Search Results (2,190)

MXenes, a class of two-dimensional transition metal carbides and nitrides, emerged as a promising material for next-generation energy storage and corresponding applications due to their unique combination of high electrical conductivity, tunable surface chemistry, and lamellar structure. This review highlights recent advances in MXene-based composites, focusing on their integration into electrode architectures for the development of supercapacitors, batteries, and multifunctional devices, including triboelectric nanogenerators. It serves as a comprehensive overview of the multifunctional capabilities of MXene-based composites and their role in advancing efficient, flexible, and sustainable energy and sensing technologies, outlining how MXene-based systems are poised to redefine multifunctional energy platforms. Electrochemical performance optimization strategies are discussed by considering surface functionalization, interlayer engineering, scalable synthesis techniques, and integration with advanced electrolytes, with particular attention paid to the development of hybrid supercapacitors, triboelectric nanogenerators (TENGs), and wearable sensors. These applications are favored due to improved charge storage capability, mechanical properties, and the multifunctionality of MXenes. Despite these aspects, challenges related to long-term stability, sustainable large-scale production, and environmental degradation must still be addressed. Emerging approaches such as three-dimensional self-assembly and artificial intelligence-assisted design are identified as key challenges for overcoming these issues. Full article

Background: Induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) hold transformative potential for cardiovascular regenerative medicine, yet their clinical application is hindered by suboptimal mitochondrial maturation and metabolic inefficiencies. This systematic review evaluates targeted strategies for optimizing mitochondrial function, integrating metabolic preconditioning, substrate selection, and pathway modulation to enhance energy production and cellular resilience. Additionally, we examine the role of extracellular matrix stiffness and mechanical stimulation in mitochondrial adaptation, given their influence on metabolism and maturation. Methods: A comprehensive analysis of recent advancements in iPSC-CM maturation was conducted, focusing on metabolic interventions that enhance mitochondrial structure and function. Studies employing metabolic preconditioning, lipid and amino acid supplementation, and modulation of key signaling pathways, including PGC-1α, AMPK, and mTOR, were reviewed. Computational modeling approaches predicting optimal metabolic shifts were assessed, alongside insights into reactive oxygen species (ROS) signaling, calcium handling, and the impact of electrical pacing on energy metabolism. Results: Evidence indicates that metabolic preconditioning with fatty acids and oxidative phosphorylation enhancers improves mitochondrial architecture, cristae density, and ATP production. Substrate manipulation fosters a shift toward adult-like metabolism, while pathway modulation refines mitochondrial biogenesis. Computational models enhance precision, predicting interventions that best align iPSC-CM metabolism with native cardiomyocytes. The synergy between metabolic and biomechanical cues offers new avenues for accelerating maturation, bridging the gap between in vitro models and functional cardiac tissues. Conclusions: Strategic metabolic optimization is essential for overcoming mitochondrial immaturity in iPSC-CMs. By integrating biochemical engineering, predictive modeling, and biomechanical conditioning, a robust framework emerges for advancing iPSC-CM applications in regenerative therapy and disease modeling. These findings pave the way for more physiologically relevant cell models, addressing key translational challenges in cardiovascular medicine. Full article

Huanglongbing (HLB), caused by Candidatus Liberibacter asiaticus (CLas), is the most devastating disease threatening global citrus production. Although no commercial citrus varieties exhibit complete HLB resistance, genotype-specific tolerance variations remain underexplored. This study conducted a comparative transcriptomic profiling of six commercially citrus cultivars in South China, four susceptible cultivars (C. reticulata cv. Tankan, Gongkan, Shatangju, and C. sinensis Osbeck cv. Newhall), and two tolerant cultivars (C. limon cv. Eureka; C. maxima cv Guanxi Yu) to dissect molecular mechanisms underlying HLB responses. Comparative transcriptomic analyses revealed extensive transcriptional reprogramming, with tolerant cultivars exhibiting fewer differentially expressed genes (DEGs) and targeted defense activation compared to susceptible genotypes. The key findings highlighted the genotype-specific regulation of starch metabolism, where β-amylase 3 (BAM3) was uniquely upregulated in tolerant varieties, potentially mitigating starch accumulation. Immune signaling diverged significantly: tolerant cultivars activated pattern-triggered immunity (PTI) via receptor-like kinases (FLS2) and suppressed ROS-associated RBOH genes, while susceptible genotypes showed the hyperactivation of ethylene signaling and oxidative stress pathways. Cell wall remodeling in susceptible cultivars involved upregulated xyloglucan endotransglucosylases (XTH), contrasting with pectin methylesterase induction in tolerant Eureka lemon for structural reinforcement. Phytohormonal dynamics revealed SA-mediated defense and NPR3/4 suppression in Eureka lemon, whereas susceptible cultivars prioritized ethylene/JA pathways. These findings delineate genotype-specific strategies in citrus–CLas interactions, identifying BAM3, FLS2, and cell wall modifiers as critical targets for breeding HLB-resistant cultivars through molecular-assisted selection. This study provides a foundational framework for understanding host–pathogen dynamics and advancing citrus immunity engineering. Full article

The synergistic development of low-permeability reservoirs such as deep coalbed methane (CBM) and tight gas has emerged as a key technology to reduce development costs, enhance single-well productivity, and improve gas recovery. However, due to fundamental differences between coal seams and tight sandstones in their pore structure, permeability, water saturation, and pressure sensitivity, significant variations exist in their flow capacities and fluid production behaviors. To address the challenges of production allocation and main reservoir identification in the co-development of CBM and tight gas within deep gas-bearing basins, this study employs the transient multiphase flow simulation software OLGA to construct a representative dual-gas co-production well model. The regulatory mechanisms of the gas–liquid distribution, deliquification efficiency, and interlayer interference under two typical vertical stacking relationships—“coal over sand” and “sand over coal”—are systematically analyzed with respect to different tubing setting depths. A high-precision dynamic production allocation method is proposed, which couples the wellbore structure with real-time monitoring parameters. The results demonstrate that positioning the tubing near the bottom of both reservoirs significantly enhances the deliquification efficiency and bottomhole pressure differential, reduces the liquid holdup in the wellbore, and improves the synergistic productivity of the dual-reservoirs, achieving optimal drainage and production performance. Building upon this, a physically constrained model integrating real-time monitoring data—such as the gas and liquid production from tubing and casing, wellhead pressures, and other parameters—is established. Specifically, the model is built upon fundamental physical constraints, including mass conservation and the pressure equilibrium, to logically model the flow paths and phase distribution behaviors of the gas–liquid two-phase flow. This enables the accurate derivation of the respective contributions of each reservoir interval and dynamic production allocation without the need for downhole logging. Validation results show that the proposed method reliably reconstructs reservoir contribution rates under various operational conditions and wellbore configurations. Through a comparison of calculated and simulated results, the maximum relative error occurs during abrupt changes in the production capacity, approximately 6.37%, while for most time periods, the error remains within 1%, with an average error of 0.49% throughout the process. These results substantially improve the timeliness and accuracy of the reservoir identification. This study offers a novel approach for the co-optimization of complex multi-reservoir gas fields, enriching the theoretical framework of dual-gas co-production and providing technically adaptive solutions and engineering guidance for multilayer unconventional gas exploitation. Full article

Digital technology has become an essential engine of green development and economic progress due to the meteoric rise of new technologies. Our paper seeks to explore the impact of digitalization on environmental, social and governance (ESG) performance in different business contexts. Data were collected from listed firms across 19 Asian countries from 2015 to 2024, covering 1839 firms, yielding 18,390 firm-year observations and establishing a balanced panel data set. We used the dynamic panel data model to test the proposed hypotheses. The findings revealed that digitalization has a significant and positive impact on ESG performance. It also revealed that environmental uncertainty moderates this relationship. Moreover, our analysis indicated that the impact of digitalization on ESG performance is stronger for product (vs. service) firms, stronger for B2B (vs. B2C) firms and stronger for firms in IT-intensive industries. In addition, the analysis indicated that the impact of digitalization on ESG performance is stronger in more dynamic, complex and munificent environments. Our examination offers meaningful implications for theory and practice by expanding our knowledge of the complex mechanism underpinning the positive correlation between digitalization and ESG performance. Full article

The complexity of assessment is a significant problem in designing renewable energy source (RES) products, especially when one wants to take into account their various aspects, e.g., technical, environmental, or social. Hence, the aim of the research is to develop a model supporting the decision-making process of RES product development based on meeting the criteria of quality, environmental impact, and social responsibility (QES). The model was developed in four main stages, implementing multi-criteria decision support methods such as DEMATEL (decision-making trial and evaluation laboratory) and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution), as well as criteria for social responsibility and environmental impact from the ISO 26000 standard. The model was tested and illustrated using the example of photovoltaic panels (PVs): (i) five prototypes were developed, (ii) 30 PV criteria were identified from the qualitative, environmental, and social groups, (iii) the criteria were reduced to 13 key (strongly intercorrelated) criteria according to DEMATEL, (iv) the PV prototypes were assessed taking into account the importance and fulfilment of their key criteria according to TOPSIS, and (v) a PV ranking was created, where the fifth prototype turned out to be the most advantageous (QES = 0.79). The main advantage of the model is its simple form and transparency of application through a systematic analysis and evaluation of many different criteria, after which a ranking of design solutions is obtained. QES ensures precise decision-making in terms of sustainability of new or already available products on the market, also those belonging to RES. Therefore, QES will find application in various companies, especially those looking for low-cost decision-making support techniques at early stages of product development (design and conceptualization). Full article

The dynamics of the vibrational mode of motion of the driving member of a nonideal system, a mixing–whipping device based on a simple slide-crank mechanism, was studied. The highly nonlinear differential equations of motion were solved numerically by the Runge–Kutta method. The interaction of the mixing–whipping device with the nonideal excitation source causes the rotational speed of the engine shaft and the rotation angle of the driving member to fluctuate, accomplishing a damped process. The parameters of the device and the nonideal energy source have an effect on the kinematic, vibrational and energy characteristics of the system. An increase in the engine’s torque, crank length, number and radius of piston holes, and piston mass, as well as a decrease in the fluid’s density, leads to a reduction in the oscillation range of the crank angle, amplitude and period of angular velocity oscillations of the engine shaft and the mixing–whipping force power. The effects of a nonideal energy source may be used in designing a mixing–whipping device based on a slider-crank mechanism to select effective system parameters and an energy-saving motor in accordance with the requirements of technological processes and products. Full article

The transition toward integrated urban–rural development represents a complex socio-technical challenge in post-poverty alleviation China. This study examines how the reform of agricultural service systems—especially the rollout of full-process socialization services—reshapes urban–rural integration by embedding new institutional, technological, and organizational structures into rural production. Drawing on staggered provincial pilot programs, we apply a double machine learning framework to assess the causal impact of service reform on the urban–rural income gap, labor reallocation, and agricultural productivity. Results show that agricultural socialization services enhance systemic efficiency by reducing labor bottlenecks, increasing technology diffusion, and fostering large-scale coordination in agricultural operations. These effects are most pronounced in provinces with stronger institutional capacity and higher levels of mechanization. The findings highlight agricultural service reform as a systemic intervention that alters resource allocation logics, drives institutional change, and fosters structural convergence across urban and rural domains. This research contributes to the understanding of agricultural modernization as a systems-engineered solution for regional inequality. Full article

The shikimate pathway is the fundamental metabolic route for aromatic amino acid biosynthesis in bacteria, plants, and fungi, but is absent in mammals. This review explores how multi-enzyme synergy and allosteric regulation coordinate metabolic flux through this pathway by focusing on three key enzymes: 3-deoxy-d-arabino-heptulosonate-7-phosphate synthase, chorismate mutase, and tryptophan synthase. We examine the structural diversity and distribution of these enzymes across evolutionary domains, highlighting conserved catalytic mechanisms alongside species-specific regulatory adaptations. The review covers directed evolution strategies that have transformed naturally regulated enzymes into standalone biocatalysts with enhanced activity and expanded substrate scope, enabling synthesis of non-canonical amino acids and complex organic molecules. Industrial applications demonstrate the pathway’s potential for sustainable production of pharmaceuticals, polymer precursors, and specialty chemicals through engineered microbial platforms. Additionally, we discuss the therapeutic potential of inhibitors targeting pathogenic organisms, particularly their mechanisms of action and antimicrobial efficacy. This comprehensive review establishes the shikimate pathway as a paradigmatic system where understanding allosteric networks enables the rational design of biocatalytic platforms, providing blueprints for biotechnological innovation and demonstrating how evolutionary constraints can be overcome through protein engineering to create superior industrial biocatalysts. Full article

Engineering of Cupriavidus necator could enable the production of various polyhydroxyalkanoates (PHAs); particularly, poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (P(3HB-co-3HH)), a biopolymer with enhanced mechanical and thermal properties compared to poly(3-hydroxybutyrate) (PHB), can be efficiently produced from vegetable oils. However, challenges remain in the recovery process, particularly in removing residual oil and minimizing degradation of the polymer structure during extraction steps. This study investigated the effects of ethanol-based defatting on the recovery and polymeric properties of P(3HB-co-3HH). The proposed method involves the addition of ethanol to the cell broth to effectively remove residual oil. Ethanol improved the separation of microbial cells from the broth, thereby streamlining the downstream recovery process. Using ethanol in the washing step increased the recovery yield and purity to 95.7% and 83.4%, respectively (compared to 87.4% and 76.2% for distilled water washing), representing improvements of 8.3% and 7.2%. Ethanol washing also resulted in a 19% higher molecular weight compared to water washing, indicating reduced polymer degradation. In terms of physical properties, the elongation at break showed a significant difference: 241.9 ± 27.0% with ethanol washing compared to water (177.7 ± 10.3%), indicating ethanol washing retains flexibility. Overall, an ethanol washing step for defatting could simplify the recovery steps, increase yield and purity, and retain mechanical properties, especially for P(3HB-co-3HH) from oils. Full article

The dramatic increase in urban construction waste poses severe environmental challenges. Utilizing waste concrete to produce recycled aggregates (RA) for manufacturing recycled concrete (RC) represents an effective strategy for resource utilization. However, inherent defects in RA, such as high porosity, microcracks, and adherent old mortar layers, lead to significant performance degradation of the resulting RC, limiting its widespread application. Traditional methods for enhancing RA often suffer from limitations, including high energy consumption, increased costs, or the introduction of new pollutants. MICP offers an innovative approach for enhancing RC performance. This technique employs the metabolic activity of specific microorganisms to induce the formation of a three-dimensionally interwoven calcium carbonate gel network within the pores and on the surface of RA. This gel network can improve the inherent defects of RA, thereby enhancing the performance of RC. Compared to conventional techniques, this approach demonstrates significant environmental benefits and enhances concrete compressive strength by 5–30%. Furthermore, embedding mineralizing microbial spores within the pores of RA enables the production of self-healing RC. This review systematically explores recent research advances in microbial mineral gel network for improving RC performance. It begins by delineating the fundamental mechanisms underlying microbial mineralization, detailing the key biochemical reactions driving the formation of calcium carbonate (CaCO₃) gel, and introducing the common types of microorganisms involved. Subsequently, it critically discusses the key environmental factors influencing the effectiveness of MICP treatment on RA and strategies for their optimization. The analysis focuses on the enhancement of critical mechanical properties of RC achieved through MICP treatment, elucidating the underlying strengthening mechanisms at the microscale. Furthermore, the review synthesizes findings on the self-healing efficiency of MICP-based RC, including such metrics as crack width healing ratio, permeability recovery, and restoration of mechanical properties. Key factors influencing self-healing effectiveness are also discussed. Finally, building upon the current research landscape, the review provides perspectives on future research directions for advancing microbial mineralization gel techniques to enhance RC performance, offering a theoretical reference for translating this technology into practical engineering applications. Full article

Boehmite has been widely used in theoretical research and industry, especially for hazardous material processing. For the liquid-phase treating process, the interfacial properties of boehmite are believed to be affected by pH conditions, which change its physicochemical behavior. However, molecular-level detection on cluster ions is challenging when using bulk approaches. Herein we employ in situ vacuum ultraviolet single-photon ionization mass spectrometry (VUV SPI-MS) coupled with a vacuum-compatible microreactor system for analysis at the liquid–vacuum interface (SALVI) to investigate the solute molecular composition of boehmite under different pH conditions for the first time. The mass spectral results show that more complex clustering of solute molecules exists at the solid–liquid (s–l) interface than conventionally perceived in a “simple” aqueous solution. Besides solute ions, such as boehmite molecules and fragments, the composition and appearance energies of these newly discovered solvated cluster ions are determined by VUV SPI-MS in different pH solutions. We offer new results for the pH-dependent effect of boehmite and provide insights into a more detailed solvation mechanism at the s–l interface. By comparing the key products under different pH conditions, fundamental understanding of boehmite dissolution is revealed to assist the engineering design of waste processing and storage solutions. Full article

The transition to clean and renewable energy sources is critically dependent on efficient hydrogen production technologies. This review surveys recent advances in photocatalytic water splitting, focusing on MNb₂O₆ nanomaterials, which have emerged as promising photocatalysts due to their tunable band structures, chemical robustness, and tailored morphologies. The objectives of this work are to (i) encompass the current synthesis strategies for MNb₂O₆ compounds; (ii) assess their structural, electronic, and optical properties in relation to photocatalytic performance; and (iii) elucidate the mechanisms underpinning enhanced hydrogen evolution. Main data collection methods include a literature review of experimental studies reporting bandgap measurements, structural analyses, and hydrogen production metrics for various MNb₂O₆ compositions—especially those incorporating transition metals such as Mn, Cu, Ni, and Co. Novelty stems from systematically detailing the relationships between synthesis routes (hydrothermal, solvothermal, electrospinning, etc.), crystallographic features, conductivity type, and bandgap tuning in these materials, as well as by benchmarking their performance against more conventional photocatalyst systems. Key findings indicate that MnNb₂O₆, CuNb₂O₆, and certain engineered heterostructures (e.g., with g-C₃N₄ or TiO₂) display significant visible-light-driven hydrogen evolution, achieving hydrogen production rates up to 146 mmol h⁻¹ g⁻¹ in composite systems. The review spotlights trends in heterojunction design, defect engineering, co-catalyst integration, and the extension of light absorption into the visible range, all contributing to improved charge separation and catalytic longevity. However, significant challenges remain in realizing the full potential of the broader MNb₂O₆ family, particularly regarding efficiency, scalability, and long-term stability. The insights synthesized here serve as a guide for future experimental investigations and materials design, advancing the deployment of MNb₂O₆-based photocatalysts for large-scale, sustainable hydrogen production. Full article

Glass powder, a non-degradable waste material, offers significant potential to reduce cement consumption and carbon emissions in concrete production. However, existing mix design methods for glass powder concrete (GPC) fail to systematically balance economic efficiency, environmental sustainability, and mechanical performance. To address this gap, this study proposes an AI-assisted framework integrating machine learning (ML) and Multi-Objective Optimization (MOO) to achieve a sustainable GPC design. A robust database of 1154 experimental records was developed, focusing on five key predictors: cement content, water-to-binder ratio, aggregate composition, glass powder content, and curing age. Seven ML models were optimized via Bayesian tuning, with the Ensemble Tree model achieving superior accuracy (R² = 0.959 on test data). SHapley Additive exPlanations (SHAP) analysis further elucidated the contribution mechanisms and underlying interactions of material components on GPC compressive strength. Subsequently, a MOO framework minimized unit cost and CO₂ emissions while meeting compressive strength targets (15–70 MPa), solved using the NSGA-II algorithm for Pareto solutions and TOPSIS for decision-making. The Pareto-optimal solutions provide actionable guidelines for engineers to align GPC design with circular economy principles and low-carbon policies. This work advances sustainable construction practices by bridging AI-driven innovation with building materials, directly supporting global goals for waste valorization and carbon neutrality. Full article

The arid-hot valleys of Sichuan Province contain extensive engineered gravel deposits, where ecological restoration has become the predominant remediation strategy. Accelerating vegetation recovery and continuously improving productivity are important prerequisites for the protection of regional biodiversity. We employed fertilization and sowing cultivation to facilitate ecological restoration. We have conducted continuous ecological experiments for two years using the following experimental treatments, covering indigenous soil, adding organic fertilizer, and applying compound fertilizer and organic fertilizer, with six types of sowing established under each soil treatment: monoculture and pairwise mixed cropping utilizing Elymus dahuricus (EDA), Dactylis glomerata (DGL), and Medicago sativa (MSA). Through the analysis of variance and the calculation of effect factors, our results indicated that compound fertilizer and organic fertilizer adding significantly improved vegetation cover and increased aboveground biomass, and the highest productivity was observed in the mixed sowing treatment of EDA and MSA. The effect coefficient model analysis further showed that the combination of EDA and MSA resulted in the highest selection and compensation effects on aboveground productivity. Two potential mechanisms drive enhanced productivity in mixed grasslands: the strengthening of the selection effect via increased legume nitrogen fixation, and the enhancement of the compensation effect through niche differentiation among species. Full article