Search Results (382)

In Liquid Composite Molding (LCM), the high variability present in reinforcement properties such as permeability creates additional challenges during the injection process, such as void formation. Although improved injection strategy designs can mitigate the formation of defects, these processes can benefit from real-time process monitoring and control to adapt the injection conditions when needed. In this study, a machine vision algorithm is proposed, with the objective of detecting and tracking both fluid flow and bubbles in an LCM setup. In this preliminary design, 3D-printed porous geometries are used to mimic the architecture of textile reinforcements. The results confirm the applicability of the proposed approach, as the detection and tracking of the objects of interest is possible, without the need to incur in elaborate experimental preparations, such as coloring the fluid to increase contrast, or complex lighting conditions. Additionally, the proposed approach allowed for the formulation of a new dimensionless number to characterize bubble transport efficiency, offering a quantitative metric for evaluating void transport dynamics. This research underscores the potential of data-driven approaches in addressing manufacturing challenges in LCM by reducing the overall process monitoring complexity, as well as using the acquired reliable data to develop robust, data-driven models that offer new understanding of process dynamics and contribute to improving manufacturing efficiency. Full article

Yield stress and thixotropy are critical rheological properties for enabling successful 3D printing of magnetic colloidal systems. However, conventional magnetic colloids, typically composed of a single dispersed phase, exhibit insufficient rheological tunability for reliable 3D printing. In this study, we developed a novel magnetic colloidal system comprising a carrier liquid, magnetic nanoparticles, and organic modified bentonite. A direct-ink-writing 3D-printing platform was specifically designed and optimized for thixotropic materials, incorporating three distinct extruder head configurations. Through an in-depth rheological investigation and printing trials, quantitative analysis revealed that the printability of magnetic colloids is significantly affected by multiple factors, including magnetic field strength, pre-shear conditions, and printing speed. Furthermore, we successfully fabricated 3D architectures through the precise coordination of deposition paths and magnetic field modulation. This work offers initial support for the material’s future applications in soft robotics, in vivo therapeutic systems, and targeted drug delivery platforms. Full article

Ancient Chinese scripts—including oracle bone carvings, bronze inscriptions, stone steles, Dunhuang scrolls, and bamboo slips—are rich in historical value but often degraded due to centuries of erosion, damage, and stylistic variability. These issues severely hinder manual transcription and render conventional OCR techniques inadequate, as they are typically trained on modern printed or handwritten text and lack interpretability. To tackle these challenges, we propose FAIR-Net, a hybrid architecture that combines the unsupervised feature learning capacity of a deep autoencoder with the semantic transparency of a fuzzy rule-based classifier. In FAIR-Net, the deep autoencoder first compresses high-resolution character images into low-dimensional, noise-robust embeddings. These embeddings are then passed into a Fuzzy Neural Network (FNN), whose hidden layer leverages Fuzzy C-Means (FCM) clustering to model soft membership degrees and generate human-readable fuzzy rules. The output layer uses Iteratively Reweighted Least Squares Estimation (IRLSE) combined with a Softmax function to produce probabilistic predictions, with all weights constrained as linear mappings to maintain model transparency. We evaluate FAIR-Net on CASIA-HWDB1.0, HWDB1.1, and ICDAR 2013 CompetitionDB, where it achieves a recognition accuracy of 97.91%, significantly outperforming baseline CNNs (p < 0.01, Cohen’s d > 0.8) while maintaining the tightest confidence interval (96.88–98.94%) and lowest standard deviation (±1.03%). Additionally, FAIR-Net reduces inference time to 25 s, improving processing efficiency by 41.9% over AlexNet and up to 98.9% over CNN-Fujitsu, while preserving >97.5% accuracy across evaluations. To further assess generalization to historical scripts, FAIR-Net was tested on the Ancient Chinese Character Dataset (9233 classes; 979,907 images), achieving 83.25% accuracy—slightly higher than ResNet101 but 2.49% lower than SwinT-v2-small—while reducing training time by over 5.5× compared to transformer-based baselines. Fuzzy rule visualization confirms enhanced robustness to glyph ambiguities and erosion. Overall, FAIR-Net provides a practical, interpretable, and highly efficient solution for the digitization and preservation of ancient Chinese character corpora. Full article

Hernia repair is among the most frequent procedures in general surgery, traditionally performed with synthetic meshes such as polypropylene. While effective in reducing recurrence, these materials are biologically inert and often trigger chronic inflammation, fibrosis, pain, and impaired abdominal wall function, with a significant impact on long-term quality of life. A comprehensive literature search was conducted in PubMed, Web of Science, and Scopus databases, and relevant preclinical, clinical, and review articles were synthesized within a narrative review framework. Recent advances in tissue engineering propose a shift from passive reinforcement to regenerative strategies based on biomimetic scaffolds, nanomaterials, and nanocomposites that replicate the extracellular matrix, enhance cell integration, and provide controlled drug delivery. Nanotechnology enables localized release of anti-inflammatory, antimicrobial, and pro-angiogenic agents, while electrospun nanofibers and composite scaffolds improve strength and elasticity. In parallel, 3D printing allows for patient-specific implants with tailored architecture and regenerative potential. Although preclinical studies show encouraging results, clinical translation remains limited by cost, regulatory constraints, and long-term safety uncertainties. Overall, these innovations highlight a transition toward personalized and regenerative hernia repair, aiming to improve durability, function, and patient quality of life. Full article

Tryptophan (Trp) and tryptamine (Tryp), critical biomarkers in mood regulation, immune function, and metabolic homeostasis, are increasingly recognized for their roles in both oral and systemic pathologies, including neurodegenerative disorders, cancers, and inflammatory conditions. Their rapid, sensitive detection in biofluids such as saliva—a non-invasive, real-time diagnostic medium—offers transformative potential for early disease identification and personalized health monitoring. This review synthesizes advancements in electrochemical sensor technologies tailored for Trp and Tryp quantification, emphasizing their clinical relevance in diagnosing conditions like oral squamous cell carcinoma (OSCC), Alzheimer’s disease (AD), and breast cancer, where dysregulated Trp metabolism reflects immune dysfunction or tumor progression. Electrochemical platforms have overcome the limitations of conventional techniques (e.g., enzyme-linked immunosorbent assays (ELISA) and mass spectrometry) by integrating innovative nanomaterials and smart engineering strategies. Carbon-based architectures, such as graphene (Gr) and carbon nanotubes (CNTs) functionalized with metal nanoparticles (Ni and Co) or nitrogen dopants, amplify electron transfer kinetics and catalytic activity, achieving sub-nanomolar detection limits. Synergies between doping and advanced functionalization—via aptamers (Apt), molecularly imprinted polymers (MIPs), or metal-oxide hybrids—impart exceptional selectivity, enabling the precise discrimination of Trp and Tryp in complex matrices like saliva. Mechanistically, redox reactions at the indole ring are optimized through tailored electrode interfaces, which enhance reaction kinetics and stability over repeated cycles. Translational strides include 3D-printed microfluidics and wearable sensors for continuous intraoral health surveillance, demonstrating clinical utility in detecting elevated Trp levels in OSCC and breast cancer. These platforms align with point-of-care (POC) needs through rapid response times, minimal fouling, and compatibility with scalable fabrication. However, challenges persist in standardizing saliva collection, mitigating matrix interference, and validating biomarkers across diverse populations. Emerging solutions, such as AI-driven analytics and antifouling coatings, coupled with interdisciplinary efforts to refine device integration and manufacturing, are critical to bridging these gaps. By harmonizing material innovation with clinical insights, electrochemical sensors promise to revolutionize precision medicine, offering cost-effective, real-time diagnostics for both localized oral pathologies and systemic diseases. As the field advances, addressing stability and scalability barriers will unlock the full potential of these technologies, transforming them into indispensable tools for early intervention and tailored therapeutic monitoring in global healthcare. Full article

A novel process for the synthesis of binder-free, porous nickel/polythiophene-functionalized sulfur (Ni/S-PTh) composite cathodes for lithium–sulfur (Li–S) batteries is introduced in this paper. Initially, a polyvinyl butyl polymer scaffold is 3D printed, then coated with a graphite-based conducting layer, and, finally, it is pulse-plated for nickel deposition to produce a high-surface-area, mechanically stable current collector. S-PTh particles are afterwards co-deposited into the Ni matrix through composite electrodeposition. After the dissolution of the polymer template, the resulting self-standing electrodes still maintain porous structure with uniform sulfur distribution and a distinct transition between the dense Ni layer and the Ni/S-PTh composite layer. Electrochemical characterization of the Ni/S-PTh composite cathodes by galvanostatic cycling at C/10 rate results in an initial specific discharge capacity of ~1120 mAh·g⁻¹ and a specific capacity of ~910 mAh·g⁻¹ after 200 cycles, resulting in a high capacity retention of ~81 %. For our novel approach, no steps at high temperatures or toxic solvents are involved and the need for polymer binders and conductive additives is avoided. These results demonstrate the potential of composite electrodeposition in combination with 3D printing for producing sustainable, high-performance sulfur cathodes with tunable architecture. Full article

The convergence of carbon nanomaterials and functional polymers has led to the emergence of smart carbon–polymer nanocomposites (CPNCs), which possess exceptional potential for next-generation sensing and actuation systems. These hybrid materials exhibit unique combinations of electrical, thermal, and mechanical properties, along with tunable responsiveness to external stimuli such as strain, pressure, temperature, light, and chemical environments. This review provides a comprehensive overview of recent advances in the design and synthesis of CPNCs, focusing on their application in multifunctional sensors and actuator technologies. Key carbon nanomaterials including graphene, carbon nanotubes (CNTs), and MXenes were examined in the context of their integration into polymer matrices to enhance performance parameters such as sensitivity, flexibility, response time, and durability. The review also highlights novel fabrication techniques, such as 3D printing, self-assembly, and in situ polymerization, that are driving innovation in device architectures. Applications in wearable electronics, soft robotics, biomedical diagnostics, and environmental monitoring are discussed to illustrate the transformative impact of CPNCs. Finally, this review addresses current challenges and outlines future research directions toward scalable manufacturing, environmental stability, and multifunctional integration for the real-world deployment of smart sensing and actuation systems. Full article

Using systematic literature observations, this study explains how 3D printing technology is being applied to innovative sustainable construction (Systematic Literature Review). Additive manufacturing, also referred to as 3D printing technology, has greatly increased productivity and adoption in the building sector. The utilization of eco-friendly materials, enhancing sustainable building practices, and the environmental impact of 3D printing technology in comparison to conventional techniques are the three primary areas of attention for this study. By reducing material waste through additive manufacturing methods, 3D printing technology may employ alternative resources like fly ash, geopolymers, and limestone calcined clay (LC3) cement, which lowers carbon emissions considerably, according to observation data. This technology also speeds up the construction process, saves costs, and enables complex architectural designs that are difficult to achieve with conventional methods. There are still a number of issues, though, such as the high upfront expenditures of supplies and equipment and the long-term robustness of the molded structures that are produced. Nevertheless, 3D printing has enormous potential to transform building methods into more effective and ecologically friendly ones as a result of technological advancements and growing knowledge of desirability. This research provides valuable insights for stakeholders in supporting wider application of this technology to achieve sustainable development goals. Full article

Additive manufacturing (AM) is gaining prominence in architecture, engineering, and construction (AEC). Within this context, robotic additive manufacturing (RAM) has emerged as a promising solution, offering enhanced flexibility and motion control for fabricating complex geometries and performing on-site production. However, it also introduces new, complex manufacturing processes that impact the design, making the control of manufacturing variables important for achieving accurate and feasible architectural results. In this sense, this study presents a systematic review of the state of the art in RAM for AEC, with a focus on extrusion-based 3D printing using flexible robotic arms and materials such as thermoplastics and paste-based mixtures (cementitious and earth-based compositions). This review includes 142 peer-reviewed journal and conference papers published between 2014 and 2025. It maps key research subfields, geographic trends, and RAM technology evolution, complemented by a bibliometric analysis of co-authorship and keyword networks. This review identifies four key areas of research: process, design, materials, and equipment. Most studies come from North America, Europe, and Asia, with clay emerging as a material receiving growing attention in construction within the RAM field. However, challenges like scalability, programming complexity, and AI integration still limit broader implementation. Full article

The research investigated the potential of five novel additively manufactured (AM) fiber-reinforced thermoplastic composite (FRTPC) configurations as alternatives for ablative thermal protection system (TPS) applications. The thermal stability and ablative behavior of ten samples developed via fused deposition modeling (FDM) three-dimensional (3D) printing out of fire-retardant thermoplastics were investigated using an in-house oxyacetylene torch bench. All samples featured an innovative internal thermal management architecture with three air chambers. Furthermore, the enhancement of thermal benefits was achieved through several approaches: ceramic coating, mechanical hybridization, or continuous fiber reinforcement. For each configuration, two samples were exposed to flame at 1450 ± 50 °C for 30 s and 60 s, respectively, with the front surface subjected to direct exposure at a distance of 100 mm during the ablation tests. Internal temperatures recorded at two back-side contact points remained below 50 °C, well under the 180 °C maximum allowable back-face temperature for TPS during testing. Continuous reinforced configurations 4 and 5 displayed higher thermal stability the lowest values in terms of thickness, mass loss, and recession rates. Both configurations showed half of the weight losses measured for the other tested configurations, ranging from approximately 5% (30 s) to 10–12% (60 s), confirming the trend observed in the thickness loss measurements. However, continuous glass-reinforced configuration 5 exhibited the lowest weight loss values for both exposure durations, benefiting from its non-combustible nature, low thermal conductivity, and high abrasion resistance intrinsic characteristics. In particular, the Al₂O₃ surface coated configuration 1 showed a mass loss comparable to reinforced configurations, indicating that an enhanced surface coat adhesion could provide a potential benefit. A key outcome of the study was the synergistic effect of the novel air chamber architecture, which reduces thermal conductivity by forming small internal air pockets, combined with the continuous front-wall fiber reinforcement functioning as a thermal and abrasion barrier. This remains a central focus for future research and optimization. Full article

Face anti-spoofing is crucial for protecting biometric authentication systems. Presentation attacks using 3D masks and high-resolution printed images present detection challenges for existing methods. In this paper, we introduce a family of specialized CNN architectures, AttackNet, designed for robust face anti-spoofing with optimized residual connections and activation functions. The study includes the development of four architectures: baseline LivenessNet, AttackNetV1 with concatenation-based skip connections, AttackNetV2.1 with optimized activation functions, and AttackNetV2.2 with efficient addition-based residual learning. Our analysis demonstrates that element-wise addition in skip connections reduces parameters from 8.4 M to 4.2 M while maintaining performance. A comprehensive evaluation was conducted on four benchmark datasets: MSSpoof, 3DMAD, CSMAD, and Replay-Attack. Results show high accuracy (approaching 100%) on the 3DMAD, CSMAD, and Replay-Attack datasets. On the more challenging MSSpoof dataset, AttackNetV1 achieved 99.6% accuracy with an HTER of 0.004, outperforming the baseline LivenessNet (94.35% accuracy, 0.056 HTER). Comparative analysis with state-of-the-art methods confirms the superiority of the proposed approach. AttackNetV2.2 demonstrates an optimal balance between accuracy and computational efficiency, requiring 16.1 MB of memory compared to 32.1 MB for other AttackNet variants. Training time analysis shows twice the speed for AttackNetV2.2 compared to AttackNetV1. Architectural ablation studies highlight the crucial role of residual connections, batch normalization, and suitable dropout rates. Statistical significance testing verifies the reliability of the results (p-value < 0.001). The proposed architectures show excellent generalization ability and practical usefulness for real-world deployment in mobile and embedded systems. Full article

Polysaccharides have emerged as promising biomaterials for 3D bioprinting due to their inherent biocompatibility, biodegradability, and structural diversity. However, their limited mechanical strength, insufficient bioactivity, and suboptimal printability hinder their direct application in fabricating complex tissue constructs. This review systematically summarizes universal modification strategies to address these challenges by tailoring polysaccharides’ physicochemical and biological properties. We first analyse the fundamental requirements of bioprinting materials, emphasising on the critical role of shear-thinning behaviours, post-printing structural fidelity, and cell-instructive functions. Subsequently, we highlight the advantages and limitations of representative polysaccharides, including chitosan, alginate, and hyaluronic acid. Chemical functionalisation, physical reinforcement, and biological hybridisation are proposed as versatile approaches to synergistically enhance printability, mechanical robustness, and bioactivity to tackle the limitations. Furthermore, dynamic crosslinking mechanisms enabling self-healing and stimuli-responsive behaviours are discussed as emerging solutions for constructing biomimetic architectures. Finally, we outline future directions in balancing material processability with cellular viability and scaling up modified polysaccharides for clinical translation. This review aims to provide a design blueprint for engineering polysaccharide-based bioinks toward next-generation regenerative medicine. Full article

The traditional construction industry significantly contributes to global resource consumption and climate change. Conventional methods limit the development of complex and multifunctional architectural forms. In contrast, 3D concrete printing (3DCP), an additive manufacturing technique, enables the creation of intricate building envelopes that integrate architectural and energy-efficient functions. Bioinspired design, recognized for its sustainability, has gained traction in this context. This study investigates the thermal and energy performance of various bioinspired and regular 3DCP infill patterns compared to conventional concrete building envelopes in tropical climates. A three-stage methodology was employed. First, bioinspired patterns were identified and evaluated through a literature review. Next, prototype models were developed using Rhino and simulated in ANSYS to assess thermal performance. Finally, energy performance was analyzed using Ladybug and Honeybee tools. The results revealed that honeycomb, spiral, spiderweb, and weaving patterns achieved 35–40% higher thermal and energy efficiency than solid concrete, and about 10% more than the 3DCP sawtooth pattern. The findings highlight the potential of bioinspired spiral infill patterns to enhance the sustainability of 3DCP building envelopes. This opens new avenues for integrating biomimicry into 3DCP construction as a tool for performance optimization and environmental impact reduction. Full article

Deep-seated tumors are challenging pathologies to treat. Currently available approaches are limited, prompting innovative solutions. Hyperthermia treatment (HT) is a thermal oncological therapy that raises tumor temperature (40–44 °C for 60 min), enhancing radio- and chemotherapy. Biomaterials loaded with magnetic particles, called magnetic scaffolds (MagSs), are used as HT agents for cancer treatment using radiofrequency (RF) heating. MagSs can be manufactured via 3D printing using fused deposition modeling to create biomimetic architectures based on triply periodic minimal surfaces (TPMSs). TPMS-based MagSs have been tested in vitro for RF HT. However, there is a lack of understanding regarding the thermal properties of TPMS MagSs for RF hyperthermia. Significant discrepancies between simulated and measured temperatures have been reported, attributed to limited knowledge of the apparent thermal conductivity of MagSs. Since planning is crucial for HT, it is fundamental to determine the thermal properties of these heterogeneous and porous composite biomaterials. Magnetic polylactic acid (PLA) scaffolds, shaped in different TPMS geometries and variable porosities, were thermally investigated in this research study. A linear relationship was found between the apparent thermal conductivity of parallelepiped and cylindrical scaffolds, and the measured values were validated using a numerical model of the RF HT test. Full article

Conventional static fixation for distal radius fractures (DRF) is clinically challenging, with methods often leading to complications such as malunion and pressure-related injuries. These issues stem from uncontrolled pressure and a lack of real-time biomechanical feedback, resulting in suboptimal functional recovery. To overcome these limitations, we engineered an intelligent, adaptive orthopedic device. The system is built on a patient-specific, 3D-printed architecture for a lightweight, personalized fit. It embeds an array of thin-film pressure sensors at critical anatomical sites to continuously quantify biomechanical forces. This data is transmitted via an Internet of Things (IoT) module to a cloud platform, enabling real-time remote monitoring by clinicians. The core innovation is a closed-loop feedback controller governed by a robust Interval Type-2 Fuzzy Logic (IT2-FLC) algorithm. This system autonomously adjusts servo-driven straps to dynamically regulate fixation pressure, adapting to changes in limb swelling. In a preliminary clinical evaluation, the group receiving the integrated treatment protocol, which included the smart splint and TCM herbal therapy, demonstrated superior anatomical restoration and functional recovery, evidenced by higher Cooney scores (91.65 vs. 83.15) and lower VAS pain scores. This proof-of-concept study validates a new paradigm for adaptive orthopedic devices, showing high potential for clinical translation. Full article