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Multifunctional Liposomes: Smart Nanomaterials for Enhanced Photodynamic Therapy
Development of Variable Elastic Band with Adjustable Elasticities for Semi-Passive Exosuits
Regenerative Strategies for Vocal Fold Repair Using Injectable Materials

Journal Description

Biomimetics

Biomimetics is an international, peer-reviewed, open access journal on biomimicry and bionics, published monthly online by MDPI. The International Society of Bionic Engineering (ISBE) is affiliated with Biomimetics.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, PMC, Ei Compendex, CAPlus / SciFinder, and other databases.
Journal Rank: JCR - Q1 (Engineering, Multidisciplinary) / CiteScore - Q2 (Biomedical Engineering)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.1 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the first half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 3.9 (2024); 5-Year Impact Factor: 4.0 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2313-7673

Latest Articles

11 pages, 1206 KB

Open AccessArticle

Designing Biomimetic Learning Environments for Animal Welfare Education: A Gamified Approach

by Ebru Emsen, Bahadir Baran Odevci, Muzeyyen Kutluca Korkmaz, Fatma Alshamsi and Alyaziya Alkaabi

Biomimetics 2025, 10(11), 769; https://doi.org/10.3390/biomimetics10110769 (registering DOI) - 13 Nov 2025

Animal welfare education requires pedagogical models that bridge conceptual knowledge with practice. This study presents GamifyWELL, a biomimetic, gamified learning environment for students, farmers, and veterinary technicians. Grounded in ecological principles of adaptation, diversification, and niche specialization, the design emulates how living systems [...] Read more.

Animal welfare education requires pedagogical models that bridge conceptual knowledge with practice. This study presents GamifyWELL, a biomimetic, gamified learning environment for students, farmers, and veterinary technicians. Grounded in ecological principles of adaptation, diversification, and niche specialization, the design emulates how living systems evolve through feedback and cooperation. These principles were translated into an instructional model that integrates a core pathway (Pre-Test, Levels 1–4, Post-Test) with optional enrichment tasks and a role-specific Reward Marketplace. Question formats are constant across levels (MCQ, image-based, video-based) while cognitive difficulty increases, culminating in Positive Welfare scenarios. We describe the learning design structure and report preliminary implementation observations using a mixed-methods evaluation plan (pre/post knowledge assessments and engagement indicators). Results from early deployment indicate strong usability and engagement, with high voluntary uptake of enrichment tasks and positive learner feedback on role-tailored rewards; full empirical testing is in progress. Findings support the feasibility and pedagogical promise of biomimetic gamification to enhance knowledge, motivation, and intended practice in animal welfare education. GamifyWELL offers a replicable framework for nature-inspired instructional design that can be extended to allied sustainability domains. Full article

(This article belongs to the Special Issue Biologically-Inspired Product Development)

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40 pages, 5680 KB

Open AccessReview

Recent Advances in the Applications of Biomaterials in Ovarian Cancer

by A M U B Mahfuz, Amol V. Janorkar, Rodney P. Rocconi and Yuanyuan Duan

Biomimetics 2025, 10(11), 768; https://doi.org/10.3390/biomimetics10110768 (registering DOI) - 12 Nov 2025

Among the gynecological cancers, ovarian cancer is the most fatal. Despite advancements in modern medicine, the survival rate is abysmally low among ovarian cancer patients. Ovarian cancer poses several unique challenges, like late diagnosis due to the initial vagueness of the symptoms and [...] Read more.

Among the gynecological cancers, ovarian cancer is the most fatal. Despite advancements in modern medicine, the survival rate is abysmally low among ovarian cancer patients. Ovarian cancer poses several unique challenges, like late diagnosis due to the initial vagueness of the symptoms and lack of effective screening protocols. Recently, biomaterials have been explored and utilized extensively for the diagnosis, treatment, and screening of ovarian malignancies. Biomaterials can help bypass the obstacles of traditional chemotherapy and enhance imaging capabilities. They are also indispensable for next-generation biosensors and tumor organoids. Biomaterials inspired by biomimetic strategies that replicate the structural, chemical, and functional properties of natural biological systems have proven to have better functionalities. While numerous review articles have examined biomaterials in oncology, there is a lack of reviews dedicated specifically to their applications in ovarian cancer. This review aims to address this critical gap by providing the first comprehensive overview of the current biomaterial research on ovarian cancer and highlighting key challenges, opportunities, and future directions in this evolving interdisciplinary field. Full article

(This article belongs to the Special Issue Functional Biomimetic Materials and Devices for Biomedical Applications: 4th Edition)

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32 pages, 12726 KB

Open AccessArticle

Arctic Puffin Optimization Algorithm Integrating Opposition-Based Learning and Differential Evolution with Engineering Applications

by Yating Zhu, Tinghua Wang and Ning Zhao

Biomimetics 2025, 10(11), 767; https://doi.org/10.3390/biomimetics10110767 (registering DOI) - 12 Nov 2025

The Arctic Puffin Optimization (APO) algorithm, proposed in 2024, is a swarm intelligence optimization. Similar to other swarm intelligence optimization algorithms, it suffers from issues such as slow convergence in the early stage, being easy to fall into local optima, and insufficient balance [...] Read more.

The Arctic Puffin Optimization (APO) algorithm, proposed in 2024, is a swarm intelligence optimization. Similar to other swarm intelligence optimization algorithms, it suffers from issues such as slow convergence in the early stage, being easy to fall into local optima, and insufficient balance between exploration and exploitation. To address these limitations, an improved APO (IAPO) algorithm incorporating multiple strategies is proposed. Firstly, a mirror opposition-based learning mechanism is introduced to expand the search scope, improving the efficiency of searching for the optimal solution, which enhances the algorithm’s convergence accuracy and optimization speed. Secondly, a dynamic differential evolution strategy with adaptive parameters is integrated to improve the algorithm’s ability to escape local optima and achieve precise optimization. Comparative experimental results between IAPO and eight other optimization algorithms on 20 benchmark functions, as well as CEC2019 and CEC2022 test functions, show that IAPO achieves higher accuracy, faster convergence, and superior robustness, securing first-place average rankings of 1.35, 1.30, 1.25, and 1.08 on the 20 benchmark functions, CEC 2019, 10- and 20-dimensional CEC 2022 test sets, respectively. Finally, simulation experiments were conducted on three engineering optimization design problems. IAPO achieved optimal values of 5.2559 × 10⁻¹, 1.09 × 10³, and 1.49 × 10⁴ for these engineering problems, ranking first in all cases. This further validates the effectiveness and practicality of the IAPO algorithm. Full article

(This article belongs to the Section Biological Optimisation and Management)

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37 pages, 7430 KB

Open AccessArticle

An Improved Crested Porcupine Optimization Algorithm Incorporating Butterfly Search and Triangular Walk Strategies

by Binhe Chen, Yaodan Chen, Li Cao, Changzu Chen and Yinggao Yue

Biomimetics 2025, 10(11), 766; https://doi.org/10.3390/biomimetics10110766 (registering DOI) - 12 Nov 2025

The Crested Porcupine Optimizer (CPO), as a newly emerging swarm intelligence algorithm, demonstrates advantages in balancing global exploration and local exploitation but still suffers from limitations in convergence speed and local exploitation precision. To address these issues, this paper proposes an enhanced variant, [...] Read more.

The Crested Porcupine Optimizer (CPO), as a newly emerging swarm intelligence algorithm, demonstrates advantages in balancing global exploration and local exploitation but still suffers from limitations in convergence speed and local exploitation precision. To address these issues, this paper proposes an enhanced variant, the Butterfly Search and Triangular Walk Crested Porcupine Optimizer (BTCPO). The method achieves a dynamic balance between exploration and exploitation by combining triangular walk to boost local exploitation and butterfly search to increase global variety. Experimental results on 23 classical benchmark functions and the CEC2021 test suite show that BTCPO outperforms CPO as well as seven state-of-the-art algorithms (DBO, HBA, BKA, HHO, GWO, GOOSE, and SSA). Specifically, BTCPO achieves the best performance on more than 80% of CEC2021 functions, with convergence speed improved by approximately 25% compared to CPO. Furthermore, BTCPO exhibits higher efficiency and usefulness in engineering design problems such as trusses, welded beams, and cantilever beams. These findings demonstrate the theoretical and practical advantages of BTCPO, making it a workable approach to solving difficult optimization problems. Full article

(This article belongs to the Special Issue Advances in Biological and Bio-Inspired Algorithms)

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43 pages, 4478 KB

Open AccessArticle

MEIAO: A Multi-Strategy Enhanced Information Acquisition Optimizer for Global Optimization and UAV Path Planning

by Yongzheng Chen, Ruibo Sun, Jun Zheng, Yuanyuan Shao and Haoxiang Zhou

Biomimetics 2025, 10(11), 765; https://doi.org/10.3390/biomimetics10110765 (registering DOI) - 12 Nov 2025

With the expansion of unmanned aerial vehicles (UAVs) into complex three-dimensional (3D) terrains for reconnaissance, rescue, and related missions, traditional path planning methods struggle to meet multi-constraint and multi-objective requirements. Existing swarm intelligence algorithms, limited by the “no free lunch” theorem, also face [...] Read more.

With the expansion of unmanned aerial vehicles (UAVs) into complex three-dimensional (3D) terrains for reconnaissance, rescue, and related missions, traditional path planning methods struggle to meet multi-constraint and multi-objective requirements. Existing swarm intelligence algorithms, limited by the “no free lunch” theorem, also face challenges when the standard Information Acquisition Optimizer (IAO) is applied to such tasks, including low exploration efficiency in high-dimensional search spaces, rapid loss of population diversity, and improper boundary handling. To address these issues, this study proposes a Multi-Strategy Enhanced Information Acquisition Optimizer (MEIAO). First, a Levy Flight-based information collection strategy is introduced to leverage its combination of short-range local searches and long-distance jumps, thereby broadening global exploration. Second, an adaptive differential evolution operator is designed to dynamically balance exploration and exploitation via a variable mutation factor, while crossover and greedy selection mechanisms help maintain population diversity. Third, a globally guided boundary handling strategy adjusts out-of-bound dimensions to feasible regions, preventing the generation of low-quality paths. Performance was evaluated on the CEC2017 (dim = 30/50/100) and CEC2022 (dim = 10/20) benchmark suites by comparing MEIAO with eight algorithms, including VPPSO and IAO. Based on the mean, standard deviation, Friedman mean rank, and Wilcoxon rank-sum tests, MEIAO demonstrated superior performance in local exploitation of unimodal functions, global exploration of multimodal functions, and complex adaptation on composite functions while exhibiting stronger robustness. Finally, MEIAO was applied to 3D mountainous UAV path planning, where a cost model considering path length, altitude standard deviation, and turning smoothness was established. The experimental results show that MEIAO achieved an average path cost of 253.9190, a 25.7% reduction compared to IAO (341.9324), with the lowest standard deviation (60.6960) among all algorithms. The generated paths were smoother, collision-free, and achieved faster convergence, offering an efficient and reliable solution for UAV operations in complex environments. Full article

(This article belongs to the Special Issue Biomimetics and Bioinspired Artificial Intelligence Applications: 2nd Edition)

28 pages, 7618 KB

Open AccessArticle

Design Methodology for a Backrest-Lifting Nursing Bed Based on Dual-Channel Behavior–Emotion Data Fusion and Biomechanical Simulation: A Human-Centered and Data-Driven Optimization Approach

by Xiaochan Wang, Cheolhee Cho, Peng Zhang, Shuyuan Ge and Liyun Wang

Biomimetics 2025, 10(11), 764; https://doi.org/10.3390/biomimetics10110764 (registering DOI) - 12 Nov 2025

Population aging and rising rehabilitation demands highlight the need for advanced assistive devices to improve mobility in individuals with motor impairments. Existing back-support lifting nursing beds often lack adequate human–machine adaptability, safety, and emotional consideration. This study presents a human-centered, data-driven optimization pipeline [...] Read more.

Population aging and rising rehabilitation demands highlight the need for advanced assistive devices to improve mobility in individuals with motor impairments. Existing back-support lifting nursing beds often lack adequate human–machine adaptability, safety, and emotional consideration. This study presents a human-centered, data-driven optimization pipeline that integrates behavior–emotion dual recognition, simulation verification, and parameter optimization with user demand mining, biomechanical simulation, and sustainable practices. The design utilizes GreenAI, focusing on low-power algorithms and eco-friendly materials, ensuring energy-efficient AI models and reducing the environmental footprint. A dual-channel data fusion method was developed, combining movement parameters from sit-to-lie transitions with emotional needs extracted from e-commerce reviews using the Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA) models. The fuzzy Kano model prioritized design objectives, identifying multi-position adjustment, joint protection, armrest optimization, and interaction comfort as key targets. An AnyBody-based human–device model quantified muscle (erector spinae, rectus abdominis, trapezius) and hip joint loads during posture changes. Simulations verified the design’s ability to improve load distribution, reduce joint stress, and enhance comfort. The optimized nursing bed demonstrated improved adaptability across user profiles while maintaining functional reliability. This framework offers a scalable paradigm for intelligent rehabilitation equipment design, with potential extension toward AI-driven adaptive control and clinical validation. This sustainable methodology ensures that the device not only meets rehabilitation goals but also contributes to a more environmentally responsible healthcare solution, aligning with global sustainability efforts. Full article

(This article belongs to the Special Issue Advanced Intelligent Systems and Biomimetics)

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23 pages, 808 KB

Open AccessArticle

ACGA a Novel Biomimetic Hybrid Optimisation Algorithm Based on a HP Protein Visualizer: An Interpretable Web-Based Tool for 3D Protein Folding Based on the Hydrophobic-Polar Model

by Ioan Sima, Daniela-Maria Cristea, Laszlo Barna Iantovics and Virginia Niculescu

Biomimetics 2025, 10(11), 763; https://doi.org/10.3390/biomimetics10110763 - 12 Nov 2025

In this study, we used the hydrophobic-polar (HP) two-dimensional square and three-dimensional cubic lattice models for the problem of protein structure prediction (PSP). This kind of lattice reduces computational time and calculations, the conformational space from

9^{n}

to

3^{n - 2}

[...] Read more.

In this study, we used the hydrophobic-polar (HP) two-dimensional square and three-dimensional cubic lattice models for the problem of protein structure prediction (PSP). This kind of lattice reduces computational time and calculations, the conformational space from

9^{n}

to

3^{n - 2}

for the 2D square lattice and

5^{n - 2}

for the 3D cubic lattice. Even within this context, it remains challenging for genetic algorithms or other metaheuristics to identify the optimal solutions. The contributions of the paper consist of: (1) implementation of a high-performing novel genetic algorithm (GA); instead of considering only the self-avoiding walk (SAW) conformations approached in other work, we decided to allow any conformation to appear in the population at all stages of the proposed all conformations biomimetic genetic algorithm (ACGA). This increases the probability of achieving good conformations (self avoiding walk ones), with the lowest energy. In addition to classical crossover and mutation operators, (2) we introduced specific translation operators for these two operations. We have proposed and implemented an HP Protein Visualizer tool which offers interpretability, a hybrid approach in that the visualizer gives some insight to the algorithm, that analyse and optimise protein structures HP model. The program resulted based on performed research, provides a molecular modeling tool for studying protein folding using technologies such as Node.js, Express and p5js for 3D rendering, and includes optimization algorithms to simulate protein folding. Full article

(This article belongs to the Special Issue Bio-Inspired Artificial Intelligence in Healthcare)

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14 pages, 812 KB

Open AccessArticle

Low-Overhead Learning: Quantized Shallow Neural Networks at the Service of Genetic Algorithm Optimization

by Fabián Pizarro, Emanuel Vega, Ricardo Soto, Broderick Crawford and José Villamayor

Biomimetics 2025, 10(11), 762; https://doi.org/10.3390/biomimetics10110762 - 12 Nov 2025

Online parameter tuning significantly enhances the performance of optimization algorithms by dynamically adjusting mutation and crossover rates. However, current approaches often suffer from high computational costs and limited adaptability to complex and dynamic fitness landscapes, particularly when machine learning methods are employed. This [...] Read more.

Online parameter tuning significantly enhances the performance of optimization algorithms by dynamically adjusting mutation and crossover rates. However, current approaches often suffer from high computational costs and limited adaptability to complex and dynamic fitness landscapes, particularly when machine learning methods are employed. This work proposes a quantized shallow neural network (SNN) as an efficient learning-based component for dynamically adjusting the mutation and crossover rates of a genetic algorithm (GA). By leveraging runtime-generated data and applying quantization techniques like Quantization-aware Training (QaT) and Post-training Quantization (PtQ), the proposed approach reduces computational overhead while maintaining competitive performance. Experimental evaluation on 15 continuous benchmark functions demonstrates that the quantized SNN achieves high-quality solutions while significantly reducing execution time compared to alternative shallow learning methods. This study highlights the potential of quantized SNNs to balance efficiency and performance, broadening the applicability of shallow learning in optimization. Full article

(This article belongs to the Section Biological Optimisation and Management)

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36 pages, 7319 KB

Open AccessArticle

An Enhanced Secretary Bird Optimization Algorithm Based on Multi Population Management for Numerical Optimization Problems

by Jin Zhu, Bojun Liu, Jun Zheng, Shaojie Yin and Meng Wang

Biomimetics 2025, 10(11), 761; https://doi.org/10.3390/biomimetics10110761 - 12 Nov 2025

The Secretary Bird Optimization Algorithm (SBOA) is a novel swarm-based meta-heuristic that formulates an optimization model by mimicking the secretary bird’s hunting and predator-evasion behaviors, and thus possesses appreciable application potential. Nevertheless, it suffers from an unbalanced exploration–exploitation ratio, difficulty in maintaining population [...] Read more.

The Secretary Bird Optimization Algorithm (SBOA) is a novel swarm-based meta-heuristic that formulates an optimization model by mimicking the secretary bird’s hunting and predator-evasion behaviors, and thus possesses appreciable application potential. Nevertheless, it suffers from an unbalanced exploration–exploitation ratio, difficulty in maintaining population diversity, and a tendency to be trapped in local optima. To eliminate these drawbacks, this paper proposes an SBOA variant (MESBOA) that integrates a multi-population management strategy with an experience-trend guidance strategy. The proposed method is compared with eight advanced basic/enhanced algorithms of different categories on both the CEC2017 and CEC2022 test suites. Experimental results demonstrate that MESBOA delivers faster convergence, more stable robustness and higher accuracy, achieving mean rankings of 2.500 (CEC2022 10-D), 2.333 (CEC2022 20-D), 1.828 (CEC2017 50-D) and 1.931 (CEC2017 100-D). Moreover, engineering constrained optimization problems further verify its applicability to real-world optimization tasks. Full article

(This article belongs to the Special Issue Advances in Biological and Bio-Inspired Algorithms)

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51 pages, 12120 KB

Open AccessArticle

Multi-Strategy Improved POA for Global Optimization Problems and 3D UAV Path Planning

by Rui Zhang, Jingbo Zhan and Jianfeng Wang

Biomimetics 2025, 10(11), 760; https://doi.org/10.3390/biomimetics10110760 - 11 Nov 2025

With the rapid development of smart manufacturing and the low-altitude economy, drone technology—as a vital component of next-generation intelligent equipment—has garnered significant attention from researchers. Path planning, one of the core challenges in drone technology advancement, directly impacts the efficiency and safety of [...] Read more.

With the rapid development of smart manufacturing and the low-altitude economy, drone technology—as a vital component of next-generation intelligent equipment—has garnered significant attention from researchers. Path planning, one of the core challenges in drone technology advancement, directly impacts the efficiency and safety of drone mission execution. However, most existing drone path planning algorithms suffer from issues such as requiring extensive interactive information or being prone to getting stuck in local optima. This study introduces a multi-strategy enhanced Pelican Optimization Algorithm (MIPOA) tailored for UAV path planning. To improve the quality of the initial population, a hybrid initialization approach combining low-discrepancy sequences with heuristic refinement is developed. The low-discrepancy component promotes a more uniform distribution across the search space, while the heuristic mechanism enhances the fitness of selected individuals and reduces redundant exploration. Furthermore, a subgroup mean-guided updating strategy is designed to accelerate convergence toward the global optimum. To maintain exploration ability, a random reinitialization boundary mechanism is incorporated, effectively preventing premature convergence. To validate the algorithm’s performance, MIPOA is compared with eleven benchmark metaheuristics on the CEC2017 test suite, and statistical analyses confirm its superior optimization capability. Finally, MIPOA is applied to 3D UAV path planning under four threat scenarios in a realistic environment, demonstrating robust adaptability and achieving successful mission completion. Full article

(This article belongs to the Special Issue Exploration of Bio-Inspired Computing: 2nd Edition)

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14 pages, 8180 KB

Open AccessArticle

Impact of Replicated Biomimetic Microstructures on the Wettability of Injection-Molded Polymer Surfaces

by Vojtěch Šorm, Jakub Bittner, Petr Lenfeld, Dora Kroisová and Štěpánka Dvořáčková

Biomimetics 2025, 10(11), 759; https://doi.org/10.3390/biomimetics10110759 - 11 Nov 2025

This article evaluates the influence of replicated natural structures, produced by micro-machining, on the wettability of plastic parts made from hydrophilic and hydrophobic polymer materials under various temperature and pressure conditions. Although many studies have focused on biomimetic surface design, the effect of [...] Read more.

This article evaluates the influence of replicated natural structures, produced by micro-machining, on the wettability of plastic parts made from hydrophilic and hydrophobic polymer materials under various temperature and pressure conditions. Although many studies have focused on biomimetic surface design, the effect of specific processing parameters on the accurate replication of natural topologies and their resulting wettability has been only partially explored. This study addresses this gap by systematically analyzing the effect of melt temperature and packing pressure on the functional replication of micro-machined biomimetic structures. The research describes the design of hierarchical microstructures inspired by biomimetics and their fabrication by micro-milling on molded parts. Test samples were prepared from polypropylene (PP), acrylonitrile butadiene styrene (ABS), and polyamide 6.6 (PA 6.6) under different processing parameters, and wettability was assessed using contact angle (CA) measurements. The results confirmed significant variations in surface wettability depending on polymer type, melt temperature, and packing pressure. For the hydrophilic relief (Rock Moss), contact angles below 90° were obtained for all tested polymers, including PP, which decreased from 98.7° on a flat surface to 82.4° at 220 °C and 500 bar. In PA 6.6, a reduction of up to 12% in contact angle was observed compared to smooth samples at 310 °C and 500 bar. For hydrophobic reliefs (Three-part Hibiscus and Tricolor Pansy), contact angles exceeded 100–110°, with the highest value of 108.3 ± 1.6° for PP at 200 °C and 500 bar. Suitable combinations of melt temperature and packing pressure enabled accurate replication of microstructures while preserving their functional wettability, demonstrating the possibility of tuning surface properties through topological design. Full article

(This article belongs to the Special Issue Bioinspired Engineered Systems)

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26 pages, 4246 KB

Open AccessReview

Hydrogel-Based Strategies for the Prevention and Treatment of Radiation-Induced Skin Injury: Progress and Mechanistic Insights

by Yinhui Wang, Huan Liu, Yushan He, Mei Li, Jie Gao, Zongtai Han, Jiayu Zhou and Jianguo Li

Biomimetics 2025, 10(11), 758; https://doi.org/10.3390/biomimetics10110758 - 11 Nov 2025

Radiation-induced skin injury (RISI) is one of the most common complications of radiotherapy, severely compromising patients’ quality of life. However, no standardized treatment has yet been established. Owing to their high water content, three-dimensional porous structure, excellent biocompatibility, and tunable functionalization, hydrogels have [...] Read more.

Radiation-induced skin injury (RISI) is one of the most common complications of radiotherapy, severely compromising patients’ quality of life. However, no standardized treatment has yet been established. Owing to their high water content, three-dimensional porous structure, excellent biocompatibility, and tunable functionalization, hydrogels have emerged as promising candidates for both the prevention and treatment of RISI. This review provides a comprehensive overview of recent advances in hydrogel-based interventions for RISI, with particular focus on material classifications and underlying mechanisms. Mechanistically, hydrogels facilitate tissue repair through multiple synergistic pathways, including antioxidation, anti-inflammation, angiogenesis, and tissue remodeling. Understanding these mechanisms not only provides a theoretical basis for the rational design of next-generation wound dressings but also enhances the translational potential of hydrogels in clinical radiotherapy. With the convergence of materials science, radiation medicine, and pharmaceutical innovation, hydrogels are poised to redefine therapeutic strategies for RISI and accelerate their clinical implementation. Full article

(This article belongs to the Section Biomimetics of Materials and Structures)

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16 pages, 2720 KB

Open AccessArticle

A Rigid-Flexible Coupled Lower Limb Exoskeleton for Enhancing Load-Bearing Ambulation

by Yong-Tang Tian, Chun-Jie Chen, Xiao-Jun Wu and Wu-Jing Cao

Biomimetics 2025, 10(11), 757; https://doi.org/10.3390/biomimetics10110757 - 10 Nov 2025

Lower limb exoskeletons significantly enhance human functionality. However, simultaneously improving the load capacity of these devices while reducing metabolic costs presents a major challenge in the industry. This paper presents a lower limb exoskeleton that integrates both rigid and flexible structures to facilitate [...] Read more.

Lower limb exoskeletons significantly enhance human functionality. However, simultaneously improving the load capacity of these devices while reducing metabolic costs presents a major challenge in the industry. This paper presents a lower limb exoskeleton that integrates both rigid and flexible structures to facilitate active assistance and passive load transfer at the hip joint. The load transfer experiments were conducted with weights of 10 kg and 15 kg. During static standing, the load transfer rates were recorded at 90.48% and 69.70%, respectively. In dynamic walking, these rates decreased to 62.07% and 43.69%. Furthermore, in metabolic experiments involving a load of 15 kg, metabolic costs in the exoskeleton assistance modes OFF (Assist OFF) and exoskeleton assistance ON (Assist ON) were reduced by 8.3% and 21.61%, respectively, compared to the exoskeleton-free condition (NE). Furthermore, the Assist ON mode further decreased metabolic costs by 13.22% compared to the Assist OFF mode. These findings demonstrate that the rigid-soft coupled lower limb exoskeleton exhibits exceptional load transfer capabilities and effective assistance, highlighting its potential to enhance human performance in weight-bearing activities. Full article

(This article belongs to the Section Biomimetic Design, Constructions and Devices)

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17 pages, 2571 KB

Open AccessArticle

Effect of Caudal Keel Structure on the Head Stability of a Bionic Dolphin Robot

by Weijie Gong, Yanxiong Wei and Hong Chen

Biomimetics 2025, 10(11), 756; https://doi.org/10.3390/biomimetics10110756 - 10 Nov 2025

To address the challenge of head stability in a biomimetic robotic dolphin during self-propulsion, this study systematically investigates the passive stabilization mechanism of a bio-inspired caudal keel. A combined experimental and computational fluid dynamics (CFD) approach was employed to evaluate four keel geometries [...] Read more.

To address the challenge of head stability in a biomimetic robotic dolphin during self-propulsion, this study systematically investigates the passive stabilization mechanism of a bio-inspired caudal keel. A combined experimental and computational fluid dynamics (CFD) approach was employed to evaluate four keel geometries across a tail oscillation frequency range of 0.5–2 Hz. The experimental results demonstrate that the optimal keel configuration reduced the standard deviation of the head pitch angle by 20.9% at 2 Hz. CFD analysis revealed a dual stabilization mechanism: an effective keel not only attenuates the intensity of the primary disturbance moment at the driving frequency but, more critically, also enhances the spectral purity of the signal by suppressing high-frequency harmonics and broadband stochastic noise through the systematic reorganization of caudal vortices. A systematic investigation of keel geometry identified non-dimensional height (h/c) as the dominant parameter, with its stabilizing effect exhibiting diminishing returns beyond an optimal range. Furthermore, a quantifiable design trade-off was established, showing an approximate 9.1% increase in the Cost of Transport (CoT) for the most stable configuration. These findings provide quantitative design principles and a deeper physical insight into the passive stabilization of biomimetic underwater vehicles, highlighting the importance of both disturbance intensity and spectral quality. Full article

(This article belongs to the Special Issue Bioinspired Aerodynamic-Fluidic Design)

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15 pages, 3390 KB

Open AccessArticle

Phytofabrication of ZIF-8 Using Mangrove Metabolites for Dual Action Against Drug-Resistant Microbes and Breast Cancer Cells

by Srinath Rajeswaran, Mithuna Shaji Kumarikrishna, Aneesh Giriprasath, Kandi Sridhar, Murugan Anbazhagan, Siva Vadivel and Maharshi Bhaswant

Biomimetics 2025, 10(11), 755; https://doi.org/10.3390/biomimetics10110755 - 8 Nov 2025

Green nanotechnology offers a sustainable and eco-friendly approach for nanoframework synthesis. The present study intended to synthesize a novel eco-friendly encapsulated Zeolitic Imidazolate Framework-8 (ZIF-8) in a one-pot method using metabolites from the mangrove plant Conocarpus erectus (CE). Gas Chromatography–Mass Spectrometry (GC-MS) analysis [...] Read more.

Green nanotechnology offers a sustainable and eco-friendly approach for nanoframework synthesis. The present study intended to synthesize a novel eco-friendly encapsulated Zeolitic Imidazolate Framework-8 (ZIF-8) in a one-pot method using metabolites from the mangrove plant Conocarpus erectus (CE). Gas Chromatography–Mass Spectrometry (GC-MS) analysis of the extract revealed the presence of important bioactive metabolites. The synthesized material was evaluated by UV-Vis spectroscopy, X-ray diffraction (XRD), particle size analysis (PSA), zeta potential measurement, high-resolution transmission electron microscopy (HR-TEM), and Fourier transform infrared (FT-IR) spectroscopy studies. The environment-friendly mangrove metabolites aided by Zeolitic Imidazolate Framework-8 was found to be crystalline, rhombic dodecahedron structured, and size dispersed without agglomeration. The nanomaterial possessed a broad antimicrobial effect on drug-resistant microorganisms, including Candida krusei, Escherichia coli, Streptococcus Sp., Staphylococcus aureus, Enterococcus Sp., Pseudomonas aeruginosa, Klebsiella pneumoniae, C. propicalis, and C. albicans. Further, its cytotoxicity against MDA-MB-231 cells was found to be efficient. The morphological alterations exhibited by the antiproliferative impact on the breast cancer cell line were detected using DAPI and AO/EB staining. Therefore, ZIF-8 encapsulated mangrove metabolites could serve as an effective biomaterial with biomedical properties in the future. Full article

(This article belongs to the Section Biomimetics of Materials and Structures)

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55 pages, 31519 KB

Open AccessReview

Learning from Nature: Bio-Inspired Designs and Strategies for Efficient On-Earth and Off-Earth Ventilation Systems

by Ulfa Riani, Noune Melkoumian, David Harvey and Rini Akmeliawati

Biomimetics 2025, 10(11), 754; https://doi.org/10.3390/biomimetics10110754 - 7 Nov 2025

Efficient ventilation systems are of paramount importance for maintaining optimal air quality in indoor and enclosed environments, both on Earth and in space. Such environments include buildings, space habitats, international space station crew quarters, tunnels, underground mines and other structures. However, conventional ventilation [...] Read more.

Efficient ventilation systems are of paramount importance for maintaining optimal air quality in indoor and enclosed environments, both on Earth and in space. Such environments include buildings, space habitats, international space station crew quarters, tunnels, underground mines and other structures. However, conventional ventilation systems encounter various challenges, including uneven air distribution, energy inefficiency, noise, and limited adaptability to fluctuating environmental conditions. Concurrently, a multitude of organisms in nature have demonstrated the capacity to construct structures that can facilitate efficient air exchange and heat regulation. Illustrative examples of such structures include ant nests, termite mounds and prairie dog burrows. The present study explores, analyses and summarizes the mechanisms, structures and strategies found in nature that can inspire the design of efficient and effective ventilation systems. The purpose of this paper is to highlight the practical implications of the aforementioned designs. To this end, it reviews the progress of research into bio-inspired ventilation, focusing on the following three areas: air regulation, component optimization and environmentally adaptive strategies. A bibliometric analysis and research trend is presented to illustrate the key developments in this field over the past 25 years. The potential of integrating the bio-inspired strategies into ventilation systems, with a particular emphasis on off-Earth habitats and underground mines, is discussed. This study provides a comprehensive overview of the development of bio-inspired ventilation systems, thereby establishing the foundation for the creation of innovative and efficient design solutions. Full article

(This article belongs to the Special Issue Biomimetic Approaches and Materials in Engineering)

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24 pages, 6188 KB

Open AccessArticle

A Bionic Sensing Platform for Cell Separation: Simulation of a Dielectrophoretic Microfluidic Device That Leverages Dielectric Fingerprints

by Reza Hadjiaghaie Vafaie, Elnaz Poorreza, Sobhan Sheykhivand and Sebelan Danishvar

Biomimetics 2025, 10(11), 753; https://doi.org/10.3390/biomimetics10110753 - 7 Nov 2025

Cancers are diseases described by the irregular spread of cells that have developed invasive features, enabling them to invade adjacent tissues. The specific diagnosis and effective management of oncological treatments depend on the timely detection of circulating tumor cells (CTCs) in a patient’s [...] Read more.

Cancers are diseases described by the irregular spread of cells that have developed invasive features, enabling them to invade adjacent tissues. The specific diagnosis and effective management of oncological treatments depend on the timely detection of circulating tumor cells (CTCs) in a patient’s bloodstream. One of the most promising approaches to CTC separation from blood fractions involves the dielectrophoresis (DEP) technique. This research presents a new DEP-based bionic system designed for MDA-MB-231 breast cancer cell isolation from white blood cell (WBC) subtypes with a viable approach to cell viability. This work leverages the principle that every cell type possesses a unique dielectric fingerprint. This dielectrophoresis microfluidic device is designed to act as a scanner, reading these fingerprints to achieve a continuous, label-free separation of cancer cells from blood components with a high efficiency. In the proposed system that consists of three different stages, the first stage allows for separating B-lymphocytes and Monocytes from Granulocytes and MDA-MB-231 cells. The separation of B-lymphocytes from Monocytes occurs in the second step, while the last step concerns the separation of Granulocytes and MDA-MB-231 cells. In the analysis, x-y graphs of the electric potentials, velocity fields, pressure distributions, and cellular DEP forces applied to the cells, as well as the resulting particle paths, are provided. The model predicts that the system operates with a separation efficiency of nearly 92%. This work focuses on an investigation of the impact of electrode potentials, the velocity of cells, the number of electrodes, the width of the channel, and the output angles on enhancing the separation efficiency of particles. Full article

(This article belongs to the Special Issue Biomimetic Approaches in Healthcare—Innovations Inspired by Nature: 3rd Edition)

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25 pages, 4931 KB

Open AccessReview

Bioinspired Drilling for Extraterrestrial Applications

by Gal-Erdene Battsengel, Noune Melkoumian, David Harvey and Rini Akmeliawati

Biomimetics 2025, 10(11), 752; https://doi.org/10.3390/biomimetics10110752 - 7 Nov 2025

This review presents the novel synthesis of nature-inspired drilling strategies specifically tailored for extraterrestrial environments, where conventional technologies fail under the environmental conditions and power and mass constraints. Biomimetic drilling, inspired by insects, mollusks, reptiles, and other organisms, offers novel solutions for extraterrestrial [...] Read more.

This review presents the novel synthesis of nature-inspired drilling strategies specifically tailored for extraterrestrial environments, where conventional technologies fail under the environmental conditions and power and mass constraints. Biomimetic drilling, inspired by insects, mollusks, reptiles, and other organisms, offers novel solutions for extraterrestrial subsurface exploration. Numerous organisms efficiently penetrate materials with low energy, using little force, and adapt to flexible substrates, which are essential capabilities for use off this planet. Traditional rotary and percussive drills do not function well under microgravity, at the end of the temperature spectrum, or in low energy and mass environments, such as landers which are typically under 300 kg and 200 W of power available. Nature-inspired approaches such as the reciprocating carpenter bee style have been shown to reduce overhead forces by as much as 50%; clam-like fluidization reduces drag by 90%; and sandfish-inspired methods improve mobility in granular media by 40%. These also improve the in situ resource utilization (ISRU) approaches for efficient sampling, water ice extraction, and planetary surface operations. This paper focuses on bio-drilling with other biological models, their engineering analogs, and exploration models for off-Earth use. Based on this synthesis, the paper recommends prioritizing dual-reciprocating and oscillatory mechanisms for near-term missions, while pursuing hybrid, AI-driven, and wear-resistant designs for long-term exploration. These approaches will help to improve penetration efficiency, reduce power demands, and extend the drilling system’s lifespan in challenging extraterrestrial environments. Full article

(This article belongs to the Special Issue Biomimetic Approaches and Materials in Engineering)

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15 pages, 964 KB

Open AccessArticle

Comparative Analysis of Flexural and Compressive Strengths of Bioactive Alkasite Compared to Other Ion-Releasing Restorative Materials

by Hanin E. Yeslam and Fatin A. Hasanain

Biomimetics 2025, 10(11), 751; https://doi.org/10.3390/biomimetics10110751 - 7 Nov 2025

Background: Ion-releasing and bioactive restorative materials are an integral part of restorative dentistry, especially in light of minimally invasive and esthetic intervention strategies. Their strength and mechanical properties directly influence their durability and indicated use. Methods: This study aimed to comparatively analyze the [...] Read more.

Background: Ion-releasing and bioactive restorative materials are an integral part of restorative dentistry, especially in light of minimally invasive and esthetic intervention strategies. Their strength and mechanical properties directly influence their durability and indicated use. Methods: This study aimed to comparatively analyze the compressive strengths, flexural strengths, and flexural moduli of bioactive Alkasite (Cention N) and other ion-releasing restorative materials, specifically a resin-modified glass ionomer (RMGIC, Fuji II LC) and a compomer (Dyract XP). Cylindrical and bar-shaped specimens were fabricated from each material (n = 6 per material and conducted test) and subjected to mechanical strength testing (compressive and flexural strength) using a 2 kN cell universal testing machine (Instron 5944) with a crosshead speed of 0.5 mm/min. Statistical analysis, using one-way ANOVA and Tukey’s HSD post hoc tests, was conducted. Results: The results revealed significant differences in mechanical properties between the tested materials. Dyract XP showed the greatest compressive and flexural strengths (170.79 ± 23.59 MPa and 114.09 ± 30.78 MPa) (p < 0.01). Fuji II LC had a significantly greater flexural modulus (10.21 ± 4.46 GPa) than Dyract XP. Conclusions: The findings indicated that the investigated compomer could produce stronger restorations than the investigated alkasite and RMGIC, which would make them preferred for posterior teeth restoration. However, the alkasite Cention N might still be a good option for the treatment of carious lesions in areas with less occlusal stress. Full article

(This article belongs to the Special Issue Functional Biomimetic Materials and Devices for Biomedical Applications: 4th Edition)

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33 pages, 6935 KB

Open AccessArticle

A Coverage Optimization Approach for Wireless Sensor Networks Using Swarm Intelligence Optimization

by Shuxin Wang, Qingchen Zhang, Yejun Zheng, Yinggao Yue, Li Cao and Mengji Xiong

Biomimetics 2025, 10(11), 750; https://doi.org/10.3390/biomimetics10110750 - 6 Nov 2025

WSN coverage optimization faces two key challenges: firstly, traditional algorithms are prone to getting stuck in local optima, leading to ‘coverage holes’ in node deployment; Secondly, in dynamic scenarios (such as imbalanced energy consumption of nodes), the convergence speed of the algorithm is [...] Read more.

WSN coverage optimization faces two key challenges: firstly, traditional algorithms are prone to getting stuck in local optima, leading to ‘coverage holes’ in node deployment; Secondly, in dynamic scenarios (such as imbalanced energy consumption of nodes), the convergence speed of the algorithm is slow, making it difficult to maintain high coverage in real time. This study focuses on the coverage optimization problem of wireless sensor networks (WSNs) and proposes improvements to the Flamingo Search Optimization Algorithm (FSA). Specifically, the algorithm is enhanced by integrating the elite opposition-based learning strategy and the stagewise step-size control strategy, which significantly improves its overall performance. Additionally, the introduction of a cosine variation factor combined with the stagewise step-size control strategy enables the algorithm to effectively break free from local optima constraints in the later stages of iteration. The improved Flamingo Algorithm is applied to optimize the deployment strategy of sensing nodes, thereby enhancing the coverage rate of the sensor network. First, an appropriate number of sensing nodes is selected according to the target area, and the population is initialized using a chaotic sequence. Subsequently, the improved Flamingo Algorithm is adopted to optimize and solve the coverage model, with the coverage rate as the fitness function and the coordinates of all randomly distributed sensing nodes as the initial foraging positions. Next, a search for candidate foraging sources is performed to obtain the coordinates of sensing nodes with higher fitness; the coordinate components of these candidate foraging sources are further optimized through chaos theory to derive the foraging source with the highest fitness. Finally, the coordinates of the optimal foraging source are output, which correspond to the coordinate values of all sensing nodes in the target area. Experimental results show that after 100 and 200 iterations, the coverage rate of the improved Flamingo Search Optimization Algorithm is 7.48% and 5.68% higher than that of the original FSA, respectively. Furthermore, the findings indicate that, by properly configuring the Flamingo population size and the number of iterations, the improved algorithm achieves a higher coverage rate compared to other benchmark algorithms. Full article

(This article belongs to the Section Biological Optimisation and Management)

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