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The advancement of heterogeneous catalysts incorporating metal clusters in the nanometric size range has garnered significant attention due to their extraordinary catalytic activity and selectivity. The detailed characterization and understanding of the atomic structure of these metal clusters within catalysts is crucial for elucidating the underlying reaction mechanisms. In the present study, a distinctive three-atom PdNi cluster, characterized by two Pd atoms at terminal positions and a central Ni atom, was synthesized over mordenite zeolite. The presence of atomic PdNi clusters within the eight-membered ring side pocket area was confirmed by multiple advanced analytical techniques, including magic-angle spinning nuclear magnetic resonance spectroscopy, synchrotron X-ray powder diffraction, extended X-ray absorption fine structure spectroscopy, and high-angle annular dark-field scanning transmission electron microscopy. The catalytic activity of the confined active species was examined by the carbene-mediated reactions of ethyl-2-diazoacetate to ethyl-2-methoxyacetate as a model reaction. Compared to the Pd-mordenite and Ni-mordenite, the PdNi-mordenite catalyst incorporates a PdNi cluster, which demonstrates a superior performance, achieving 100% conversion and high selectivity under the same reaction conditions. Our study elucidates the potential of constructing bimetallic clusters in zeolites, providing valuable insights for developing new heterogeneous catalysts applicable to a wide range of catalytic processes. [ABSTRACT FROM AUTHOR]

Designing high-performance thermal catalysts with stable catalytic sites is an important challenge. Conventional wisdom holds that strong metal-support interactions can benefit the catalyst performance, but there is a knowledge gap in generalizing this effect across different metals. Here, we have successfully developed a generalizable strong metal-support interaction strategy guided by Tammann temperatures of materials, enabling functional oxide encapsulation of transition metal nanocatalysts. As an illustrative example, Co@BaAl2O4 core@shell is synthesized and tracked in real-time through in-situ microscopy and spectroscopy, revealing an unconventional strong metal-support interaction encapsulation mechanism. Notably, Co@BaAl2O4 exhibits exceptional activity relative to previously reported core@shell catalysts, displaying excellent long-term stability during high-temperature chemical reactions and overcoming the durability and reusability limitations of conventional supported catalysts. This pioneering design and widely applicable approach has been validated to guide the encapsulation of various transition metal nanoparticles for environmental tolerance functionalities, offering great potential to advance energy, catalysis, and environmental fields. The authors report a synthetic strategy to create core@shell catalysts using strong metal-support interactions and low-Tammann-temperature compounds. The resulting materials are highly stable and may be useful in industrial applications. [ABSTRACT FROM AUTHOR]

Fuel cells are new energy technologies with broad application prospects. Carbon-supported platinum-based catalysts (Pt/C) are the most commonly used fuel cell electrode catalysts, but Pt/C has poor stability and high cost, which severely limits the large-scale application of fuel cells. Diamond and covalent carbides (silicon carbide and boron carbide) have excellent physicochemical stability and become important basic materials for the preparation of highly stable and low-cost fuel cell catalysts due to their strong covalent bonds. This paper introduces the unique advantages of diamond, silicon carbide, and boron carbide as electrocatalysts and summarizes the related research results. [ABSTRACT FROM AUTHOR]

In recent years,. the rapid development of the Yangtze River Delta in China has led to increasingly serious regional eco-environmental problems. Therefore, it is of great significance for the construction of ecological civilization to study the ecosystem health in the Yangtze River Delta. In this paper, the assessment framework of "Vigor-Organization-Resilience" was used to assess the ecosystem health index (EHI) of the Yangtze River Delta from 2000 to 2020, and then the spatial autocorrelation method was used to analyze the agglomeration of EHI in 314 counties in this region. Finally, the eXtreme Gradient Boosting (XGBoost) model and the SHapley Additive exPlanation (SHAP) model were combined to explore the synergistic impact of EHI driving factors. The results show that (1) from 2000 to 2020, the EHI in the Yangtze River Delta is at the level of ordinary health, and gradually decreased; (2) the EHI has significant spatial positive correlation and aggregation; (3) the driving factors in descending order of importance are urbanization level (UL), precipitation (PRE), PM2.5 (PM), normalized difference vegetation index (NDVI), and temperature (TEMP); and (4) the relationship between UL and EHI is logarithmic; PRE and EHI are quartic polynomial; PM, NDVI, TEMP, and EHI are quadratic polynomial. The results of this paper are of great significance to the management and restoration of the ecosystem in this region [ABSTRACT FROM AUTHOR]

At present, the remote sensing (RS) thermal infrared (TIR) images that are commonly used to obtain land surface temperature (LST) are contaminated by clouds and thus cannot obtain spatiotemporal integrity of LST. To solve this problem, this study combined a physical model with strong interpretability with a data-driven model with high data adaptability. First, the physical model (Weather Research and Forecast (WRF) model) was used to generate LST source data. Then, combined with multisource RS data, a data-driven method (random forest (RF)) was used to improve the accuracy of the LST, and a model framework for a data-driven auxiliary physical model was formed. Finally, all-weather MODIS-like data with a spatial resolution of 1 km were generated. Beijing, China, was used as the study area. The results showed that in cases of more clouds and fewer clouds, the reconstructed all-weather LST had a high spatial continuity and could restore the spatial distribution details of the LST well. The mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (ρ) in the case of more (fewer) clouds were ranked as follows: MAE < 1 K (< 2 K), RMSE < 2 K (< 2 K), and ρ > 0.9. The errors obeyed an approximately normal distribution. The total MAE, RMSE, and ρ were 0.80 K, 1.09 K, and 0.94 K, respectively. Generally, the LST reconstructed in this paper had a high accuracy, and the model could provide all-weather MODIS-like LST to compensate for the disadvantages of satellite TIR images (i.e., contamination by clouds and inability to obtain complete LST values). [ABSTRACT FROM AUTHOR]

Spatiotemporal dynamic vegetation changes affect global climate change, energy balances and the hydrological cycle. Predicting these dynamics over a long time series is important for the study and analysis of global environmental change. Based on leaf area index (LAI), climate, and radiation flux data of past and future scenarios, this study looked at historical dynamic changes in global vegetation LAI, and proposed a coupled multiple linear regression and improved gray model (CMLRIGM) to predict future global LAI. The results show that CMLRIGM predictions are more accurate than results predicted by the multiple linear regression (MLR) model or the improved gray model (IGM) alone. This coupled model can effectively resolve the problem posed by the underestimation of annual average of global vegetation LAI predicted by MLR and the overestimate predicted by IGM. From 1981 to 2018, the annual average of LAI in most areas covered by global vegetation (71.4%) showed an increase with a growth rate of 0.0028 a–1; of this area, significant increases occurred in 34.42% of the total area. From 2016 to 2060, the CMLRIGM model has predicted that the annual average global vegetation LAI will increase, accounting for approximately 68.5% of the global vegetation coverage, with a growth rate of 0.004 a−1. The growth rate will increase in the future scenario, and it may be related to the driving factors of the high emission scenario used in this study. This research may provide a basis for simulating spatiotemporal dynamic changes in global vegetation conditions over a long time series. [ABSTRACT FROM AUTHOR]

Urban renewal can transform areas that are not adapted to modern urban life, allowing them to redevelop and flourish; however, the renewal process generates many new construction sites, producing environmentally harmful construction dust. The widespread use of urban green plastic cover (GPC) at construction sites and the development of high-resolution satellites have made it possible to extract the spatial distribution of construction sites and provide a basis for environmental protection authorities to protect against dust sources. Existing GPC extraction methods based on remote sensing images are either difficult to obtain the exact boundary of GPC or cannot provide corresponding algorithms according to different application scenarios. In order to determine the distribution of green plastic cover in the built-up area, this paper selects a variety of typical machine learning algorithms to classify the land cover of the test area image and selects K-nearest neighbor as the best machine learning algorithm through accuracy evaluation. Then multiple deep learning methods were used and the top networks with high overall scores were selected by comparing various aspects. Then these networks were used to predict the GPC of the test area image, and the accuracy evaluation results showed that the segmentation accuracy of deep learning was much higher than that of machine learning methods, but it took more time to predict. Therefore, combining different application scenarios, this paper gives the corresponding suggested methods for GPC extraction. [ABSTRACT FROM AUTHOR]

Olverembatinib represents the third-generation breakpoint cluster region protein-Abelson-murine leukemia 1 (BCR-ABL1) tyrosine kinase inhibitor with oral bioavailability, which can be used to overcome the T315I mutation in Philadelphia chromosome–positive (Ph +) leukemia. BCR-ABL-independent resistance to olverembatinib has been reported among patients in various clinical cases. However, the mechanism of olverembatinib resistance has rarely been reported. This study has illustrated bone marrow cell transcriptome and metabolome profiles among Ph + acute lymphoblastic leukemias (ALL) cases pre- and post-olverembatinib resistance. The transcriptome studies demonstrated that PI3K/AKT, purine metabolism, and other signaling pathways could play a vital role in olverembatinib resistance. As suggested by metabolomics, olverembatinib resistance in Ph + ALL was associated with purine metabolism alterations. Subsequently, high-performance liquid chromatography along with real-time quantitative PCR was utilized to measure purine metabolism-related mRNA levels and metabolism expression levels between olverembatinib resistance and sensitive cell lines. Our results elucidate the mechanism of olverembatinib resistance in Ph + ALL at transcriptome and metabolome levels, which facilitate a better understanding of olverembatinib resistance and hence may prove crucial in identifying novel drugs to tackle this conundrum. [ABSTRACT FROM AUTHOR]

It is of great significance for broadening the electrochemical window of poly(ethylene oxide) (PEO)–based solid electrolyte to match with high voltage lithium cobalt oxide for the commercial application of solid-state lithium-ion batteries. In this paper, LiPO2F2 was introduced as an additive to increase the stability of PEO-based polymer solid electrolytes. After LiPO2F2 addition, the oxidative decomposition voltage of PEO increased from 3.8 to 4.8 V. The LiCoO2/PEO-LiTFSI-LiPO2F2/Li cell showed a specific discharge capacity of 103.2 mAh g−1 at 0.2 C after 100 cycles with a capacity retention of 83.8%, which is much better than that of LiCoO2/PEO-LITFSI/Li cell (maintained at 8.7 mAh g−1 after only 70 cycles). The improved electrochemical performance can be attributed to the LiPO2F2 decomposition earlier than PEO during the cycles, and the products acted as a protective layer to stabilize PEO at high voltage. The novel LiPO2F2-modified PEO electrolyte strategy provides a new approach for solid-state batteries, especially for their high voltage applications. [ABSTRACT FROM AUTHOR]

The increased growth of vegetation has the potential to slow global climate warming. Therefore, analyzing and predicting the response assessment of Chinese vegetation to climate change is of great significance to studies of global warming. In this paper, we examine the spatiotemporal dynamics of vegetation leaf area index (LAI) values in China from 1981 to 2017 and their correlations with meteorological (hydrothermal) factors based on trend analysis and correlation analysis. We further construct an LAI prediction model based on hydrothermal conditions. The climate data obtained under different scenarios in the CMIP5 and CMIP6 climate models were used to predict the dynamic change trend of vegetation LAI from 2021 to 2100. The results show that most areas of China (72.82%) showed an improving trend in vegetation LAI from 1981 to 2017, during which the annual average LAI value increased at a rate of 0.0029 year−1. Vegetation LAI in China was significantly correlated with climatic factors (temperature, precipitation, and evapotranspiration), and the LAI prediction model constructed based on hydrothermal conditions had a high accuracy (Pearson's Cor value is 0.9729). From 2021 to 2100, approximately 2/3 of China's vegetation LAI area showed an improvement trend, and the impact of climate change on vegetation LAI predictions under the high emission scenario was greater than that under the low emission scenario. This research can provide a basis for studies on the climatic drivers of vegetation change and the global vegetation dynamic model. [ABSTRACT FROM AUTHOR]

Porous graphitic carbon nanorings (PGCNs) are proposed by smart catalytic graphitization of nano-sized graphene quantum dots (GQDs). The as-prepared PGCNs show unique ring-like morphology with diameter around 10 nm, and demonstrate extraordinary mesoporous structure, controllable graphitization degree and highly defective nature. The mechanism from GQDs to PGCNs is proven to be a dissolution-precipitation process, undergoing the procedure of amorphous carbon, intermediate phase, graphitic carbon nanorings and graphitic carbon nanosheets. Further, the relationship between particles size of GQDs precursor and graphitization degree of PGCNs products is revealed. The unique microstructure implies PGCNs a broad prospect for energy storage application. When applied as negative electrode materials in dual-carbon lithium-ion capacitors, high energy density (77.6 Wh·kg−1) and super long lifespan (89.5% retention after 40,000 cycles at 5.0 A·g−1) are obtained. The energy density still maintains at 24.5 Wh·kg−1 even at the power density of 14.1 kW·kg−1, demonstrating excellent rate capability. The distinct microstructure of PGCNs together with the strategy for catalytic conversion from nanocarbon precursors to carbon nanorings opens a new window for carbon materials in electrochemical energy storage. [ABSTRACT FROM AUTHOR]

Flow and heat transfer of a novel turbine shroud with throttle chamber under two kinds of orifice arrangements were numerically studied. The original shroud model composed of the impingement holes, film holes and jet channel. The two modified models have identical geometrical spacing except for the number and location of the orifices in the upper plate of the throttle chamber added in the jet channel. Different pressure values were set at the outlets of different row film holes simulating the mainstream favorable pressure gradient. The ratios of inlet total pressures of impingement holes to outlet static pressures of the third row film holes ranged from 1.6 to 3.6. The Nusselt number distributions were validated by the experimental data. The main target of this study was to quantify the impact of the throttle chamber on the flow regulation and internal heat transfer characteristics. The flow factor, relative mass flow rate, the Nusselt number and the heat transfer factors on the target walls were presented. It is found that the mass flow rate distributions in the film hole rows become more reasonable by the modification of location and number of the orifices on the throttle chamber. The throttle chamber decreases the heat transfer on the target walls. [ABSTRACT FROM AUTHOR]

A growing number of electrochemical oxidation system was employed for the degradation of refractory contaminants. In this study, a boron-doped diamond (BDD) anode/Ti cathode equipped in the differential column batch reactor (DCBR) was utilized for electrochemical oxidation of ciprofloxacin (CIP). The feed solution within the DCBR system was confirmed as a uniform flow state through a computational fluid dynamics (CFD) simulation analysis. The results showed that the BDD anode/Ti cathode electrochemical system was with a high efficiency oxidation performance when treating the CIP contaminant. The CIP was completely degraded within 20 min, and over 50% DOC removed after 120 min. Therefore, two-stage electrochemical oxidation mechanism was proposed. Four major factors, the initial concentration, current density, pH, and electrolyte concentration, on the CIP degradation efficiency were systematically investigated. The CIP degradation curve followed pseudo first-order degradation kinetics. The electric efficiency per order (EE/O) of the electrochemical oxidation system was calculated to determine an optimal operation condition. Moreover, the oxidation intermediates were identified with a mass spectrometry (LC/MS/MS) and the degradation pathways were proposed in this study. The destruction of quinolone moiety and piperazine ring and fluorine substitution were the three possible degradation pathways during BDD anode oxidation process. [ABSTRACT FROM AUTHOR]

Film cooling performance about a row of dual-fanned holes with injection angles of 30°, 60 ° and 90° were experimentally investigated at blowing ratios of 1.0 and 2.0. Dual-fanned hole is a novel shaped hole which has both inlet expansion and outlet expansion. A transient thermochromic liquid crystal technique was used to reveal the local values of film cooling effectiveness and heat transfer coefficient. The results show that injection angles have strong influence on the two dimensional distributions of film cooling effectiveness and heat transfer coefficient. For the small injection angle of 30 degree and small blowing ratio of 1.0, there is only a narrow spanwise region covered with film. The increase of injection angle and blowing ratio both leads to the enhanced spanwise film diffusion, but reduced local cooling ability far away from the hole. Injection angles have comprehensive influence on the averaged film cooling effectiveness for various x/d locations. As injection angles are 30 and 60 degree, two bands of high heat transfer coefficients are found in mixing region of the gas and coolant. As injection angle increases to 90 degree, the mixing leads to the enhanced heat transfer region near the film hole. The averaged heat transfer coefficient increases with the increase of injection angle. [ABSTRACT FROM AUTHOR]

The degradation kinetics and mechanism of the antiepileptic drug oxcarbazepine (OXC) by UV-activated persulfate oxidation were investigated in this study. Results showed that UV/persulfate (UV/PS) process appeared to be more effective in degrading OXC than UV or PS alone. The OXC degradation exhibited a pseudo-first order kinetics pattern and the degradation rate constants ( k) were affected by initial OXC concentration, PS dosage, initial pH, and humic acid concentration to different degrees. It was found that low initial OXC concentration, high persulfate dosage, and initial pH enhanced the OXC degradation. Additionally, the presence of humic acid in the solution could greatly inhibit the degradation of OXC. Moreover, hydroxyl radical (OH•) and sulfate radical (SO••) were identified to be responsible for OXC degradation and SO• made the predominant contribution in this study. Finally, major intermediate products were identified and a preliminary degradation pathway was proposed. Results demonstrated that UV/PS system is a potential technology to control the water pollution caused by emerging contaminants such as OXC. [ABSTRACT FROM AUTHOR]

Targeted therapy based on ALK tyrosine kinase inhibitors (ALK-TKIs) has made significant achievements in individuals with EML4-ALK (echinoderm microtubule-associated protein-like 4 gene and the anaplastic lymphoma kinase gene) fusion positive nonsmall-cell lung cancer (NSCLC). However, a high fraction of patients receive inferior clinical response to such treatment in the initial therapy, and the exact mechanisms underlying this process need to be further investigated. In this study, we revealed a persistently activated PI3K/AKT signaling that mediates the drug ineffectiveness. We found that genetic or pharmacological inhibition of ALK markedly abrogated phosphorylated STAT3 and ERK, but it failed to suppress AKT activity or induce apoptosis, in EML4-ALK-positive H2228 cells. Furthermore, targeted RNA interference of PI3K pathway components restored sensitivity to TAE684 treatment at least partially due to increased apoptosis. Combined TAE684 with PI3K inhibitor synergistically inhibited the proliferation of EML4-ALK-positive cells in vitro and significantly suppressed the growth of H2228 xenografts in vivo, suggesting the potential clinical application of such combinatorial therapy regimens in patients with EML4-ALK positive lung cancer. [ABSTRACT FROM AUTHOR]

When predicting aerosol optical depth (AOD) values for geographical weighted regression (GWR) and geographical and temporal weighted regression (GTWR), the input variables are influenced by multiple collinearity. Additionally, too many input variables make the model computationally complex, and too few input variables can reduce the prediction accuracy. In this study, a nonlinear principal component analysis geographical weighted regression method (NLPCA-GWR) and a nonlinear principal component analysis geographical and temporal weighted regression method (NLPCA-GTWR) are proposed. The NLPCA-GWR and NLPCA-GTWR methods use nonlinear principal component analysis (NLPCA) to reduce the dimensionality of several related variables that influence the AOD and to obtain several comprehensive indicators. The obtained comprehensive indicators are used as dependent variables that are input into the GWR and GTWR models to predict AOD values. To test the effectiveness of the NLPCA-GWR and NLPCA-GTWR methods, this paper uses Beijing, Tianjin, and Hebei AOD data from April 2015; air quality data; meteorological data; and geospatial data as experimental data to model and compare the GWR and GTWR methods with the same number of input variables. The results show that the MAE, RMSE, AIC, R2, and R j 2 of the NLPCA-GWR method are 13.58%, 6.99%, 13.86%, 4.07%, and 3.96% higher, respectively, than those of the GWR method. Compared with the GTWR method, the NLPCA-GTWR method improved the MAE, RMSE, AIC, R2, and R j 2 by 6.53%, 2.91%, 2.17%, 1.14%, and 1.14%, respectively. [ABSTRACT FROM AUTHOR]