This study evaluated the cooling performance of an electric vehicle heat sink manufactured using additive manufacturing (AM) with a topology-optimized design, compared with a conventionally manufactured pin-fin heat sink. The experimental results showed that the topology-optimized heat sink improved the cell cooling coefficient by up to 42.6% compared to the conventional heat sink, leading to an estimated 7.6% extension in battery lifetime. This study also assessed the environmental and life cycle cost (LCC) implications of this extended battery life, revealing that battery production emits approximately seven tons of CO2-equivalent (CO2-eq) greenhouse gases per pack; however, longer battery life reduces the frequency of battery replacement and the overall demand for battery production. Under a scenario where the topology-optimized heat sink achieves a 15% market penetration by 2040, the cumulative reduction in greenhouse-gas emissions is projected to reach 2.4 MtCO2-eq. LCC analysis further indicated that despite the higher manufacturing cost of the AM heat sink, the increased battery longevity lowers total operating cost by approximately 5.3%. These findings show that enhanced functionality of optimized components can simultaneously improve performance and reduce LCC. This study’s evaluation framework for assessing environmental impacts and costs across the product life cycle provides a transparent and consistent basis for selecting appropriate manufacturing technologies for component production.

The second member of the Paleogene Funing Formation (E1f2) in the Gaoyou Sag, Subei Basin, is a promising shale oil target, yet its organic matter (OM) enrichment mechanisms remain poorly understood. This study integrates petrological and multi-proxy geochemical analyses to investigate lithofacies, paleoenvironmental evolution, and OM enrichment of the E1f2 shale. Seven lithofacies types transition upward from laminated (submembers III to V) to blocky structures (submembers I to II). TOC and hydrocarbon potential increase stepwise from bottom to top, with Type II OM dominant. The paleoenvironment evolved from arid, saline, and semideep lacustrine with strong terrigenous input (V); through semiarid to arid, brackish to saline, and semideep to deep lacustrine with peak productivity (III to IV); to humid to semiarid, fresh to brackish, and deep lacustrine with minimal terrigenous input (I to II). Anoxia persisted throughout. OM enrichment is jointly controlled by paleoclimate, water depth, paleosalinity, and terrigenous input, with paleoproductivity subordinate and redox conditions insignificant. Critically, terrigenous input exerts a non-linear dual control, defining an optimal window where nutrient supply, dilution, and oxidation are balanced. The highest OM enrichment in submembers I to II results precisely from terrigenous input falling within this window. This challenges productivity/preservation-dominant paradigms and provides a new framework for shale oil sweet-spot prediction in saline lacustrine basins.

With the increasing development and utilization of natural resources, the importance of rock property characterization is becoming increasingly prominent. Artificial intelligence (AI) technology, with its rapid and accurate identification and analysis capabilities, is driving the evolution of a new generation of intelligent rock property analysis technologies. This paper systematically reviews the application and development trends of AI in rock property analysis. Key topics include: using AI methods to identify and analyze rock structure, composition, and texture; introducing commonly used AI models, analytical metrics, and public datasets in this field to help researchers more comprehensively evaluate model performance and match appropriate rock data; and summarizing AI solutions, future challenges, and coping strategies in four key areas of rock property analysis. This study emphasizes that the current application of AI methods to rock property analysis still faces challenges such as data quality, model generalization, and interpretability. To address these challenges, this paper proposes constructive suggestions, including the development of industry standards for intelligent rock analysis, the integration of petrological theory and fluid dynamics equations, and the adoption of weakly supervised learning strategies, in order to overcome existing technical bottlenecks.

Recycled carbon fibres frequently exhibit degraded surface functionality owing to prior matrix removal processes, limiting their compatibility with contemporary epoxy resin systems. This study proposes a nanobubble-based surface treatment route designed to restore and enhance the surface characteristics of recycled carbon fibres without aggressive chemical oxidation. The study generated ozone and carbon dioxide nanobubbles in aqueous media and experimentally investigated the effects of nanobubble treatment on the surface properties and adhesive behaviour of recycled carbon fibres. Surface chemical changes were examined using X-ray photoelectron spectroscopy, which revealed an increase in oxygen-containing functional groups due to the nanobubble treatment, indicating improved surface polarity and potential for chemical interaction with epoxy networks. The practical effectiveness of the treatment was assessed via a pinhole pull-out test that served as an indirect measure of interfacial adhesion with epoxy resin, especially the combination of ozone nanobubbles and recycled carbon fibres. Notably, the nanobubble-treated recycled carbon fibres exhibited an increase in the adhesion compared with untreated recycled carbon fibres, rising from 84.5 ± 11.5 MPa to 138.5 ± 14.8 MPa, reflecting enhanced wetting behaviour and stronger fibre–matrix interfacial bonding. Overall, the proposed nanobubble processing route offers a mild, scalable, and environmentally favourable method for restoring surface reactivity in recycled carbon fibres, supporting their reintegration into high-performance composite applications.

An improved depleting sand fracture model was derived in this work using Finite Element Methods, taking into consideration the effect of pore pressure and production on in situ stresses. Sets of governing equations from the commercial finite element simulator COMSOL Multiphysics were used to obtain a model that compares well with the existing fracture model, mainly based on the Mohr–Coulomb failure criterion. The model uniquely couples reservoir depletion-induced stress evolution with fracture initiation and propagation within a unified finite element framework. A constant overburden load was used since its value majorly depends on depth, and the formation is assumed to be fixed at the bottom. The reservoir is assumed to be depleting at a constant rate with no water injection to assist pressure, with an average porosity of 25% and an average permeability of 251 mD at the beginning of production. The reservoir compacted during production, and in turn, porosity and permeability were reduced over the years of observation. Fracturing was observed to be much easier for the depleted reservoir, since horizontal stresses, which might have created friction, are reduced during reservoir production, signifying that for depleted reservoirs, a small fracture pressure is required. Created fractures are observed to propagate in the direction of the maximum horizontal stress and perpendicular to the direction of the minimum horizontal stress.

Climate change and water pollution are the global focus to address the mitigation measures that will improve water quality and wastewater management. The recent increase in dye pollution from the manufacturing sector, particularly the textile industries, has increased the demand for advanced wastewater treatment technologies that are sustainable and affordable. The abundant, renewable lignocellulosic fiber-based adsorbents have emerged as a promising alternative for removing a wide range of heavy metals and dyes. This review highlights in detail various sources of fibers, their physicochemical compositions, and different modification techniques that improve their selectivity and adsorption capacity, particularly for dye removal. The complementary impacts of the presence of the inherent functional groups, specific surface charges, and the primary adsorption mechanisms that can significantly enhance dye selectivity have been well addressed. While the modified fibers demonstrated the promising removal efficiency of above 90% at the laboratory scale, challenges remain in terms of their adsorption kinetics, regeneration efficiency, and long-term stability for large-scale industrial settings. Hence, future studies should focus on enhancing fiber properties for sustainable industrial applications, high-performance, and multifunctionality through a promising hybrid modification technique that will bridge the gap into large industrial implementation.

This study introduces an electrochemical sensing platform based on bimetallic CoAg/rGO and CuAg/rGO nanocomposites for the detection of 1,3-dinitrobenzene (DNB), a highly toxic nitroaromatic compound commonly encountered in industrial effluents and contaminated water systems. The prepared nanocomposites were characterized using SEM, TEM, AFM, XPS, and electrochemical techniques, providing detailed insight into their structural, morphological, and surface properties relevant to electrochemical sensing. The electrochemical behavior of DNB was investigated in phosphate buffer solutions using cyclic voltammetry under optimized experimental conditions. Both CoAg/rGO and CuAg/rGO electrodes exhibited pronounced electrocatalytic activity towards the reduction in DNB, characterized by well-defined reduction peaks. The developed sensors exhibited good analytical performance, with limits of detection of 2.21 µM and 2.47 µM for the CuAg/rGO and CoAg/rGO electrodes, respectively, both showing linear responses in the concentration range of 5–50 µM. Moreover, a clear response to DNB was obtained in the presence of phenols as interferents as well as in spiked real water samples. The integration of characterization results with electrochemical measurements and validation in real water samples supports process-oriented research in environmental monitoring and electrochemical process control. These results confirm that bimetallic rGO-based nanocomposites represent efficient and cost-effective electrode materials for the electrochemical detection of 1,3-dinitrobenzene.

Thermal conductivity is a crucial parameter for heat transfer in asphalt pavements, especially in cold regions where electrically heated snow-melting systems are used. Steel slag, an industrial by-product with high thermal conductivity, holds significant potential to enhance the thermal performance of asphalt mixtures. However, its thermal behavior is influenced by various factors. This study established a thermal conductivity database consisting of 200 samples from published experimental studies, incorporating data collection, graphical digitization, and physically constrained expansion. Mixture composition, volumetric structure, and steel slag properties were used as input variables, with thermal conductivity as the output. Five machine learning models including k-nearest neighbors regression, decision tree, random forest, support vector regression, and gradient boosting were developed. Among them, random forest and gradient boosting showed the highest accuracy and robustness. Feature importance analysis revealed that steel slag content is the primary factor affecting thermal conductivity, while material properties and gradation parameters play secondary roles. This data-driven framework facilitates the efficient prediction and design of thermal conductivity in steel slag asphalt mixtures, supporting the engineering application of functional asphalt pavements.

Leuenbergeria bleo (Kunth) DC. (Cactaceae), previously classified as Pereskia bleo, represents a phylogenetically basal cactus species with a disjunct distribution across Central America, Southeast Asia, and southern China. Phytochemical investigations have traditionally emphasized small-molecule secondary metabolites, including phenolics, alkaloids, and terpenoids, which contribute to antioxidant and anti-cancer activities. However, recent peptidomic analyses have expanded this chemical space through the discovery of bleogens, a family of hyper-stable, cysteine-rich microproteins with specific antifungal and wound-healing properties. This review systematically integrates botanical characteristics, ethnomedicinal applications, and pharmacological profiles, providing a comparative analysis of the plant’s small-molecule constituents versus its peptidyl biologics. It identifies the co-existence of these distinct chemical classes as a defining feature of the plant’s efficacy while highlighting the need for future research into their potential interactions.

This paper presents a comparative analysis of direct expansion solar-assisted heat pumps (DX-SAHP) with and without photovoltaic cells (PVT-DX-SAHP). Two sets of experiments were conducted to validate the mathematical model: one with PVT-DX-SAHP and another with DX-SAHP. The mathematical model used in this study is based on physical equations using energy, mass, and entropy balance. The heat exchangers were modeled using the moving boundaries technique. The compressor and photovoltaic cells were modeled using a black box model with equations for the efficiencies of these components. The low-global-warming-potential (GWP) refrigerant R290 (propane) was used in this study. Two independent sets of experimental data were used to validate the model, with a mean absolute deviation of 4.8%. The results indicated that PVT-DX-SAHP had a higher season performance factor (SPF) and a lower total equivalent warming impact (TEWI) compared to DX-SAHP, with an average SPF increase of 81% and a TEWI reduction of 30%. The findings also showed that PVT-DX-SAHP had a 24% higher longer payback period than DX-SAHP.