Layered Double Hydroxide Derivatives for Polyolefin Upcycling.

While Ru-catalyzed hydrogenolysis holds significant promise in converting waste polyolefins into value-added alkane fuels, a major constraint is the high cost of noble metal catalysts. In this work, we propose, for the first time, that Co-based catalysts derived from CoAl-layered double hydroxide (LDH) are alternatives for efficient polyolefin hydrogenolysis. Leveraging the chemical flexibility of the LDH platform, we reveal that metallic Co species serve as highly efficient active sites for polyolefin hydrogenolysis. Furthermore, we introduced Ni into the Co framework to tackle the issue of restricted hydrogenation ability associated with contiguous Co-Co sites. In-situ analysis indicates that the integration of Ni induces electron transfer and facilitates hydrogen spillover. This dual effect synergistically enhances the hydrogenation/desorption of olefin intermediates, resulting in a significant reduction in the yield of low-value CH4 from 27.1 to 12.6%. Through leveraging the unique properties of LDH, we have developed efficient and cost-effective catalysts for the sustainable recycling and valorization of waste polyolefin materials.

Mo2C coated with Ni nanoparticles as the cathode catalyst towards efficient hydrogen evolution reaction: an experimental and computational investigation.

Although Mo2C and earth-abundant 3d transition metals are regarded as potential catalysts to replace noble metal catalysts for effective hydrogen evolution reaction, their large-scale application is still inhibited by their own defects. Here, a facile thermal treatment method for nonprecious metal catalysts is developed to prepare a porous Ni/Mo2C composite catalyst. The loading density of Ni nanoparticles on the Mo2C surface has an important effect on the activity of the catalyst. By optimizing the Ni doping ratio, the Ni-40/Mo2C-17 sample exhibits the lowest onset overpotential and lowest overpotential at 10 mA cm-2 in both acidic and alkaline electrolytes, compared to other reported Ni- and Mo2C-based catalysts. In addition, theoretical calculations have also confirmed the synergistic effect between Ni nanoparticles and Mo2C, which can balance the thermodynamics between H adsorption and desorption of H2. This work provides an avenue for designing high-performance water-splitting catalytic materials using low-cost species, which exhibit excellent HER activity in a wide pH range.

The Role of Bismuth in Suppressing the CO Poisoning in Alkaline Methanol Electrooxidation: Switching the Reaction from the CO to Formate Pathway.

While tuning the electronic structure of Pt can thermodynamically alleviate CO poisoning in direct methanol fuel cells, the impact of interactions between intermediates on the reaction pathway is seldom studied. Herein, we contrive a PtBi model catalyst and realize a complete inhibition of the CO pathway and concurrent enhancement of the formate pathway in the alkaline methanol electrooxidation. The key role of Bi is enriching OH adsorbates (OHad) on the catalyst surface. The competitive adsorption of CO adsorbates (COad) and OHad at Pt sites, complementing the thermodynamic contribution from alloying Bi with Pt, switches the intermediate from COad to formate that circumvents CO poisoning. Hence, 8% Bi brings an approximately 6-fold increase in activity compared to pure Pt nanoparticles. This notion can be generalized to modify commercially available Pt/C catalysts by a microwave-assisted method, offering opportunities for the design and practical production of CO-tolerance electrocatalysts in an industrial setting.

Tuning Adsorbate-Mediated Strong Metal-Support Interaction by Oxygen Vacancy: A Case Study in Ru/TiO2.

The adsorbate-mediated strong metal-support interaction (A-SMSI) offers a reversible means of altering the selectivity of supported metal catalysts, thereby providing a powerful tool for facile modulation of catalytic performance. However, the fundamental understanding of A-SMSI remains inadequate and methods for tuning A-SMSI are still in their nascent stages, impeding its stabilization under reaction conditions. Here, we report that the initial concentration of oxygen vacancy in oxide supports plays a key role in tuning the A-SMSI between Ru nanoparticles and defected titania (TiO2-x). Based on this new understanding, we demonstrate the in situ formation of A-SMSI under reaction conditions, obviating the typically required CO2-rich pretreatment. The as-formed A-SMSI layer exhibits remarkable stability at various temperatures, enabling excellent activity, selectivity and long-term stability in catalyzing the reverse water gas-shift reaction. This study deepens the understanding of the A-SMSI and the ability to stabilize A-SMSI under reaction conditions represents a key step for practical catalytic applications.

A hybrid model for tuberculosis forecasting based on empirical mode decomposition in China.

BACKGROUND: Pulmonary Tuberculosis is a major public health problem endangering people's health, a scientifically accurate predictive model is of great practical significance for the prevention and treatment of pulmonary tuberculosis. METHODS: The reported incidence data of pulmonary tuberculosis were from the National Public Health Science Data Center ( https://www.phsciencedata.cn/ ). The ARIMA, LSTM, EMD-SARIMA, EMD-LSTM, EMD-ARMA-LSTM models were established using the reported monthly incidence of tuberculosis reported in China from January 2008 to December 2018. The MSE, MAE, RMSE and MAPE were used to evaluate the performance of the models to determine the best model. RESULTS: Comparing decomposition-based single model with undecomposed single model, it was found that: when predicting the incidence trend in the next year, compared with SARIMA model, the MSE, MAE, RMSE and MAPE of EMD-SARIMA decreased by 39.3%, 19.0%, 22.1% and 19.8%, respectively. The MSE, MAE, RMSE and MAPE of EMD-LSTM were reduced by 40.5%, 12.8%, 22.9% and 12.7%, respectively, compared with the LSTM model; Comparing the decomposition-based hybrid model with the decomposition-based single model, it was found that: when predicting the incidence trend in the next year, compared with EMD-SARIMA model, the MSE, MAE, RMSE and MAPE of EMD-ARMA-LSTM model decreased by 21.7%, 10.6%, 11.5% and 11.2%, respectively. The MSE, MAE, RMSE and MAPE of EMD-ARMA-LSTM were reduced by 16.7%, 9.6%, 8.7% and 12.3%, respectively, compared with EMD-LSTM model. Furthermore, the performance of the model were consistent when predicting the incidence trend in the next 3 months, 6 months and 9 months. CONCLUSION: The prediction performance of the decomposition-based single model is better than that of the undecomposed single model, and the prediction performance of the combined model using the advantages of different models is better than that of the decomposition-based single model, so the EMD-ARMA-LSTM combination model can improve the prediction accuracy better than other models, which can provide a theoretical basis for predicting the epidemic trend of pulmonary tuberculosis and formulating prevention and control policies.

Study on the prediction effect of a combined model of SARIMA and LSTM based on SSA for influenza in Shanxi Province, China.

BACKGROUND: Influenza is an acute respiratory infectious disease that is highly infectious and seriously damages human health. Reasonable prediction is of great significance to control the epidemic of influenza. METHODS: Our Influenza data were extracted from Shanxi Provincial Center for Disease Control and Prevention. Seasonal-trend decomposition using Loess (STL) was adopted to analyze the season characteristics of the influenza in Shanxi Province, China, from the 1st week in 2010 to the 52nd week in 2019. To handle the insufficient prediction performance of the seasonal autoregressive integrated moving average (SARIMA) model in predicting the nonlinear parts and the poor accuracy of directly predicting the original sequence, this study established the SARIMA model, the combination model of SARIMA and Long-Short Term Memory neural network (SARIMA-LSTM) and the combination model of SARIMA-LSTM based on Singular spectrum analysis (SSA-SARIMA-LSTM) to make predictions and identify the best model. Additionally, the Mean Squared Error (MSE), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used to evaluate the performance of the models. RESULTS: The influenza time series in Shanxi Province from the 1st week in 2010 to the 52nd week in 2019 showed a year-by-year decrease with obvious seasonal characteristics. The peak period of the disease mainly concentrated from the end of the year to the beginning of the next year. The best fitting and prediction performance was the SSA-SARIMA-LSTM model. Compared with the SARIMA model, the MSE, MAE and RMSE of the SSA-SARIMA-LSTM model decreased by 38.12, 17.39 and 21.34%, respectively, in fitting performance; the MSE, MAE and RMSE decreased by 42.41, 18.69 and 24.11%, respectively, in prediction performances. Furthermore, compared with the SARIMA-LSTM model, the MSE, MAE and RMSE of the SSA-SARIMA-LSTM model decreased by 28.26, 14.61 and 15.30%, respectively, in fitting performance; the MSE, MAE and RMSE decreased by 36.99, 7.22 and 20.62%, respectively, in prediction performances. CONCLUSIONS: The fitting and prediction performances of the SSA-SARIMA-LSTM model were better than those of the SARIMA and the SARIMA-LSTM models. Generally speaking, we can apply the SSA-SARIMA-LSTM model to the prediction of influenza, and offer a leg-up for public policy.

The relationship between social capital and health from a configuration perspective: an evidence from China.

BACKGROUND: The debate on the relationship between social capital and health is still ongoing. To enhance previous research, this study uses data drawn from China to analyse the situations in which social capital is related to good health and the various configurations that result in good health outcomes. METHODS: Using the data of China Family Panel Studies, the conditions of age, gender, marriage, education, income, structural social capital and cognitive social capital were included to analyse the sufficient and necessary conditions for achieving good general health and their different configurations using the fsQCA method. RESULTS: None of the listed conditions were prerequisites for excellent general health in terms of either their presence or their absence. The sufficiency analysis found 11 configurations with an average of 3-4 conditions per configuration; in no configuration was the condition of social capital present alone. Structured social capital and cognitive social capital exhibited negative states in configurations 1 and 2, respectively. The most prevalent factor in all configurations was the condition of age. CONCLUSIONS: The relationship between social capital and health is both positive and negative, with cognitive social capital playing a larger role in the positive relationship than structural social capital. Social capital is neither a necessary nor a sufficient condition for health, and it must be combined with a variety of other factors to promote health. A variety of methods can be used to promote an individual's health, as different populations require different approaches to good general health, and no single pathway applies to all populations. In the Chinese population, an individual's age is a significant determinant of their health status.

An explainable artificial intelligence framework for risk prediction of COPD in smokers.

BACKGROUND: Since the inconspicuous nature of early signs associated with Chronic Obstructive Pulmonary Disease (COPD), individuals often remain unidentified, leading to suboptimal opportunities for timely prevention and treatment. The purpose of this study was to create an explainable artificial intelligence framework combining data preprocessing methods, machine learning methods, and model interpretability methods to identify people at high risk of COPD in the smoking population and to provide a reasonable interpretation of model predictions. METHODS: The data comprised questionnaire information, physical examination data and results of pulmonary function tests before and after bronchodilatation. First, the factorial analysis for mixed data (FAMD), Boruta and NRSBoundary-SMOTE resampling methods were used to solve the missing data, high dimensionality and category imbalance problems. Then, seven classification models (CatBoost, NGBoost, XGBoost, LightGBM, random forest, SVM and logistic regression) were applied to model the risk level, and the best machine learning (ML) model's decisions were explained using the Shapley additive explanations (SHAP) method and partial dependence plot (PDP). RESULTS: In the smoking population, age and 14 other variables were significant factors for predicting COPD. The CatBoost, random forest, and logistic regression models performed reasonably well in unbalanced datasets. CatBoost with NRSBoundary-SMOTE had the best classification performance in balanced datasets when composite indicators (the AUC, F1-score, and G-mean) were used as model comparison criteria. Age, COPD Assessment Test (CAT) score, gross annual income, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), anhelation, respiratory disease, central obesity, use of polluting fuel for household heating, region, use of polluting fuel for household cooking, and wheezing were important factors for predicting COPD in the smoking population. CONCLUSION: This study combined feature screening methods, unbalanced data processing methods, and advanced machine learning methods to enable early identification of COPD risk groups in the smoking population. COPD risk factors in the smoking population were identified using SHAP and PDP, with the goal of providing theoretical support for targeted screening strategies and smoking population self-management strategies.

Integrated Photochromic-Photothermal Processes for Catalytic Plastic Upcycling.

Incorporating high-energy ultraviolet (UV) photons into photothermal catalytic processes may enable photothermal-photochemical synergistic catalysis, which represents a transformative technology for waste plastic recycling. The major challenge is avoiding side reactions and by-products caused by these energetic photons. Here, we break through the limitation of the existing photothermal conversion mechanism and propose a photochromic-photothermal catalytic system based on polyol-ligated TiO2 nanocrystals. Upon UV or sunlight irradiation, the chemically bonded polyols can rapidly capture holes generated by TiO2 , enabling photogenerated electrons to reduce Ti4+ to Ti3+ and produce oxygen vacancies. The resulting abundant defect energy levels boost sunlight-to-heat conversion efficiency, and simultaneously the oxygen vacancies facilitate polyester glycolysis by activating the nucleophilic addition-elimination process. As a result, compared to commercial TiO2 (P25), we achieve 6-fold and 12.2-fold performance enhancements under thermal and photothermal conditions, respectively, while maintaining high selectivity to high-valued monomers. This paradigm-shift strategy directs energetic UV photons for activating catalysts and avoids their interaction with reactants, opening the possibility of substantially elevating the efficiency of more solar-driven catalysis.

Entropy Confinement Promotes Hydrogenolysis Activity for Polyethylene Upcycling.

Chemical upcycling that catalyzes waste plastics back to high-purity chemicals holds great promise in end-of-life plastics valorization. One of the main challenges in this process is the thermodynamic limitations imposed by the high intrinsic entropy of polymer chains, which makes their adsorption on catalysts unfavorable and the transition state unstable. Here, we overcome this challenge by inducing the catalytic reaction inside mesoporous channels, which possess a strong confined ability to polymer chains, allowing for stabilization of the transition state. This approach involves the synthesis of p-Ru/SBA catalysts, in which Ru nanoparticles are uniformly distributed within the channels of an SBA-15 support, using a precise impregnation method. The unique design of the p-Ru/SBA catalyst has demonstrated significant improvements in catalytic performance for the conversion of polyethylene into high-value liquid fuels, particularly diesel. The catalyst achieved a high solid conversion rate of 1106 g ⋅ gRu -1 ⋅ h-1 at 230 °C. Comparatively, this catalytic activity is 4.9 times higher than that of a control catalyst, Ru/SiO2 , and 14.0 times higher than that of a commercial catalyst, Ru/C, at 240 °C. This remarkable catalytic activity opens up immense opportunities for the chemical upcycling of waste plastics.

The enhancer rare germline variation rs548071605 contributes to lung cancer development.

Rare germline variations contribute to the missing heritability of human complex diseases including cancers. Given their very low frequency, discovering and testing disease-causing rare germline variations remains challenging. The tag-single nucleotide polymorphism rs17728461 in 22q12.2 is highly associated with lung cancer risk. Here, we identified a functional rare germline variation rs548071605 (A>G) in a p65-responsive enhancer located within 22q12.2. The enhancer significantly promoted lung cancer cell proliferation in vitro and in a xenograft mouse model by upregulating the leukemia inhibitory factor (LIF) gene via the formation of a chromatin loop. Differential expression of LIF and its significant correlation with first progression survival time of patients further supported the lung cancer-driving effects of the 22q-Enh enhancer. Importantly, the rare variation was harbored in the p65 binding sequence and dramatically increased the enhancer activity by increasing responsiveness of the enhancer to p65 and B-cell lymphoma 3 protein, an oncoprotein that assisted the p65 binding. Our study revealed a regulatory rare germline variation with a potential lung cancer-driving role in the 22q12.2 risk region, providing intriguing clues for investigating the "missing heritability" of cancers, and also offered a useful experimental model for identifying causal rare variations.

Ultra-Stable CsPbX3 @Pyrophosphate Nanoparticles in Water over One Year.

All-inorganic lead halide perovskite (CsPbX3 , X = Cl, Br, I, or their mixture) nanocrystals (NCs) have achieved inspiring advancements in optoelectronic fields but still suffer from poor durability when exposed to environmental stimuli such as water, irradiation and heat. Herein, a strategy of employing pyrophosphate as the inert shell for CsPbX3 NCs is reported. The strong binding between pyrophosphate and CsPbBr3 surface can stabilize the perovskite structure well. The as-obtained core@shell CsPbBr3 @NH4 AlP2 O7 NCs exhibit impressive stability against water and maintain the initial optical properties with negligible change in 400 days. Furthermore, significant improvement of irradiation/thermal resistance is realized due to the protecting role of pyrophosphate. The NCs can retain 100% and ≈90% of the original PL after hundreds of heating/cooling cycles and several hundred hours of UV light irradiation, respectively. As a result, the core@shell products can be directly used for high-resolution inkjet printing, enabling the printed fluorescent information to be resistant under harsh environmental conditions. This work provides a promising way for the synthesis of highly stable encapsulated perovskite NCs and demonstrates a great potential in practical applications.

Suppressing Dehydroisomerization Boosts n-Butane Dehydrogenation with High Butadiene Selectivity.

Butadiene (BD) is a critical raw material in chemical industry, which is conventionally produced from naphtha cracking. The fast-growing demand of BD and the limited oil reserve motivate chemists to develop alternative methods for BD production. Shale gas, which mainly consists of light alkanes, has been considered as cheap raw materials to replace oil for BD production via n-butane direct dehydrogenation (n-BDH). However, the quest for highly-efficient catalysts for n-BDH is driven by the current drawback of low BD selectivity. Here, we demonstrate a strategy for boosting the selectivity of BD by suppressing dehydroisomerization, an inevitable step in the conventional n-BDH process which largely reduces the selectivity of BD. Detailed investigations show that the addition of alkali-earth metals (e. g., Mg and Ca) into Pt-Ga2 O3 /S10 catalysts increases Pt dispersity, suppresses coke deposition and dehydroisomerization, and thus leads to the significant increase of BD selectivity. The optimized catalyst displays an initial BD selectivity of 34.7 % at a n-butane conversion of 82.1 % at 625 °C, which outperforms the reported catalysts in literatures. This work not only provides efficient catalysts for BD production via n-BDH, but also promotes the researches on catalyst design in heterogeneous catalysis.

Research on the predictive effect of a combined model of ARIMA and neural networks on human brucellosis in Shanxi Province, China: a time series predictive analysis.

BACKGROUND: Brucellosis is a major public health problem that seriously affects developing countries and could cause significant economic losses to the livestock industry and great harm to human health. Reasonable prediction of the incidence is of great significance in controlling brucellosis and taking preventive measures. METHODS: Our human brucellosis incidence data were extracted from Shanxi Provincial Center for Disease Control and Prevention. We used seasonal-trend decomposition using Loess (STL) and monthplot to analyse the seasonal characteristics of human brucellosis in Shanxi Province from 2007 to 2017. The autoregressive integrated moving average (ARIMA) model, a combined model of ARIMA and the back propagation neural network (ARIMA-BPNN), and a combined model of ARIMA and the Elman recurrent neural network (ARIMA-ERNN) were established separately to make predictions and identify the best model. Additionally, the mean squared error (MAE), mean absolute error (MSE) and mean absolute percentage error (MAPE) were used to evaluate the performance of the model. RESULTS: We observed that the time series of human brucellosis in Shanxi Province increased from 2007 to 2014 but decreased from 2015 to 2017. It had obvious seasonal characteristics, with the peak lasting from March to July every year. The best fitting and prediction effect was the ARIMA-ERNN model. Compared with those of the ARIMA model, the MAE, MSE and MAPE of the ARIMA-ERNN model decreased by 18.65, 31.48 and 64.35%, respectively, in fitting performance; in terms of prediction performance, the MAE, MSE and MAPE decreased by 60.19, 75.30 and 64.35%, respectively. Second, compared with those of ARIMA-BPNN, the MAE, MSE and MAPE of ARIMA-ERNN decreased by 9.60, 15.73 and 11.58%, respectively, in fitting performance; in terms of prediction performance, the MAE, MSE and MAPE decreased by 31.63, 45.79 and 29.59%, respectively. CONCLUSIONS: The time series of human brucellosis in Shanxi Province from 2007 to 2017 showed obvious seasonal characteristics. The fitting and prediction performances of the ARIMA-ERNN model were better than those of the ARIMA-BPNN and ARIMA models. This will provide some theoretical support for the prediction of infectious diseases and will be beneficial to public health decision making.

Structural, elastic and electronic properties of atomically thin pyridyne: theoretical predictions.

In this paper, we propose a new acetylenic carbon material called pyridyne, which is composed of acetylenic linkages and pyridine rings. From first-principles calculations, we investigate the structural, elastic and electronic properties of pyridyne. It is found that the structure of pyridyne is stable at 300 K and its stability is comparable to experimentally synthesized graphdiyne and graphtetrayne. Compared with graphene or graphyne, pyridyne possesses more diverse pores and reduced delocalization of electrons. The in-plane stiffness of pyridyne is 183 N m-1 with a Poisson's ratio of 0.304. Pyridyne is found to be a semiconductor with a direct band gap of 0.91 eV. The intrinsic electron mobility can reach 6.08 × 104 cm2 V-1 s-1, while the hole mobility can reach 1.82 × 104 cm2 V-1 s-1.

Application of a novel hybrid algorithm of Bayesian network in the study of hyperlipidemia related factors: a cross-sectional study.

BACKGROUND: This article aims to understand the prevalence of hyperlipidemia and its related factors in Shanxi Province. On the basis of multivariate Logistic regression analysis to find out the influencing factors closely related to hyperlipidemia, the complex network connection between various variables was presented through Bayesian networks(BNs). METHODS: Logistic regression was used to screen for hyperlipidemia-related variables, and then the complex network connection between various variables was presented through BNs. Since some drawbacks stand out in the Max-Min Hill-Climbing (MMHC) hybrid algorithm, extra hybrid algorithms are proposed to construct the BN structure: MMPC-Tabu, Fast.iamb-Tabu and Inter.iamb-Tabu. To assess their performance, we made a comparison between these three hybrid algorithms with the widely used MMHC hybrid algorithm on randomly generated datasets. Afterwards, the optimized BN was determined to explore to study related factors for hyperlipidemia. We also make a comparison between the BN model with logistic regression model. RESULTS: The BN constructed by Inter.iamb-Tabu hybrid algorithm had the best fitting degree to the benchmark networks, and was used to construct the BN model of hyperlipidemia. Multivariate logistic regression analysis suggested that gender, smoking, central obesity, daily average salt intake, daily average oil intake, diabetes mellitus, hypertension and physical activity were associated with hyperlipidemia. BNs model of hyperlipidemia further showed that gender, BMI, and physical activity were directly related to the occurrence of hyperlipidemia, hyperlipidemia was directly related to the occurrence of diabetes mellitus and hypertension; the average daily salt intake, daily average oil consumption, smoking, and central obesity were indirectly related to hyperlipidemia. CONCLUSIONS: The BN of hyperlipidemia constructed by the Inter.iamb-Tabu hybrid algorithm is more reasonable, and allows for the overall linking effect between factors and diseases, revealing the direct and indirect factors associated with hyperlipidemia and correlation between related variables, which can provide a new approach to the study of chronic diseases and their associated factors.

Exploratory study on classification of diabetes mellitus through a combined Random Forest Classifier.

BACKGROUND: Diabetes Mellitus (DM) has become the third chronic non-communicable disease that hits patients after tumors, cardiovascular and cerebrovascular diseases, and has become one of the major public health problems in the world. Therefore, it is of great importance to identify individuals at high risk for DM in order to establish prevention strategies for DM. METHODS: Aiming at the problem of high-dimensional feature space and high feature redundancy of medical data, as well as the problem of data imbalance often faced. This study explored different supervised classifiers, combined with SVM-SMOTE and two feature dimensionality reduction methods (Logistic stepwise regression and LAASO) to classify the diabetes survey sample data with unbalanced categories and complex related factors. Analysis and discussion of the classification results of 4 supervised classifiers based on 4 data processing methods. Five indicators including Accuracy, Precision, Recall, F1-Score and AUC are selected as the key indicators to evaluate the performance of the classification model. RESULTS: According to the result, Random Forest Classifier combining SVM-SMOTE resampling technology and LASSO feature screening method (Accuracy = 0.890, Precision = 0.869, Recall = 0.919, F1-Score = 0.893, AUC = 0.948) proved the best way to tell those at high risk of DM. Besides, the combined algorithm helps enhance the classification performance for prediction of high-risk people of DM. Also, age, region, heart rate, hypertension, hyperlipidemia and BMI are the top six most critical characteristic variables affecting diabetes. CONCLUSIONS: The Random Forest Classifier combining with SVM-SMOTE and LASSO feature reduction method perform best in identifying high-risk people of DM from individuals. And the combined method proposed in the study would be a good tool for early screening of DM.

Revealing the Correlation between Catalytic Selectivity and the Local Coordination Environment of Pt Single Atom.

Single atom catalysts (SACs) have recently attracted great attention in heterogeneous catalysis and have been regarded as ideal models for investigating the strong interaction between metal and support. Despite the huge progress over the past decade, the deep understanding on the structure-performance correlation of SACs at a single atom level still remains to be a great challenge. In this study, we demonstrate that the variation in the coordination number of the Pt single atom can significantly promote the propylene selectivity during propyne semihydrogenation (PSH) for the first time. Specifically, the propylene selectivity greatly increases from 65.4% to 94.1% as the coordination number of Pt-O increases from ∼3.4 to ∼5, whereas the variation in the coordination number of Pt-O slightly influences the turnover frequency values of SACs. We anticipate that the present work may deepen the understanding on the structure-performance of SACs and also promote the fundamental research in single atom catalysis.

Bismuth Oxyhydroxide-Pt Inverse Interface for Enhanced Methanol Electrooxidation Performance.

Developing efficient Pt-based electrocatalysts for the methanol oxidation reaction (MOR) is of pivotal importance for large-scale application of direct methanol fuel cells (DMFCs), but Pt suffers from severe deactivation brought by the carbonaceous intermediates such as CO. Here, we demonstrate the formation of a bismuth oxyhydroxide (BiOx(OH)y)-Pt inverse interface via electrochemical reconstruction for enhanced methanol oxidation. By combining density functional theory calculations, X-ray absorption spectroscopy, ambient pressure X-ray photoelectron spectroscopy, and electrochemical characterizations, we reveal that the BiOx(OH)y-Pt inverse interface can induce the electron deficiency of neighboring Pt; this would result in weakened CO adsorption and strengthened OH adsorption, thereby facilitating the removal of the poisonous intermediates and ensuring the high activity and good stability of Pt2Bi sample. This work provides a comprehensive understanding of the inverse interface structure and deep insight into the active sites for MOR, offering great opportunities for rational fabrication of efficient electrocatalysts for DMFCs.

Fluorogenic Probes/Inhibitors of ß-Lactamase and their Applications in Drug-Resistant Bacteria.

ß-Lactam antibiotics are generally perceived as one of the greatest inventions of the 20th century, and these small molecular compounds have saved millions of lives. However, upon clinical application of antibiotics, the ß-lactamase secreted by pathogenic bacteria can lead to the gradual development of drug resistance. ß-Lactamase is a hydrolase that can efficiently hydrolyze and destroy ß-lactam antibiotics. It develops and spreads rapidly in pathogens, and the drug-resistant bacteria pose a severe threat to human health and development. As a result, detecting and inhibiting the activities of ß-lactamase are of great value for the rational use of antibiotics and the treatment of infectious diseases. At present, many specific detection methods and inhibitors of ß-lactamase have been developed and applied in clinical practice. In this Minireview, we describe the resistance mechanism of bacteria producing ß-lactamase and further summarize the fluorogenic probes, inhibitors of ß-lactamase, and their applications in the treatment of infectious diseases. It may be valuable to design fluorogenic probes with improved selectivity, sensitivity, and effectiveness to further identify the inhibitors for ß-lactamases and eventually overcome bacterial resistance.