of standard commercial Pt/C catalysts.

[Polybasic research on the biopharmaceutical characteristics of 20 (S)-protopanaxadiol].

In this study, the biopharmaceutical properties of 20 (S)-protopanaxadiol (PPD) were studied. Firstly, the equilibrium solubility and apparent oil/water partition coefficient of PPD were used to predict the absorption in vivo. Meanwhile the membrane permeability and absorption window were studied by Caco-2 cell model and single-pass intestinal perfusion model. Furthermore, the bioavailability and metabolism were combined to study the absorption properties and metabolic properties in vivo. All of them were used to provide theoretical and practical foundation for designing PPD preparation. The results showed that PPD is poorly water-soluble, and the equilibrium solubility in water is only 35.24 mg x L(-1). The oil-water partition coefficient is 46.21 (logP = 1.66). By Caco-2 cell model, the results showed PPD uptake in general, and it also has efflux. By in situ intestinal perfusion model, the results showed that the absorption of PPD in the intestine is good, and the effective permeability coefficient were duodenum > jejunum > ileum > colon. The oral bioavailability of PPD was 29.39%. It was not well. Metabolic studies showed PPD in vivo presented a wide spread metabolism. So the main factors that restricted oral bioavailability of PPD were the poor solubility and first-pass effect.