Ultra-high-performance concrete (UHPC) with adapted rheology continues to attract interest considering the requirement for novel processing techniques such as self-consolidating, pumping, spraying, and three-dimensional (3D) printing. The rheology of UHPC is complex due to its high solid volume fraction, low water content, and wide range of constituent materials that affect its flow properties. This work provides guidance for tailoring the mixture proportioning of UHPC to secure proper rheological properties and performance of UHPC for various applications. In the first part of this work, key physical, physicochemical, and chemical factors that can affect the rheological properties of UHPC are discussed. Rheological measurement methods and interpretation of the test results are provided to accurately determine the rheological parameters. The effects of constituent materials on the yield stress, viscosity, thixotropy, and structural build-up of UHPC are elaborated. The rheological parameters can increase by up to 100 times with the decrease in water-to-binder ratio. Such an increase can be reduced to less than 10 times through optimization of the particle size distribution and selection of superplasticizer. Rheology control strategies for UHPC for various applications are outlined. Multiple chemical admixtures with an organized molecular architecture must be used to achieve contradictory rheological requirements (e.g., low yield stress but high viscosity; low dynamic yield stress but high static yield stress). Finally, challenges and future demands to fine-tune the rheological properties of sustainable UHPC are showcased. Of special interest in future studies is the interaction between low-clinker binder and chemical admixtures and its effect on the microstructure of fresh UHPC.
Current fatigue load models (FLMs) for road bridges are typically calibrated based on steel and reinforced-concrete bridges. These models are not likely applicable to fiber–polymer composite bridges, primarily because the damage rate—defined as the slope of the fatigue load versus life (F–N) curve of the composites—is significantly lower for composites than for steel. To address this limitation, a methodology is proposed to derive FLMs for composite road–bridge superstructures, and its application was demonstrated via a case study of a composite bridge deck. The adhesive deck joint was identified as the fatigue-critical location. Given the typically short spans of current composite bridges, a double-axle FLM was adopted, and the axle loads were calibrated using a composite road bridge that has been in service in Switzerland since 2012. Three different slopes of the F–N curve were assumed, all representative of composites. Calibration was based on a series of real traffic loads from the three countries, obtained from weigh-in-motion (WIM) measurements. The FLM axle loads were calibrated at specified numbers of cycles such that the damage generated by the FLM was equal to that in real traffic. Large axle loads were the predominant contributors to damage even at low cycle counts. The relationship between the axle loads and the number of cycles was established as a reference for testing across the three slopes. Characteristic axle loads increased with slope and decreased with cycle number. The results of this study represent an initial step toward developing an FLM for composite road bridge superstructures. Further calibration of bridges with varied geometries and composite materials is required to derive a generally applicable, parametrized model.
Seismic strengthening with carbon fiber reinforced polymer (CFRP) sheets is a proven technique for improving the capacity and ductility of concrete members and has been used worldwide. In this study, the effects of multi-hazard loading on the behavior of reinforced concrete beams strengthened with CFRP and ultra-high-performance concrete (UHPC) jackets are investigated. This work is an unprecedented initiative in the rehabilitation sector. Based on a previously conducted experimental study, where load reversals are performed at elevated temperatures varying from 25 to 175 ℃, analytical responses are investigated focusing on the performance degradation, uncertainty quantification, hysteresis, and pinching mechanisms of the retrofitted beams. The uncertainty index, which measures the extent of the anomaly caused by the multi-hazard loading, clarified that the pinching of hysteresis loops during loading processes is the predominant factor causing a loss of energy dissipation capacity in the thermocyclic distress. The development of uncertainty correlated with the degree of drift ratios, demonstrated by the increased uncertainty index of 0.37 at 175 ℃, and the beam pinching is controlled by the retrofit schemes. The adjusted stiffness of the loops represents the accumulated damage and deformation resistance; meanwhile, the evolution of irreversible pinching alters hysteretic configurations in the subsequent unloading phases. The Eigen hysteretic properties of the beams are extracted to understand the contribution of individual modes to the progression of uncertainties. The first mode dominates the fourth mode by a factor of up to 127.9. Design recommendations are suggested to estimate thermocyclic damage in the strengthened beams with performance degradation factors ranging from 1.00 to 0.45, contingent upon temperature.
Carbon fibers have excellent properties, including high strength, light weight, corrosion resistance, and high durability; therefore, they are widely used in various fields. Carbon fibers possess excellent electrical conductivity and electrochemical stability, and they can be used as electrode materials for functionalized applications in civil engineering. This study explores the evolution mechanism of the electrochemical properties of carbon fibers and carbon fiber composites used as anodes. This study further focuses on the collaborative intervention technique of impressed current cathodic protection and structural strengthening (ICCP-SS) for reinforced concrete structures, as well as the non-destructive recycling of carbon fibers based on their electrochemical evolution mechanism. This study aims to provide new ideas for the functionalization of carbon fiber composites in civil engineering.
Cementitious composites are ubiquitous in infrastructure, but their durability is often limited by interfacial weaknesses, particularly when bonded to epoxy polymers. These organic-inorganic interfaces are critical in applications such as fiber-reinforced polymer repair, strengthening, and additive concrete manufacturing. However, debonding and cracking at the epoxy-calcium-silicate-hydrate (CSH) interface under conditions such as elevated temperature, moisture, or fatigue-loading remain long-standing challenges. A fundamental understanding of the atomic-level interactions governing macroscopic interfacial properties is essential to optimize the performance of these composites; however, these remain largely unexplored by conventional analytical techniques owing to the buried nature of the nanoscale epoxy-CSH interface. In this study, we employed classical molecular dynamics (MD) simulations to elucidate the molecular mechanisms associated with epoxy-modifier-incorporated (nanofillers-incorporated) epoxy prepolymer (i.e., liquid epoxy) adsorption, wetting, and adhesion to CSH surfaces. We investigated three epoxy systems: epoxy prepolymer, acrylamide-modified (AM-modified) epoxy prepolymer, and nanosilica-reinforced (nano-SiO2-reinforced) epoxy prepolymer, with their pre-curing interactions with CSH simulated for the first time. Key interfacial parameters, including interphase formation, wetting ratio, atomic-density profile, monomer-concentration profile, interfacial ionic and hydrogen bonding, interaction energy, and work of adhesion, were rigorously analyzed. Our MD simulations revealed that the diethyltoluenediamine (DETDA) epoxy hardener preferentially adsorbs over bisphenol F diglycidyl ether (DGEBF) resin on the CSH surface, leading to a hardener-rich interphase. Both resin and hardener components were found to concentrate within approximately 5 Å of the CSH surface and exhibit molar ratios significantly higher than that of the bulk, suggestive of a potentially stiffer interphase. The incorporation of AM was found to significantly enhance interfacial adhesion and improve adsorption and wetting through strong electrostatic and hydrogen bonding, resulting in a 16.43% increase in the work of adhesion compared to neat epoxy. The nanosilica filler, while improving interfacial interactions, aggregated and yielded a modest enhancement of 4.45% in the work of adhesion. These findings provide fundamental and novel molecular-level insights for the rational design of epoxy-modified cementitious composites with enhanced interfacial-bonding, durability, and mechanical properties. This study underscores the critical role played by nanofiller selection in tailoring interfacial properties and offers valuable guidelines for material formulations that optimize the interphase region in high-performance polymer-cement hybrid materials.
The concept of multiscale fibrous reinforcements in cementitious matrices is characterized by a wide range of scales from distributed nanomaterials and chopped short fibers to continuous fibrous reinforcements. Based on fibrous reinforcements at multiple scales, this study elaborately optimizes mechanical behavior by tailoring the types and volume fraction of fibers and develops a cementitious composite, flexible ultra-high performance reinforced cementitious composite (FHPRC), with 160 MPa compressive strength, 36 MPa tensile strength, over 1% ultimate tensile strain, less than 0.1 mm crack width, and significant post-yield stiffness. FHPRC combines the superior strength and durability of ultra-high performance concrete (UHPC) with the high ductility and crack control capacity of engineered cementitious composite. We demonstrated the effectiveness of the material design strategy through experimental and numerical examinations. The effects of the types of short fibers (steel with a designed length of 13 mm and glass with a length of 50 mm), fiber-reinforced polymers (FRPs) (carbon and glass), and textile configuration on the flexural behavior were analyzed. To capture their flexural behavior, several numerical models have been developed to optimize FHPRCs. Furthermore, the layered shell finite element model (FEM) based on the smeared crack approach considerably simplifies the numerical effort required to simulate intense matrix cracking. However, no realistic constitutive model for any composite containing one or more reinforcing fibers for layered shell FEMs has been developed. Hence, an equivalent constitutive model for layered shells was established to analyze the flexural behavior of FHPRCs. The model proved that the combination of UHPC and carbon FRP textiles yielded a superior composite. The research results provide valuable insights into the evolving field of advanced construction materials and engineering.
The conventional method for analyzing the durability of concrete structures is complex and time-consuming. The values of durability parameters are closely linked with the specific mix proportions of concrete and its service environment. For a reliable service life assessment, it is crucial to accurately determine critical physical indicators within the design model, such as the chloride diffusion coefficient, the time-dependent index of chloride diffusion coefficient, and the critical chloride concentration. However, efficiently and reliably deriving these physical parameters remains a significant challenge. This study develops three-dimensional (3D) heterogeneous models of cement-based materials from the microscale to the mesoscale to quantitatively calculate the chloride diffusion coefficient of concrete at the macroscale. Then, a dataset containing more than 2000 groups of marine concrete was analyzed to reliably determine the values and distribution types of relevant durability parameters. Finally, service life evaluations based on the theoretical model of chloride diffusion and reliability theory are applied to the marine concrete structure in Northeast Asia. This methodology bypasses the lengthy process of conventional exposure testing, demonstrating enhanced efficiency and providing a novel approach to the durability assessment of concrete structures in the marine environment.
Concrete manufacturing is a significant contributor to carbon emission. This paper presents an extensive study on compression-casting, which is a newly developed and highly efficient concrete manufacturing technology. Compression-casting is a mechanical process that improves the properties of all types of concrete materials without using chemical additives or mineral admixtures. It is particularly suitable for producing high-quality concrete from secondary/substandard or waste/recycled aggregate materials, thereby promoting green, low-carbon, and sustainable construction. This paper introduces compression-casting and presents the experimental results of the material and relevant fiber-reinforced polymer (FRP)-reinforced structures. Various applications of the compression-casting technology on inferior concrete materials, including rubber, recycled aggregate, desert sand, and recycled powder concrete, have also been introduced. The behavior of FRP-reinforced structural members of compression-cast concrete (CCC) is evaluated and compared with that of normal concrete (NC). It is concluded that compared to NC, CCC has the following advantages: improved mechanical properties, microstructure, durability, cement saving, economy, and reduced carbon dioxide emissions. Finally, we provide recommendations for addressing CCC structure brittleness. As a result, this method can reshape the future of concrete manufacturing.
This study demonstrates the synthesis of nanosilica aerogels (NSAs) from waste glass using a CO2-based extraction process. The process was optimized by varying key reaction parameters, including extraction temperature, reaction duration, NaOH concentration, and waste glass fineness. The resulting silicate precursors, which demonstrated high CO2 capture efficiency, were used to prepare NSA particles. The synthesized NSA exhibited an extremely high surface area and porosity; thus, these can be used as a value-added, lightweight, and reactive supplementary cementitious material for producing thermally insulating concrete. The incorporation of NSA accelerated hydration, with nucleation and pozzolanic effects contributing to 21 % and 3 %, respectively, to the hydration process. The initial hydration acceleration was attributed to the extremely high surface area of NSA, which facilitated the precipitation of hydration products on its surface. At later stages of hydration, the pozzolanic reaction of NSA promoted the formation of calcium silicate hydrate (C-S-H) in the cement matrix. This reaction increased the chain length of the C-S-H gel, resulting in a more robust and interconnected gel network. The densification effect mitigated potential mechanical property losses caused by the porous nature of NSA. Additionally, the porous structure of NSA significantly reduced the matrix density, leading to lower thermal conductivity and improved insulation performance. This study presents a new approach for valorizing recycled glass, promoting CO2 sequestration, and producing high-value aerogels for use as additives in the development of lightweight insulating concrete.
Microfibers (less than 100 µm in diameter) are commonly employed in structural applications to minimize early shrinkage cracking and lower pore pressure during fires. For any application, micro fiber-reinforced concrete (FRC) structural behavior and durability must be estimated using the mechanical constitutive law. Formulating a mechanical constitutive law for FRC presents several difficulties in terms of comprehending the physical principles and employing suitable numerical techniques. A novel model called “Lattice Discrete Particle Model for micro-FRC (LDPM-MicroF)” is presented to simulate the fracture behavior of black micro-FRC. An equivalent fiber diameter coefficient has been defined to balance modeling accuracy and computational cost so that the LDPM-MicroF model can simulate the mechanical responses of engineered cementitious composites. The unimodal variation in tensile strength caused by the increase in microfiber dose is assessed and quantitatively reproduced by LDPM-MicroF predictions. This phenomenon is explained by a combination of mesoscopic mechanisms and the “near-field effect” of the fibers. A small number of microfibers can improve the strength of the matrix and thus slightly the tensile strength. However, when the dosage of microfibers exceeds a certain amount, the tensile strength decreases as the contribution of the fiber bridging force to the strength becomes lower than that of the replaced matrix. This research has provided new insights into the physical comprehension of the mechanical properties of micro-FRC, which has significant implications for the field of study.
Ensuring the long-term durability of glass fiber-reinforced polymer (GFRP) bars poses a significant challenge in practical applications, particularly in marine environments. Moisture absorption by GFRP bars causes hydrolysis and plasticization of the polymer matrix, resulting in a decline in both their stiffness and strength. Furthermore, the penetration of detrimental ions (e.g., OH–) with moisture accelerates the degradation of GFRP bars. Therefore, clarifying the moisture absorption behavior of GFRP bars is crucial for investigating their durability. Previous studies have indicated that the initial moisture absorption in GFRP bars conforms to the Fickian model. However, with prolonged exposure, anomalous diffusion behavior emerges, characterized as non-Fickian diffusion. This paper reviews existing models for non-Fickian diffusion, highlighting their shortcomings. Gravimetric experiments were then conducted on GFRP bars with diameters of 6, 10, and 14 mm, immersed in portable water at temperatures approximately 23, 40, and 60 ℃. Based on the test results and underlying mechanisms, an improved model, named the Weibull relaxation (WR) model, was proposed and validated using the particle swarm optimization (PSO) algorithm for regression analysis. The new model not only exhibits better agreement with the test results but also incorporates fitting parameters with clear physical interpretations. Its distinct advantage over existing models is that it is able to more realistically capture the mechanisms governing the moisture absorption of GFRP bars.
Limestone calcined clay cements (LC3) present a sustainable alternative to traditional Portland cement due to their potential to significantly reduce CO2 emissions. However, LC3 cements often exhibit slower early-age strength development, which poses a challenge for their broader adoption. This study critically examines various technological approaches to enhance the early-age strength of LC3 cements, focusing on physical, chemical, and hybrid methods. The findings highlight the effectiveness of increased clinker and supplementary cementitious material (SCM) fineness, chemical acceleration using calcium silicate hydrate (C-S-H) seeds and admixtures that promote the reaction of aluminate phases. Additionally, the study assesses the environmental impact of these strategies, evaluating their global warming potential (GWP) in relation to strength performance. The results demonstrate that while these methods effectively improve early-age strength, they may also inadvertently increase late-age strength, necessitating a balanced approach to optimize both performance and sustainability. This research provides a comprehensive framework for advancing LC3 adoption in high early-age demand applications.
The integrity of organic-inorganic interface determines the performance of composite material systems, such as concrete reinforced with basalt fiber reinforced polymer. The integrity of the interface, which depends on the epoxy resin, may be degraded in harsh environments such as in seawater and concrete alkaline environments. In this study, a novel resin cross-linked with polydimethylsiloxane (PDMS) was developed to enhance the performance of composite material in harsh environments. The long-term mechanical strength of the composite (after modification to enhance its hydrophobicity) increased by 20% in concrete alkaline environments, based on micro-and macro-experiments. This improvement is attributed to cross-linking between PDMS and epoxy molecules and the formation of PDMS phase-separated circular domains, which achieve dynamic equilibrium and simultaneously enhance the densification and hydrophobicity. Molecular dynamics simulations revealed that PDMS reinforces interface adhesion and significantly improves the corrosion resistance by facilitating covalent bond formation at the resin-fiber and even resin-concrete interfaces. This study provides a feasible strategy and atomic insights for durability enhancement of composite with similar organic-inorganic interfaces in concrete structures, thus advancing the safety and service life in marine engineering.
This paper presents a reliability-based code calibration procedure aimed at determining optimal partial safety factors to be employed in design rules for pultruded glass fibre-reinforced polymer (pGFRP) I-section beams, susceptible to web-crippling, namely under end-two-flange (ETF) and interior-two-flange (ITF) loading cases with unfastened flanges. Following a comprehensive state-of-the-art literature overview regarding web-crippling of pGFRP beams, the description of a direct strength method (DSM)-based resistance model for the two web-crippling loading cases is presented. Next, attention is turned to the detailed description of the (structural) reliability-based code calibration procedure for both loading cases. This includes detailing all the necessary steps and pertinent data, encompassing the adopted probabilistic models for material properties, loads, geometrical dimensions, and resistance model uncertainty. Then, the results obtained through the application of the code calibration procedure are presented, validated, and discussed. Lastly, the paper closes by presenting relevant concluding remarks, which include recommendations for the partial safety factors to be applied in the resistance model for pGFRP I-beams under ETF and ITF loading cases.
Distributed fiber-optic sensors (DFOS), which are based on optical frequency-domain reflectometry (OFDR), provide high-resolution strain measurements and have promising application in structural health monitoring. This study introduces a novel steel fibe-reinforced polymer composite bar (SFCB) with self-sensing, structural reinforcement, and damage control features designed to evaluate the response and damage status of concrete members. Investigating the force transfer mechanism between SFCB and concrete is essential for understanding concrete cracking behavior and establishing a reliable damage evaluation approach. Initially, tension tests were conducted on SFCB concrete members to investigate the effects of cover depth and bonding mechanism (concrete type and surface treatment of the SFCB) on the end effects, along with a test procedure designed to effectively eliminate the end effects. The results indicate that the use of members with small cover depths, surface sandblasted SFCB, and geopolymer concrete (GPC) can reduce the impact of the end effects. The tracking and quantification of particular crack progressions were subsequently assessed through the integration of a digital image correlation (DIC) system and a DFOS system. Finally, based on the results from which the influence of end effects has been eliminated, a theoretical model for the response of SFCB concrete tension members was proposed, along with a model for damage variables that is independent of geometry and material behaviors.
Carbon dioxide (CO2)-consuming strain-hardening cementitious composites (CC-SHCCs) are sustainable and highly ductile materials characterized by outstanding tensile properties and carbon consumption capacity. This study proposes the use of nanobubble water (NBW) for CC-SHCCs based on the durable physical properties and element transport capabilities of nanobubbles. The analysis of porosity characteristics of NBW and CO2-capturing NBW (NBW + C) shows that nanobubbles reduce micro-sized porosity, while significantly enhancing nanosized porosity. The use of NBW led to an improvement in compressive strength, and NBW + C highlighted the benefits of CO2 capture by achieving a maximum of 128.5 MPa. In the direct tensile test, the specimens using NBW and NBW + C as the mixing water exhibited improved load distribution and extended strain-hardening regions. The specimen utilized NBW + C both in the mixing and curing stages and achieves a strain capacity of 7.79 % and an energy absorption capacity (g-value) of 1023 kJ·m−3, which represents exceptional tensile characteristics. Chemical analysis showed that the introduction of nanobubbles and increased CO2 concentration promoted the transfer and accumulation of calcium and hydroxide ions, accelerating the formation of calcium silicate hydrate (C-S-H) gels and calcium carbonate (CC). In particular, an exceptionally high net CO2 consumption capacity () of 2.333 was achieved when NBW + C was used as mixing and curing water.
In recent decades, fiber-reinforced polymer (FRP) bar-reinforced concrete (FRP-RC) beams have attracted considerable attention as a corrosion-free beam form. However, FRP-RC beams have limited practical applications owing to their much lower ductility compared to conventional steel bar-reinforced concrete (steel-RC) beams. To improve the ductility of FRP-RC beams, this study presents an innovative sectional form for FRP-RC beams in which the compression zone concrete is confined with FRP spirals/hoops. With such a sectional form, the ductility of the beam is derived from the ductile behavior of confined concrete in the compression zone instead of the ductile behavior of steel yielding in a conventional steel-RC beam. An experimental program consisting of eight large-scale FRP-RC beams was conducted to validate the effectiveness of this sectional form, in which the variables investigated include the pitch of the FRP spiral used to offer confinement, amount of longitudinal tension FRP bars, and FRP confinement configuration. The test results demonstrate the effectiveness of FRP confinement in improving the ductility and load-carrying capacity of FRP-RC beams; for one of the beams tested in the present study, ductility was increased by 150% with only a corresponding cost increase of around 7%. The significant effects of design parameters on the behavior of such FRP-RC beams are clearly revealed. Finally, a finite element model for such FRP-RC beams was established and verified using the test results.
Confined concrete has garnered significant attention owing to its advantages, such as enhanced strength, improved deformation capacity, and associated benefits when used in engineering structures. Although existing constitutive models reasonably predict the behavior of actively and passively confined concrete columns under axial compression, they are not designed to model concrete behavior in various stress states encountered in practice, including eccentrically compressed concrete columns passively confined by fiber-reinforced polymer (FRP) sheets. Accordingly, this study presents a new three-dimensional damaged-plasticity model for confined concrete under various stress states, based on the well-known Lubliner–Lee damaged-plasticity model. A key advancement involves a capped potential surface with a bulged triangular deviatoric trace under compression-dominated stress states and a Drucker–Prager potential surface under tension-dominated stress states, connected by a smooth transition. The potential surface, along with a properly designed yield surface, hardening rule, and evolution law for internal vari- ables, makes the proposed model well suited to capturing concrete behavior under various stress states, including triaxial compression, tension–compression, and loading–unloading. The constitutive model is first validated against monotonic and cyclic axial-compression data for actively and passively confined concrete and then verified using eccentric-compression results for FRP-confined concrete. The validation confirms the capability and accuracy of the proposed model to capture concrete behavior under various stress states.
In the context of carbon neutrality, the large-scale commercialization of clean-energy generation demands an innovative engineering paradigm that can ensure the reliability and durability of the associated critical mechanical equipment. In this study, structural-integrity challenges that are encountered during the clean-energy transition were investigated, and advancements in accurate lifetime-design methodologies were explored. This study addressed the complexities of multi-mode damage interactions and demonstrated the effects of such interactions on the critical mechanical equipment. By tracking the evolution of lifetime-design approaches, the fundamental aspects of damage-driven lifetime-design methodologies were determined. A case study that involved creep–fatigue–oxidation interactions demonstrated the simplicity and high accuracy of the modeling methodology that was developed during this study for industrial applications. To evaluate the carbon-reduction benefits that are associated with lifetime extension, a three-level quantitative criterion, which links prediction scatter, extension potential, and net emissions reduction, was developed. Hierarchical Bayesian modeling was also implemented to capture multi-level uncertainties across various regions and energy types, thereby providing probabilistic insights into diverse operational scenarios. In the future, accurate lifetime design is expected to be integrated into a full-chain technical tetrahedron for structural-integrity evaluations; thus, it will redefine the role of engineering in the design, manufacture, operation, and maintenance of mechanical equipment that is critical for a sustainable future.
This paper presents a comprehensive review of the development and advancements in ultra-precision grating displacement sensors, emphasizing their fundamental measurement theories, technology evolutions, device development, current applications, and future trends. Grating displacement sensors, utilizing optical interference and photoelectric conversion principles, deliver exceptional resolution and accuracy, making them vital in high-precision fields such as semiconductor manufacturing, aerospace, advanced metrology, and microfabrication. The review examines key innovations in grating sensor technologies, including the integration of digital interference measurement methods, advanced signal demodulation techniques, and the application of pseudo-random binary codes for absolute displacement measurements. Furthermore, the paper assesses the impact of novel materials, sensor designs, and miniaturization on sensor performance, particularly in enhancing sensitivity, reducing environmental susceptibility, and improving long-term stability. A comparison of domestic and international research progress in grating sensor technologies is provided, identifying critical gaps and emerging research areas. Looking ahead, the outlook for the field underscores the potential for integration into digital twin techniques, artificial intelligence (AI), and hybrid sensor systems that combine displacement measurement with other sensing capabilities. The paper concludes by addressing challenges in the field, such as improving the signal-to-noise ratio (SNR), enhancing sensor integration, and reducing production costs, while also spotlighting opportunities for further innovations to meet the escalating demands for ultra-precision measurements in next-generation manufacturing and other advanced applications. This review aims to provide a thorough understanding of the current state of ultra-precision grating displacement sensors and their potential to shape the future of high-precision measurement technologies.
Artificial intelligence for the intelligent Internet of Everything (I-IoE) establishes a four-dimensional interconnection among people, data, processes, and things, providing possibilities for the next generation of industrial revolution. Against the ultra-high power consumption brought by massive connectivity, passive backscatter communication has emerged as a promising paradigm for the I-IoE. However, even with a well-designed communication module, integrating sensing components significantly multiplies the overall system complexity and cost in the context of the {0,1} modulation paradigm. In this paper, we introduce a [0,1] modulated backscatter architecture that achieves the seamless integration of passive sensing and communication. Unlike the conventional backscatter of reading, encoding, and reflecting, the proposed [0,1] modulated backscatter directly converts environmental physical quantities into continuous frequency-modulated square waves. Building on this concept, we propose an intelligent detection method based on graph dimensionality reduction. This method achieves low computational complexity at the receiver by leveraging a pre-measured dataset. We then present a hardware communication system that performs data collection, transmission, and demodulation, providing experimental validation for the proposed architecture. Furthermore, simulation results verify the feasibility of a continuous frequency division multiple-access method for large-scale tags. The experimental results demonstrate the superior performance of the proposed scheme. This work provides a potential breakthrough for achieving ultra-low-power integrated sensing and communication in sixth-generation wireless communication networks.
Perovskite solar cells (PSCs) have been undergoing rapid development with the vast combinatorial explo- ration of recipes; however, the related research suffers from time-consuming trial-and-error synthesis and labor-intensive fabrication. As a promising alternative, interconnected robotic boxes that integrate fabrication and characterization enable high-throughput experimentation and data collection; however, the resulting numerical datasets are often insufficiently analyzed and fail to provide effective feedback for semantic recipe optimization. Here, we conceived and realized an emerging scientific tool of robotic boxes enabled by a domain-specific recipe language model (RLM) and a coordinating language agent for PSCs research. The developed agent features two loops of seven artificial intelligence (AI) layers, in which both numerical and semantic recipes were continuously learned and optimized from the literature and robotic corpora for iterative fine-tuning of the RLM. Guided by the agent, 11 robotic boxes executed the controllable synthesis, fabrication, and characterization of 50 764 PSCs, increasing the power conver- sion efficiency (PCE) to 27.0% (26.5% certified). Simultaneously, more than 578 million tokens were gen- erated and augmented to improve the ability to recommend a recipe and mechanistic reasoning, achieving an overall score of about 80% based on the dedicated evaluation criteria. Thus, such agentic robotic boxes provide an advanced tool for the next-generation synthesis, fabrication, characterization, and even mechanistic reasoning of PSCs and beyond.
To deal with global water shortage, water vapor in the air is regarded as a freshwater source that cannot be ignored, especially in water-scarce regions. Adsorption-based atmospheric water harvesting (AAWH) is a promising option that utilizes the interaction between the internal structure of the adsorbent material and water molecules to accumulate water. Novel water harvesting adsorbents such as zeolites, metal–organic frameworks (MOFs), covalent organic frameworks (COFs), and hydrogen-bonded organic frameworks (HOFs) have given new perspectives to fundamentally improve the effectiveness of AAWH, benefiting from their steep S-shaped isotherms. Based on the selection criteria derived from the principle of AAWH, 5 zeolites, 20 MOFs, 6 COFs, and one HOF are identified as potential candidates. From the analysis of the water adsorption mechanisms of these four types of materials and summarizing the latest progress in AAWH devices, the next-generation AAWH technology should focus on the scale-up of well-designed adsorbent components and their integration into efficient system devices to promote the practical application of AAWH.
The rapid pace of modern life and the demand for a high-quality cooking experience have clashed with traditional cooking methods. Artificial intelligence (AI) provides a new means for the development of smart kitchens and enables their revolution. However, the knowledge involved in AI is extremely broad, and has a high learning threshold. Furthermore, the existing research is disorganized and lacks systematic reviews, which may hinder the development of smart kitchens to a large extent. This study systematically analyzed the entire construction process and research on smart kitchen equipment empowered by AI. It was built on the three most important parts of equipment development and added to mainstream smart kitchen equipment. In particular, this study provides a comprehensive overview of the latest advances and existing defects in the research on modern smart kitchens under the influence of AI from four perspectives: hardware, information transmission, software, and equipment development. We hope this will guide the design of future studies. Among them, software is emphatically analyzed as the most intuitive achievement and the hottest research in AI. This includes detailed information on the training methods, datasets used, and performance comparisons. Additionally, under the AI-enabling smart kitchen revolution, the challenges and possible solutions faced by the new generation of smart kitchens have been proposed. These recommendations aim to eliminate bias in AI-powered kitchen technology, address the interoperability challenges between different manufacturers, and improve the usability of smart kitchen devices. This study offers guidance for an in-depth study of intelligent kitchen modernization from both theoretical and practical perspectives.
Biocompatible hydrogels are highly valuable for wound management; however, improving their mechanical compatibility and achieving controlled drug release for dynamic wound treatment remain challenging. Inspired by skin structure and function, a novel mechanically-responsive hydrogel was developed using drug-loaded liposomes as structural units. The crosslinked hydrogel network was generated via free-radical polymerization of acrylamide, incorporating double-bond-functionalized liposomes as crosslinkers. Deformable liposomes endowed the hydrogel with improved mechanical properties and enabled controlled drug release in response to mechanical deformation. The rifampin-loaded mechanically-responsive hydrogel exhibited strong antimicrobial activity both in vitro and in vivo. In addition, anti-inflammatory effects and enhanced wound-healing properties were observed in dynamic wound environments. These findings indicate that mechanically-responsive skin-mimicking hydrogels offer a promising strategy for dynamic wound management.
Gliomas are aggressive brain tumors associated with poor prognosis, necessitating advanced in vitro models for efficient drug screening. In this study, we develop a microfluidic glioma-on-a-microsphere model based on compartmentalized hydrogel microspheres with engineered rough surfaces. Each microsphere enables to accurately structure U251 glioma cells, M2-polarized THP-1 macrophages, and endothelial cells that can form a functional endothelial barrier on the Matrigel-modified microsphere surface. The model exhibited high cell viability, controllable molecular permeability, and upregulated expression of glioma-associated genes compared to conventional two-dimensional (2D) cultures. To validate the practicality of this platform in drug evaluation, chlorogenic acid was applied, resulting in suppressed gene expression, repolarization of macrophages toward an M1 phenotype, and significant alterations in key metabolites including tryptophan, glutamate, and lactate. Overall, this hydrogel microsphere-based glioma model offers a high-throughput and physiologically relevant platform for drug screening, disease modeling, and personalized therapeutic development.
This study introduces a novel conceptual framework to understand the transformative impact of Artificial Intelligence (AI) on global supply chains. We propose a Three-Chain Four-Intelligence framework that systematically analyzes how AI reconfigures supply chain architecture and capabilities through enhanced contextual awareness. The Three-Chain perspective examines how AI transforms the logistics chain (physical flow), information chain (data flow), and value chain (value creation) from fragmented operations to synchronized intelligent ecosystems. The Four-Intelligence pathway maps the evolutionary progression from digital connectivity to operational optimization, collaborative ecosystems, and ultimately self-evolving intelligent systems. AI serves as an orchestrating force that processes rich contextual information spanning product attributes, market dynamics, environmental conditions, and operational realities. We demonstrated the practical application of the framework through a comprehensive case study of JD.com, where AI implementation across all dimensions yielded quantifiable improvements. Our analysis reveals that the most transformative supply chain advancements emerge at the intersection of multiple chains with increasingly sophisticated contextual awareness. The paper concludes by identifying six emerging research frontiers, such as generative AI integration with decision optimization.