Conventional cutting fluids often rely on non-renewable mineral oils and hazardous additives, raising serious environmental and occupational safety concerns. Developing green, high-performance alternatives is therefore imperative for advancing sustainable manufacturing. Amphiphilic Fe3O4@SiO2-C12 core-shell nanoparticles were synthesized and used to stabilize vegetable oil-in-water Pickering emulsions as novel nano-cutting fluids. Systematic characterization confirmed the successful formation of superparamagnetic nanoparticles with tunable wettability. Response surface methodology (RSM) optimized the formulation to pH 8.9, 2.9 wt% cottonseed oil, and 0.18 wt% nanoparticles, achieving a zeta potential of −42.533 mV and wear scar diameter of 0.393 mm. The optimized Pickering emulsion demonstrated superior performance versus the commercial cutting fluid, including a low friction coefficient of 0.0666, a 17.09% reduction in wear scar diameter, a 10.21% decrease in tapping torque, and a 15.40% enhancement in thermal conductivity (0.645 W/(m·K)). X-ray photoelectron spectroscopy (XPS) and scanning electron microscope (SEM)-energy-dispersive spectroscopy (EDS) revealed a triple synergistic lubrication mechanism involving physical adsorption, tribochemical Fe2O3/FeO film formation, and nanoparticle-enabled micro-bearing and heat conduction. The Pickering emulsion also demonstrated excellent magnetic responsiveness, enabling rapid, magnetically controlled demulsification and oil-water separation for recycling. In this work, a high-performance and environmentally friendly cutting fluid was developed and evaluated for sustainable manufacturing applications.
Panicle-grain nitrogen (PGN) supply effectively promotes rice grain filling and increases yield, but its effects on grain filling under reduced drip irrigation and the underlying physiological mechanisms are unclear. A three-year field experiment (2023–2025) evaluated the effects of two drip irrigation amounts (D1, 10,200 m³ ha⁻¹; D2, 8966 m³ ha⁻¹) and three PGN levels (N60, 60 kg ha⁻¹; N120 (CK), 120 kg ha⁻¹ ; and N180, 180 kg ha⁻¹ ) on root-shoot endogenous hormone levels, grain-filling characteristics and yield in rice. Compared with those under D1, under D2, the abscisic acid (ABA) content increased by 10.7%, and the timing of peak ABA accumulation and maximum grain filling rate occurred earlier in the inferior grains; however, the active inferior-grain filling period was shortened by 2.8 days, and the yield decreased by 3.1–9.0% under D₂. Compared with N60, the N120 and N180 treatments increased spikelet-root activity (SRA) by 5.0–244.9% and indole-3-acetic acid (IAA) levels and IAA/ABA ratios in roots, leaves, and grains by 2.3%–242.6%, thereby increasing the mean grain-filling rate (Gmean) by 0.8–9.5%. PLS-PM revealed that under D2, inferior grain filling was positively associated with yield. Notably, compared with D1N120, D2N180 altered the IAA metabolic characteristics of inferior grains, increasing SRA (19.4%) and Gmean (8.24%), while maintaining a relatively high grain yield. In conclusion, the application of 180 kg ha⁻¹ panicle-grain nitrogen fertilizer under reduced drip irrigation (8966 m³ ha⁻¹) improved inferior grain filling by increasing root physiological activity and regulating the IAA/ABA balance, thereby maintaining a higher yield.
To clarify microscopic competitive adsorption characteristics of methane (CH4) and carbon dioxide (CO2) on coal surfaces, a constitutive model for CO2–CH4 competitive adsorption on coal surfaces is derived based on the Lennard-Jones intermolecular potential theory. The model accuracy has verified against both molecular dynamics simulation results and competitive adsorption experimental data. Model analysis results reveal that: the densities of CO2 and CH4 both increase initially and then decrease with decreasing distance from coal surface. The location of maximum CO2 density is closer to the surface than that of CH4, which is a characteristic parameter characterizing the CO2 competitive adsorption superiority. The average molar fraction of CO2 in adsorption region is significantly higher than that in the free phase. The CO2 competitive adsorption selectivity coefficient decreases by a power law and gradually approaches 1 with increasing CO2 mole fraction in free region. The CO2 competitive adsorption superiority weakens significantly when the CO2 mole fraction in free region exceeds 40%. During pressurization below 10 megapascal (MPa), the free CO2 gradually transforms into the adsorbed state, and CO2 competitive adsorption superiority is enhanced. The CO2 competitive adsorption selectivity coefficients show linearly upward trend with increasing pressure and power-law downward trend with increasing temperature.
Remote sensing Water Body Change Detection (WBCD) aims to detect water body surface changes from bi-temporal images of the same geographic area. Recently, the lack of application-oriented high-resolution datasets for WBCD largely restricts its implementation in fine-grained urban and rural application scenarios. Meanwhile, existing deep learning-based methods fail to fully exploit the synergy of spatial semantic and structural information in deep features in WBCD networks. To resolve these concerns, we first propose HSRW-CD, a novel WBCD dataset with spatial resolution finer than 3 m, containing abundant image pairs covering diverse water body types. Besides, we further design a Spatial Semantics and Continuity Perception (SSCP) attention module, consisting of Multi-Semantic spatial Attention (MSA), Structural Relation-aware Global Attention (SRGA), and Channel-wise Self-Attention (CSA), which sequentially enhances semantic representation, models water body spatial continuity, and computes channel similarity with dual priors. As a plug-and-play module for WBCD, SSCP can be seamlessly integrated into existing models. Extensive experiments on HSRW-CD, Water-CD and LEVIR-CD datasets validate its efficiency and generalization. The proposed dataset and code are available at https://github.com/QingMa1/SSCP.
This research’s theme is to examine the mediating role of digital innovation and internet consumer finance from a multidimensional perspective, investigating the influence of psychological factors on consumer online buying behavior. Using a survey design, data were collected from 943 participants via an online questionnaire on the Qualtrics platform (e.g., via Google Surveys, Facebook, WeChat, and email), employing a purposive sampling method. A quantitative research method was performed utilizing PLS-SEM with importance-performance map analysis (IPMA) applied for data analysis. Findings provide significant theoretical and empirical support for, and are consistent with, the research objective. The results show that motivation (MOTV) and trust (TRST) significantly and positively influence consumer online buying behavior (COBB). Specifically, motivation drives the development of digital innovation and internet consumer finance, while trust critically influences both digital innovation and consumer behavior. Digital innovation (DIGINOV) and internet consumer finance (ICF serve as mediators, with motivation strongly driving digital innovation and trust playing a critical role in both mediators. Among the key determinants, digital innovation exerts a greater influence on consumer behavior than Internet consumer finance. On the other hand, perceived risk was found to have a detrimental effect on both online purchasing behavior and DIGINOV, although this effect was not statistically significant. Governments, stakeholders, and policymakers should prioritize digital business sustainability to assist internet users in making educated financial decisions and engaging in safe online shopping. This study is confined to a single region; subsequent research should investigate digital marketplaces throughout Asian nations to have a more comprehensive insight into online customer purchasing behavior.
