The mechanical properties of calcium (alumino) silicate hydrates (C-(A)-S-H) represent a critical focus within the cement and concrete industry. This review begins by summarizing particle-and subparticle-scale models of C-(A)-S-H. Building on these models, the effects of chemical composition and microstructure on the intrinsic mechanical properties of C-(A)-S-H, as determined by high-pressure X-ray diffraction, are described. Existing studies have demonstrated that increasing the Ca/Si ratio and Al incorporation enhances the intrinsic mechanical properties of C-(A)-S-H. Advanced techniques, such as high-pressure Raman spectroscopy and synchrotron radiation-based techniques, have been employed to elucidate the origins of intralayer sliding and preferred intragranular orientation, offering insights into the fundamental mechanisms of creep. Compared with intralayer sliding, the preferred intragranular orientation of cement-based materials significantly contributes to creep in C-(-A)-S-H, establishing a direct link to macroscopic creep behavior. Based on these findings, this review summarizes several “bottom-up” strategies for strengthening and toughening C-(A)-S-H.
This article reviews traditional practices in atmospheric data assimilation and explores scientific strategies to assimilate data at resolutions spanning from tens of kilometers to even hundreds of meters. Such advances leverage the latest technological developments in computing hardware and methods. Two focal points are specifically chosen to illustrate the opportunities and the associated challenges: ① How to fully exploit satellite-observed cloud and rainband structures in tropical cyclones for high-resolution data assimilation; and ② which traditional data assimilation core techniques need re-evaluation and improvement. Specific topics include making an innovative advancement in all-sky brightness temperature assimilation by seeking the connection between satellite observed brightness temperature and unobservable relative vorticity within tropical cyclones; constructing a global data assimilation framework suitable for satellite-orbit-data observation times; developing advanced data thinning and quality control methods to avoid losing the most needed atmospheric small-scale and large gradient structural information. Finally, how to effectively integrate meteorological data assimilation with deep learning is discussed, such as incorporating AI physical parameterization models into 4D-Var system; combining image assimilation with AI to make prediction from image data; making data assimilation and meteorological deep learning mutually beneficial.
Over the past 30 years, metal additive manufacturing (AM) has advanced rapidly, reaching major milestones that have transformed the manufacturing landscape. This layer-by-layer fabrication technique offers exceptional design freedom and manufacturing flexibility, delivering notable performance and economic benefits across sectors such as aerospace and the automotive industry. This study methodically analyses various printing processes, identifies common defects in metallic materials, and proposes effective strategies for their mitigation. We elucidate the complex relationships among various manufacturing methods, microstructures, and their resulting performances. Considering the rapid advancement of artificial intelligence, we outline its various applications within the field of AM. Within the framework of Industry 5.0, the integration of high-throughput experimentation and materials genome engineering (MGE) is expected to substantially expedite the discovery of novel materials. Moreover, agents developed via the integration of large language models with AM expertise are poised to provide innovative approaches for optimising process parameters and enhancing decision-making accuracy. With continued advancements in AM agents, cloud computing, renewable energy, and structural design principles, the realisation of smart AM factories based on these technologies is becoming increasingly achievable. This improvement is expected to propel metal AM into a new era characterised by intelligence and customisation, fostering substantial progress and transformative shifts in materials science and manufacturing engineering.
Future decarbonized and resilient energy systems will rely on significant demand-side flexibility resources to accommodate high penetration of intermittent renewable energy, particularly considering the rise in extreme weather events due to climate change. These resources must manage their load or generation, inevitably affecting end-user interests, such as comfort, productivity, and convenience. To address the supply-demand imbalance from a human-system interaction perspective, this study proposes “ergonomics in energy use,” a framework connecting the energy system to humans (i.e., energy users) through various flexibility resources, each characterized by a physical machine model, a target parameter, and a human evaluation model. The framework was demonstrated in an office building featuring three flexibility resources: an air-conditioning system, electric vehicles with smart chargers, and a lighting system. The framework was found to provide optimal operational strategies for these resources to minimize user dissatisfaction in various real-time load shedding and day-ahead scheduling programs. Based on the framework, we further present a novel method for demand flexibility quantification, defined as the maximum change in energy use for a given increment in service quality impact (using indices such as predicted percentage dissatisfied). This study shifts the perception of demand flexibility from a purely engineering concept to a social engineering concept, fostering human-centric energy system design, operation, and evaluation while paving the way for a new theory called “ergonomics in energy use.”
Intelligentization presently emerges as the primary direction for future developments of road infrastructure, providing specific scenarios that integrate conventional transport infrastructure research with cutting-edge technologies, such as artificial intelligence (AI), the Internet of Things (IoT), big data, and new forms of business, including automated driving and intelligent connected vehicles. The key technologies for the construction and operation of smart roads include digital sensor networks, intelligent management systems, and interconnected service frameworks, among which sensor networks provide a data foundation. This study focuses on monitoring and detection technologies for road service performance, which constitute an integral part of the digital sensor networks of smart roads. Reviews were conducted, and observations were made, from three perspectives: embedded sensing of road service performance, automated detection of road surface defects, and intelligent identification of hidden road defects. Advancements and existing challenges faced by monitoring and detection technologies for road service performance were examined, and applications of AI in monitoring road service performance and detecting road service problems were elucidated. Finally, a roadmap for future research on sensing and detection for AI-powered road service performance was proposed. Breakthroughs are expected in four areas: establishing a “space-air-ground” multi-source three-dimensional monitoring and detection system, developing monitoring and detection technologies based on multi-source data fusion algorithms, building a digital twin base integrating the physical structures of roads, and creating a road control and service system that integrates end-edge-cloud collaboration. This comprehensive approach aims to advance the key technologies and theoretical foundations that are essential for the construction and operation of smart roads.
Hybrid models that integrate physics-based (PB) and data-driven (DD) approaches have gained increasing attention in intelligent manufacturing. To address the dual challenges of limited dynamic adaptability in static hybrid models and the difficulty of traditional residual learning in handling unmodeled dynamics, we propose a PB-DD hybrid model using reinforcement learning (RL) and adversarial learning (AL), namely, RA-PBDD, which is different from a DD-centric hybrid approach that often suffers from limited training data representativeness, whereas PB-centric hybrid methods face challenges because of model incompleteness. RL is employed for one-time parameter identification in PB models, overcoming the limitations of conventional stepwise techniques. A residual value model using AL with neural networks is proposed, which achieves improved accuracy through iterative refinement compared with standard learning approaches. Additionally, we analyze the operational principles of the PB, DD, and hybrid PB-DD models from a state-space perspective which is a particular angle to systematically examine and contrast the characteristics of these model types. The effectiveness of RA-PBDD is validated through a case involving a machine tool feed system. The results confirm the superiority of RA-PBDD in both prediction and generalizability accuracy over the stand-alone PB, DD models and other two hybrid models. Specifically, it improves the prediction accuracy by up to 73% from 15.4 to 4.2 μm and machining precision by up to 46.7% from 15.2 to 8.1 μm.
Emerging evidence highlights the oral-gut axis as an important contributor to the pathogenesis of inflammatory bowel disease (IBD); however, the specific role of the tongue coating microbiota remains poorly understood. To comprehensively delineate oral-gut microbial alterations in IBD, we analyzed tongue coating and fecal microbiota from 596 participants, including 278 patients with Crohn’s disease (CD), 91 with ulcerative colitis (UC), and 227 healthy controls (HCs), using 16S ribosomal RNA (rRNA) gene sequencing. Patients with IBD exhibited a distinct dysbiotic signature in tongue coating microbiota, characterized by increased abundances of Streptococcus, Prevotella, and Gemella in both CD and UC patients, alongside a CD-specific depletion of commensal taxa such as Neisseria and Fusobacterium compared with HCs. Notably, a similar oral microbial shift was observed in a spontaneous enteritis mouse model, which demonstrated enrichment of oral Streptococcus and concordant increases in Prevotella across both oral and intestinal niches. To establish causality, tongue coating microbiota from CD patients were transplanted into antibiotic-pretreated mice, resulting in significantly exacerbated colitis relative to recipients of HC-derived microbiota. Mechanistically, Streptococcus strains isolated from CD patients promoted Th1 cell polarization in vitro and aggravated colitis in vivo, implicating these orally derived pathobionts in the amplification of intestinal inflammation. Together, these findings identify the tongue coating microbiota as a previously underappreciated mediator of gut inflammation in IBD, support its potential utility as a non-invasive biomarker for CD, and provide mechanistic evidence that oral microbial dysbiosis can actively drive intestinal immune pathology.
Wastewater-based surveillance (WBS) has emerged as an effective tool for monitoring infectious diseases. However, its broader application is often constrained by operational resources and data complexities. Herein, we developed an integrated framework that synergistically integrated WBS with the Research Index, China’s leading online search query platform, to enhance early warning capability for infectious diseases using coronavirus disease 2019 (COVID-19) as a case study. A total of 1164 influent wastewater samples were collected from 12 wastewater treatment plants in Nanning, China, over a one-year period (February 2023-January 2024), and RNA was quantified using reverse transcription quantitative polymerase chain reaction (RT-qPCR). The 7-day flow-weighted moving average concentration (FWMAC) was calculated and evaluated in relation to 16 population surveillance indicators and 126 Baidu search terms. Remarkably, the 7-day FWMAC preceded clinical indicators by 1-7 days and demonstrated strong correlations with multiple epidemiological metrics, including reported cases (the coefficient of determination (R2) = 0.92), diagnosed cases in fever clinics (R2 = 0.72), positive diagnoses in fever clinics (R2 = 0.86), and hospitalizations (R2 = 0.78). Distributed lag nonlinear models were employed to define actionable and clinically relevant risk thresholds. We then identified three key Baidu search terms (“second positive,” “four stages of COVID-19 clinical progression,” and “ibuprofen”). Their
Bio-tar, a byproduct of biomass pyrolysis, poses environmental and processing challenges owing to its tendency to clog pipelines and its ecotoxicity. Converting bio-tar into functional carbon materials offers a sustainable route for waste valorization; however, the underlying thermal polymerization mechanisms remain poorly understood. Herein, we present a single-functional model-compound-assisted analytical strategy to elucidate reaction pathways and polymerization mechanisms in multifunctional group coupling systems. By constructing a model bio-tar (M-bio-tar) that reflects the chemical heterogeneity of real samples, we uncover a temperature-dependent, stage-specific polymerization mechanism comprising volatile release ( ≤ 200 °C), radical-driven crosslinking polymerization (200–400 °C), and carbon skeleton consolidation ( ≥ 300 °C). Radical dynamics involving alkyl and hydroxyl radicals (R• and HO•) are key contributors to crosslinking processes, while oxygenated intermediates, such as aldehydes and furans, enhance polymerization efficiency via synergistic Diels–Alder and cyclization reactions. Structural evolution analyses reveal temperature-dependent trade-offs among graphitization, dehydrogenation, and porosity development. Temperature-mediated graphitization and heteroatom elimination result in bio-carbons with tunable physicochemical properties. Thermodynamic calculations support the proposed oxygen-regulated reaction pathways and reveal the catalytic roles of unsaturated functionalities. These findings establish a mechanistic framework for engineering bio-tar-derived carbon materials that integrates biomass utilization and advanced material design, thereby advancing the rational development of sustainable carbon materials for energy and environmental applications within a circular bioeconomy.
As sixth-generation (6G) wireless communications push edge nodes toward higher densities and multi-task collaboration, environment-responsive reconfigurable antennas that operate without auxiliary control circuitry can simultaneously provide sensing, communications, and lower node-level power budgets, offering a viable route to overcoming front-end bottlenecks. Here, we integrate an environment-responsive passive actuator that is sensitive to multiple ambient factors into the antenna architecture and demonstrate a prototype whose geometry, operating frequency, and radiation pattern can all be dynamically reconfigured. A layered actuator comprising an ultrathin electrodeposited nickel (Ni)-aurum (Au) substrate and a cobalt (Co)-doped brinessite-type manganese dioxide (δ-MnO₂) actuating transition etal oxide (TMO) delivers large strain, rapid response, and straightforward fabrication. The Ni-Au substrate functions as the antenna radiator, while the actuating layer generates reversible stress under ambient stimuli that reshapes the radiation structure, unifying actuation and radiation within a single structure. Systematic studies of actuation behaviors, material properties, radiation performance, and energy-harvesting application show that the actuator is well suited to adaptive communication and to electromagnetic devices that can sense environmental factors, as envisioned for edge nodes. The proposed strategy can be extended to a broad range of antenna topologies and even to metasurfaces. These findings demonstrate the potential of TMO-based actuators in the development of next-generation intelligent antennas for adaptive wireless communication systems.
With the advancement of next-generation fighter aircraft, the escalating cooling demands of thermal management in aircraft and their engines are approaching the thresholds of conventional heat sinks, including ram air and fuel. A variable cycle engine (VCE), characterized by its third-stream design, facilitates potential multi-heat sink coordination within the fuel thermal management system (FTMS). Despite the use of decoupled VCE and FTMS modeling in previous research, the heat sink potential of internal secondary bypass air remains largely unexplored and unquantified, with its feedback effects on VCE energy efficiency also lacking rigorous investigation. Driven by the background, this study proposes a novel coupling of VCE and FTMS design. By leveraging multidisciplinary simulations, we provide the first quantitative analysis of the heat sink efficacy of secondary bypass air across representative flight missions and elucidate its synergistic mechanism with fuel. Investigations reveal that compared with ram air, secondary bypass air markedly reduces the thermal accumulation by 36.57%–74.06%. This improved thermal performance is accompanied by a 2.17%–4.10% decrease in the hot-return fuel flow. Intriguingly, the induced specific fuel consumption penalty throughout various typical flight missions consistently remains below 0.8%, thereby demonstrating the economic efficiency and sustainable benefits of employing secondary bypass air for thermal management. Furthermore, this study presents the first optimization strategy for allocating heat transfer area. Specifically, an area ratio of 0.6 between the ram air and secondary bypass air significantly lowers the system hot-return fuel temperature by 2.68%. This work validates quantitative evidence for secondary bypass air–FTMS coupling and establishes a foundation for system-level thermal management schemes in advanced fighter aircraft and engine designs.
Pesticide spraying is a primary approach for the chemical management of pests, diseases, and weeds. The efficient design of formulations and their accurate application constitute a systems engineering problem that combines formulation chemistry, application technology, and agronomic practice. The droplet size distribution (DSD), which is determined by the physicochemical properties of the formulation during atomization, has a direct influence on deposition efficiency, drift potential, and control performance. Conventional atomization indicators, such as volume median diameter (VMD), Sauter mean diameter (D32), and relative span (RS), do not adequately provide a quantitative connection between the physicochemical properties of formulations and the droplet size spectrum needed for effective biological control. A comprehensive atomization quality index that incorporates formulation properties and the optimal droplet size is therefore urgently required. To fill this gap, five atomization parameters, relative diffusion ratio (RD), RS, fractal dimension (FD), drift droplet proportion (V150, ≤ 150 lm diameter), and Dv0.5 (also referred to as the VMD), were chosen to construct the atomization bridging index (ABI). The ABI allows a comprehensive quantitative assessment of atomization quality and shows a strong correlation with the physicochemical microstructure of the formulation. The analysis indicates that micelle–polymer complexes generated through polymer–surfactant interactions markedly improve the ABI by jointly lowering dynamic surface tension (DST) and increasing viscosity. The evaluation findings show that the associative polyethylene oxide (PEO)/sodium dodecyl sulfate (SDS) system demonstrates outstanding performance over a broad pressure range. At 250 kPa, the PEO/SDS system at 1× the critical micelle concentration (CMC) reaches the highest ABI value, corresponding to the Class I category, and is therefore recommended as the optimal spraying system. Overall, ABI establishes a robust physicochemical-spray linkage framework and provides new guidance for identifying key formulation parameters in pesticide adjuvant design.
Agricultural decision support systems (ADSS) are designed to translate complex data into actionable insights for farm management. However, a significant gap persists between the growing skill of subseasonal-to-seasonal (S2S) climate forecasts and their operational use in supporting tactical agricultural decisions. This review critically examines the current state and future trajectory of ADSS, with a focus on bridging this gap. The analysis reveals that while modern ADSS excel in operational and strategic planning using historical data, they largely fail to integrate operational S2S forecasts, leaving farmers vulnerable to near-term climate anomalies. Advances and challenges are synthesized across three interconnected fronts: the methodological pipeline for integrating S2S forecasts, including downscaling, bias correction, and uncertainty quantification; the imperative of participatory design and coproduction to enhance usability and adoption; and the transformative potential of artificial intelligence and machine learning under the emerging Agriculture 5.0 paradigm. The next generation of ADSS must evolve into interactive, uncertainty-aware platforms that facilitate exploratory decision-making through bidirectional feedback loops. By synthesizing these insights, this review proposes a conceptual framework and research agenda for developing ADSS capable of truly supporting climate-resilient agriculture.
Despite the critical role of rural energy transitions in global decarbonization, the spatial alignment between regional energy demand and local biomass resource endowments remains insufficiently explored. Here, we develop a spatially explicit framework that integrates agricultural, energy, population and carbon datasets to compare rural energy consumption and straw resource distribution in China and globally. We find that although rural electrification in China now covers approximately 41% of the total area, mitigation benefits are constrained by a coal-dominated supply (61%), indicating that electrification does not deliver decarbonization uniformly. On the resource side, China generates approximately 0.95 Gt of crop residues from major food crops annually, corresponding to 0.54 gigatonnes of standard coal equivalent (Gtce), yet less than 10% is utilized for fuel, underscoring a paradox of resource abundance versus underutilization. By incorporating collection-radius thresholds, we delineate suitability zones, identifying major grain belts such as northeast China and the Yangtze River Plain as appropriate for large-scale deployment, whereas hilly and fragmented agricultural regions are more compatible with decentralized pathways. At the global scale, straw resources and rural populations are strongly coupled in south Asia, southeast Asia, and Sub-Saharan Africa, yet disparities in per capita availability highlight divergent transition pathways across income groups. Our study establishes a spatially explicit basis for linking rural energy demand with straw resource suitability, providing evidence to guide differentiated deployment strategies and to support rural clean energy transitions.
There are critical opportunities and profound challenges at the food–water–energy nexus. Crucial to the global nexus is the role of groundwater. Groundwaters provide 99% of the world’s useable freshwater supplies. Irrigation is required for much of the world’s food production, as 40% of the food grown globally uses irrigation. Yet some 10% of today’s irrigation water is “stolen” from tomorrow, as it relies on today’s use of non-renewable groundwater. With “business-as-usual,” it is predicted we will reach “peak groundwater” around 2050. We review groundwater-protection measures under two contrasting food-production systems: viticulture in temperate New Zealand, and halophyte production for food, fibre, and fuel in the hyper-arid United Arab Emirates (UAE). Some 75% of New Zealand’s wine is produced in the Marlborough region, and the vines generally require irrigation using groundwater. Ourin-situ measurements of drainage and leaching show that there is net drainage-recharge of groundwater, and that the nitrate concentrations in the leachates are less than the drinking water standard. Groundwater is being protected though a net aquifer recharge of about 100 mm·y−1. Our work in the UAE focussed on groundwater protection under the production of the obligate halophyte Salicornia using irrigation with the reject brine from a desalination unit. A leaching-fraction is needed to despatch the residual salts out of the rootzone back to groundwater. Our heuristic modelling predicts a hyperbolic rise in aquifer salinity. From an initial salt concentration of 18.8 kg·m−3 the concentration of salt would be predicted to reach about 100 kg•m−3 in just 40 years. Solutions to extend the life of these groundwaters could involve the use of zero-liquid-discharge desalination, or the conjunctive use of alternative waters, either directly, or indirectly via managed aquifer recharge. There are opportunities and challenges at the nexus. Solutions will rely on engineering science and technology.
The current Von-Neumann architecture cannot support emerging applications in artificial intelligence, necessitating a new computing paradigm that can solve complex tasks. Memristor-based near-memory computing demonstrates the potential beyond Von-Neumann computers. However, due to device limita- tions, memristor-based hardware with bulky peripheral circuits cannot handle complex tasks and is sen- sitive to noise. In this study, we leverage the physical stochasticity of a diffusive device and the inherent parallelism of the crossbar structure to implement a true stochastic Bayesian machine based on an Au/Ag/ Al2O3/Pt/Ti memristor crossbar array circuit. The proposed system supports long probabilistic sequence inference with superior soft-error robustness and enables probabilistic hardware to handle high-feature tasks such as image recognition for the first time. We have constructed a diffusive memristor device with the corresponding compact model and measured the signal-transmission characteristics of the crossbar array prototype circuit, revealing severe signal distortion due to parasitic effects. Moreover, we propose a signal-reconstruction method that effectively improves the performance of Bayesian machine circuits. Our work points to new directions for exploring circuit design and large-scale integration methods in probabilistic computing hardware.
China, the world’s largest producer and consumer of coal, possesses extensive underground space in coal-bearing regions (CBRs). With the low-carbon transition of the national energy system, the utilization of underground coal-mine spaces for energy storage has attracted significant attention. This paper presents a comprehensive review of the types, engineering suitability, safety constraints, and future pathways of underground energy storage (UES) technologies in China’s coal mines based on 1044 related publications from Web of Science and China National Knowledge Infrastructure (CNKI). The results indicate that China’s CBRs overlap substantially with solar, wind, and geothermal resources, accounting for 82.2%, 88.3%, and 85.2% of the national resource distribution, respectively, demonstrating substantial potential for integrated renewable-energy development. On this basis, the principal UES technologies applicable to abandoned and producing coal mines are summarized, identifying key technological requirements and safety challenges under different geological conditions. In addition, functional backfill technologies are emerging as valuable tools for the full carbon cycle by enabling underground CO2 sequestration, geothermal heat extraction, and strategic underground storage of energy carriers. However, large-scale deployment of UES in coal mines remains constrained by geological uncertainty in deep mining environments, cyclic loading effects, and coupled thermal–hydrological–mechanical disturbances. Long-term cavern stability, sealing integrity, and leakage risks require further validation. Future progress will hinge on intelligent material design, digital-twin monitoring, multi-field coupled stability assessment, and multi-energy complementary systems, which may provide a potential pathway for the low-carbon transformation of China’s CBRs.
Stem cell-based embryo models (SCBEMs) have emerged as important tools in developmental biology and regenerative medicine, providing novel perspectives for deciphering the mechanisms of early embryogenesis and advancing clinical applications. This review systematically summarizes recent progress in the field and proposes a three-dimensional (3D) “potency-state-lineage” classification framework that integrates the intrinsic properties of stem cells used for model construction, as well as outlining the logical framework underlying model development. This review highlights the ability of pluripotent stem cells (PSCs) to self-organize into 3D embryoids or organoids that mimic natural embryos or specific tissues. These models offer unprecedented access for studying human development while circumventing the ethical and practical constraints associated with direct research on human embryos. Moreover, these models serve as important platforms for investigating developmental disorders, environmental influences, and disease modeling. This review further explores the transformative potential of SCBEMs in personalized tissue engineering, drug screening, and livestock breeding while emphasizing the urgent need for ethical guidelines and standardization in this rapidly evolving field. SCBEM technology continues to advance and holds promise for enhancing the understanding of developmental processes and providing support for the development of therapeutic strategies.
The full-section asphalt concrete waterproof layer (FACWL) has garnered significant attention for its outstanding ability to reduce frost heave and thaw-related weakening in railway track beds, particularly in seasonally frozen regions. To explore the dynamic properties of the FACWL, a fractional-order constitutive model was utilized to characterize the viscoelastic behavior of asphalt concrete. Additionally, a vehicle–track coupled finite element (FE) model and the numerical approach incorporating the fractional-order constitutive model were developed and validated via experimental and field testing. Simulation results indicate that applying the FACWL reduces the vertical dynamic response of each structural layer, vertical peak accelerations across the subgrade surface layer exhibited reductions exceeding 30% in both positive and negative directions. Moreover, the tensile strain at the bottom of the FACWL remained relatively low, less than 100 με. Compared with conventional waterproof sealing layers, the viscoelastic nature of the FACWL facilitates energy dissipation, effectively decreasing the overall vibrational amplitude and vertical deformation within the track structure by more than 20%. Consequently, the FACWL plays a crucial role in ensuring the long-term stability of the subgrade and minimizing vibrations in the track system.
Rising freshwater scarcity in arid regions requires advanced desalination and resource recovery technologies to address brackish water threats to agriculture, drinking water, and ecosystems. Charge-asymmetric Janus nanofiltration (NF) membranes demonstrate significant potential for resource recovery in brackish water treatment, particularly enabling efficient ion-selective separation. However, current models remain limited to qualitative speculation regarding the ion transport mechanisms in Janus membranes, as they fail to incorporate quantitative descriptions of axial charge heterogeneity. Herein, we address this gap by integrating axial charge distribution into the Donnan Steric Pore Model with Dielectric Exclusion (DSPM-DE). A charge-asymmetric Janus membrane (R90-AC) was fabricated by coating a positively charged polyelectrolyte layer onto a commercial NF membrane (R90-SC). The axial-charge-distributed DSPM-DE model reduced prediction deviations for six ions in brackish water systems to < 8 %, outperforming conventional models (deviations > 16 %). Theoretical simulations revealed an intrinsic electric field (18.87 mV·μm−1) within Janus structures, driving anomalous electromigration contributions exceeding 100 % for cations and −50 % to −20 % for anions. This “electrostatic diode” effect was experimentally validated, with Mg2+ forward flux (2.69 × 10−2 mol·m−2·h−1) surpassing reverse flux (2.98 × 10−3 mol·m−2·h−1) by nearly an order of magnitude. The study bridges theoretical modeling and practical structure design, offering a robust framework for tailoring charge-asymmetric structures in ion-selective separations.
This study proposes a continuous-flow microwave system as an innovative alternative to conventional steam-based ultra-high-temperature (UHT) processing for liquid foods. To address the complex interactions between microwave energy and fluid matter, we investigated the feasibility and stability of both the three-stub tuner method and electromagnetic-black-hole (EBH) technology. Through an integrated electromagnetic-heat-fluid coupling model, we demonstrated that while the stub tuner system achieved heat conversion efficiencies comparable to the EBH configuration under specific optimized conditions, it exhibited significant parameter sensitivity during tuning processes. In contrast, the EBH system maintained consistent high efficiency across dielectric constant variations (ε′ = 10–80) through its gradient-index structure, though it required precise dielectric loss control to prevent efficiency decay. Proteomic analysis revealed temperature-dependent alterations in milk serum proteins following microwave-UHT treatment. Compared to raw milk, samples processed at 110, 130, and 150 °C showed 401, 426, and 305 significantly upregulated proteins, respectively, with the 130 °C treatment group demonstrating optimal protein preservation. Gene ontology (GO) analysis identified 73 differentially expressed proteins functionally associated with immune regulation, particularly enriched in complement activation and inflammatory response pathways. These findings suggest that moderate microwave-UHT treatment enhances the diversity and quantity of proteins in the milk serum phase, thereby indirectly improving nutritional and functional properties. This study provides insights into the design of continuous-flow microwave-UHT methods and elucidates the impact of high-temperature, short-time microwave processing on the protein composition and potential nutritional properties of bovine milk.
The proposed steel–ultra-high-performance concrete composite truss (SUCT) arch bridge addresses three major technical problems of conventional arch bridges and extends the span to 600–1000 m. An intelligent cyclic construction method and an automated sliding connector are proposed for the SUCT arch ring to reduce construction costs and mitigate stress superposition. This method involves multiple closures of the arch ring from inside to outside. This paper introduces the cyclic construction method, compares it with existing methods, and investigates the mechanical behavior and load distribution of the arch ring through experimental analysis in both slidable and locked states of the sliding connectors. An optimized design for sliding connectors was also developed. The results indicate that the cyclic construction method offers distinct advantages over traditional techniques, ensuring the construction feasibility of SUCT arch bridges. The sliding connectors released most vertical shear forces, and the corresponding bending moments were transferred to the inner arch. Consequently, approximately 10% of the load was transferred to the inner arch, representing approximately 20% of the load in the locked state. However, approximately 25.5% of the negative bending moments were transferred to the inner arch in spring conditions. During the arch ring’s asymmetric loading process, the inner arch’s left spring cracked and crushed before the outer arch due to stress superposition. By optimizing the sliding surface to a vertical orientation (0°) and lubricating the sliding plate and chute, negative bending moments and vertical loads were effectively isolated from the inner arch, eliminating stress superposition and significantly enhancing crack resistance and load-bearing capacity. With the optimized design, the load borne by the innermost arch was reduced to 48% and 85% compared to the non-sliding connector scheme and the original design. Furthermore, the load distribution across all truss arch rows was uniform. These findings advance both the theoretical understanding and practical implementation of innovative arch bridge construction, offering insights for the infrastructure sector.