Search Results (1,276)

Highly permeable and selective membranes are crucial for energy-efficient gas separation. Two-dimensional (2D) graphitic carbon nitride (g-C₃N₄) has attracted significant attention due to its unique structural characteristics, including ultra-thin thickness, inherent surface porosity, and abundant amine groups. However, the interfacial defects caused by poor compatibility between g-C₃N₄ and polymers deteriorate the separation performance of membrane materials. In this study, amino-functionalized g-C₃N₄ nanosheets (CN@PEI) was prepared by a post-synthesis method, then blended with the polymer Pebax to fabricate Pebax/CN@PEI mixed matrix membranes (MMMs). Compared to g-C₃N₄, MMMs with CN@PEI loading of 20 wt% as nanofiller exhibited a CO₂ permeance of 241 Barrer as well as the CO₂/CH₄ and CO₂/N₂ selectivity of 39.7 and 61.2, respectively, at the feed gas pressure of 2 bar, which approaches the 2008 Robeson upper bound and exceeded the 1991 Robeson upper bound. The Pebax/CN@PEI (20) membrane showed robust stability performance over 70 h continuous gas permeability testing, and no significant decline was observed. SEM characterization revealed a uniform dispersion of CN@PEI throughout the Pebax matrix, demonstrating excellent interfacial compatibility between the components. The increased free volume fraction, enhanced solubility, and higher diffusion coefficient demonstrated that the incorporation of CN@PEI nanosheets introduced more CO₂-philic amino groups and disrupted the chain packing of the Pebax matrix, thereby creating additional diffusion channels and facilitating CO₂ transport. Full article

Ensuring food safety in complex supply chains requires evaluation frameworks that integrate multiple indicators, account for their interdependencies, and incorporate historical performance. This study proposes a novel RM–Shapley–FAHP framework that combines the Fuzzy Analytic Hierarchy Process, Shapley value contribution analysis, and a reputation decay mechanism to construct a dynamic, multi-year assessment model. The framework evaluates six governance subsystems, mitigates indicator redundancy, and links past performance to current risk posture. Applied to a leading food enterprise over three years, the method demonstrated superior consistency, interpretability, and operational relevance compared to FAHP, entropy weighting, and equal-weight baselines. The results demonstrate that RM–Shapley–FAHP framework effectively supports balanced development in food safety governance by capturing temporal dynamics and interdependencies, offering interpretable and operationally relevant guidance for decision makers. In future work, this framework may be extended with machine learning to improve adaptability for multi-dimensional and time-series evaluations, noted here as a research prospect rather than a present contribution. Full article

Waste cooking oil (WCO), a significant urban waste stream, presents untapped potential for synthesizing high-value materials. This study introduces an innovative “epoxidation-hydrolysis-blending” strategy to conveniently transform WCO into a highly flexible, photocurable elastomer suitable for 3D printing. Initially, WCO is converted into WCO-based hydroxy fatty acids (WHFA) via epoxidation and hydrolysis, yielding linear chains functionalized with multiple hydrogen-bonding sites. Subsequently, blending WHFA with hydroxyethyl acrylate (HEA) yields a novel photocurable WHFA/HEA elastomer. This elastomer exhibits excellent dimensional accuracy during vat photopolymerization 3D printing. Within the WHFA/HEA system, WHFA acts as a dual-functional modifier: its flexible alkyl chains enhance conformational freedom through plasticization while serving as dynamic hydrogen-bonding cross-linking sites that synergize with HEA chains to achieve unprecedented flexibility via reversible bond reconfiguration. Mechanical testing reveals that the optimized WHFA/HEA elastomer (mass ratio 1:3) exhibits ultra-high flexibility, with an elongation at break of 1184.66% (surpassing pure HEA by 360%). Furthermore, the elastomer demonstrates significant weldability (44.23% elongation retention after 12 h at 25 °C), physical reprocessability (7.60% elongation retention after two cycles), pressure-sensitive adhesion (glass interface adhesion toughness: 32.60 J/m²), and notable biodegradability (14.35% mass loss after 30-day soil burial). These properties indicate broad application potential in flexible electronics, biomedical scaffolds, and related fields. This research not only pioneers a low-cost route to multifunctional photocurable 3D printing materials but also provides a novel, sustainable solution for the high-value valorization of waste cooking oil. Full article

This study constructs a neural-network-inspired multi-layer machine learning model (RQLNet) to measure and analyze the effects of tail risk spillover and its associated sensitivities to macroeconomic factors among petroleum supply chain enterprises. On this basis, the study constructs a tail risk spillover network and analyzes its network-level structural features. The results show the following: (1) The proposed model improves the accuracy of tail risk measurement while addressing the issue of excessive penalization in spillover weights, offering enhanced interpretability and structural stability and making it particularly suitable for high-dimensional tail risk estimation. (2) Tail risk spillovers propagate from up- and midstream to downstream and ultimately to end enterprises. Structurally, the up- and midstream are the main sources, whereas the downstream and end enterprises are the primary recipients. (3) The tail risk sensitivities of Chinese petroleum supply chain enterprises exhibit significant differences across macroeconomic factors and across types of enterprises. Overall, the sensitivities to CIMV and LS are higher. (4) The network evolves in stages: during trade frictions, spillovers accelerate and core nodes strengthen; during public-health events, intra-community cohesion increases and cross-community spillovers decline; in the recovery phase, cross-community links resume and concentrate on core nodes; and during geopolitical conflicts, spillovers are core-dominated and cross-community transmission accelerates. Full article

Global Value Chains (GVCs) have driven the worldwide dispersion of manufacturing but remain highly vulnerable to macro-level shocks, including financial crises, geopolitical tensions, and the COVID-19 pandemic. These shocks expose manufacturing supply chains (MSCs) to systemic risks, but limited research has explored how GVC reconfiguration mediates their impact on manufacturing supply chain resilience (MSCR). To address this gap, this study conducts an enhanced bibliometric–systematic literature review (B-SLR) of 120 peer-reviewed articles. The findings reveal that macro-level shocks induce GVC reconfigurations along geographical, value, and governance dimensions, which in turn trigger MSCR through node- and link-level mechanisms. MSCR represents a manufacturer-centered capability that enables MSCs to preserve, realign, and enhance value amid shocks. Building on these insights, this research proposes a multi-tier strategy encompassing firm-level practices, inter-firm collaborations, and policy interventions. This study outlines three key contributions. First, at the theoretical level, it embeds MSCR within a GVC framework, clarifying how GVC reconfiguration mediates SCR under macro-level shocks. Second, at the methodological level, it ensures corpus completeness through snowballing and refines bibliometric mapping with multi-dimensional visualization. Third, at the managerial level, it provides actionable guidance for firms, industry alliances, and policymakers to align MSCR strategies with the dynamics of global production networks. Full article

Hydrogels are crosslinked polymer chains that form a three-dimensional network, widely used for wound dressing due to their ability to absorb significant amounts of fluid. This study aimed to develop a hydrogel patch for wound dressing with self-healing properties, particularly for joints and stretchable body parts, providing a physical barrier while maintaining an optimal environment for wound healing. Polyvinyl alcohol (PVA) and sodium carboxymethyl cellulose (Na CMC) were crosslinked with borax, which reacts with the active hydroxyl groups in both polymers to form a hydrogel. The patches were loaded with ciprofloxacin HCl (CIP), a broad-spectrum antibiotic used to prevent and treat various types of wound infections. Hydrogels were subjected to rheological, morphological, antimicrobial, self-healing, ex vivo release, swelling, cytotoxicity, wound healing, and stability studies. The hydrogels exhibited shear-thinning, thixotropic, and viscoelastic properties. Microscopic images of the CIP hydrogel patch showed a porous, crosslinked matrix. The antimicrobial activity of the patch revealed antibacterial effectiveness against five types of Gram-positive and Gram-negative bacteria, demonstrating a minimum inhibitory concentration of 0.05 μg/mL against E. coli. The swelling percentage was found to be 337.4 ± 12.7%. The cumulative CIP release percentage reached 103.7 ± 3.7% after 3 h, followed by zero-order release kinetics. The stability studies revealed that the crossover point shifted toward higher frequencies after 3 months of storage at room temperature, suggesting a relaxation in the hydrogel bonds. The cytotoxicity study revealed that the CIP hydrogel patch is non-cytotoxic. Additionally, the in vivo study demonstrated that the CIP hydrogel patch possesses wound-healing ability. Therefore, the CIP PVA/Na CMC/Borax patch could be used in wound dressing. Full article

Electronic-waste (e-waste) management is a key challenge in engineering smart cities due to its rapid accumulation, complex composition, sparse data availability, and significant environmental and economic impacts. This study employs a bespoke machine learning infrastructure on an Indian e-waste supply chain network (SCN) focusing on the three pillars of sustainability—environmental, economic, and social. The economic resilience of the SCN is investigated against external perturbations, like market fluctuations or policy changes, by analyzing six stochastically perturbed modules, generated from the optimal point of the original dataset using Monte Carlo Simulation (MCS). In the process, MCS is demonstrated as a powerful technique to deal with sparse statistics in SCN modeling. The perturbed model is then analyzed to uncover “hidden” non-linear relationships between key variables and their sensitivity in dictating economic arbitrage. Two complementary ensemble-based approaches have been used—Feedforward Neural Network (FNN) model and Random Forest (RF) model. While FNN excels in regressing the model performance against the industry-specified target, RF is better in dealing with feature engineering and dimensional reduction, thus identifying the most influential variables. Our results demonstrate that the FNN model is a superior predictor of arbitrage conditions compared to the RF model. The tangible deliverable is a data-driven toolkit for smart engineering solutions to ensure sustainable e-waste management. Full article

Growing global supply chain uncertainties significantly threaten China’s forage import security. The evolving characteristics of the global forage trade network directly impact the stability of China’s supply. This study constructs a directed, weighted trade network based on global forage products trade data (2000–2024). Using complex network analysis methods, it systematically analyzes the network’s topological structure and evolutionary patterns, with a focus on their impact on China’s import security. The study addresses the following questions: What evolutionary patterns does the global forage trade network exhibit in terms of its topological structure? How does the evolution of this network impact the import security of forage products in China, specifically regarding supply chain stability and risk resilience? The research findings indicate the following: (1) From 2000 to 2024, the total volume of global forage products trade increased by 48.17%, primarily driven by forage products excluding alfalfa meal and pellets, which accounted for an average of 82.04% of volume annually. Additionally, the number of participating countries grew by 21.95%. (2) The global forage products trade network follows a power–law distribution, characterized by increasing network density, a clustering coefficient that initially declines and then rises, and a shortening of the average path length. (3) The core structure of the global forage products trade network shows an evolutionary trend of diffusion from core nodes in North America, Oceania, and Asia to multiple core nodes, including those in North America, Oceania, Europe, Africa, and Asia. (4) China’s forage products trade network displays distinct phase characteristics; however, imports face significant risks from high supply chain dependency and exposure to international price fluctuations. Based on these conclusions, it is recommended that China actively expands trade relations with potential product-exporting countries in Africa, encouraging enterprises to “go global.” Additionally, China should establish a three-dimensional supply chain security system, comprising maritime, land, and storage components, to enhance risk resistance and import safety. Full article

With the continuous progress and innovation of petroleum engineering technology, the development of new oilfield additives with superior environmental benefits has attracted widespread attention. Konjac glucomannan (KGM) is a natural resource characterized by abundant availability, low cost, biodegradability, and environmental compatibility. Konjac gum easily forms a weak gel network in water, but its water solubility and thermal stability are poor, and it is easily degraded at high temperatures. Therefore, its application in drilling fluid and fracturing fluid is limited. In this paper, a method of carboxymethyl modification of KGM was developed, and a carboxymethyl group was introduced to adjust KGM’s hydrogel forming ability and stability. Carboxymethylated Konjac glucomannan (CMKG) is a water-soluble anionic polysaccharide derived from natural Konjac glucomannan. By introducing carboxymethyl groups, CMKG overcomes the limitations of the native polymer, such as poor solubility and instability, while retaining its safe and biocompatible nature, making it an effective natural polymer additive for oilfield applications. The results show that when used as a drilling fluid additive, CMKG can form a stable three-dimensional gel network through molecular chain cross-linking, significantly improving the rheological properties of the mud. Its unique gel structure can enhance the encapsulation of clay particles and inhibit clay hydration expansion. When used as a fracturing fluid thickener, the viscosity of the gel system formed by CMKG at 0.6% (w/v) is superior to that of the weak gel system of KGM. The heat resistance/shear resistance tests confirm that the gel structure remains intact under high-temperature and high-shear conditions, meeting the sand-carrying capacity requirements for fracturing operations. The gel-breaking experiment shows that the system can achieve controlled degradation within 300 min, in line with on-site gel-breaking specifications. This modification process not only improves the rheological properties and water solubility of the CMKG gel but also optimizes the gel stability and controlled degradation through molecular structure adjustment. Full article

Hydrogels comprise three-dimensional networks of hydrophilic polymers and have attracted considerable interest in various sectors, including the biomedical, pharmaceutical, agricultural, and food industries. These materials offer significant benefits for food packaging applications, such as high mechanical strength and excellent water absorption capacity, thereby contributing to the extension of product shelf life. Therefore, the aim of this study is to compare the performance of citric acid and glutaraldehyde as crosslinking agents in gelatine-based hydrogels reinforced with cellulose nanocrystals (CNC), contributing to the development of safe and environmentally responsible materials. The hydrogels were prepared using the casting method and characterised in terms of their physical, mechanical, and structural properties. The results indicated that hydrogels crosslinked with glutaraldehyde exhibited higher opacity, lower transparency, and greater mechanical strength, whereas those crosslinked with citric acid demonstrated improved clarity, reduced water permeability, and enhanced swelling capacity. The incorporation of CNC further improved mechanical strength, reduced weight loss, and altered both surface homogeneity and optical properties. Microstructural results obtained by SEM were consistent with the mechanical properties evaluated (TS, %E, and EM). The Gel-ca hydrogel displayed the highest elongation value (98%), reflecting better cohesion within the polymeric matrix. In contrast, films incorporating CNC exhibited greater roughness and cracking, which correlated with increased rigidity and mechanical strength, as evidenced by the high Young’s modulus (420 MPa in Gel-ga-CNC2). These findings suggest that the heterogeneity and porosity induced by CNC limit the mobility of polymer chains, resulting in less flexible and more rigid structures. Additionally, the DSC analysis revealed that gelatine hydrogels did not exhibit a well-defined Tg, due to the predominance of crystalline domains. Systems crosslinked with citric acid showed greater thermal stability (higher Tm and ΔH_m values), while those crosslinked with glutaraldehyde, although mechanically stronger, exhibited lower thermal stability. These results confirm the decisive effect of the crosslinking agent and CNC incorporation on the structural and thermal behaviour of hydrogels. In this context, the application of hydrogels in packaged products represents an eco-friendly alternative that enhances product presentation. This research supports the reduction in plastic consumption whilst promoting the principles of a circular economy and facilitating the development of materials with lower environmental impact. Full article

Critical thinking, as a crucial component of 21st-century core competencies, poses significant challenges for effective assessment in educational evaluation. This study proposes an automated assessment method for critical thinking in middle school Chinese language based on a Bidirectional Encoder Representations from Transformers—Convolutional Neural Network (BERT-CNN) hybrid model, achieving a multi-dimensional quantitative assessment of students’ critical thinking performance in writing through the synergistic effect of deep semantic encoding and local feature extraction. The research constructs an annotated dataset containing 4827 argumentative essays from three middle school grades, employing expert scoring across nine dimensions of the Paul–Elder framework, and designs three types of logic chain extraction algorithms: argument–evidence mapping, causal reasoning chains, and rebuttal–support structures. Experimental results demonstrate that the BERT-CNN hybrid model achieves a Pearson correlation coefficient of 0.872 in overall assessment tasks and an average F1 score of 0.770 in logic chain recognition tasks, outperforming the traditional baseline methods tested in our experiments. Ablation experiments confirm the hierarchical contributions of semantic features (31.2%), syntactic features (24.1%), and logical markers (18.9%), while revealing the model’s limitations in assessing higher-order cognitive dimensions. The findings provide a feasible technical solution for the intelligent assessment of critical thinking, offering significant theoretical value and practical implications for advancing educational evaluation reform and personalized instruction. Full article

The curing of silicone networks from dimethylsilanediol and methylsilanetriol chainbuilder–crosslinker precursor mixtures is investigated from combined quantum/molecular mechanics simulations. Upon screening different crosslinker content from 5 to 15%, we provide a series of atomic-resolution bulk models all featuring 98–99% curing degree, albeit at rather different arrangement of the chains and nodes, respectively. To elucidate the nm scale alignment of the polymer networks, we bridge scales from atomic simulation cells to graph theory and demonstrate the analyses of 3-dimensional percolation of -O-Si-O- bonds, polydimethylsiloxane branching characteristics and the interpenetration of loops. Our findings are discussed in the context of the available experimental data to relate heat of formation, curing degree and elastic properties to the molecular scale structural details—thus promoting the in-depth understanding of silicone resins. Full article

As a vital driver of supply chain management, data has evolved into both a foundational resource and a critical production factor for optimizing supply chains and mitigating risk. This study adopts a four-dimensional framework (i.e., visibility, coordination, flexibility, and redundancy) to investigate how data asset information disclosure (DAID) shapes supply chain risk (SCR). Relative to the existing literature, this paper contributes by examining the determinants of supply chain risk from the perspective of data asset information disclosure and by conducting empirical analyses using double debiased machine learning and causal mediation analysis. The results show that DAID significantly lowers SCR, with results robust to multiple sensitivity checks. Economically, a one-standard-deviation increase in DAID leads to an average decline in SCR of 0.63%. Causal mediation analysis, aligned with the theoretical dimensions, reveals that DAID mitigates SCR through four channels: enhancing information transparency, improving visibility, strengthening agile responsiveness, and increasing supply chain concentration. Heterogeneity tests reveal stronger effects among firms facing fewer financing constraints, operating in more marketized environments, and designated as chain master firms. Further evidence suggests that reduced SCR promotes a greater capacity for coordinated innovation within the supply chain. Full article

Sperm membrane lipids play a crucial role in male fertility, influencing sperm motility, viability, and functional competence. This study comprehensively characterizes the phospholipid and sphingolipid composition in highly motile human spermatozoa obtained through the swim-up method, a widely used technique in assisted reproductive technology (ART). Using two-dimensional thin-layer chromatography and phosphorus analysis, we identified choline glycerophospholipids (CGP, 45%), ethanolamine glycerophospholipids (EGP, 26%), and sphingomyelin (SM, 17%) as predominant phospholipids, with minor components including cardiolipin, lysophospholipids, phosphatidylinositol, phosphatidylserine, phosphatidic acid, and neutral lipids. Gas chromatography analysis of glycerophospholipids (GPL) revealed a high long chain (C20–C22) polyunsaturated fatty acids (PUFA) content (46.3%), particularly docosahexaenoic acid (DHA, 22:6n-3), which was more abundant in CGP (46%) than EGP (26%). Sphingolipid analysis indicated that ceramide (Cer) and SM shared similar fatty acid profiles due to their metabolic relationship, with very-long-chain (VLC) PUFA (≥C26) being more prevalent in SM (10%) than in Cer (6%). Additionally, argentation chromatography identified highly unsaturated VLCPUFA species in Cer, including 28:3n-6, 28:4n-6, and 30:4n-6, which had not been previously quantified in motile human spermatozoa. Given the essential function of sphingolipid metabolism in spermatogenesis, capacitation, and acrosomal exocytosis, our findings suggest that the balance of VLCPUFA-containing SM and Cer could play a role in sperm performance and fertilization potential. This study provides novel insights into the lipid signature of human sperm and highlights the relevance of membrane lipid remodeling for male fertility and ART outcomes. Full article

A series of cobalt(II) dicyanamide (dca⁻) coordination polymers with substituted pyrazines (pyz) and pyrimidines (pym) as auxiliary ligands have been synthesized and structurally characterized to investigate the influence of the type and substitution pattern of the auxiliary ligand on the dimensionality and topology of the resulting frameworks. As a result of our studies, 13 novel heteroleptic cobalt(II) dicyanamide coordination polymers were obtained, and their crystal structures were determined by single-crystal X-ray diffraction. Eight of the investigated compounds exhibit a single-chain structure composed of [Co(L_pyz/_pym)₂]²⁺ units bridged via double μ_1,5–dca ligands. In two complexes, neutral triple-chain topologies were observed, in which double μ_1,5– and single μ_1,3,5–dca bridges connect two crystallographically independent cobalt(II) ions, both being six-coordinate in tetragonally elongated octahedral environments. Two- and three-dimensional architectures were confirmed only in the case of Co(II) compounds with 2,6–Me₂pyz and 4-NH₂-pym co-ligand, respectively The cobalt(II) complexes described herein have also been compared with dicyanamide-based cobalt(II) systems incorporating pyrazine- and pyrimidine-like ligands. These structural relationships are of high significance for the rational design and synthesis of heteroleptic cobalt(II) dicyanamide systems. Full article