Search Results (614)

Although microbial self-healing concrete technology has been widely studied, limited attention has been paid to the effect of the dosage of microbial self-healing materials on concrete crack repair performance. To address this, this study investigates the influence of the dosage of microbial self-healing materials on the crack repair performance of concrete using planar thin-plate specimens. The results are summarized as follows: (1) Increasing the dosage of microbial self-healing materials effectively delays the initial cracking time of concrete specimens. When the dosage levels were 10%, 20%, and 30%, the initial cracking time was prolonged by 50%, 65%, and 70%, respectively, compared with the blank group without microbial addition. (2) After 28 d of water spraying and coating curing, the total crack area of concrete decreased significantly compared with that at the early age (1 d). For dosages of 0%, 10%, 20%, and 30% of microbial self-healing materials, the total crack area per unit surface area decreased by 12.2%, 21.9%, 22.7%, and 31.8%, respectively, compared with the initial stage. (3) Through X-ray diffraction (XRD), thermogravimetric analysis (TG/DTG), and morphological characterization, the presence of microbial mineralization products, including calcite and vaterite, on the concrete crack surfaces was confirmed. Full article

The detector array chips can be used to capture the transient space-time signal of the pulse radiation field. It is mainly composed of a photoelectric array detector and a readout circuit. However, the metal leads used to connect the detector and the readout circuit have long spacing. This can easily introduce additional delays, resulting in a decrease in the response performance of the chip, which cannot meet the goal of simultaneous transmission of ultra-fast detection signals. In recent years, the rapid development of three-dimensional interconnect technology has enabled the chip to achieve shorter interconnect spacing, smaller parasitic parameters and smaller delay time, thereby improving system response performance. The integrated detector array chips composed of three-dimensional interconnects has the advantages of fast signal interconnection transmission speed, high bandwidth, process compatibility and functional expansion compared with the traditional planar architecture. At the same time, there are some limitations and challenges. Therefore, this paper mainly reviews the evolution characteristics of the stacked architecture of the detector array chips, the process development and the nanosecond-level transmission integration challenges. This paper effectively incorporates the three into a unified framework. This provides a solution for the realization of integrated nanosecond detector array chips. Furthermore, it promotes the application and expansion of the chip in the pulse radiation field diagnosis technology. Full article

Pixelated detectors based on inorganic scintillation materials are widely used in radiation detection systems for medical imaging and many other fields of science and technology. A substantial application is X-ray scanning using flat-panel detectors (FPDs) for both fluorography and mammography. In this article, the detection properties of the monolithic planar ceramic scintillation elements are reported for the first time. A high-light yield (Gd,Y)₃Al₂Ga₃O₁₂:Ce,Mg garnet-type scintillation material was used to form square-shaped pixels, while a material of similar composition was used as a substrate. Green bodies were successfully fabricated by a digital light processing (DLP) 3D printing method. Subsequent debinding and pressureless high-temperature sintering resulted in composite elements consisting of two layers with different chemical compositions. The lower bulk layer consisted of transparent, non-luminescent garnet, whereas the upper pixelated layer, with pixel dimensions of 230 × 230 µm, was made of scintillation material. The spatial resolution of the matrices under UV light and alpha-particle excitation was evaluated. It was confirmed that the spatial resolution of the matrices produced by the developed technology is approximately 0.4 times the pixel size. The proven ability of the integrated technology of inorganic scintillation matrix production opens the way for future improvement in spatial resolution through optimizing the printed pixel dimensions. Full article

This paper presents a rule-based LVS-driven methodology for parasitic RC extraction from CMOS layouts for post-layout SPICE simulation. The proposed approach operates directly within foundry-qualified rule environments, ensuring consistency with Process Design Kits (PDKs) and enabling seamless integration with existing design and verification flows without requiring field-solver execution during the production extraction flow. The methodology provides a generalized framework for deriving electrical parameters from layout geometries and is applicable to interconnects, contacts, vias, and gate structures in multilayer CMOS technologies. By decomposing conductive regions into directional components and applying geometric and Boolean operations, the method captures the impact of layout topology and process-dependent features on circuit-level behavior. In addition, a model-order reduction technique based on π-equivalent representations is introduced to simplify the resulting networks while preserving timing accuracy. This enables the scalable simulation of complex layouts with reduced computational overhead. The proposed framework supports layout optimization, variability-aware design, and process-technology co-design, particularly for mature and advanced planar nodes. The methodology is evaluated using register-file layout test cases and post-layout SPICE simulations. The results show that the proposed rule-based extraction and RC-merging flow preserve timing behavior while reducing netlist complexity. Full article

Coal has been Malaysia’s primary energy source for electricity generation for the past few decades, resulting in increased greenhouse gas emissions and irreversible environmental damage. Solid Oxide Fuel Cells (SOFCs) have emerged as a viable clean-energy alternative to mitigate these environmental effects. There has been significant emphasis on developing pollution-free technology, with limited attention given to the environmental impact of SOFC. Research and development efforts have primarily focused on the design and technical aspects of SOFC. Prior to the introduction of SOFC to market, quantifying the environmental footprint of SOFC manufacturing is necessary to support a sustainable energy transition. This study conducts a comprehensive Life Cycle Assessment (LCA) of SOFC manufacturing in accordance with ISO 14040 and 14044 standards. The analysis focuses on a planar electrolyte-supported SOFC with a lifespan of 4.57 years, using a functional unit of 1 kWh electrical output. The Environmental Footprint (EF) 3.1 method implemented in GaBi Software was used for the impact assessment. Key environmental impact categories considered in the LCA include Climate Change (CC), Acidification Potential (AP), Eutrophication Potential (EP), Ozone Depletion Potential (ODP), Photochemical Ozone Formation (POF), and Human Toxicity Potential (HTP). The total climate change impact is approximately 19.674 kg CO₂ eq./kWh, with the Balance of Plant (BoP) phase contributing 91% of this impact, while the fuel cell stack phase contributes 1.25%. The study identifies key areas for improvement, primarily related to BoP and other high-impact processes, and emphasizes the importance of targeted measures to effectively reduce the environmental impacts associated with SOFC manufacturing. Full article

Brain-on-a-chip (BOC) refers to a miniaturized in vitro platform that integrates living neuronal networks on a micro-engineered chip, enabling the simulation of brain functions, neural activities and physiological responses. BOC technology is an advanced evolution of microphysiological systems (MPS) and Lab-on-a-Chip platforms, providing novel paradigms for in vitro modeling and exploring early-stage biocomputing by interfacing living neural networks with engineered electronics. Microelectrode arrays (MEAs) serve as the critical physical interface for bidirectional communication in these systems. In this review, we systematically examine the technological landscape and engineering requirements of MEAs tailored for BOC applications, evaluating them across electrical characteristics, structural properties, and biocompatibility. Two primary classes of current MEA technologies, including planar arrays for 2D neural cultures and 3D flexible arrays for brain organoids, are discussed in detail. We highlight the transition from passive planar electrodes to high-density active CMOS and TFT-based arrays, and detail how 3D flexible MEAs utilize endogenous integration and exogenous wrapping strategies to overcome tissue-mechanics mismatches. Furthermore, the integration of MEAs with microfluidics, optoelectronics, and electrochemical sensors to enable multimodal monitoring is explored. With the advantages of the various MEAs, the application of MEAs for BOC, particularly in biological computing and network plasticity research, is discussed. Finally, future technological developments in scalability bottlenecks, chronic stability, and the incorporation of artificial intelligence for MEAs of BOC are prospected. Full article

Surface rock movement can lead to geological or environmental problems such as surface subsidence, ground fissure development, and deformation of engineering structures, and its evolution process exhibits significant spatiotemporal heterogeneity. Therefore, conducting high-precision, spatiotemporally continuous monitoring of surface deformation is of great significance for revealing subsidence mechanisms, assessing potential risks, and guiding disaster reduction decisions. GNSS and InSAR have become mainstream methods for monitoring surface deformation, but they still have limitations in terms of spatial sparsity, 3D deformation inversion capability, and data gaps in areas of strong deformation. To address these issues, this paper takes the Jinchuan copper-nickel mine’s No. 2 mining area as the research object and comprehensively utilizes multi-source monitoring data from GNSS and InSAR to construct a joint inversion model of the surface 3D deformation field based on posterior variance component estimation, achieving adaptive optimization of weight allocation and collaborative solution of 3D deformation. To address the issue of InSAR decorrelation in areas of strong deformation, which leads to missing deformation information, a fitting and estimation approach was applied to supplement six decorrelated points that spatially coincide with GNSS stations. These points are located in key deformation areas, and their reconstruction effectively improves the completeness and reliability of the deformation field in critical regions. Based on this, an automated solution process for the fusion model is implemented using MATLAB R2022b, and the joint inversion yields spatiotemporally continuous 3D deformation fields in the northward, eastward, and vertical directions. The results show that compared with traditional monitoring methods, the proposed fusion model exhibits higher inversion accuracy and stability under different InSAR technology conditions, effectively suppressing the impact of single data source errors on the overall solution results. Among them, SBAS-InSAR shows slightly higher accuracy in the vertical direction, while PS-InSAR achieves higher accuracy in the planar direction, as indicated by lower RMSE and MAE values. The research results improve the accuracy and reliability of surface deformation monitoring in mining areas, providing important technical support for safe mining and refined management. Full article

This paper examines the integration of three-dimensional (3D) printing and robotic fabrication in contemporary architectural design, with a focus on overcoming the technical limitations that constrain large-scale adoption. While additive manufacturing enables the production of complex geometries and customized structures, its standalone application remains limited by fixed build volumes, planar deposition, lack of tensile reinforcement, open-loop process control, and single-process extrusion. To address these constraints, the paper proposes a functional integration framework that systematically maps robotic fabrication capabilities onto these five critical limitations. Evidence from recent studies demonstrates that such integration has already led to measurable advances, including up to a 90-fold increase in printable volume through mobile robotic systems, robotically fabricated reinforcement systems (e.g., Mesh Mold) achieving post-crack behavior comparable to conventional reinforced concrete, and the implementation of closed-loop sensor-based process control to enhance interlayer bonding. Despite these achievements, interdisciplinary collaboration across architecture, structural engineering, materials science, and robotics remains largely fragmented and is predominantly confined to academic and pilot-scale projects, such as the ETH Zurich DFAB House. Regulatory progress is also limited, with only isolated code-compliant implementations under frameworks such as ICC-ES AC509 and ISO/ASTM 52939. Persistent barriers including high capital costs, loss of information in BIM-to-fabrication workflows, anisotropic material behavior, and the absence of long-term durability standards continue to restrict widespread adoption. These findings suggest that advancing robotic additive manufacturing in architecture requires not only technological innovation but also coordinated cross-disciplinary integration, standardized testing protocols, and harmonized regulatory frameworks. Full article

The functional performance and structural integrity of natural rock materials under fluctuating environmental stressors are pivotal for their advanced applications. As a non-ionizing and radiation-free technology, terahertz (THz) spectroscopy offers a safe and promising alternative for non-destructive testing (NDT), uniquely capable of being deployed in open and unshielded environments. However, limited penetration depth, exacerbated by both the dense geological matrix and the extreme sensitivity of THz waves to moisture states, has long hindered its widespread application in rock characterization. This study establishes a quantitative Terahertz Time-Domain Spectroscopy (THz-TDS) framework to characterize four lithologies under drying–wetting cycles. Exponential signal attenuation across thicknesses was quantified based on the Beer–Lambert law, with attenuation coefficients ranging from 0.15 to 0.74 per millimeter. Planar transmission imaging successfully visualizes lithologic and moisture-dependent heterogeneity: limestone exhibits a dense, homogeneous structure with stable amplitude distribution; sandstone and purple sandstone show parallel statistical trends, reflecting uniform pore networks; and granite demonstrates the most pronounced imaging contrast under varying moisture states, driven by complex grain-boundary scattering. The findings reveal that THz transmission is dictated by the synergistic effects of mineral compositions and pore structures: scattering at grain boundaries and fractures leads to significant energy dissipation, whereas clay-rich lithologies exhibit the highest sensitivity to moisture variations due to water adsorption and interfacial polarization effects. As an exploration of THz technology in the non-destructive evaluation of rock materials, these findings establish an analytical framework for the quantitative assessment of microstructure evolution. Full article

GaN-on-Si light-emitting devices have been widely studied in the field of opto-electronics, while their optical performance and characterization accessibility are severely limited by the strong visible light absorption of the native silicon substrate. Conventional substrate transfer technologies often suffer from inherent thermal, optical, or mechanical bottlenecks. In this study, we developed a robust wafer-level substrate transfer strategy for 8-inch green GaN-on-Si light-emitting device wafers, utilizing a hybrid planarization process combined with SiO₂–SiO₂ direct bonding. The hybrid planarization precisely eliminated the 900 nm macroscopic steps, achieving sub-nanometer surface roughness for high-yield wafer bonding. We systematically investigated the physical evolution during substrate removal. Results indicate that the removal of the thick native silicon and high-stress buffer layers effectively released the additional in-plane biaxial compressive stress within the multiple quantum wells (MQWs), thereby mitigating the quantum-confined Stark effect (QCSE). Benefiting from the elimination of the light-absorbing silicon substrate and the incorporation of a built-in back-surface reflector (BSR), the transferred devices achieved a remarkable 1.9-fold enhancement in relative optical performance, albeit with an inherent trade-off of increased reverse leakage current while preserving basic diode functionality. Furthermore, optothermal dynamic analysis at high injection levels suggests a potential localized thermal bottleneck at the thick SiO₂ bonding interface, where a hypothesized heat-induced spectral red shift may counteract the carrier-screening blue shift. This work provides a feasible wafer-level substrate transfer process for GaN-on-Si devices and offers systematic experimental insights into stress relaxation and optothermal behaviors during the substrate transfer process. Full article

While the demand for free-form architecture (FFA) has increased with advancements in computer-aided design (CAD) technology, the rationalization of complex surfaces into fabricable panels remains a significant challenge due to high production costs and technical complexity. Practical pain points, such as the prohibitive cost of unique molds and the inefficiency of manual data processing during design iterations, pose substantial economic risks. This study proposes an intelligent surface rationalization framework that integrates parametric design with machine learning algorithms in Autodesk^TM Dynamo Studio, a plug-in to Revit. A data-driven classification workflow was developed using four key geometric parameters—planarity, principal curvature (PC), Gaussian curvature (GC), and mean curvature (MC). Two unsupervised learning algorithms, a Gaussian mixture model and K-means clustering, were compared for their classification performance. As a result of two case studies, free-form surface classification by a Gaussian mixture model (CGMM) demonstrated flexibility in modeling complex surface data by probabilistically managing the uncertainty of the curvature distribution, and free-form surface classification by K-means clustering (CKC) was confirmed to be effective for the rapid classification of large-scale panel data. Optimizing the proportion of flat and single-curved panels through the proposed workflow contributes to deriving a reasonable balance between design intent and construction costs/constructability at the early design stage, and strengthening risk management capabilities for FFA. Full article

This work examines the aerodynamic efficiency improvement achieved by integrating C-type winglets into a small-scale Blended Wing Body (BWB) Unmanned Aerial Vehicle (UAV). The platform, designated S-3M, is an evolution of the RX-3 1:3 sub-scale demonstrator developed and flight-tested by the Laboratory of Fluid Mechanics and Turbomachinery (LFMT) during the DELAER project. The S-3M is redesigned for catapult launch and Intelligence–Surveillance–Reconnaissance (ISR) missions, supporting a useful payload of up to 5 kg. Strict dimensional, cost, and development constraints posed challenges in preserving aerodynamic efficiency and achieving sufficient stability margins. To meet these requirements, the design incorporates C-type winglets, tailored to enhance aerodynamic performance while providing stabilizing effects. Their integration enabled an increase in gross take-off weight (GTOW) and payload capacity, while ensuring adequate trimming without the need for a conventional horizontal tail. The aerodynamic development of the winglets and the overall configuration is supported by Computational Fluid Dynamics (CFD) analyses, followed by performance calculations. S-3M was manufactured by Carbon Fiber Technologies (CFT) and successfully flight-tested by LFMT, validating the design choices. Overall, the study demonstrates that C-type winglets can significantly improve efficiency and expand the operational envelope of BWB UAVs, highlighting the value of non-planar lifting surfaces in modern UAV design. Full article

Accurate measurements of light–matter interactions at subwavelength scales are critical for advancing nanophotonic and quantum optical technologies. In this paper, we present the far-field terahertz (THz) spectroscopy of a single planar meta-atom of subwavelength dimensions embedded within a square or circular aperture on a thin free-standing metal film. The meta-atom, composed of concentric disk and ring structures interconnected by narrow bridges, was fabricated by a mask-less direct laser ablation (DLA) technique to exhibit a pronounced transmission peak near a resonance frequency of 0.35 THz. We propose a novel spectral analysis framework that accounts for aperture-to-beam area mismatch suppressing non-resonant background contributions originating from edge diffraction and aperture discontinuities which are commonly encountered in subwavelength geometries. This technical analysis yields transmission spectra with improved accuracy providing good agreement with finite-difference time-domain (FDTD) simulations. A foundation for precise optical characterization of a single subwavelength size resonator is demonstrated paving the way for applications in quantum sensing, meta-surface design, and low-dimensional optoelectronic systems. Full article

Asphalt pavement distress detection plays a pivotal role in highway maintenance, providing an essential basis for optimizing maintenance strategies and allocating funding. Consequently, quick detection and efficient identification of distress are crucial for enhancing the quality of highway maintenance. This study aims to acquire high-precision distress data using 3D laser point cloud technology, identify distress types via the YOLO algorithm, and extract geometric features such as length and angle. Specifically, a recognition method based on 3D laser point cloud images is proposed, where point cloud data are converted into planar images for processing. Experimental results indicate that the laser point cloud detection achieves millimeter-level precision, the distress recall rate exceeds 85%, and the identification precision reaches 79.5%, demonstrating satisfactory detection accuracy and efficiency. Full article

The crystallinity of cubic silicon carbide (3C-SiC) epilayers is improved through the use of a novel wafer bonding and regrowth technique resulting in a reduction in planar defects. The process involves the epitaxial growth of a 3–6 µm thick 3C-SiC seed on silicon (Si), which is polished and bonded to a new handle wafer before the original substrate and defective interface region of the 3C-SiC epilayer are removed. Further epitaxial growth on this Bonded Switchback template results in higher quality 3C-SiC epilayers through the reduction in crystal mosaicity, stacking fault defects, and elimination of interface voids. The process could be applied to 3C-SiC grown on both on- and off-axis substrates, and the form of the new handle has no impact on the growth process, enabling this technology to be applied to sapphire or hexagonal 4H-SiC substrates. The use of such substrates would overcome the thermal budget limitations of Si substrates for 3C-SiC heteroepitaxy and ion implantation. Bonded Switchback can improve material quality for applications in power electronics, as well as see the heterogeneous integration of 3C-SiC into other device structures, potentially leading to a new range of hybrid 3C-SiC/Si devices without the high density of defects observed at the interface between these two materials. Full article