Journal of Bionic Engineering

Progress in Passive Radiative Cooling Materials: From Material Selection, Preparation Process, Structural Design to Applications

Yuqi Zhuansun, Yunhai Ma, Hanliang Ding, Shichao Niu, Zhiwu Han, Luquan Ren

Journal of Bionic Engineering. 2026, 23 (1): 1-33. DOI: 10.1007/s42235-025-00820-7

Abstract ( 25 )

Radiative cooling passively emits heat to outer space without energy input, offering promise for energy-efficient thermalmanagement. It is an important solution to promote the low-carbon environmental protection strategy. With the continuousdevelopment of radiative cooling technologies, the material selection, preparation process, structural design, and application fields have also made more diverse progress. Therefore, this review aims to systematically introduce the fundamentalconcepts and underlying principles of radiative cooling. A summary of the commonly used materials for radiative coolingis provided. In addition, the advanced fabrication processes and structural designs of radiative cooling materials are furtherexplored and discussed. Subsequently, the unique functions of radiative cooling materials are highlighted to enhance theirapplicability and usefulness across various fields. An overview of combining radiative cooling materials with differentfields is also provided. In reality, these applications hold the potential to improve thermal management across a range offields. Finally, it summarizes the shortcomings and great potential of radiative cooling materials in various fields. It alsolooks forward to the future, aiming to promote the progress and widespread adoption of radiative cooling technologies.

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Catalase-powered Micro/Nanorobots: Propulsion Mechanisms and Biomedical, Environmental, and Industrial Applications

Jitendra Gupta, Abdulla Ahmed Al-dulaimi, Mudher Kadhem, Irfan Ahmad, S. Renuka Jyothi, Rajashree Panigrahi, Indu Singh, Surbhi Singh, Nafaa Farhan Muften & Yasser Fakri Mustafa

Journal of Bionic Engineering. 2026, 23 (1): 34-54. DOI: 10.1007/s42235-025-00812-7

Abstract ( 21 )

Micro/nanorobots represent a groundbreaking advancement in nanotechnology, with applications spanning medicine, environmental remediation, and industrial processes. A major challenge in their development is achieving efficient and biocompatible propulsion. Enzyme-driven propulsion, particularly using catalase, offers a promising solution due to its abilityto decompose hydrogen peroxide (H?O?) into water and oxygen, generating thrust for autonomous movement. Comparedto metal-based catalysts, catalase-powered systems exhibit superior biocompatibility and lower toxicity, making themideal for biomedical applications. This review explores the role of catalase in micro/nanorobot propulsion, highlightingself-propulsion mechanisms, different nanorobot types, and their applications in drug delivery, infection treatment, cancertherapy, and biosensing. Additionally, recent advancements in biodegradable enzyme-powered nanorobots and their potential in overcoming biological barriers are discussed. With further research, catalase-driven nanorobots could revolutionizetargeted therapy and diagnostic techniques, paving the way for innovative solutions in nanomedicine.

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A Decade of Soft Robotic Manipulators: Advances in Design, Modeling, Control, and Emerging Challenges

Elsayed Atif Aner, Omar M. Shehata, Mohammed Ibrahim Awad, Nancy E. ElHady

Journal of Bionic Engineering. 2026, 23 (1): 55-98. DOI: 10.1007/s42235-025-00819-0

Abstract ( 17 )

Soft robotic manipulators represent a rapidly evolving field characterized by inherent compliance, adaptability, and safeinteractions within unstructured environments. Over the past decade (2015–2025), significant advancements have transformed their capabilities through novel designs inspired by biological systems, advanced modeling frameworks, sophisticated control strategies, and integration into diverse real-world applications. Recent innovations in multifunctional materials and emerging actuation technologies have markedly expanded manipulator performance, reliability, and dexterity.Concurrently, developments in modeling have progressed from simplified geometric methods toward highly accuratephysics-based and hybrid data-driven approaches, substantially improving real-time prediction and controllability. Coupledwith these developments, adaptive and robust control strategies–including learning-based techniques–have enabled unprecedented autonomy and precision in challenging application domains such as Minimally Invasive Surgery (MIS), precisionagriculture, deep-sea exploration, disaster recovery, and space missions. Despite these remarkable strides, key challengesremain, notably regarding scalability, long-term material durability, robust integrated sensing, and standardized evaluationprocedures. This review comprehensively synthesizes recent advances, critically evaluates state-of-the-art methodologies,and systematically identifies existing gaps to provide a clear roadmap and targeted research directions, guiding futuredevelopments toward the broader adoption and optimal utilization of soft robotic manipulators.

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From Biohybrid Actuators To Smart Manufacturing: Advancing Microrobots for Minimally Invasive Medicine

Wenqi Zhang, Gongxin Li, Xiaoli Luan, Fei Liu

Journal of Bionic Engineering. 2026, 23 (1): 99-125. DOI: 10.1007/s42235-025-00824-3

Abstract ( 16 )

Microrobotic systems are emerging as transformative technology for minimally invasive medicine, driven by innovationsin actuation mechanisms, advanced fabrication paradigms, and multifunctional system integration. This comprehensivereview analyzes the evolution of microrobotic technologies through three critical dimensions: (1) actuation modalities,including magnetic, optical, acoustic, chemical, and biological actuation, with a focus on the synergistic advantagesof hybrid actuation strategies in complex internal physiological environments; (2) Fabrication methods cover technologies such as photolithography, microinjection molding, self-assembly, and 3D printing, emphasizing innovative strategiesinvolving multi-technology integration and collaborative manufacturing of bio/non-bio hybrid materials; (3) Internal physiological applications involve disease diagnosis, targeted drug delivery, minimally invasive surgery, tissue engineering, andcell manipulation, highlighting the broad prospects of microrobots in precision medicine. Despite remarkable progress,critical challenges remain, including low actuation efficiency, as seen in acoustic systems, limited biocompatibility, exemplified by the toxicity of hydrogen peroxide in chemical actuation, delayed clinical translation, and other related challengesthat must be addressed to advance the field.

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Structural Characteristics of Bamboo and Research Progress in Bamboo-Inspired Composites

Xinyan Hu, Ziyang Zhang, Yuping Tao, Xinyuan Zhou, Hang Zhao, Shanyu Han, Changlei Xia

Journal of Bionic Engineering. 2026, 23 (1): 126-138. DOI: 10.1007/s42235-025-00814-5

Abstract ( 11 )

Bamboo is a natural composite that has inspired the design of biomimetic composites due to its unique multi-scale structure and outstanding mechanical properties. This paper first presents the structural features of bamboo, detailing the hydrophobic wax and silica layer of the surface, the functionally graded vascular bundles of the wall for optimized toughness,and the hollow, multi-node architecture of the stem for overall stability and bending resistance. Subsequently, this studysurveys recent sustainability and designability advances in bamboo-inspired composites. Inspiration from the bamboo surface has spurred the creation of materials with enhanced functionalities, such as transparent composites and high-stiffnessstructural materials. Imitation of the wall structure has led to the development of high-strength and tough materials,with the discussion covering examples such as hydrogels, polymer composites, and metal-matrix composites. Inspirationfrom the stem structure has yielded lightweight composites with excellent energy absorption and stability, exemplifiedby advanced linear materials like resilient yarns and tendon sutures, as well as functional structures like flexible sensors.These biomimetic designs show significant potential across numerous fields, including construction, healthcare, urbanrail transit, wearable electronics, and mechanical engineering. Finally, this paper discusses the current limitations andchallenges to understanding bamboo’s structural characteristics towards the development of bamboo-inspired composites.Future research directions are proposed, including understanding bamboo’s structure, designing novel biomimetic composites, and optimizing their structure to develop bamboo-inspired functional materials.

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Research on the Active and Passive Motion Characteristics of Bioinspired Soft Actuators

Qi Shen, Jinzhu Zhang, Xiaoyan Xiong, Hongjie Du, Shiyu Li

Journal of Bionic Engineering. 2026, 23 (1): 139-158. DOI: 10.1007/s42235-025-00822-5

Abstract ( 23 )

The soft actuator is characterized by high safety, flexibility, and adaptability. It is capable of both active and passive deformations. This paper presents a discrete degree of freedom (DOF) method for soft actuators to reveal DOF characteristics.The method draws on the superposition mechanism of the deformation characteristics of the sarcomere in the skeletalmuscles of living organisms. Firstly, the multi-DOF deformation characteristics of the soft actuator are discretized intosuperimposed combinations of single-DOF micro-units. Then, the soft actuator was determined to contain deformationcharacteristics such as extension-contraction, bending, and twisting. Eighteen types of micro-units with basic deformation characteristics were obtained depending on the axis and orientation. Further, the mapping relationship between thecombination of micro-units and the motion characteristics of the soft actuator based on the GF set theory was established.Finally, an active–passive DOF co-structured soft actuator (APCSA) was developed. The graphical approach analyzes theexperimental results, and it can be concluded that active and passive DOFs can coexist in the composite deformation ofthe soft actuator.

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Design and Control of a Bionic Inspection Robot for Suspension Bridge Main Cables

Shengkai Liu, Chao Wang, Xiaoqiang Yuan, Ning Ding

Journal of Bionic Engineering. 2026, 23 (1): 159-174. DOI: 10.1007/s42235-025-00818-1

Abstract ( 23 )

The main cable is the primary load-bearing component of a suspension bridge, continuously exposed to harsh environmental conditions, such as wind and rain, throughout the year. These adverse conditions contribute to varying degreesof degradation and damage to the main cable, necessitating regular inspections to prevent catastrophic failures. Traditional manual inspection methods not only suffer from low efficiency but also pose significant safety risks to personnel.To address these challenges and ensure the safe and effective inspection of suspension bridge main cables, this studyintroduces a novel cooperative climbing robot, designated as Main Cable Robot Version II (CCRobot-M-II), inspired bythe locomotion of the inchworm. The robot employs an alternating opening and closing mechanism of four gripper sets,mimicking the inchworm’s movement to achieve efficient crawling along the suspension bridge handrails. This paperprovides a comprehensive analysis of the structural design, key components, and motion mechanisms of CCRobot-M-II.A detailed force analysis of the robot’s crawling process is also presented, followed by the design of the control systemand the development of an efficient motion control algorithm. Laboratory experiments demonstrate that the robot achievesa positional error of 0–0.64% during crawling, with a maximum average crawling speed of 7.6 m/min. Furthermore, thebiomimetic design enables the robot to overcome obstacles up to 30 mm in height and possess the capability to handlesuspension bridge cables with spans ranging from 740 to 1100 mm. Finally, CCRobot-M-II successfully conducted aninspection of the main cable on a suspension bridge, marking the world’s first successful deployment of a climbing robotfor main cable inspection on a suspension bridge.

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A Bio-inspired Bubble Artificial Muscles and TacTip Perception-driven Tri-legged Robot for Obstacle Avoidance

Chaoqun Xiang, Zhengwei Zhong, Wenqiang Wu, Xiaocong Chen, Yisheng Guan, Tao Zou

Journal of Bionic Engineering. 2026, 23 (1): 175-191. DOI: 10.1007/s42235-025-00801-w

Abstract ( 28 )

Legged robots have considerable potential for traversing unstructured situations; nonetheless, their inflexible frameworksoften constrain adaptability and obstacle negotiation. The study article presents a revolutionary Soft Tri-Legged Robot(STLR) that improves movement and obstacle-avoidance skills by using a bio-inspired pneumatic artificial muscle (BubbleArtificial Muscles) and a bio-inspired tactile sensor (TacTip). The STLR is activated by BAMs, which are flexible, pneumatic-driven actuators that provide fine control over forward, backward, and steering movements. Obstacle identificationand avoidance are facilitated by the TacTip sensor, which delivers tactile input for traversing unstructured terrains. Wedelineate the mechanical features of the BAMs, assess the functionality of the robot’s legs, and elaborate on the incorporation of the tactile sensing system. Experimental results demonstrate that the STLR can effectively achieve multi-directionalflexible movement and obstacle avoidance through a cross-modal perception-actuation mechanism. This study highlightsthe promise of soft robotics for search and rescue, medical aid, and autonomous exploration, while delineating difficultiesand opportunities for future improvements in functionality and efficiency.

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Bioinspired Dual-layered Soft-rigid Gripper for Reduced Damage and Improved Grasping Stability with Real-time Classification

Wenhui Li, Liangsong Huang, Yuxia Li

Journal of Bionic Engineering. 2026, 23 (1): 192-224. DOI: 10.1007/s42235-025-00823-4

Abstract ( 12 )

Soft grippers research is gaining increasing attention for their flexibility. However, the conventional soft gripper primarily focuses on soft fingers, without considering the palm. This makes grasping forces concentrated in the fingertip areas,resulting in objects being prone to damage and instability during handling, especially for delicate items. Additionally,pre-transportation classification faces challenges: tactile methods are complex, visual methods are environment-sensitive,and both struggle with similar objects. To address these problems, inspired by the human hand’s transition between fingergrasp and palm support and the lotus’s hierarchical structure, this paper proposes a dual-layer gripper, named IOSGripper. It features four pneumatic soft fingers and a rotational soft-rigid palm. Through coordinated control of the fingersand palm, it transitions concentrated fingertip squeeze force to distributed palm support force, reducing squeeze force andsqueeze duration. Moreover, it integrates a range sensor and four load cells, enabling stable and accurate measurementsof the object’s height and weight. Furthermore, a classifier is developed based on K-nearest neighbor algorithm, allowingreal-time object classification. Experiments demonstrate that compared to only using soft fingers, the IOSGripper significantly reduces the squeeze force on the objects (with 0 N squeeze force during palm support) and damage on the delicateobject, while improving grasping stability. Its height and weight measurement errors are within 2 mm and 1 g, respectively.And it achieves high accuracy in three test scenarios, including classifying similar objects. This study provides usefulinsights for the design of soft grippers capable of human-like grasping and sorting tasks.

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Semi-supervised Risk Assessment Research for Intelligent Vehicles Inspired by Collective Biological Risk-avoidance Behaviors

Hongyu Hu, Zhonghua Xiong, Zhengyi Li, Tianjun Sun, Rui Ran

Journal of Bionic Engineering. 2026, 23 (1): 225-238. DOI: 10.1007/s42235-025-00800-x

Abstract ( 17 )

To address the critical challenge of risk perception and assessment for autonomous vehicles in dynamic interactive environments, this study proposes a semi-supervised spatiotemporal interaction risk cognition network with attention mechanism (SS-SIRCN), inspired by the behavioral adaptation patterns of biological groups under external threats. First, bythoroughly analyzing the dynamic interaction characteristics exhibited by typical biological collectives when exposed torisk, the study reveals the underlying patterns of trajectory changes influenced by external danger. Then, an attentionbased spatiotemporal risk cognition network is designed to establish a mapping between driving behavior features andpotential driving risks.Finally, a semi-supervised learning framework is employed to enable risk assessment for autonomous vehicles using only a small amount of labeled data.Experimental results on real-world vehicle trajectory datasetsdemonstrate that the proposed method achieves a risk prediction accuracy of 90.76%, outperforming other baseline modelsin performance.

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AP-D: A Thickness Optimization Method of Back Protection Material for Humanoid Robot

Chao Sun, Lianqiang Han, Lingxuan Zhao, Taiping Wu, Qingqing Li, Xuechao Chen, Zhangguo Yu, Qiang Huang

Journal of Bionic Engineering. 2026, 23 (1): 239-256. DOI: 10.1007/s42235-025-00802-9

Abstract ( 10 )

Protective hardware is essential for mitigating damage caused by unavoidable falls in humanoid robots. Despite notableprogress in fall protection hardware, the theoretical foundation for modeling and the feasibility of conducting full-scalefall experiments on robots or their surrogates remain somewhat limited. This paper proposes a method for optimizing thethickness of Expandable Polyethylene (EPE), which is used as back protection for the Chubao humanoid robot, based onsmall-scale impact test data to predict full-scale behavior. The optimal thickness is defined as a balance between compactdesign and protective effectiveness. An equivalent impact model characterized by four parameters: contact area S, massm, fall height h, and cushioning material thickness d is introduced to describe impact conditions. The relationship betweenthe peak impact acceleration ap and material thickness d, which forms the core of the method and gives rise to the nameAP-D, is analyzed through their plotted curves. After introducing three characteristic parameters and two correction factors, the relationship among the aforementioned variables is derived. Subsequently, both the optimal thickness do and itscorresponding peak impact acceleration ao p are predicted via nonlinear and linear regression models. Finally, the accuracyand effectiveness of the theoretically derived optimal thickness are validated on both a dummy and the actual robot. Withthe cushioning material applied, the peak chest acceleration is reduced to 41.57g for the dummy and 32.08g for the robot.

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A Spiderweb-inspired Electrostatic-microstructure Hybrid Adhesive Flexible Net for Space Debris Capture

Ruilin Li, Peng Qiao, Lifu Liu, Zean Yuan, Shuhong Huang, Huijiang Wang, Rui Chen

Journal of Bionic Engineering. 2026, 23 (1): 257-273. DOI: 10.1007/s42235-025-00826-1

Abstract ( 16 )

The net capturing method holds great potential for space debris removal due to its adaptability to the various target shapesand high fault tolerance. However, the capture mechanisms of current rope nets, which rely solely on a passive wrapping mechanism, limit their capacity to capture objects within a specific size range and make it challenging to handleunexpected situations. Inspired by spider webs, which combine wrapping and adhering to capture prey of various sizes,we present a new type of net (envelope diameter: 208.49 mm) for on-orbit capture. This net adopts a spiral symmetricstructure similar to spider webs, incorporates electrostatic-microstructure hybrid adhesives, and increases the maximumcontact area by 38.31%, allowing it to capture debris ranging from fragments smaller than the mesh size (envelope diameter: 2.7 mm - 4.4 mm) to larger objects (envelope diameter: 270 mm), and effectively grasps flexible items (450 mm2),planar items (350 mm2) and three-dimensional items (160 mm3). Moreover, to validate the net’s capability for wrappingand adhesion, simulations and experiments are demonstrated that this dual capture method can effectively handle varioustargets.

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Deep Learning in Electromyography Signal-based Lower Limb Angle Prediction and Activity Classification

Gundala Jhansi Rani, Mohammad Farukh Hashmi

Journal of Bionic Engineering. 2026, 23 (1): 274-290. DOI: 10.1007/s42235-025-00813-6

Abstract ( 14 )

This research presents a Human Lower Limb Activity Recognition (HLLAR) system that identifies specific activities andpredicts the angles of the knees simultaneously, based on the EMG signals. The HLLAR systems streamlines the researchon the lower limb activities. The HILLAR model includes Discrete Hermite Wavelets Transform-based Synchrosqueezing(DHWTS), Deep Two-Layer Multiscale Convolutional Neural Network (DTLMCNN), and Generalized Regression NeuralNetwork (GRNN) as feature extraction, activity recognition, and knee angle prediction respectively. Electromyographysignal-based automatic lower limb activity detection is crucial to rehabilitation and human movement analysis. Yet severalof these methods face issues in feature extraction in complex data, overlapping signals, extraction of crucial parameters,and adaptation constraints. This research aims classify lower limb activities and predict knee joint angles from electromyography signals using HILLAR model. The model is validated on two datasets, comprising 26 subjects performing threeclasses of activities: walking, standing, and sitting. The proposed model obtained a classification accuracy of 99.95%,along with significant achievements in precision (99.93%), recall (99.91%), and F1-score (99.93%). The generalizedregression neural network predicted angles of the knee joint with a root mean squared error of 1.25%. Robustness isdemonstrated through consistent results in five-fold cross-validation and statistical significance testing (p-value=0.004,McNemar’s test). Additionally, the proposed model showed superior performance over baseline methods by reducing errorrates by 18% and decreasing processing time to 0.98 s.

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Transcutaneous Electrical Nerve Stimulation at Proximal Brachial Plexus to Evoke Tactile Sensation in the Hand

Lizhi Pan, Jiapeng Lun, Zhihao Ren, Haifeng Zhao, Ruinan Mu, Jianmin Li

Journal of Bionic Engineering. 2026, 23 (1): 291-301. DOI: 10.1007/s42235-025-00810-9

Abstract ( 15 )

Tactile feedback is critical for human interaction with external information. Similarly, tactile feedback can enrich theuser’s sensations when using prosthesis. To explore a potential scheme for tactile feedback, this study applied a non-invasive Transcutaneous Electrical Nerve Stimulation (TENS) to elicit tactile sensations in the hand, which involved mediannerve, ulnar nerve, and radial nerve. Ten able-bodied subjects (8 males, 2 females) were recruited to participate in thestudy. An array of 4 × 2 electrodes was positioned on the medial aspect of the brachii muscle’s short head in the upperarm, which is in proximity to the median nerve, ulnar nerve, and radial nerve. Different electrode pairs were randomlyselected to elicit distinct sensations at various positions on the hand, and the subjects reported the sensory areas. Then,the sensory areas and sensory thresholds were confirmed through psychophysical methods. According to the experimentalresults, tactile sensations were elicited at different locations on the subjects’ hand through TENS of different electrodepairs. All subjects reported extensive and detailed sensory areas in the fingers, palm, and dorsum, corresponding to thesensory innervation areas of different nerves. The study effectively demonstrated the ability of TENS in evoking tactilefeedback in the hand, paving the way for future optimization and development of prosthetic hands.

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Tactile Sensor for Subcutaneous Vocal Organ Vibrations Inspired by Otolith Cilia

Chang Ge

Journal of Bionic Engineering. 2026, 23 (1): 302-310. DOI: 10.1007/s42235-025-00811-8

Abstract ( 15 )

Tactile sensing of subcutaneous organ vibrations provides a promising route toward human–machine interfaces and wearable diagnostics, particularly for voice rehabilitation and silent-speech communication. Here, we present a bioinspiredpiezoelectric vibration sensor that mimics the graded stiffness and stress-based transduction mechanism of otolithic ciliain the human vestibular system. The device consists of a trapezoidal cantilever array with tip inertial masses, fabricatedthrough a hybrid stereolithography 3D printing and laser micromachining process for rapid prototyping without cleanroomfacilities. Finite-element modeling and experimental measurements demonstrate a fundamental resonance near 1.2 kHz,a 5% flat-bandwidth of 350 Hz, and an in-band charge sensitivity of 3.17 pC/g. A wearable proof-of-concept test furtherverifies the sensor’s ability to reproducibly distinguish phoneme-specific vibration patterns in both time and frequencydomains. This work establishes a foundation for bioinspired tactile sensing front-ends in wearable voice interfaces andother intelligent diagnostic systems integrated with machine-learning algorithms.

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Bionic Design of Copper-doped Mesoporous Silica with Enhanced Hydrogel Mechanical Properties and its Promising Application in Bone-defect Regeneration

Han Yang, Ya Fang, Jiaming Cui, Xueheng Sun, Tianchang Wang, Liang Feng, Hao Yang, Changru Zhang, Bide Xu, Xiaojun Zhou, Jinwu Wang, Xudong Wang

Journal of Bionic Engineering. 2026, 23 (1): 311-325. DOI: 10.1007/s42235-025-00821-6

Abstract ( 15 )

Treating bone defects complicated by bacterial infections remains a significant clinical challenge. Drawing inspirationfrom the human body’s bone repair mechanisms, the use of biomimetic methods to design tissue engineering scaffoldsis of great significance for bone repair. This study synthesized copper (Cu)-doped mesoporous silica nanoparticles (Cu@MSN) modified with hydroxyethyl methacrylate to obtain methacrylated Cu@MSN (Cu@MSNMA). Furtheremore, biomimetic nanocomposite hydrogels were prepared by adding Cu@MSNMA to a GelMA/gelatin solution. This hydrogelachieves multi-modal bone tissue biomimicry: (i) GelMA/gelatin mimics the matrix components in bone ECM, ensuringbiocompatibility while promoting cellular behavior (such as adhesion, proliferation, and differentiation); (ii) GelMA/gelatin and the crosslinking sites introduced by Cu@MSNMA form a stable porous network structure, achieving structuraland mechanical biomimicry to provide necessary support for bone defects; (iii) The elemental biomimicry of Si and Cu inCu@MSNMA achieves efficient osteogenic induction. The effect of different proportions of Cu@MSNMA on the physical properties of the composite hydrogels was investigated to determine the optimal proportion. The results indicated thatthe mechanical properties of hydrogel were enhanced with the increasing Cu@MSNMA mass ratio. Notably, 5% NPs/GelMA/gelatin hydrogel exhibited excellent mechanical property compared to the GelMA/gelatin hydrogel. In vitro andvivo cellular experiments demonstrated a significant enhancement in antibacterial and osteogenic induction with Cu@MSNMA addition. In conclusion, the proposed nanocomposite hydrogel with biomimetic components and ion-regulatingproperties can serve as a multifunctional scaffold, offering antimicrobial properties for infected bone regeneration, andguide for future research in bone regeneration and three-dimensional printing.

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Construction of Bionic Non-Smooth Surface of Cu-Based Friction Materials Based on Finite Element Method

Lekai Li, Juxiang Zhu, Zhaohua Yao, Mengting Xing, Yitong Tian, Ma Yunhai

Journal of Bionic Engineering. 2026, 23 (1): 326-340. DOI: 10.1007/s42235-025-00815-4

Abstract ( 13 )

To solve the problem of abnormal abrasion of Cu-Based Friction Materials (CBFMs), Bionic Non-Smooth Surface (BNS)on friction surface of CBFMs was constructed based on bionic principles, and the optimal bionic prototype was selectedby Finite Element Method (FEM). In addition, the bionic parameters were optimized by Response Surface Method (RSM).Samples holding BNS were prepared by Laser Processing, tribological properties were tested by a Friction and Wear Testerand worn surface morphology was characterized by a Scanning Electron Microscope (SEM). The results showed that BNSon friction surface could regulate the stress distribution and alleviate the peak stress. Among all samples, the coupledtexture of pit-hexagonal got the minimum peak stress. During braking, bionic texture could also collect wear debris orchange the motion forms from sliding to rotation, which can reduce abnormal abrasion. The wear rate was reduced by19.31%. The results in this paper can provide a new idea for enhancing the tribological properties of CBFMs, and canalso lay the foundation for further research of bionic tribology.

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Marine Shell-Inspired Laser Surface Texturing: Characterizing the Surface Properties of Co-Based Alloy

Şefika Kasman, Sertan Ozan

Journal of Bionic Engineering. 2026, 23 (1): 341-353. DOI: 10.1007/s42235-025-00712-w

Abstract ( 11 )

This study investigated surface roughness, the wettability behavior, and surface energy of Co-based alloy specimenstextured using the biomimetic Laser Surface Texturing (LST) method. The surface texture was inspired by the patternsfound on marine shells. The impacts of the parameters on wettability, Surface Free Energy (SFE), surface topography, andtexture roughness generated by the laser beam tracking a spiral path were investigated. Reducing spiral pitch producesmore complicated and chaotic surface patterns. Most surfaces are hydrophobic, and surface roughness and topographyinfluence the Contact Angle (CA). Topography and roughness were affected by frequency and scanning speed; a decreasein scanning speed and frequency generated more chaotic and irregular surface textures. With general factorial analysis andAnalysis of Variance (ANOVA), our statistical study reveals that accounting for 88% of the influence, the scanning speedis the primary factor influencing surface roughness. On the other hand, the spiral pitch is essential for defining the structural features of the surface, even if it less influences roughness. The SFE of laser-textured CoCr28Mo alloy specimenswas optimizable within the range of 14–32 mN/m. The relevant findings offer valuable insights into optimizing LST forthe specific surface properties of the Co-based alloy.

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Design and Optimization of Bio-inspired Herringbone Textured Bearing for Turbocharger Using Artificial Intelligence Technique

Hara Prakash Mishra, Suraj Kumar Behera

Journal of Bionic Engineering. 2026, 23 (1): 354-379. DOI: 10.1007/s42235-025-00807-4

Abstract ( 13 )

Floating ring bearings are widely used in high-speed turbomachinery such as turbochargers and turbogenerators. Researchers have recently explored various surface texturing strategies on the inner surface of floating rings to enhance bearingperformance. In this study, the herring patterns are textured on the inner surface of the floating ring. This pattern is inspiredby the secondary flight feathers of the Indian pigeon, which aid the bird in reducing viscous drag during flight. The resulting Herringbone Textured Floating Ring Bearing (HTFRB) is investigated for its potential application in locomotive turbochargers. The HTFRB is numerically modeled using the Reynolds equation to evaluate the bearing’s pressure distributionand static characteristics, including load-carrying capacity, power loss, and side leakage. Dynamic characteristics aredetermined by solving the zeroth- and first-order perturbed Reynolds equation. A Sobol sensitivity analysis is conductedto quantify the influence of groove parameters — helix angle, groove depth, groove width ratio, and number of grooves— on bearing performance metrics. An artificial intelligence-based optimization framework, integrating artificial neuralnetworks and adaptive neuro-fuzzy inference systems, is developed to maximize load carrying capacity while minimizing power loss, side leakage, and friction coefficient. The optimized texture parameters obtained from this framework areemployed to validate the ANN model and evaluate the static and dynamic characteristics of the HTFRB. The dynamiccoefficients of the HTFRB are further employed to evaluate the stability and robustness of the turbocharger rotor-HTFRBsystem. This study underscores the potential of combining bio-inspired texture design with numerical modeling and AIbased optimization to develop high-performance HTFRB.

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Pain Induced by Friction Based on fMRI and EEG

Shousheng Zhang, Wei Tang, Yangyang Xia, Xingxing Fang, Zhouqing Xu

Journal of Bionic Engineering. 2026, 23 (1): 380-393. DOI: 10.1007/s42235-025-00808-3

Abstract ( 12 )

Pain, as a common symptom, seriously affects the patient’s health. The aim of this work was to study the physiologicalresponses of the brain and identify the features of Electroencephalography (EEG) signals related to friction pain. Theresults showed that the primary brain activation evoked by friction pain was located in the Prefrontal Cortex (PFC). Theactivation area decreased, and the negative activation intensity in the PFC region increased with increasing intensity ofpain. The inhibitory interactions between different brain regions, especially between the PFC and primary somatosensorycortex (SI) regions were enhanced, and excitatory-inhibitory connections between the medial and lateral pain pathwayswere balanced during pain perception. The percentage power spectral density of the α rhythm (Dα), dominant singularitystrength (αpeak) and longest vertical line (Vmax) of EEG signals induced by pain significantly decreased, and the percentage power spectral density of the β rhythm (Dβ) significantly increased. The combination of multiple features of Dα, Dβ,αpeak and Vmax could significantly improve the average recognition accuracy of different pain states. This study elucidatedthe neural processing mechanisms of friction-induced pain, and EEG features associated with friction pain were extractedand recognized. It was helpful to study the brain feedback mechanisms of pain and control signals of Brain-ComputerInterface (BCI) system related to pain.

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Engineering a Bilayered Scaffold as a Potential Cardiac Patch: From Scaffold Design to In Vitro Assessment

Adile Yuruk, Ayhan Duzler, Sevil Dincer Isoglu, Ismail Alper Isoglu

Journal of Bionic Engineering. 2026, 23 (1): 394-415. DOI: Engineering a Bilayered Scaffold as a Potential Cardiac Patch: From Scaffold Design to In Vitro Asse

Abstract ( 12 )

In this study, we developed a novel bilayered scaffold consisting of a bottom layer composed of the DecellularizedBovine Pericardium (DP) coated with Polyaniline Nanoparticles (PANINPs) and a top layer made of an electrospunPoly(lactic-co-glycolic acid)/Gelatin (PLGA/Gel) membrane incorporated with Vascular Endothelial Growth Factor (VEGF) and hawthorn extract. Functionally, the DP supplies native Extracellular Matrix (ECM) componentsand mechanical support, while PANINPs provide conductivity. The electrospun PLGA/Gel layer mimics fibrousECM. It incorporates bioactives, with VEGF promoting pro-angiogenic stimulation and hawthorn extract enhancing anticoagulant activity, as well as increasing surface hydrophilicity. The tissue adhesive ensures the interfacialintegrity between the two layers. Decellularization efficiency was confirmed histologically using 4′,6-diamidino-2-phenylindole (DAPI) and Hematoxylin–Eosin (H&E) staining. The DP exhibited a DNA content of 115.9 ± 47.8ng/mg DNA, compared to 982.88 ± 395.42 ng/mg in Native Pericardium (NP). The PANINPs had an average particle size of 104.94 ± 13.7 nm. The conductivity of PANINPs-coated decellularized pericardium was measured to be9.093 ± 8.6 × 10- 4 S/cm using the four-point probe method. PLGA/Gel membranes containing hawthorn extract (1%,5%, 10%, and 15% w/v) and VEGF (0.1 μg/mL, 0.5 μg/mL, and 1 μg/mL) were fabricated by electrospinning, resulting in fiber diameters between 850 and 1200 nm and pore sizes between 14 and 20 μm. The anticoagulant efficiencyof the membranes containing hawthorn extract reached 430 s in the Activated Partial Thromboplastin Time Assay(aPTT). Mechanical testing revealed a tensile strength of 22.70 ± 6.33 MPa, an elongation of 53.58 ± 10.63%, andYoung’s modulus of 0.67 ± 0.10 MPa. The scaffold also exhibited over 91% cell viability and excellent cardiomyocyte adhesion. The hemolysis ratio was determined to be 0.421 ± 0.191%, which confirms its blood compatibility.Our results indicate that the proposed bilayered scaffold can be a promising candidate for cardiac patch applications.

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Hydrogen Ion Escape from Water’s Body-Centered Cubic Lattice for Modelling IPMC’ Electromechanical Behavior

Dehai Zhang, Chenyu Xu, Jingxin Zhou, Zhiqiang Zhang, Zhimin Xu, Yihao Li, Dongjie Guo

Journal of Bionic Engineering. 2026, 23 (1): 416-430. DOI: 10.1007/s42235-025-00809-2

Abstract ( 12 )

Ion-exchange Polymer-Metal Composites (IPMCs) gain huge attentions due to large deformation, rapid electromechanicalresponse, and high energy conversion efficiency. Deflection of IPMC arises from the volumetric swelling effect inducedby the concentration gradient of hydrated cations between the two electrodes, thus the volume of hydrated cation determines the motion magnitude and direction of IPMC. H ion is one of the most commonly used driving cations for IPMC.However, due to its unique characteristics, particularly the inability to accurately quantify its hydration volume, existingliteratures primarily focus on the physical driving models for metallic cations, i.e., Na+, no driving model for the H ionis reported until now. This paper proposes a novel model of H ion escape from the water’s body-centered cubic lattice tocount the hydration volume. Number (n) of water molecules carried by the H ion is solved by combining the LennardJones potential energy function with Maxwell’s velocity distribution. The specific n value is equivalent to 4.04 for the Hion inside Nafion electrolyte under a 3.0 V DC electric field. Substituting it into the classic Friction Model (proposed byTadokoro et al. at 2000), actuation behaviors of H ion driven IPMC were therefore achieved through Matlab calculationsand Abaqus simulations. The calculated results of dynamic displacement and force highly match to the experimental dataform the Nafion IPMC actuator driven by same electric field, showing a highly reliability of the established escape model.

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Gekko Japonicus Algorithm: A Novel Nature-inspired Algorithm for Engineering Problems and Path Planning

Ke Zhang, Hongyang Zhao, Xingdong Li, Chengjin Fu, Jing Jin

Journal of Bionic Engineering. 2026, 23 (1): 431-471. DOI: 10.1007/s42235-025-00805-6

Abstract ( 16 )

This paper introduces a novel nature-inspired metaheuristic algorithm called the Gekko japonicus algorithm. The algorithm draws inspiration mainly from the predation strategies and survival behaviors of the Gekko japonicus. The mathematical model is developed by simulating various biological behaviors of the Gekko japonicus, such as hybrid locomotion patterns, directional olfactory guidance, implicit group advantage tendencies, and the tail autotomy mechanism.By integrating multi-stage mutual constraints and dynamically adjusting parameters, GJA maintains an optimal balancebetween global exploration and local exploitation, thereby effectively solving complex optimization problems. To assessthe performance of GJA, comparative analyses were performed against fourteen state-of-the-art metaheuristic algorithmsusing the CEC2017 and CEC2022 benchmark test sets. Additionally, a Friedman test was performed on the experimental results to assess the statistical significance of differences between various algorithms. And GJA was evaluated usingmultiple qualitative indicators, further confirming its superiority in exploration and exploitation. Finally, GJA was utilizedto solve four engineering optimization problems and further implemented in robotic path planning to verify its practicalapplicability. Experimental results indicate that, compared to other high-performance algorithms, GJA demonstrates exceptional performance as a powerful optimization algorithm in complex optimization problems. We make the code publiclyavailable at: https://github.com/zhy1109/Gekko-japonicusalgorithm

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Fatigue Detection with Multimodal Physiological Signals via Uncertainty-Aware Deep Transfer Learning

Kourosh Kakhi, Hamzeh Asgharnezhad, Abbas Khosravi, Roohallah Alizadehsani, U. Rajendra Acharya

Journal of Bionic Engineering. 2026, 23 (1): 472-487. DOI: 10.1007/s42235-025-00827-0

Abstract ( 10 )

Accurate detection of driver fatigue is essential for improving road safety. This study investigates the effectiveness ofusing multimodal physiological signals for fatigue detection while incorporating uncertainty quantification to enhancethe reliability of predictions. Physiological signals, including Electrocardiogram (ECG), Galvanic Skin Response (GSR),and Electroencephalogram (EEG), were transformed into image representations and analyzed using pretrained deep neural networks. The extracted features were classified through a feedforward neural network, and prediction reliabilitywas assessed using uncertainty quantification techniques such as Monte Carlo Dropout (MCD), model ensembles, andcombined approaches. Evaluation metrics included standard measures (sensitivity, specificity, precision, and accuracy)along with uncertainty-aware metrics such as uncertainty sensitivity and uncertainty precision. Across all evaluations,ECG-based models consistently demonstrated strong performance. The findings indicate that combining multimodal physiological signals, Transfer Learning (TL), and uncertainty quantification can significantly improve both the accuracy andtrustworthiness of fatigue detection systems. This approach supports the development of more reliable driver assistancetechnologies aimed at preventing fatigue-related accidents.

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An Improved Machine Learning Model for Screening and Activity Prediction of Receptor Tyrosine Kinase

Huanghui Xia, Huangzhi Xia, Jianzhong Huang

Journal of Bionic Engineering. 2026, 23 (1): 488-523. DOI: 10.1007/s42235-025-00816-3

Abstract ( 12 )

Aberrant activation of Receptor Tyrosine Kinases (RTKs) is a well-established trigger of tumorigenesis, and the overuse of RTK inhibitors often leads to drug resistance and tumor recurrence. While current Drug-Target Interaction (DTI)prediction methods (including those based on heterogeneous information networks) have shown promise, they remainlimited in their ability to fully capture the nature of DTIs and often lack interpretability. To overcome these limitations,this study introduces a novel hybrid optimization model termed MDBO-RF, which integrates a Modified Dung BeetleOptimizer (MDBO) with Random Forest (RF). The key innovation lies in the enhancement of the DBO algorithm througha quaternion-based learning mechanism and the Cauchy mutation strategy, specifically designed to overcome the slowconvergence and susceptibility to local optima that plague traditional metaheuristic algorithms used for hyperparametertuning. The model leverages commonly used molecular descriptors to enhance the prediction of Tyrosine Kinase (TK)inhibitory activity and enable efficient compound screening. Our results demonstrate that MDBO-RF achieves a 3.41%increase in prediction accuracy compared to the standard RF model and outperforms several other contemporary machinelearning approaches. The model effectively streamlines the RTK inhibitor screening process by improving predictionaccuracy in multi-target competitive binding scenarios and reducing false-positive screening due to off-target effects. Thiswork underscores the value of hybrid optimization strategies in bioinformatics and provides a robust, interpretable toolfor accelerating drug discovery.

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Collaborative Area Coverage Method for UAV Swarm Under Complex Boundary Conditions: A Region Partitioning Approach

Jiabin Yu, Haocun Wang, Bingyi Wang, Yang Lu, Xin Zhang, Qian Sun, Zhiyao Zhao

Journal of Bionic Engineering. 2026, 23 (1): 524-548. DOI: 10.1007/s42235-025-00817-2

Abstract ( 15 )

Unmanned aerial vehicles (UAVs) are widely utilized in area coverage tasks due to their flexibility and efficiency in geographic information acquisition. However, complex boundary conditions in actual water area maps often reduce coverageefficiency. To address this issue, this paper proposes a map preprocessing algorithm that linearizes boundary lines andprocesses concave areas into concave polygons, followed by gridding the map. Additionally, a collaborative area coveragemethod for UAV swarms is introduced based on region partitioning, which considers the comprehensive cost of energyconsumption and time. An improved Hungarian algorithm is utilized for region partitioning, and a Dubins-A*-based plowing area full coverage path planning method is proposed to achieve path smoothing and collaborative coverage of eachpartition. Two sets of simulation experiments are conducted. The first experiment verifies the effectiveness of the mappreprocessing algorithm, and the second compares the proposed collaborative area coverage algorithm with other methods,demonstrating its performance advantages.

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