Journal of Bionic Engineering最新文献

This article presents the design of a microfabricated bio-inspired flapping-wing Nnano Aaerial Vvehicle (NAV), driven by an electromagnetic system. Our approach is based on artificial wings composed of rigid bodies connected by compliant links, which optimise aerodynamic forces though replicating the complex wing kinematics of insects. The originality of this article lies in a new design methodology based on a triple equivalence between a 3D model, a multibody model, and a mass/spring model (0D) which reduces the number of parameters in the problem. This approach facilitates NAV optimisation by using only the mass/spring model, thereby simplifying the design process while maintaining high accuracy. Two wing geometries are studied and optimised in this article to produce large-amplitude wing motions (approximately (40^circ )), and enabling flapping and twisting motion in quadrature. The results are validated thanks to experimental measurements for the large amplitude and through finite element simulations for the combined motion, confirming the effectiveness of this strategy for a NAV weighing less than 40 mg with a wingspan of under 3 cm.

Pulmonary embolism (PE) can range from minor, asymptomatic blood clots to life-threatening emboli capable of obstructing pulmonary arteries, potentially leading to cardiac arrest and fatal outcomes. Due to this significant mortality risk, risk stratification is essential following PE diagnosis to guide appropriate therapeutic intervention. This study proposes a machine learning-based methodology for PE risk stratification, utilizing clinical data from a cohort of 139 patients. The predictive framework integrates an enhanced binary Honey Badger Algorithm (BCCHBA) with the K-Nearest Neighbor (KNN) classifier. To comprehensively evaluate the performance of the core optimization algorithm (CCHBA), a series of benchmark function tests were conducted. Furthermore, diagnostic validation tests were performed using real-world PE patient data collected from medical facilities, demonstrating the clinical significance and practical utility of the BCCHBA-KNN system. Analysis revealed the critical importance of specific indicators, including neutrophil percentage (NEUT%), systolic blood pressure (SBP), oxygen saturation (SaO₂%), white blood cell count (WBC), and syncope. The classification results demonstrated exceptional performance, with the prediction model achieving 100% sensitivity and 99.09% accuracy. This approach holds promise as a novel and accurate method for assessing PE severity.

Intraoral scanning has become integral to digital workflows in dental implantology, offering a more efficient and comfortable alternative to conventional impression techniques. For complete edentulism, accurate scanning is crucial to successful full-arch dental implant rehabilitation. However, the absence of well-defined anatomical landmarks can lead to cumulative errors during merging sequential scans, often surpassing acceptable thresholds. Current mitigation strategies rely on manual adjustments in Computer-Aided Design (CAD) software, a time-intensive process that depends heavily on the operator’s expertise. This study presents a novel segment-match-correct process automation workflow to enhance full-arch intraoral scans’ positioning accuracy and efficiency. By leveraging 3D registration algorithms, the proposed method improves implant positioning accuracy while significantly reducing manual labor. To assess the robustness of this workflow, we simulated four types of noise to evaluate their impact on scanning errors. Our findings demonstrate that the process automation workflow reduces dentist workload from 5-8 minutes per scan to less than 1 min (about 57 seconds) while achieving a lower linear error of 45.16 ± 23.76 (upmu hbox {m}), outperforming traditional scanning methods. We could replicate linear and angular deviations observed in real-world scans by simulating cumulative errors. This workflow improves the accuracy and efficiency of complete-arch implant rehabilitation and provides a practical solution to reduce cumulative scanning errors. Additionally, the noise simulations offer valuable insights into the origins of these errors, further optimizing IntraOral Scanner (IOS) performance.

This paper presents an innovative design of a Bionic Powered Ankle Prosthesis (BPAP) utilizing a muscle recruitment mechanism-inspired clutch, aimed at achieving biomimetic simulation of ankle muscle function. To address the varying stiffness requirements of the prosthesis across different gaits, the clutch dynamically switch between different rope bundle combinations. The mechanical characteristics during the load traction process of the selected flexible ropes are also studied. A mathematical model of the series elastic drive system is established. The optimization is carried out with the minimum peak power required by the motor as the optimization goal. The experimental results show that by applying a clutch with human muscle recruitment mechanism, the proposed prosthesis can achieve better energy efficiency at different speeds.

Marine biofouling is the undesired attachment and formation of marine organisms on surfaces, which adversely affects ship maintenance, economic costs, and ecosystem health. Despite remarkable advancements in antifouling coatings, developing formulations that simultaneously achieve environmental benign and high antifouling performances remains a critical challenge. Herein, drawing inspiration from the natural antifouling mechanisms including the superhydrophobicity of lotus leaves and biochemical defense of coral mucus, we developed a Superhydrophobic Antifouling Coating (SHAC) incorporating coral mucus-derived agents. This biomimetic design synergistically integrates physical anti-adhesion of superhydrophobic surfaces with chemical repellency of antifouling agents, yielding outstanding antifouling performance. The SHAC demonstrates remarkable durability (withstanding 100 abrasion cycles), sustained superhydrophobicity (contact angle > 150°), and outstanding antifouling efficacy (92% bacterial and 96% algal inhibition). Marine field tests demonstrate significant reduction in fouling organism attachment over 30 days. Our work presents an eco-friendly and high-performance solution to marine biofouling, bridging the gap between sustainability and effectiveness in antifouling technology.

Recent advances in bone regeneration have introduced the concept of four-dimensional (4D) scaffolds that can undergo morphological and functional changes in response to external stimuli. While several studies have proposed patient-specific designs for defect sites, they often fail to adequately distinguish the advantages of 4D scaffolds over conventional 3D counterparts. This study aimed to investigate the potential benefits of 4D scaffolds in clinically challenging scenarios involving curved defects, where fixation is difficult. We proposed the use of Shape-Memory Polymers (SMPs) as a solution to address critical issues in personalized scaffold fabrication, including dimensional accuracy, measurement error, and manufacturing imprecision. Experimental results demonstrated that the Curved-Layer Fused Deposition Modeling (CLFDM) scaffold, which offers superior conformability to curved defects, achieved significantly higher interfacial contact with the defect area compared to traditional Fused Deposition Modeling (FDM) scaffolds. Specifically, the CLFDM scaffold facilitated bone regeneration of 25.59 ± 4.72 mm³, which is more than twice the 9.37 ± 1.36 mm³ observed with the 3D FDM scaffold. Furthermore, the 4D CLFDM scaffold achieved 75.38 ± 11.65 mm³ of new bone formation after four weeks, approximately three times greater than that of the 3D CLFDM scaffold, regardless of surface micro-roughness. These results underscore that improved geometrical conformity between the scaffold and the defect site enhances cellular infiltration and contributes to more effective bone regeneration. The findings also highlight the promise of 4D scaffolds as a compelling strategy to overcome geometric and dimensional mismatches in the design of patient-specific scaffolds.

Gait coordination in lower limbs plays a critical role in maintaining stability of the human body during walking. For transfemoral amputees, the absence of limbs disrupts this coordination, reducing prosthesis control accuracy. Hip-knee coordination mapping offers a feasible solution for lower-limb prosthesis control, involving the generation of a reference trajectory for the knee joint by leveraging information from the hip. However, current reference trajectories are usually derived from static models, which cannot generate reference trajectories robustly when dealing with perturbations. Therefore, this paper introduces a time-dependent model based on the Delayed Feedback Reservoir (DFR) for hip-knee coordination in lower-limb prosthetic control. Experimental results show that DFR outperforms classical gait planning approaches when facing perturbations, achieving a 20% lower Root Mean Square Error (RMSE) and reducing residuals by up to 18.14 degrees. This research contributes to understanding gait mapping approaches and emphasizes the potential of time-dependent models for robust and strong lower-limb prosthetic control. The discovery provides a novel way to enhance the perturbation adaptability of prosthetic control.

Valvular Heart Disease (VHD), including stenosis and regurgitation, is a significant contributor to global cardiovascular morbidity. Current prosthetic solutions mechanical and bioprosthetic heart valves each present major limitation. Mechanical valves require lifelong anticoagulation due to thrombogenicity, while bioprosthetic valves suffer from structural degeneration and limited durability. Polymeric Heart Valves (PHVs) have emerged as promising alternatives, aiming to integrate the mechanical resilience of synthetic materials with the biocompatibility and hemodynamic performance of natural valves. Recent studies have explored advanced polymers such as Polyhedral Oligomeric Silsesquioxane–Polycarbonate–Urea–Urethane (POSS-PCU), Silicone–Polyurethane Urea (SiPUU), and nanocomposites like Polyvinyl Alcohol (PVA) and SIBS for their enhanced thromboresistance, calcification resistance, and long-term mechanical durability. Complementary to material innovation, fabrication methods such as 3D printing, Melt Electrospinning Writing (MEW), and Focused Rotary Jet Spinning (FRJS) offer patient-specific designs and microstructural control. This review systematically compares traditional and next-generation prostheses, examines mechanical and biological performance, and discusses critical design challenges including porosity, thrombogenicity, and leaflet calcification. Finite Element Analysis (FEA) and Computational Fluid Dynamics (CFD) are highlighted for optimizing design and simulating physiological conditions. By presenting recent preclinical progress and manufacturing strategies, this review outlines a translational roadmap toward clinically viable, biomimetic polymeric heart valves capable of addressing the needs of both adult and pediatric patients. Compared to traditional bioprosthetic tissues, advanced polymers offer better resistance to calcification, reduced thrombogenicity, and tunable mechanical properties.

Annelid-inspired robots exhibit excellent motion adaptability and structural compliance, enabling them to navigate confined, hazardous, or complex environments such as pipelines, soil, or the gastrointestinal tract. This review summarizes key developments in their bionic part design, actuation methods, material selection, and performance characteristics. Comparative analyses show that different actuation strategies (e.g., pneumatic, shape memory alloys, and electroactive polymers, etc.) need to be weighed in terms of their advantages, limitations, and applicable environments. Materials like silicone rubber and SMA are evaluated for their strength, flexibility, and energy performance. Quantitative benchmarks of velocity, load capacity, and energy consumption are presented to highlight design-performance correlations. Prospective research directions include the integration of multifunctional adaptive materials, real-time feedback sensing systems, and scalable architectures for autonomous operation in unstructured environments.

The stiffness information of the grasped object at the initial contact stage can be effectively used to adjust the grasping force of the prosthetic hand, thereby preventing damage to the object. However, the object’s deformation and contact force are often minimal during the initial stage and not easily obtained directly. Additionally, stiffness estimation methods for prosthetic hands often require contact sensors, which can easily lead to poor contact issues. To address the above issues, this paper proposes the model-based stiffness estimation of grasped objects for underactuated prosthetic hands without force sensors. First, the kinematic model is linearized at the contact points to achieve the estimation of the linkage angles in the underactuated prosthetic hand. Secondly, the motor parameters are estimated using the Kalman filter method, and the grasping force is obtained from the dynamic model of the underactuated prosthetic hand. Finally, the contact model of the prosthetic hand grasping an object is established, and an online stiffness estimation method based on the contact model for the grasped object is proposed using the iterative reweighted least squares method. Experimental results show that this method can estimate the stiffness of grasped objects within 250 ms without contact sensors.