中国石化

The Beibuwan Basin is a Cenozoic extensional basin whose Leiqiong region hosts the youngest intraplate volcanic field in South China. Although a mantle plume has been proposed for this magmatism, its location and timing, along with the crustal melting condition, remain controversial. The tectono-thermal evolution of this basin, critical for understanding geodynamic processes and geothermal genesis, remains poorly constrained. Here we employ a multi-episodic finite extension model to quantify lithospheric stretching factors, strain rates, and thermal structure along three profiles and 19 wells. Results show total stretching factors ranging from 1.04 to 1.76, with peak strain rates during the Paleocene, middle Eocene, and Oligocene. Basement heat flow increased progressively from 60 to 62.5 mW/m2 at 65 Ma to 67-85.5 mW/m2 at 23.5 Ma, subsequently decreasing to 65-82 mW/m2 at present, indicating incomplete lithospheric thermal equilibration. Present-day Moho temperatures vary between 534 degrees C and 735 degrees C, with average Curie interface depth and thermal lithospheric thickness of 22 and 78 km, respectively. Mean present-day mantle heat flow is 45 mW/m2, constituting 69% of the terrestrial heat flow. Model results indicate that Paleogene extension greatly enhanced mantle heat flow and temperatures, promoting potential crustal melting at 25-35 km depth. Limited anomalous post-rift subsidence, accompanied by localized uplift during the early Miocene and Quaternary, indicates the presence of hot mantle upwelling that provided an additional heat source. These findings offer geothermal evidence that the interplay between lithospheric extension and hot mantle upwelling drove the late Cenozoic magmatism in the Leiqiong area.

The aerobic oxidation of HMF to FDCA offers a promising pathway for biomass utilization, especially for producing the biobased polymer PEF. Herein, a series of Mn2O3 with various specific surface areas were prepared via a facile two-step process. The catalytic results demonstrated that Mn2O3 exhibited a higher intrinsic activity (3.3 mg(FDCA)& centerdot;m(-2)& centerdot;h(-1)) than common manganese oxides, such as Mn3O4 and different crystalline phases of MnO2 (alpha, beta, and delta). Under optimized conditions, the Mn2O3-140-A catalyst achieved an FDCA selectivity of 91.3% at similar to 100% HMF conversion, with a carbon balance of 97.8%. Combined characterization and catalytic testing revealed a clear positive linear correlation between specific surface area and activity, attributed to the increased concentration of accessible active sites associated with oxygen vacancies. Kinetic analysis identified the slowest steps in the two reaction pathways (DFF path and HMFCA path) and their dependence on reaction temperature. These findings demonstrate that Mn2O3 can act as a promising catalyst for HMF aerobic oxidation to FDCA, and provide valuable insights for the design of advanced Mn2O3 catalysts.

0 INTRODUCTION Shale gas reservoirs possess considerable resource potential and economic viability,making it attractive tar-gets for development (Zou et al.,2023;Hughes,2013). In the past 15 years,China has achieved major break-throughs in marine shale gas exploration and develop-ment within the Middle-Upper Yangtze Platform. Five shale gas fields with proven reserves exceeding 100 bil-lion cubic meters,including Fuling,Weiyuan,Changning,Weirong and Qijiang,have been proven in the Sichuan Basin (Guo et al.,2016). Among these shale gas field,Fuling shale gas field is the first large-scale shale gas field in China,with its cumulative production exceeded 66.5 billion cubic meters by June 2025. The exploration and discovery of these large-scale shale gas fields confirm the immense resource potential of marine shale gas in China.

Paired electrochemical valorization reactions (EVRs) offer a sustainable route for co-producing value-added chemicals, yet their efficiency is hindered by overlapping potential windows with water electrolysis reactions (WER). Here, an asymmetric CuOx@Ag electrocatalyst platform is proposed that decouples both anodic and cathodic EVRs from WER via Ag-mediated oxophilicity modulation. Atomic-level Ag incorporation suppresses *OH/H2O adsorption and widens the potential windows to 422 mV (anode) and 502 mV (cathode) in a glycerol oxidation | 4-nitrophenol reduction pair. The system achieves > 90% Faradaic efficiency and > 70 mmol h(-1) cm(-2) productivity. In situ and DFT analyses reveal that Ag-induced weakening of Cu-O interactions underlies the WER suppression mechanism. Techno-economic and life-cycle evaluations indicate 31.6% cost reduction and 57.4% lower CO2 emissions versus thermochemical routes. The CuOx@Ag platform thus provides a scalable and general strategy for low-carbon electrosynthesis across diverse redox systems.

China targets carbon peak by 2030 and neutrality by 2060. Catalytic CO2 hydrogenation to methanol using renewable H2 is a promising route for carbon recycling and emission reduction. However, developing efficient catalysts is hindered by complex structure-performance relationships. This study addresses this by constructing a dataset (112 samples and 28 features: composition, processing, and properties) and applying machine learning to predict Cu-based catalyst performance. Six models-random forest, XGBoost, LightGBM, gradient boosting, SVR, and DNN-were compared for predicting CO2 conversion, CH3OH selectivity, and yield. Optimal models varied as follows: SVR best predicted CO2 conversion (test R 2 = 0.245), XGBoost excelled for CH3OH selectivity (test R 2 = 0.922 and MSE = 46.508), whereas CH3OH yield prediction was poor (LightGBM best and R 2 = 0.006). SHAP analysis revealed key nonlinear feature contributions (e.g., Cu content, GHSV, and temperature). Focusing on CH3OH selectivity, XGBoost was optimized as follows: multialgorithm voting and stepwise elimination identified 12 key features (e.g., Zn salt type, In content, and drying time). Bayesian hyperparameter tuning boosted performance (test R 2 = 0.9352). SHAP provided interpretability and design guidance. Bootstrap resampling validated reliability (95% CIs). An online prediction platform enhanced screening efficiency. Despite strong test performance, cross-validation R 2 (0.7473) indicates a need for larger datasets. This work provides a robust data-driven framework for optimizing CO2-to-methanol catalysts, demonstrating ML's potential in catalysis research.