Search Results (14,148)

This review evaluates recent progress in nanozyme-based biosensors for detecting circulating tumour cells, nucleic acids, and protein biomarkers, with particular attention to how peroxidase-, oxidase-, and catalase-like reactions enhance signal generation across electrochemical, optical, and microfluidic platforms. The roles of iron oxide–gold composites, silica nanostructures, quantum dots, and hybrid nanomaterials in improving analytical performance, enabling multiplexed detection, and facilitating assay miniaturization are critically assessed. Advances such as amplification-free detection approaches, smartphone-compatible point-of-care systems, and AI-assisted data analysis are discussed in relation to their translational potential. Key barriers, including regulatory requirements, reproducibility concerns, and manufacturing scalability, are also evaluated. By integrating mechanistic understanding with practical considerations for clinical deployment, this review outlines how next-generation nanozyme-based biosensors may strengthen early cancer detection, real-time monitoring, and precision oncology. Full article

While valorisation pathways are increasingly promoted as sustainable solutions, their ability to genuinely minimise environmental harm and contribute to long-term material circularity remains uneven. This study systematically identifies and maps existing valorisation routes across the EU and UK, with particular attention to their environmental performance and economic viability through a material value retention lens. A literature review highlights a spectrum of practices—from soil amendment and composting to bioenergy recovery and bio-based construction materials—each offering different sustainability benefits but varying significantly in their capacity to preserve material quality and function. To address the absence of robust comparative approaches, this paper introduces a novel evaluative framework centred on intrinsic material value retention, a key principle in sustainable and circular material systems. Building on established scholarship, the framework provides a structured means of comparing valorisation options based on how effectively they conserve material properties, particularly in terms of the material’s structural and functional values, and enable high-value reuse. Supported by a dedicated classification tool and a set of guiding questions refined through expert interviews, the framework complements existing environmental assessment methods by foregrounding material circularity. In doing so, it supports more integrated, holistic decision-making for the development of a resilient and sustainable circular bioeconomy. This research is intended for academic audiences and may also be of relevance to industry practitioners. Full article

Virtual Reality (VR) has emerged as a powerful tool for improving training strategies in advanced manufacturing through immersive experiences. Within this context, this study examines the impact of two training strategies, VR and Video-Based (VB) instructions, on system performance (execution time and human errors) in a cooperative Human–Robot Interaction (HRI) assembly task. Overall, 26 participants completed the task after receiving either VR or VB training, and a sub-sample of 6 people per group returned one month later to repeat the task, enabling an evaluation of performance over time. Objective and subjective metrics were collected, and statistical and effect size analyses were conducted to compare training effects across sessions. Results show that execution times and number of errors were comparable between VR and VB in the first real session. After one month, both groups exhibited improved performance, but VR-trained participants retained, on average, lower error rates, with a 71% reduction and the number of errors dropping to zero, and more stable error patterns, whereas VB-trained participants displayed greater variability and occasional accuracy degradation during repeated task execution. Moreover, within-group comparisons show that VR training is more effective for accuracy-critical cooperative HRI tasks. At the same time, VB remains a low-cost option for time-focused contexts, shedding light on how training modalities influence learning and forgetting in Industry 5.0. Full article

Power electronic systems are often engineered through a sequential–iterative workflow in which hardware parameters are initially sized from steady-state, ripple, thermal, and electromagnetic-compatibility constraints, and controllers are subsequently tuned to satisfy dynamic and closed-loop performance requirements. While converters are inherently designed for closed-loop operation, increasing power density, uncertainty, and distributed interaction make the underlying design process resemble a strategic interplay among multiple decision-makers, including hardware designers, control algorithms, loads, disturbances, and manufacturing constraints. This paper develops a unifying game-theoretic perspective on the optimal design and control of power electronic systems. Classical concepts—such as robust control, worst-case design, droop-based load sharing, and tolerance allocation—are reinterpreted as equilibrium solutions of zero-sum, Stackelberg, non-cooperative, or cooperative games. Beyond a conceptual taxonomy, two illustrative simulation case studies are provided: (i) a Stackelberg hardware–controller co-design of a buck converter, demonstrating simultaneous passive-component reduction and improved transient performance relative to a conservative sequential design; and (ii) a droop-controlled parallel-converter example contrasting Nash and cooperative equilibria, explicitly quantifying trade-offs between bus-voltage regulation, current-sharing fairness, and conduction losses. By framing power electronic design and control as interacting strategic processes rather than isolated optimization stages, the paper aims to show that game theory can serve as a structured and practically interpretable framework for distributed and uncertainty-aware power electronic systems. Full article

This research study conducts a computational analysis of a two-terminal (2T) Perovskite-on-silicon (PVK-Si) solar cell with a tandem configuration. The motivation for this analysis arises from the outstanding potential of PVK-Si solar cells to surpass the efficiency limitations of conventional photovoltaic technology. The tandem configuration utilizes a combination of CsSnBr₃ in the top sub-cell and crystalline silicon (c-Si) in the bottom sub-cell. After optimizing parameters of the top sub-cell (FTO/TiO₂/CsSnBr₃/rGO/Au), which included the thicknesses of CsSnBr₃ (500 nm), TiO₂ (40 nm), rGO (50 nm), the interface defects (10¹³ cm⁻²), and the bandgap of CsSnBr₃ (1.78 eV), the PVK-Si tandem device was simulated. As a result, the top CsSnBr₃ sub-cell achieved an efficiency of 21.62%, while the bottom silicon sub-cell achieved an efficiency of 23.48%. Subsequently, the sub-cells were interconnected in series using filtered spectra and current-density matching. After interpolating the J-V curves, the tandem exhibited a global efficiency of 29.76%, a fill factor (FF) of 85.30%, a matched current density (J_SC) of 19.02 mA/cm², and an open-circuit voltage (V_OC) of 1.83 V. The EQE results confirmed efficient photon management via complementary sub-cell absorption. The performance is competitive with experimental lead-based tandems and exceeds that of current lead-free simulations. Therefore, this research proposes a viable pathway for the development of non-toxic, cost-effective tandem solar systems with manufacturing capabilities. Full article

Carbon fiber-reinforced thermoplastic composite drilling is a secondary manufacturing process because the quality of drilled holes affects assembly system performance, structure, and sustainability. This paper compares all drill coating types and cutting conditions for PEEK-CF30 composite drilling utilizing a hybrid experimental–statistical method. DLC-, TiN-, and TiCN-coated HSS drills, as well as cutting speed and feed rate were tested using the Taguchi L27 design. Performance indicators were measured by including thrust force, surface roughness, drilling torque, and energy consumption. Experimental results showed that increasing cutting speed and feed rate increased the thrust force while decreasing torque and energy consumption. Smearing on the hole surface, chip adhesion, and short fiber adhesion/pull were identified as indicators of poor surface quality, and these occurrences increased with increasing drill coating removal at high cutting parameters. In terms of overall performance, the TiCN-coated drill created the lowest thrust force (50.85 N), surface roughness (1.038 µm), torque (17.54 Ncm), and energy consumption (136.45 J) at high feed conditions. Taguchi-based gray relational analysis methodology revealed that the TiCN-coated drill, a cutting speed of 40 m/min, and a feed rate of 0.1 mm/rev are the optimum parameters. Second-order prediction models developed for all responses proved to have high predictive capabilities with coefficients of determination above 94%. Ultimately, drill coating quality considerably affected surface integrity and drilling energy consumption performance in drilling PEEK-CF30. A hybrid optimization and modeling framework demonstrates that the drill quality cutting parameter will allow for optimum selection to ensure efficient processing of advanced thermoplastic composites. Full article

Distributed hybrid flow-shop scheduling problems (DHFSPs) are widely encountered in manufacturing systems. Their complexity increases significantly when multiple overhead cranes are used for material handling. This paper investigates a distributed hybrid flow-shop scheduling problem with multiple overhead crane transportation (DHFSP-MCT), aiming to simultaneously minimize makespan and total energy consumption (including machining and transport). A hierarchical reinforcement learning-based bi-population collaborative metaheuristic algorithm (HRL-BCMA) is proposed. In HRL-BCMA, an iterated greedy strategy is first adopted to generate an initial population. Then, a two-level reinforcement learning framework is designed: a high-level agent decides when to release jobs to the shop floor, while a low-level agent based on a graph isomorphism network selects improvement operators. Furthermore, a bi-population co-evolutionary framework and a knowledge-informed strategy are introduced to enhance solution quality and diversity. Experimental evaluations on both randomly generated instances and a real-world-inspired aluminum manufacturing case show that HRL-BCMA reduces makespan by 8.6% and total energy consumption by 12.3% on average compared to the best existing algorithm (CBMA) while achieving superior Pareto front coverage. These results demonstrate the effectiveness of the proposed method for green scheduling problems with crane transport constraints. Full article

The optimization of impact localization in 3D-printed structures is critical for their application in smart monitoring and damage detection systems. This study investigates the influence of infill density on the accuracy of low-velocity impact localization in 3D-printed plates. Specimens with cubic infill patterns and varying densities (30%, 50%, and 100%) were fabricated and subjected to impacts with varying locations and magnitudes using two different sensor network configurations. A genetic algorithm integrated with continuous wavelet transform was employed to simultaneously determine impact coordinates and group velocity. Key findings reveal that lower infill structures act as mechanical low-pass filters, producing clean and low-frequency signals, while higher densities support complex wave propagation with higher energy and broader frequency content. The dominant frequency of first arrival shifts toward lower values with increasing impact energy across all densities. Group velocity increases with both impact energy and infill density. For 30% infill, it averages around 450 m/s, while for 100% infill it exceeds 800 m/s. The genetic algorithm demonstrated robust performance across all experimental conditions, simultaneously estimating impact coordinates and group velocity with average errors below 6% for all infill densities. Spatial probability mass functions revealed tightly clustered predictions around true impact locations, with maximum probabilities reaching 68% and uncertainties below 5%. Computational efficiency varied modestly with infill density. These findings provide quantitative relationships between infill density, wave propagation characteristics, and localization performance for designing a reliable structural health monitoring of additively manufactured structures. Full article

Biodegradable metals represent a paradigm shift in orthopedic fixation by providing temporary mechanical support synchronized with bone healing while eliminating long-term complications associated with permanent implants. Conventional bioinert alloys, including stainless steels, Ti-based alloys, and Co-Cr alloys, exhibit high elastic moduli that induce stress shielding and often require secondary removal surgeries. In response, resorbable metallic systems based on Mg, Zn, and Fe have emerged as promising alternatives. Among these, Fe-Mn-C alloys stand out for load-bearing applications due to their exceptional strength-ductility balance governed by twinning-induced plasticity mechanisms, tunable degradation behavior, and intrinsic magnetic resonance imaging compatibility through austenitic phase stabilization. Focusing on Fe-Mn-C alloys, this review critically examines the metallurgical design principles underlying stacking fault energy optimization, phase stability, and Mn-controlled electrochemical behavior. Processing innovations, such as additive manufacturing, are discussed as tools to architecture porosity, refine microstructure, and accelerate degradation by graded designs while preserving mechanical structural support during healing. Hybrid metallic-bioactive systems, surface functionalization strategies, and functionally graded porous architectures were evaluated as advanced approaches to enhance osteointegration and modulate degradability. Despite these advances, significant barriers remain for clinical translation. Persistent discrepancies between in vitro and in vivo degradation rates, often attributed to biological encapsulation and degradation product accumulation, complicate lifetime prediction. Localized corrosion at microstructural heterogeneities such as twin boundaries and phase interfaces can undermine structural reliability under load-bearing conditions. Moreover, predictive multi-physics modeling frameworks capable of coupling electrochemical kinetics, mechanical loading, microstructural evolution, and bone remodeling remain underdeveloped, limiting reliable safety-margin estimation. Regulatory progress is further hindered by the absence of standardized testing protocols specifically tailored to Fe-based biodegradable alloys, including harmonized degradation rate windows, validated corrosion-mechanics coupling methodologies, and clinically defined Mn ion release thresholds. This review aims to discuss whether Fe-based alloys, especially Fe-Mn-C alloys, can transition from promising laboratory materials to clinically viable next-generation orthopedic implants capable of delivering patient-specific, mechanically compatible, and biologically synchronized temporary fixation. Full article

6063 aluminum alloy has broad application prospects in aerospace and microelectronic thermal management systems due to its good thermal conductivity and moderate strength. However, its extremely high hot cracking susceptibility during the laser powder bed fusion (LPBF) process limits the direct manufacturing of complex components. This study proposes a strategy combining material composition modification with advanced structural design. By introducing TiH₂ nanoparticles (1.0~4.5 wt.%) to modify the 6063 aluminum alloy powder, Diamond-type porous structures based on triply periodic minimal surfaces (TPMS) were successfully fabricated using LPBF technology. The results show that the introduction of TiH₂ significantly suppresses the solidification cracking of the aluminum alloy. The underlying mechanism is that the L1₂-structured Al₃Ti particles, generated by the in situ decomposition of TiH₂ in the melt pool, provide high-density heterogeneous nucleation sites. This leads to a drastic decrease in the average grain size from 30.46 μm to 0.75 μm (a reduction of 97.5%), achieving a remarkable columnar-to-equiaxed transition (CET). In terms of mechanical properties, the 3.0 wt.% TiH₂ addition group exhibits excellent plateau stress (28.5 MPa) and energy absorption capacity, which is mainly attributed to the synergistic effect of fine-grain strengthening and Orowan dispersion strengthening. Thermal tests reveal that the thermal conductivity of the 3.0 wt.% group reaches 123 W/(m·K) at 100 °C. The healing of cracks reconstructs the macroscopic heat conduction paths, resulting in a significant improvement in thermal conductivity compared with the unmodified group. This work provides a theoretical reference for the development of high-performance, crack-free, and multi-functional integrated aluminum alloy components via additive manufacturing. Full article

Background: Total knee arthroplasty (TKA) is a common procedure aimed at alleviating knee pain and restoring function in patients with degenerative joint diseases. Traditional implants are typically designed to restore mechanical knee alignment, but personalized implants have shown promise in improving clinical outcomes. The Origin^® individualized TKA system provides a tailored approach to knee reconstruction by utilizing preoperative 3D planning to create individualized implants and cutting guides based on each patient’s unique anatomy. Surgical Technique: The Origin^® system employs a preoperative computed tomography (CT) scan and Knee-Plan^® software to design individualized implants that optimize alignment and joint anatomy. The surgical technique involves the use of patient-specific cutting guides for precise bone resections and the insertion of either cruciate-retaining (CR) or posterior-stabilized (PS) implants, depending on individual patient needs. This process aims to replicate the pre-arthritic alignment and kinematics of the pre-arthritic knee. Postoperative Protocol: The postoperative protocol allows for immediate weight-bearing, and patients are guided through a structured rehabilitation program to ensure optimal recovery. Full range-of-motion exercises begin early to promote knee mobility and strength. Discussion: The individualized TKA system offers several advantages, including precise restoration of pre-arthritic anatomy, reduced bone resection, and improved implant fit. These benefits are particularly valuable in patients with unique anatomical challenges, such as deformities or previous surgeries. Despite the potential advantages, challenges remain, including the costs and time associated with individualized manufacturing, as well as increased radiation exposure from the required CT scans. Conclusions: The Origin^® individualized TKA system represents a significant advancement in knee arthroplasty by providing a tailored approach to patient care. Future studies are needed to further evaluate the long-term outcomes and cost-effectiveness of this personalized system compared to conventional TKA approaches. Full article

With the strategic shift toward reducing reliance on critical raw materials, Cobalt-free eutectic high-entropy alloys (EHEAs) have emerged as a pivotal frontier for high-performance structural applications. This review systematically elucidates the synergistic relationship between Co-free alloy design and the non-equilibrium solidification mechanisms of Selective Laser Melting (SLM). The ultra-high cooling rates (10⁵–10⁸ K/s) inherent in SLM are shown to refine eutectic lamellae to the sub-micron scale (typically <300 nm), effectively suppressing the macro-segregation common in conventional casting. We evaluate the design principles of Al-Cr-Fe-Ni and related systems, noting that SLM-processed Co-free EHEAs frequently achieve yield strengths exceeding 1000 MPa and ultimate tensile strengths (UTSs) surpassing 1300 MPa, while maintaining tensile elongations above 10%—a significant improvement over the coarse-grained structures produced by traditional methods. Furthermore, the study identifies critical processing windows, such as laser energy densities (60–120 J/mm³), required to mitigate micro-cracking and achieve near-full density (>99.5%). By synthesizing recent experimental breakthroughs and AI-driven modeling, this review provides a quantitative roadmap for the precision manufacturing of cost-effective, high-performance EHEAs, bridging the gap between theoretical alloy design and industrial additive manufacturing. Full article

Despite strong policy support for prefabricated interior decoration systems (PIDSs) in China, residential consumer uptake remains limited. Existing research has focused primarily on adoption drivers or industry-side promotion; in contrast, in this study, Innovation Resistance Theory (IRT) is employed to investigate the functional and psychological barriers to consumer acceptance in the Chinese residential market. Utilizing data from 476 Chinese consumers, partial least squares structural equation modeling (PLS-SEM) is applied to test a hierarchical mediation framework. The results demonstrate that functional obstacles, specifically risk and usage barriers, do not exhibit a direct association with resistance intention; rather, a significant indirect effect via perceived value and image is observed. Notably, the tradition barrier emerged as the most dominant predictor of resistance, reflecting a deep-seated cultural path dependency on traditional masonry methods and a perceived loss of construction rituals that disrupts system adoption. Furthermore, multi-group analysis (MGA) reveals a paradox of experience: while uninitiated users are resistant based on abstract stereotypes, those with traditional renovation experience are driven by status quo bias, and early adopters of PIDSs are resistant due to negative disconfirmation regarding usage friction and functional inflexibility. These findings suggest that, to achieve system equilibrium, the industry must transition from an industry-centric narrative to one focused on premium quality and user-centric design. Practical implications include the need to de-stigmatize prefabrication as precision manufacturing and to align policy and market interventions more closely with the concerns of individual end-consumers in order to improve residential market acceptance. Full article

Acoustic emission (AE) monitoring in metal additive manufacturing (AM) requires compact sensors capable of high-frequency operation, broad bandwidth, and high sensitivity. However, increasing structural stiffness to achieve high resonance frequencies typically reduces electromechanical sensitivity. This work presents a finite element study of a coupled-resonator capacitive micromachined ultrasonic transducer (CMUT) designed to address this trade-off. The proposed architecture integrates three mechanically coupled silicon membranes with a stepped capacitive cavity that increases capacitance while preserving structural stiffness, enabling enhanced sensitivity without compromising high-frequency operation. COMSOL Multiphysics simulations were used to evaluate modal characteristics and frequency response under DC pre-stressed conditions. Modal coupling produced closely spaced resonances that broadened the effective bandwidth, while the stepped cavity significantly increased voltage output through improved electromechanical coupling. Compared to a single-resonator flat-cavity design, the coupled stepped-cavity configuration demonstrated nearly a threefold enhancement in output voltage while maintaining operation near 100 kHz. Additionally, adjusting the central resonator length enabled controlled frequency tuning for scalable array implementation. These results establish a proof of concept for a high-frequency, high-sensitivity micro-electro-mechanical systems (MEMS) CMUT architecture suitable for distributed AE monitoring in advanced manufacturing environments. Full article

Although nanomedicine has enabled significant advances in drug delivery, the clinical translation of conventional synthetic nanocarriers is limited by immune clearance, non-specific biodistribution, and gastrointestinal instability. This poses major challenges for therapy targeting the intestines. Cell membrane-coated nanotechnology (CMCT) and membrane vesicle-based systems have emerged as biomimetic platforms integrating synthetic nanomaterials with naturally derived biological interfaces. These biohybrid systems inherit biological functions originating from cells, including immune evasion, prolonged circulation, lesion homing, and microenvironment-responsive interactions, through the direct transfer of intact membrane components. This review summarizes recent advances in CMCT and membrane vesicle-based strategies for intestinal drug delivery. It covers fabrication methodologies, programmable manufacturing approaches, and functional regulation enabled by diverse membrane sources and hybrid engineering designs. Applications in inflammatory bowel disease, colorectal cancer, and intestinal infections are highlighted, emphasizing key therapeutic mechanisms, such as targeting inflammation, neutralizing toxins, modulating the immune system, and regulating the microbiome. We also discuss the major challenges of translation, such as preserving membrane and coating integrity, ensuring oral stability, achieving batch reproducibility, and ensuring biosafety. Overall, this review establishes a conceptual and engineering framework to guide the transition of membrane-based nanocarriers from passive biomimicry to adaptive, clinically translatable intestinal delivery systems. Full article