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Improved, highly active cathode materials are needed to promote the commercialization of ceramic fuel cell technology. However, the conventional trial-and-error process of material design, characterization and testing can make for a long and complex research cycle. Here we demonstrate an experimentally validated machine-learning-driven approach to accelerate the discovery of efficient oxygen reduction electrodes, where the ionic Lewis acid strength (ISA) is introduced as an effective physical descriptor for the oxygen reduction reaction activity of perovskite oxides. Four oxides, screened from 6,871 distinct perovskite compositions, are successfully synthesized and confirmed to have superior activity metrics. Experimental characterization reveals that decreased A-site and increased B-site ISAs in perovskite oxides considerably improve the surface exchange kinetics. Theoretical calculations indicate such improved activity is mainly attributed to the shift of electron pairs caused by polarization distribution of ISAs at sites A and B, which greatly reduces oxygen vacancy formation energy and migration barrier. The slow research cycle of material design, characterization and testing has hampered the development of new cathode materials for solid oxide fuel cells. Here the authors develop a machine-learning approach, which makes use of ionic Lewis acid strength as a descriptor, for discovery of improved perovskite oxide cathodes.

Traditional building materials have significant limitations in function and performance: insulation materials are easy to peel and age, waterproof materials have a short life, and fireproof materials have degraded flame retardancy. These shortcomings cannot meet the needs of modern buildings for energy efficiency, safety and durability. Therefore, it is imperative to study gradient building materials that integrate function and structure. In this study, based on the non-Fourier heat conduction law, a heat wave propagation model is established to derive a complete analytical solution for the heat wave scattering field of a subsurface circular defect in an exponentially gradient material. The effects of thermal diffusion length ( µ / a ), wave number ( ka ), non-uniformity coefficient ( σ ₁ a ), and defect embedding ratio ( b / a ) on the surface temperature distribution are systematically analysed by the wavefunction expansion method and the virtual mirror technique combined with the independently developed numerical procedure. The results show that: the peak temperature amplitude occurs in the region directly in front of the scatterer; the thermal fluctuation effect is significantly enhanced with the increase of the thermal diffusion length or the decrease of the defect size; the temperature fluctuation response is strengthened by the high modulation frequency (large ka ) and the shallow burial depth of the defects; and the increase of the non-uniformity parameter of the material σ ₁ a results in the increase of the surface temperature. The study confirms the limitations of traditional Fourier’s law in short-pulse heat conduction scenarios, and the results provide theoretical basis and data support for the design of functional gradient materials and nondestructive inspection by infrared thermography.

We presented an antibiotic-loaded γ-cyclodextrin metal-organic framework that delivered antibiotics suitable for the treatment of bacterial infections. The γ-cyclodextrin metal-organic framework was developed using γ-cyclodextrin and potassium ion via the ultrasonic method. The antibiotic (florfenicol and enrofloxacin) was primarily encapsulated into the pore structures of γ-CD-MOF, which allowed the sustained release of antibiotics over an extended period of time in vitro and in vivo. Notably, antibiotics-loaded γ-CD-MOF showed much superior activity against bacteria than free antibiotics (lower MIC value) and displayed better long-lasting activity (longer antibacterial time). The antibiotics-loaded γ-CD-MOF showed nontoxic and perfect biocompatibility to mammalian cells and tissues both in vitro and in vivo. These materials thus represent a novel drug-delivery device suitable for antibiotic therapy. This research is of great significance for reducing the generation of bacterial resistance and providing new ideas for the application of γ-CD-MOF.

Background Massive hemoptysis is a life-threatening condition that requires immediate treatment. This study aimed to retrospectively analyze the outcome of bronchial artery embolization (BAE) for massive hemoptysis, as well as potential factors that may contribute to the recurrence of hemoptysis after BAE. Methods A total of 105 patients with massive hemoptysis treated with BAE were analyzed. Results The immediate control rate of bleeding was 84.8% (67/79); however, during the 36-month follow-up, 45.3% (29 out of 64) of the patients had recurrent hemoptysis after BAE. Comorbidities, pituitary hormone treatment, the angiographic appearance of artery dilation and hypertrophy, and the materials used for BAE were significantly correlated with the success rate of the BAE, while lack of pituitary hormone treatment and existence of arterio-arterial or arteriovenous fistula were risk factors for the recurrence of hemoptysis after BAE. Only a small proportion of patients (9/105, 8.6%) had mild complications after BAE treatment. Conclusion Findings suggest that BAE continues to be an effective treatment for massive hemoptysis in emergency settings. Moreover, the treatment of underlying pulmonary diseases and comorbidities is important to increase BAE’s success rate of BAE and decrease the risk of recurrent hemoptysis after BAE.

This research examines the scattering of elastic waves and the phenomenon of dynamic stress concentration in piezoelectric smart building materials and structures containing holes of arbitrary shapes. It is grounded in the principles of elastic dynamics theory. The analysis leverages Liu’s complex variable function and conformal mapping methods to scrutinize the dynamic stress distribution in proximity to a solitary elliptical hole and a pair of circular holes. The study delves into the influence of various factors, including the incident wave number, elliptical eccentricity, and hole spacing, on the dynamic stress concentration factor. The findings reveal that, although the dynamic stress concentration factor exhibits predictable patterns as the wave number fluctuates, it remains highly susceptible to changes in these parameters, demonstrating symmetrical yet irregular variations. This research is crucial for addressing the challenges posed by holes and defects in piezoelectric materials during engineering design and service.

Accurately predicting the deviation factor (Z-factor) of natural gas is crucial for the estimation of natural gas reserves, evaluation of gas reservoir recovery, and assessment of natural gas transport in pipelines. Traditional machine learning algorithms, such as Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Artificial Neural Network (ANN) and Bidirectional Long Short-Term Memory Neural Networks (BiLSTM), often lack accuracy and robustness in various situations due to their inability to generalize across different gas components and temperature-pressure conditions. To address this limitation, we propose a novel and efficient machine learning framework for predicting natural gas Z-factor. Our approach first utilizes a signal decomposition algorithm like Variational Mode Decomposition (VMD), Empirical Fourier Decomposition (EFD) and Ensemble Empirical Mode Decomposition (EEMD) to decouple the Z-factor into multiple components. Subsequently, traditional machine learning algorithms is employed to predict each decomposed Z-factor component, where combination of SVM and VMD achieved the best performance. Decoupling the Z-factors firstly and then predicting the decoupled components can significantly improve prediction accuracy of all traditional machine learning algorithms. We thoroughly evaluate the impact of the decoupling method and the number of decomposed components on the model’s performance. Compared to traditional machine learning models without decomposition, our framework achieves an average correlation coefficient exceeding 0.99 and an average mean absolute percentage error below 0.83% on 10 datasets with different natural gas components, high temperatures, and pressures. These results indicate that hybrid model effectively learns the patterns of Z-factor variations and can be applied to the prediction of natural gas Z-factors under various conditions. This study significantly advances methodologies for predicting natural gas properties, offering a unified and robust solution for precise estimations, thereby benefiting the natural gas industry in resource estimation and reservoir management.

This study presents a novel approach for optimizing hydraulic fracture propagation and fracturing design in deep coalbed methane (CBM) reservoirs, specifically focusing on the Changqing Oilfield in the eastern Ordos Basin. The increasing demand for clean energy underscores the strategic importance of CBM as an unconventional natural gas resource. However, significant variability in fracturing effectiveness has limited efficient production from deep CBM reservoirs. Unlike previous studies that primarily focus on shallow coal reservoirs, this work goes beyond existing efforts by developing detailed structural and geomechanical models incorporating well log data from 31 wells, allowing for a more accurate simulation of fracture propagation under varying fracturing conditions. Through numerical simulations, the study identifies key parameters—such as segment cluster ratio, fluid volume, and injection rate—that significantly influence fracture length and stimulated reservoir volume. The results indicate that optimizing fluid volume and segment cluster ratio can enhance fracture propagation and improve the total stimulated reservoir volume, particularly in reservoirs with stronger rock plasticity and higher permeability. These findings provide valuable insights into the optimization of hydraulic fracturing designs, contributing to improved gas production efficiency and better reservoir stimulation in deep CBM reservoirs.

Lithofacies form the basis for evaluating shale gas fields and play an important role in gas reservoir enrichment. The accurate identification of shale lithofacies is key for exploration and development. Based on well‐logged data, the accuracy of mineral content prediction using machine‐learning regression models is not ideal. Therefore, feature derivation was introduced to enhance the correlation between minerals and lithofacies and improve the data expression ability. Four machine‐learning models for mineral regression were established based on feature‐derived data sets: LightGBM, XGBoost, artificial neural network, and support vector machine. By calculating the evaluation metrics of each model, we found that LightGBM had the best prediction performance. To compare and confirm the accuracy of the model in identifying lithofacies, this study established a new method, MT‐LightGBM, which combines the LightGBM mineral content regression model with mineral ternary diagrams to identify lithofacies. By using the MT‐LightGBM model and LightGBM classification models to identify the target lithofacies, it was found that the accuracy of lithofacies identification of MT‐LightGBM reached 94%. This accuracy is high and is of great significance for understanding and evaluating underground shale reservoirs.

The measurement of turbulence is necessary to quantify the vertical, diapycnal transport of heat, water and substances influencing climate, nutrient supply and marine ecosystems. As specialist instrumentation and ship-time are required to conduct microstructure measurements to quantify turbulence intensity, there is a need for more inexpensive and easy measurement methods. This study demonstrated that the turbulent energy dissipation rate, ε , estimated from fast-response thermistors Fastip Probe model 07 (FP07) with the depth-average of a > 10 m depth interval well agreed with those from current shear probes to a range of 10 –11  W/kg (m 2 s −3 ) in the two casts of the most accurate and stable free-fall vertical microstructure profiler, VMP6000 in the Oyashio water. This range cannot be measured with velocity shear probes equipped in smaller profilers in which the lower limit of ε  > O (10 –10 ) W/kg. These results extend turbulence measurements using the FP07 to 10 –11  W/kg. They may be especially useful for turbulence observations in deep oceans where ε is generally weak (< 10 –10  W/kg). As FP07 are much less sensitive to instrument vibrations than current shear and may be attached to various observational platforms such as temperature-conductivity-depth (CTD) profilers and floats. The CTD-attached FP07 observations near the VMP6000 profiles demonstrated their capabilities in the ε range of 10 –11 –10 –8  W/kg by data screening using a W sd > 0.1 ( W - 0.3 ) criterion (1 s mean lowering rate W m/s and its standard deviation W sd ) under rough conditions where the cast-mean W sd > 0.07 m/s and the standard deviation of W sd in each cast σ >0.05 m/s.

Applying catalysts for electrochemical energy conversion holds great promise for developing clean and sustainable energy sources. One of the main advantages of electrocatalysis is its ability to reduce conversion energy loss significantly. However, the wide application of electrocatalysts in these conversion processes has been hindered by poor catalytic performance and limited resources of catalyst materials. To overcome these challenges, researchers have turned to two‐dimensional (2D) materials, which possess large specific surface areas and can easily be engineered to have desirable electronic structures, making them promising candidates for high‐performance electrocatalysis in various reactions. This comprehensive review focuses on engineering novel 2D material‐based electrocatalysts and their application to seawater splitting. The review briefly introduces the mechanism of seawater splitting and the primary challenges of 2D materials. Then, we highlight the unique advantages and regulating strategies for seawater electrolysis based on recent advancements. We also review various 2D catalyst families for direct seawater splitting and delve into the physicochemical properties of these catalysts to provide valuable insights. Finally, we outline the vital future challenges and discuss the perspectives on seawater electrolysis. This review provides valuable insights for the rational design and development of cutting‐edge 2D material electrocatalysts for seawater‐electrolysis applications. This comprehensive review focuses on engineering novel 2D material‐based electrocatalysts and their application to seawater splitting. The review briefly introduces the mechanism of seawater splitting and the primary challenges of 2D materials. Highlight the unique advantages and regulating strategies for seawater electrolysis based on recent advancements. Provides valuable insights for the rational design and development of cutting‐edge 2D material electrocatalysts for seawater‐electrolysis applications.