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Biomimetics seeks to translate principles from living systems into innovative engineering solutions by drawing on the remarkable efficiency, adaptability, and multifunctionality found in nature [...].
In beetles, the elytra play critical roles in wing protection, locomotion, and communication, and their surface properties vary across ecological gradients. To explore the characteristics of the elytral surface on aquatic beetles, their microstructure and wettability, we studied six Neotropical species from the families Dytiscidae (Rhantus souzannae and Laccophilus fasciatus), Hydrophilidae (Tropisternus chalybeus and Tropisternus cf. mergus) and Gyrinidae (Gyrinus costaricensis and Dineutus solitarius), representing different habitats and trophic groups. We used scanning electron microscopy, profilometry and contact angle measurements to characterize the elytra surface and wettability properties. Elytral microstructures varied among species, both species of Gyrinidae and Dytiscidae exhibited more complex elytral structures including micro-reticulation (polygonal patterns) and strial reticulation for G. costaricensis. Hydrophilidae species presented a smooth elytral surface without polygonal patterns and showed the lowest roughness value for T. cf mergus with 0.594 μm, compared to D. solitarius with roughness of 2.776 μm. Although contact angles did not differ significantly among species, all exceeded 90°, indicating overall hydrophobic features. The microstructural diversity and water-repellent properties documented provide key insights into the biology of the group and offer valuable foundation for futures studies in functional biology and biomimetics.
Mussels possess a remarkable natural ability to adhere on wet surfaces such as rocks in marine environments. This adhesion is attributed to proteins that undergo phase separation to form coacervates. These coacervates can further assemble into systems that evolve into structured fluids, where a polymer-rich viscoelastic phase becomes continuous while the solvent-rich phase remains dispersed. Here, we report a poly(acrylic acid)-based system functionalized with cationic and hydrophobic moieties that reproduces key features of this behavior. Upon pH variation, the polymer undergoes self-coacervation, leading to the formation of dense droplets. These droplets progressively sediment and coalesce, resulting in a local increase in concentration. This process is accompanied by a transition toward a continuous polymer-rich phase in which water droplets are retained, consistent with a phase inversion driven by concentration increase. Rheological measurements show that this transition is associated with a marked increase in viscoelasticity and interfacial adhesion, as quantified by tack measurements. These results highlight how the balance between electrostatic and hydrophobic interactions governs the transition from a dispersed coacervate state to a continuous viscoelastic material with adhesive properties. This bioinspired system offers a promising strategy for the development of effective underwater adhesives and opens new avenues in the design of biomimetic materials for wet-surface bonding applications.
Biological cellular structures have developed excellent mechanical properties through natural selection, providing inspiration for the design of protective engineering structures. This study aims to uncover the unique cellular structure of the white jade bodhi (WJB) and its intrinsic relationship with mechanical performance. Based on its structural characteristics, a new type of bionic cell structure was designed, and its energy absorption behavior was systematically studied through numerical simulations and experiments. Quasi-static and split Hopkinson pressure bar dynamic compression tests and numerical simulations were conducted to study the mechanical response of the WJB cellular structure. The results reveal that the WJB possesses a unique hierarchical cellular structure consisting of primary elliptical closed-cells and secondary circular open-cells. Its mechanical behavior exhibits significant strain rate sensitivity and superior energy absorption compared to other natural cellular materials. Meanwhile, the specific energy absorption of the bio-inspired WJB cellular structure is 936% higher than that of aluminum foam, and 187% higher than that of three-layered hexagonal cellular structure. The improvement in its energy absorption performance is attributed to the crack initiation and propagation mechanism of the circular pores, as well as the alternating bending and progressive collapse mechanism of the cellular walls.
The structurally novel marine alkaloid aleutianamine was reported to have potent and selective activity against the PANC-1 pancreatic cancer cell line, inspiring the development of three exceptional recent syntheses. Our efforts toward a bioinspired, "Kita-style" synthesis met with challenges until we adopted the presumably biomimetic rearrangement of N-tosyl dihydrodiscorhabdin B discovered by Tokuyama and co-workers. With our previously reported, scalable synthesis of the tricyclic pyrroloiminoquinone (PIQ) core and the design of a brominated and sulfenylated tyramine reaction partner, a convergent condensation set up for a modified Tokuyama endgame. Early incorporation of the bromine atom increased convergence and obviated a troublesome late-stage halogenation. The discovery that efficient oxidative thioaminal formation required only oxygen permitted a synthesis of discorhabdin B in only nine steps in the longest linear sequence (LLS). Notably, no pyrrole N-protecting group was used throughout the sequence leading to discorhabdin B. Conversion to aleutianamine without N-protection was not so efficient, but delivered the target in only 10 steps LLS; alternatively, N-tosylation permitted higher yielding rearrangement, per Tokuyama. In this manner, we were able to make tens of milligrams of aleutianamine, permitting evaluation of its activity against the NCI 60-cell panel (plus 6 additional pancreatic cancer cell lines), wherein it showed potent activity against several pancreatic, leukemia, and melanoma cell lines. In addition, aleutianamine, N-Ts aleutianamine, desbromoaleutianamine, discorhabdin B, and the simpler PIQ makaluvamine J were evaluated against three pancreatic cancer cell lines, and each compound showed submicromolar activity in all cases. Critically, the more readily available discorhabdin B was equipotent to aleutianamine, showing that aleutianamine is not special among the broader family with regard to pancreatic cancer cytotoxicity. We thus optimized our route to discorhabdin B, which provided nearly 350 mg in one pass, setting the stage for further collaborative investigations. We also provide a potential mechanism for aleutianamine to undergo bioreductive activation and covalent modification of biomolecules and a proposal that makaluvamine F─thought to be a potential precursor of discorhabdin B and aleutianamine─might in fact be a degradation product of discorhabdin B.
Sodium metal batteries hold great promise for next-generation high-energy-density storage. However, their practical implementation is constrained by electrolyte flammability and unstable electrolyte-electrode interfaces. Herein, the quasi-solid-state composite electrolyte is developed by constructing a biomimetic hierarchical ion-transport architecture inspired by plant root system. Long-range ion-percolation pathways are established by KH560-functionalized halloysite nanotubes (KHNTs) serving as "primary roots," while in situ polymerized poly(1,3-dioxolane) (PDOL) "lateral roots" bridge inorganic-organic interfaces to form a seamless ion-relay network. Furthermore, the internal Al-OH groups of KHNT promote anion dissociation and capture. Whereas, the external O-Si-O linkages compete with PDOL for coordination sites, effectively accelerating Na+ hopping kinetics and ion conduction. Consequently, the ionic conductivity of 5.62 ms cm-1, the Na+ transference number of 0.766 are achieved, alongside a electrochemical window up to 5.1 V. Symmetric Na||Na cells with stable cycling exceeding 3600 h, and Na||Na₃V₂(PO₄)₃ cells maintain 92.8% capacity after 1600 cycles at 2C. Additionally, pouch cells test further confirm excellent thermal and mechanical robustness with ultralow heat release.
Carbon-based aqueous zinc-ion batteries (CAZBs) require stable operation under extremely low temperatures for practical applications, but they are hindered by sluggish Zn2 + transport within the diffusion layer and desolvation barriers in the Helmholtz layer. Here, a bio-inspired interface engineering strategy-derived from the high-volume, high-speed, and high-efficiency signal processing capability of the cerebral cortex-is employed to construct hierarchical carbon spheres with sulcus-gyrus architectures (HCSs-sg). Such HCSs-sg can effectively imitate the dense neuron distribution in the cerebral cortex and lead to a sharp increase in pseudocapacitive active sites. This biomimetic configuration generates directional micro-electric fields and ionic concentration gradients, which synergistically accelerate Zn2 + transport through diffusion-driven migration and coulombic forces. Simultaneously, the high-curvature sulcus-gyrus exhibits enhanced Zn2 + adsorption energy and reduced desolvation barriers, thereby facilitating efficient desolvation and rapid charge transfer at subzero temperatures. As a result, the optimized product delivers a specific capacity of 70 mAh g- 1 at 0.1 A g- 1 under -25°C and maintains a stable coulombic efficiency of nearly 100% over 10 000 cycles at 1 A g- 1. This biomimetic interface engineering approach can provide a potential design route for aqueous battery applications under extreme-temperature conditions.
In many robotic applications, such as humanoids, wearable robots or supernumerary limbs, there is a growing shift from rigid, traditional mechanisms toward softer, more compliant systems. This trend is driven by the need for safer physical human-robot interaction and the ability to operate in unstructured environments. In this work, we present a hybrid approach to controlling a single-degree-of-freedom robotic joint that combines a rigid frame with soft pneumatic actuators to enable both precise and versatile interaction. The joint is bioinspired, as it is powered by a couple of actuators mounted in an antagonist configuration, such as the human elbow. A key innovation is the design of pneumatic artificial muscles using thermoplastic polyurethane, which achieve high isometric force (400 N at 240 kPa), significant stretchability (80 mm), and low density(0.3gcm-3), making them competitive with state-of-the-art alternatives. Two model-free controllers were developed to independently regulate joint position and stiffness. Angle control achieved high precision (<2∘RMSE) with minimal overshoot (<1%) and fast response (rise time<1.3s). Stiffness control modulated joint compliance across a range of 0.054-0.076 Nm deg-1, with the expected trade-off of reduced workspace. A final proof-of-concept demonstrated the concurrent use of both controllers to modulate the joint's dynamic behavior in response to external disturbances. While future work will address multi-DOF coordination and wearable integration, this study represents a foundational step toward safe, adaptable robotic actuation through the combination of rigid structures and soft actuation.
This study investigates the porous structure of Royal Water Lily Leaf vein cross-sections, integrating macroscopic structural observations, quasi-static compression experiments, and finite element simulations to systematically explore the influence of gradient fractal characteristics on mechanical performance and energy absorption behavior. First, the geometric features of the vein cross-sections were extracted through macroscopic measurements, and a parametric model incorporating key variables-porosity, pore ellipticity, and distribution density coefficient-was established. Single-factor analysis reveals that porosity plays a dominant role in determining the overall load-bearing capacity and energy absorption capability; pore ellipticity primarily affects local deformation modes and plateau-stage stability; while the distribution density coefficient significantly regulates the progressive and uniform deformation behavior. Subsequently, a multi-factor coupling model based on the Box-Behnken response surface methodology was developed to investigate the interactions among structural parameters. The results showed that the three variables exhibited significant synergistic effects rather than simple monotonic relationships. Within the investigated range, the optimized configuration (porosity = 30%, ellipticity = 1.6, distribution density coefficient = 1.5) achieved excellent comprehensive performance, with SEA = 115.75 J/kg, MCF = 248.2 N, and CFE = 0.445. Further analysis revealed that the porous vein structure does not exhibit strict self-similar fractal geometry but instead presents a gradient fractal characteristic with hierarchical progression and regional heterogeneity. During compression, the structure undergoes progressive collapse from the inner region outward, enabling staged load-bearing and efficient energy dissipation. These findings provide theoretical support and engineering guidance for the design and optimization of lightweight bioinspired porous energy-absorbing structures.
The development of efficient catalysts for nitrogen conversion to ammonia is critical for a sustainable alternative to the energy-intensive Haber-Bosch process. Yet, rational catalyst design remains highly challenging, compounded by complex structure-function relationships within realistic conditions. Herein, we present an integrated computational framework combining quantum chemical calculations with 27 machine learning models to predict experimental catalytic metrics in metal-ligand complexes. The models are trained and validated on a large experimental database and demonstrate high predictive accuracy across multiple tasks. For classification, family 1 and family 2 catalysts achieved test accuracies up to 1. Regression models yield test R2 values of 0.91 and 0.88 for turnover frequency (TOF) and turnover number (TON) predictions in family 1, and 0.96 and 0.99 in family 2. Notably, the models accurately capture time-dependent variability of TOF and TON for new complexes, with predicted values closely matching experimental results. Moreover, strong transfer learning capability is observed for structurally distinct coordination architectures. Feature interpretation reveals clear design principles for optimal catalysts involving metal spin state, ligand geometry, charge distribution, and experimental conditions. Together, this study established an efficient and practical framework for discovery and inverse design of high-performance catalysts under realistic conditions, with broader relevance to electrocatalysis.
The growing prevalence of age-related limb loss underscores the need for prosthetic technologies that restore not only motor function but also authentic sensory feedback. Current prosthetic systems largely depend on sensory substitution or signal remapping, which fall short of replicating natural somatosensory signals. In this work, we develop a plant-enhanced bionic mechanoreceptor that mimics biological touch by converting mechanical stimuli into ionic signals. Incorporating bio-derived pollen microgels into the hydrogel matrix introduces interfacial ion-anchoring sites that strengthen cation-matrix interactions, enhance ionic polarization, and significantly amplify the piezoionic output. This enhancement arises from pressure-driven asymmetric ion transport within the ionically conductive hydrogel. As a result, the output signal increases by up to 12-fold, providing a simple and accessible strategy to improve the sensitivity of piezoionic mechanoreceptors. Then, we demonstrate the integration of ten such mechanoreceptors into a robotic prosthetic arm and utilize a deep learning algorithm to interpret the complex signal patterns. The system achieves accurate recognition of object interaction, validating the potential for naturalistic tactile feedback. This platform offers a scalable, biomimetic solution for developing next-generation sensory-augmented prostheses and may inform future designs in neuroprosthetics and human-machine interfaces.
Aquatic resistance training is a key technique within both sports training and osteomuscular rehabilitation, typically featuring the use of devices such as swimming parachutes to augment the hydrodynamic drag of the trainee. Conventional swimming parachutes, however, are limited by the classical strong (quadratic) scaling of drag force with velocity: to maintain a consistent target resistance, the swimmer must maintain a constant speed and the parachute size must be closely adjusted to this speed. In this work, we present and evaluate a bio-inspired approach to overcome these limitations. Many flexible biological structures, both terrestrial and aquatic, show weakened drag-velocity scaling, and thereby more consistent drag loading: an effect measured by the Vogel exponent (V). We design a swimming parachute with squid-inspired morphology that uses structural flexibility to weaken drag-velocity scaling, and evaluate it experimentally under the hydrodynamic conditions associated with crawl swimming. This evaluation confirms low Vogel exponent (-0.9 <V< -1.3), corresponding to linear-to-sublinear scaling of drag force with velocity, across several different morphological configurations. This demonstration of improved resistance load consistency via flexible bio-inspired morphology has the potential to reduce the risk of injury in aquatic resistance training, and provides a novel connection between biological fluid-structure interaction and biomedicine.
Puncture is ubiquitous in nature, serving functions ranging from predation and protection to feeding and reproduction, and it has inspired extensive research into both the mechanics of natural puncture strategies and their application to materials testing. As a characterization method, puncture integrates principles of fracture mechanics, large-deformation elasticity, and contact phenomena, making it particularly suited to capture the complex failure of biological tissues and bio-inspired materials. Over the past few decades, studies have applied puncture testing to estimate material properties such as elastic modulus and fracture toughness across a broad array of soft solids. In this review, we present a brief overview of puncture characterization theory, followed by a survey of experimental puncture results spanning biological tissues, bio-derived materials, and bio-mimetic materials. We examine how probe size, probe shape, sample geometry, and rate-dependent material behavior influence puncture outcomes and the extent to which existing theory accounts for them. Although structural and mechanical variability across biological material systems limits direct cross-study comparison, the scaling relationships and survey methodology presented here offer a practical framework for organizing puncture data and identifying where trends emerge under comparable testing conditions. We conclude with an outlook on emerging opportunities, including the adaptation of puncture testing for high-throughput biomaterials screening and cell culture characterization.
Odor source localization in turbulent environments remains a major challenge for autonomous robots, as odor plumes are highly intermittent, spatially fragmented, and often lack stable concentration gradients. Here, we propose a bio-inspired navigation framework that translates key principles of bumblebee olfactory cognition into robotic decision-making. First, classical conditioning and olfactorily triggered spatial memory experiments demonstrated that bumblebees could form robust odor memories and that training frequency is positively correlated with both proboscis extension response retention and spatial directional preference. Based on these biological findings, a bio-inspired navigation framework, termed Bio-Nav, is constructed by integrating a Partially Observable Markov Decision Process, a Hidden Markov Model, short-term memory, long-term directional reference memory, fuzzy inference, and value iteration. High-fidelity two-dimensional turbulent simulations show that the proposed algorithm substantially outperforms moth-inspired search, Infotaxis, and standard POMDP-based navigation. In 100 Monte Carlo trials, Bio-Nav achieved a success rate of 96.0%, an average of 20.3 search steps, an average path length of 155.1 cm, and a path-to-straight-line distance ratio of 1.6. Even under strong turbulence, the success rate remained above 91%. These results indicate that memory-perception coupling, inspired by bumblebee navigation, provides an effective and robust strategy for odor source localization in complex turbulent environments, offering a generalizable principle for bio-inspired robotic search under uncertainty.
Currently, small moving object detection remains a highly challenging problem, primarily attributable to four critical factors: limited pixel coverage, blurred texture features, indistinguishability from small-object-like background features (i.e., false positives), and vulnerability to environmental noise interference. The remarkable sensitivity of the Drosophila visual system to small moving objects, which originates from a specialized type of neuron known as "lobula columnar 11" (LC11), has provided inspiration for addressing this challenge. Current bio-inspired visual models have achieved certain advances. However, detection performance against real-world complex dynamic natural environments still requires further improvement. To address the challenge of limited detection accuracy for small moving objects against real-world complex dynamic natural environments, this paper proposes a Motion Small Object Detection (MSOD) model inspired by the Drosophila Vision Small Object Motion Sensitivity (DVSOMS) mechanism, namely DVSOMS-MSOD. The model consists of four stages: The first stage is preliminary processing of visual stimuli, where visual stimuli are perceived, converted to grayscale, and blurred. The second stage is the motion neural pathway, where visual signals are first decomposed into parallel ON and OFF neural pathway signals; then, the neural feedback mechanism is incorporated between the medulla and lobula complex, and the complete Hassenstein-Reichardt correlator (HRC) is integrated into the lobula complex; finally, the LC11 neuron is utilized to detect small moving objects and extract their location information. The third stage is the contrast neural pathway, where visual signals are first processed by the central and surrounding local neighborhoods, then local contrast information is calculated. The fourth stage is the integration of motion and contrast neural pathways, where the mushroom body generates motion trajectories using the location information of small moving objects, and subsequently generates contrast trajectories using the local contrast information and motion trajectories to more finely detect small moving objects. Under real-world complex dynamic natural environment datasets, compared with conventional machine learning methods for moving object detection, the proposed model achieved improvements of 77.82% and 78.70% in detection performance and output quality, respectively, while reducing running time by 10.60%. Compared with bio-inspired visual models for small moving object detection, the proposed model achieved improvements of 28.24% and 43.15% in detection accuracy and detection performance, respectively, but the running time increased by 43.40%. The proposed model demonstrates certain advantages in detection performance, output quality, and detection accuracy; however, its real-time performance still warrants further optimization.
Dental enamel is constantly challenged by acidic conditions that disrupt the balance between demineralization and remineralization. Despite proven efficacy, current remineralization strategies face limitations, which prompt exploration of novel biomimetic approaches. Aspartic acid, a calcium-binding amino acid abundant in enamel matrix proteins, was hypothesized to serve as a potential enhancer of enamel remineralization. This study investigates its potential in combination with calcium sources, comparing the efficacy of these formulations to fluoride as a conventional market benchmark. The remineralization potential of the experimental formulations was evaluated using a standardized in vitro bovine enamel model quantified by surface microhardness recovery (%SMHR). Polished enamel samples were subjected to demineralization in acidic conditions (pH = 4.5) for 60 min, in a solution containing 50 mM acetic acid, 2.2 mM calcium nitrate, 2.2 mM potassium phosphate monobasic and 0.1 ppm sodium fluoride. After rinsing with deionized water to arrest acid activity, the demineralized samples were incubated with solutions containing aspartic acid, calcium sources, their combinations and fluoride (as positive control) for 16 h at 37 °C. Enamel surface microhardness was measured before and after treatment to assess remineralizing effectiveness of test systems. While 0.5% aspartic acid alone caused enamel demineralization (mean %SMHR = -37.32 ± 24.64), its combinations with calcium sources outperformed fluoride: 0.5% Asp + 1% tricalcium phosphate system demonstrated a mean %SMHR of 42.03 ± 19.45 - significantly higher than fluoride (14.12% ± 13.40%; p = 0.0181), a system of 0.5% Asp + 1% dicalcium phosphate dihydrate in another experiment achieved a mean %SMHR of 45.43 ± 14.64- also significantly superior to fluoride (5.15% ± 4.84%, p = 0.0058). Other formulations of Asp with calcium sources showed remineralizing potential but lacked statistical superiority to fluoride. These results suggest that aspartic acid-calcium formulations may offer potential advantages over fluoride-based approaches in promoting enamel remineralization. However, further investigation is needed to elucidate the underlying mechanisms and establish clinical efficacy.
Lipases are pivotal biocatalysts for the tailored synthesis of structured lipids, owing to their capacity for precise triglyceride modification. Natural lipases contain an independent substrate-binding domain, termed the lid region, that regulates substrate selectivity and establishes a hydrophilic microenvironment for selective binding to the glycerol backbone of triglycerides. Drawing inspiration from this mechanism, this study has designed and constructed a series of biomimetic metal-organic frameworks with graded hydrophilicity to act as artificial lipases. Systematic investigations revealed a significant positive correlation between the hydrophilicity of artificial lipases and their oriented binding preference for the glycerol backbone (the polar head) of triglyceride molecules over the hydrophobic fatty acid alkyl chains, thereby successfully mimicking the substrate recognition mechanism of natural lipases. Of these, the hydrophilic artificial lipases exhibited the highest glycerol hydrophilic end-oriented binding, applied to the acidolysis of camellia oil for structured lipid synthesis. The artificial lipase demonstrated superior catalytic efficiency, achieving a total capric acid incorporation of 29.09%, which significantly exceeded that of the natural lipase. Furthermore, it exhibited pronounced regioselectivity, with capric acid incorporation of 41.33% at the sn-1,3 positions, in stark contrast to only 4.62% at the sn-2 position. This high sn-1,3 regioselectivity, coupled with superior overall activity, validates the efficiency and advantage of the hydrophilic microenvironment biomimetic strategy in synthesizing rapidly digestible, low-accumulating sn-1,3-specific medium- and long-chain triglycerides.
In recent years, cell-derived vesicle-modified biomaterials (CDVMBs) have been considered a promising strategy to overcome the limitations of traditional biomaterials in tissue repair and regeneration. By combining biomaterial carriers with cell-derived vesicles, CDVMBs integrate the functional advantages of the carriers, including support for cell adhesion, colonization, proliferation, and functionalization, while also incorporating the bioactive properties of cell-derived vesicles. In this review, the term "cell-derived vesicles" refers to two distinct bioinspired components used for carrier modification: cell membrane vesicles, which mainly retain membrane-associated receptors and interfacial biological functions, and exosomes, which are nanosized extracellular vesicles enriched in bioactive cargos such as proteins and nucleic acids. Accordingly, CDVMBs can mimic either the surface biological properties of source cells or the signaling functions mediated by exosomal cargos, thereby promoting interactions with damaged tissues and stimulating tissue regeneration. Based on the biomaterial biomimetic strategy and vesicle source, CDVMBs are classified into cell membrane-camouflaged biomaterials (CMCBs) and exosome-modified biomaterials (EMBs). This review summarizes their engineering strategies, biological mechanisms, and versatile applications for tissue repair, and further discusses the current challenges and future perspectives for clinical translation. Taken together, the integration of biomaterial carriers with cell-derived vesicles establishes a versatile bioinspired framework for engineering regenerative microenvironments and advancing tissue repair and regeneration.
Wireless Sensor Networks (WSNs) enable energy-efficient data collection in dynamic environments but continue to face the dual challenges of severely constrained node energy and the spatiotemporal heterogeneity of data traffic. Inspired by honeybee foraging behavior, this paper proposes a hybrid optimization framework that integrates mixed-integer linear programming (MILP) and Markov decision processes (MDP), utilizing Q-learning for adaptive decision-making. The proposed framework systematically maps the dual-layer decision-making mechanism of honeybee foraging onto a synergistic architecture combining MILP-based global planning and MDP-based local adaptation, offering a novel bio-inspired solution for mobile sink trajectory planning and adaptive routing. Specifically, the upper-level MILP module simulates a colony-level global assessment of distant nectar sources, generating an initial global trajectory by determining the optimal access sequence of cluster heads to minimize the movement cost of the mobile sink. The lower-level Q-learning module simulates the individual-level local adaptation, where bees adjust harvesting behavior in real-time based on nectar quality and distance. This module continuously optimizes routing parameters based on real-time network states, including residual energy, the ratio of surviving nodes, data queue lengths, and cluster head density. The algorithm employs an ϵ-greedy strategy to balance exploration and exploitation, while a periodic decision-update mechanism is introduced to harmonize computational efficiency with learning stability. Furthermore, a multi-objective reward function is designed to jointly optimize energy efficiency, network lifetime, end-to-end latency, and path length. Extensive simulation results demonstrate that the proposed MILP-MDP hybrid framework significantly outperforms several representative baseline algorithms in terms of network lifetime extension and energy balance. These findings validate that the integration of bio-inspired foraging strategies and reinforcement learning provides an efficient and robust solution for trajectory planning and adaptive routing in dynamic WSNs.
The increasing deployment of wind energy has brought renewed attention to aeroacoustic noise generated by wind turbine blades, where broadband noise is primarily associated with vortex shedding at the trailing edge (TE) and leading edge (LE) of airfoils. Owls, particularly Tyto alba, exhibit wing morphologies such as serrations, velvet-like surfaces, and fringes that enable silent flight through aerodynamic noise suppression. This study presents a scoping review of the scientific literature on owl-inspired serration strategies applied to aerodynamic airfoils and wind turbine blades. The literature search was conducted across major academic databases, including Scopus, ScienceDirect, SpringerLink, and MDPI, covering publications from 1970 to 2025. A total of 69 experimental and numerical studies focusing on LE and TE serrations was analyzed. The review integrates aeroacoustic analysis with bio-inspired design perspectives. The analyzed studies consistently show that serrated geometries modify vortex dynamics and turbulence structures, leading to measurable acoustic benefits. Experimentally, the largest reductions reported for aerodynamic airfoils reached about 7 dB for both LE and TE serrations, mainly as broadband noise attenuation, in specific frequency ranges. Numerically, the highest reported reduction reached up to 21 dB for a serrated TE configuration, corresponding to spectral SPL reduction mainly below 1.6 kHz. The reviewed studies also indicate that the associated aerodynamic response is strongly configuration-dependent, ranging from limited penalties to measurable changes in lift, drag, power output, or structural loading. Numerical simulations further support experimental findings and highlight the importance of geometric parameters such as serration amplitude, wavelength, and spacing. Overall, bio-inspired serrations represent a promising passive strategy for aeroacoustic noise mitigation in wind turbines, drones, and rotating aerodynamic systems. Future research should focus on the multi-objective optimization of serration geometry, large-scale experimental validation, and the integration of bio-inspired concepts into industrial blade designs.