Search Results (503)

This work introduces an original sequential bio-inspired strategy combining lytic phage pretreatment with TiO₂–thyme essential oil (TEO) photocatalysis, achieving near-complete inhibition of both biofilm initiation and maturation. By simultaneously targeting planktonic cells, mature biofilms, and extracellular DNA (eDNA), this approach addresses key mechanisms involved in biofilm persistence. Pseudomonas aeruginosa ATCC 4114 was selected as the biological model due to its relevance in water distribution systems and its strong biofilm-forming ability. Experimental results showed that phage pretreatment alone inhibited biofilm formation by planktonic cells by up to 99.6% (inactivation rate constant k = 0.034 min⁻¹) and weakened bacterial attachment in mature biofilms by 89.06% (k = 0.011 min⁻¹). To further enhance photocatalytic efficacy, titanium dioxide (TiO₂) was combined with TEO at 0.05% (v/v) as a bio-inspired photosensitizer. UV–Vis spectroscopy confirmed TiO₂-TEO interactions that extended light absorption into the visible region (400–700 nm), thereby enhancing photocatalytic efficiency. This combination was designed to suppress residual biofilm development and disrupt extracellular DNA (eDNA), a critical component of biofilm structure and stability. The integrated approach involving phage pretreatment followed by TiO₂–TEO (0.05%) photocatalysis achieved 99.99% inhibition of both biofilm initiation and maturation phases, with significantly increased kinetic parameters (A = 2.62 for planktonic cells and A = 3.65 for sessile cells; k = 0.076 min⁻¹ and 0.063 min⁻¹, respectively; p < 0.01). This study provides novel insights into water disinfection strategies using photocatalytic treatment, emphasizing the importance of monitoring post-treatment bacterial virulence factor expression. Full article

Water scarcity constitutes a major global challenge. Biomimetic water collection materials, which mimic the efficient water capture and transport mechanisms, offer a crucial approach to addressing the water crisis. This review summarizes the research progress on biomimetic water collection materials, focusing on biological prototypes, operational mechanisms, and core aspects of biomimetic design. Typical water-collecting biological surfaces in nature exhibit distinctive structure–function synergy: spider silk achieves directional droplet transport via periodic spindle-knot structures, utilizing Laplace pressure difference and surface energy gradient; the desert beetle’s back features hydrophilic microstructures and a hydrophobic waxy coating, forming a fog-water collection system based on heterogeneous wettability; cactus spines enhance droplet transport efficiency through the synergy of gradient grooves and barbs; and shorebird beaks enable rapid water convergence via liquid bridge effects. These biological prototypes provide vital inspiration for the design of biomimetic water collection materials. Drawing on biological mechanisms, researchers have developed diverse biomimetic water collection materials. This review offers a theoretical reference for their structural design and performance enhancement, highlighting bio-inspiration’s core value in high-efficiency water collection material development. Additionally, this paper discusses challenges and opportunities of these materials, providing insights for advancing the engineering application of next-generation high-efficiency biomimetic water collection materials. Full article

Humans could sensitively perceive and identify objects through dense mechanoreceptors distributed on the skin of curved fingertips. Inspired by this biological structure, this study presents a general conformal integration method for flexible tactile sensors on curved fingertip surfaces. By adopting a spherical partition design and an inverse mode auxiliary layering process, it ensures the uniform distribution of stress at different curvatures. The sensor adopts a 3 × 3 tactile array configuration, replicating the 3D curved surface distribution of human mechanoreceptors. By analyzing multi-point outputs, the sensor reconstructs contact pressure gradients and infers the softness or stiffness of touched objects, thereby realizing both structural and functional bionics. These sensors exhibit excellent linearity within 0–100 kPa (sensitivity ≈ 36.86 kPa⁻¹), fast response (2 ms), and outstanding durability (signal decay of only 1.94% after 30,000 cycles). It is worth noting that this conformal tactile fingertip integration method not only exhibits uniform responses at each unit, but also has the preload-free advantage, and then performs well in pulse detection and hardness discrimination. This work provides a novel bioinspired pathway for conformal integration of tactile sensors, enabling artificial skins and robotic fingertips with human-like tactile perception. Full article

Inspired by the Tesla valve, the island-type fishway is a novel design whose biological performance remains unelucidated. This study integrated hydraulic experiments, CFD modeling, and 3D computer vision to investigate the passage performance and swimming behavior of juvenile silver carp (Hypophthalmichthys molitrix). The results confirmed high biological feasibility, with upstream success rates exceeding 70%. The island and arc-baffle configuration create a heterogeneous flow field with an S-shaped main flow and low-velocity zones; each island unit contributes 8.9% to total energy dissipation. Critically, fish utilize a multi-dimensional navigation strategy to avoid high-velocity cores: temporally adopting an intermittent “rest-burst” pattern for energetic recovery; horizontally following an “Ω”-shaped bypass trajectory; and vertically preferring the bottom boundary layer. Passage failure was primarily linked to suboptimal path selection near the high-velocity main flow. These findings demonstrate that fishway effectiveness depends less on bulk hydraulic parameters and more on the spatial connectivity of hydraulic refugia aligning with fish behavioral traits. This study provides a scientific basis for optimizing eco-friendly hydraulic structures. Full article

While drug-eluting cardiovascular devices, including drug-eluting stents and drug-coated balloons, have significantly reduced restenosis rates, they remain limited by delayed vascular healing, chronic inflammation, and late adverse events. These limitations reflect a fundamental mismatch between current device pharmacology, which relies on nonselective antiproliferative drugs, and the highly coordinated, cell-specific programs that orchestrate vascular repair. Extracellular vesicles (EVs), nanometer-scale membrane-bound particles secreted by virtually all cell types, provide a biologically evolved platform for intercellular communication and cargo delivery. In the cardiovascular system, EVs regulate endothelial regeneration, smooth muscle cell phenotype, extracellular matrix remodeling, and macrophage polarization through precisely orchestrated combinations of miRNA, proteins, and lipids. Here, we synthesize mechanistic insights into EV biogenesis, cargo selection, recruitment, and functional effects in vascular healing and inflammation and translate these into a formal framework for EV-inspired device engineering. We discuss how EV-based or EV-mimetic coatings can be designed to sense the local microenvironment, deliver encoded biological “instruction sets,” and function within ECM-mimetic scaffolds to couple local stent healing with systemic tissue repair. Finally, we outline the manufacturing, regulatory, and clinical trial issues that must be addressed for EV-inspired cardiovascular devices to transition from proof of concept to clinical reality. By shifting the focus from pharmacological suppression to biological regulation of healing, EV-based strategies offer a path to resolve the long-standing tradeoff between restenosis prevention and durable vascular healing. Full article

Recent advances in automated learning techniques have enhanced the analysis of biomedical signals for detecting sleep stages and related health abnormalities. However, many existing models face challenges with imbalanced datasets and the dynamic nature of evolving sleep states. In this study, we present a robust algorithm for classifying sleep states using electroencephalogram (EEG) data collected from 33 healthy participants. We extracted dynamic, brain-inspired features, such as microstates and Lempel–Ziv complexity, which replicate intrinsic neural processing patterns and reflect temporal changes in brain activity during sleep. An optimal feature set was identified based on significant spectral ranges and classification performance. The classifier was developed using a convolutional neural network (CNN) combined with gated recurrent units (GRUs) within a reinforcement learning framework, which models adaptive decision-making processes similar to those in biological neural systems. Our proposed biomimetic framework illustrates that a multivariate feature set provides strong discriminative power for sleep state classification. Benchmark comparisons with established approaches revealed a classification accuracy of 98% using the optimized feature set, with the framework utilizing fewer EEG channels and reducing processing time, underscoring its potential for real-time deployment. These findings indicate that applying biomimetic principles in feature extraction and model design can improve automated sleep monitoring and facilitate the development of novel therapeutic and diagnostic tools for sleep-related disorders. Full article

Contemporary neural and generative architectures are deficient in self-preservation mechanisms and sustainable stability. In uncertain or noisy situations, they frequently demonstrate oscillatory learning, overconfidence, and structural deterioration, indicating a lack of biological regulatory principles in artificial systems. We present Bio-RegNet, a meta-homeostatic Bayesian neural network architecture that integrates T-regulatory-cell-inspired immunoregulation with autophagic structural optimization. The model integrates three synergistic subsystems: the Bayesian Effector Network (BEN) for uncertainty-aware inference, the Regulatory Immune Network (RIN) for Lyapunov-based inhibitory control, and the Autophagic Optimization Engine (AOE) for energy-efficient regeneration, thereby establishing a closed energy–entropy loop that attains adaptive equilibrium among cognition, regulation, and metabolism. This triadic feedback achieves meta-homeostasis, transforming learning into a process of ongoing self-stabilization instead of static optimization. Bio-RegNet routinely outperforms state-of-the-art dynamic GNNs across twelve neuronal, molecular, and macro-scale benchmarks, enhancing calibration and energy efficiency by over 20% and expediting recovery from perturbations by 14%. Its domain-invariant equilibrium facilitates seamless transfer between biological and manufactured systems, exemplifying a fundamental notion of bio-inspired, self-sustaining intelligence—connecting generative AI and biomimetic design for sustainable, living computation. Bio-RegNet consistently outperforms the strongest baseline HGNN-ODE, improving ARI from 0.77 to 0.81 and NMI from 0.84 to 0.87, while increasing equilibrium coherence $\kappa$ from 0.86 to 0.93. Full article

This study presents a magnetically actuated soft robot inspired by the peristaltic locomotion of flatworms, designed to replicate the biological locomotion of worms to achieve robust maneuverability. Fabricated entirely from photocurable soft resin, the robot features a flexible elastomeric body and two webbed fins with embedded soft magnets. By applying a vertically oscillating magnetic field, the robot achieves forward crawling through the coordinated bending and lifting of fins, converting oscillating magnetic fields into continuous undulatory motion that mimics the gait of flatworms. The experimental results demonstrate that the system maintains consistent bidirectional velocities in the range of 4–7 mm/s on flat surfaces. Beyond linear locomotion, the robot demonstrates effective terrain adaptability, navigating complex topographies, including curved obstacles up to 16 times its body thickness, by autonomously adopting a high-lifting kinematic strategy to overcome gravitational resistance. Furthermore, load-carrying tests reveal that the robot can transport a 6 g payload without velocity degradation. These findings underscore the robot’s efficacy in overcoming mobility constraints, highlighting promising applications in fields requiring non-invasive intervention, such as biomedical capsule endoscopy and industrial pipeline inspection. Full article

Detecting vehicles in remote sensing images of construction sites captured by Unmanned Aerial Vehicles (UAVs) faces severe challenges, including extremely small target scales, high inter-class visual similarity, cluttered backgrounds, and highly variable imaging conditions. To address these issues, we propose BSGNet (Biomimetic Sharpening and Global Receptive Field Network)—a novel detection architecture that synergistically fuses biologically inspired visual mechanisms with global receptive field modeling. Inspired by the Sustained Contrast Detection (SCD) mechanism in frog retinal ganglion cells, we design a Perceptual Sharpening Module (PSM). This module combines dual-path contrast enhancement with spatial attention mechanisms to significantly improve sensitivity to the high-frequency edge structures of small targets while effectively suppressing interfering backgrounds. To overcome the inherent limitation of such biomimetic mechanisms—specifically their restricted local receptive fields—we further introduce a Global Heterogeneous Receptive Field Learning Module (GRM). This module employs parallel multi-branch dilated convolutions and local detail enhancement paths to achieve joint modeling of long-range semantic context and fine-grained local features. Extensive experiments on our newly constructed UAV Construction Vehicle (UCV) dataset demonstrate that BSGNet achieves state-of-the-art performance: obtaining 64.9% APs on small targets and 81.2% on the overall mAP@0.5 metric, with an inference latency of only 31.4 milliseconds, outperforming existing mainstream detection frameworks in multiple metrics. Furthermore, the model demonstrates robust generalization performance on public datasets. Full article

Drug–drug interactions (DDIs) can cause adverse reactions or reduce the efficiency of a drug. Using computers to predict DDIs is now critical in pharmacology, as this reduces risks, improves drug outcomes and lowers healthcare costs. Clinical trials are slow, expensive, and require a lot of effort. The use of artificial intelligence (AI), primarily in the form of machine learning (ML) and its subfield deep learning (DL), has made DDI prediction more accurate and efficient when handling large datasets from biological, chemical, and clinical domains. Many ML and DL approaches are bio-inspired, taking inspiration from natural systems, and are considered part of the broader class of biomimetic methods. This review provides a comprehensive overview of AI-based methods currently used for DDI prediction. These include classical ML algorithms, such as logistic regression (LR) and support vector machines (SVMs); advanced DL models, such as deep neural networks (DNNs) and long short-term memory networks (LSTMs); graph-based models, such as graph convolutional networks (GCNs) and graph attention networks (GATs); and ensemble techniques. The use of knowledge graphs and transformers to capture relations and meaningful data about drugs is also investigated. Additionally, emerging biomimetic approaches offer promising directions for the future in designing AI models that can emulate the complexity of pharmacological interactions. These upgrades include using genetic algorithms with LR and SVM, neuroevaluation (brain-inspired model optimization) to improve DNN and LSTM architectures, ant-colony-inspired path exploration with GCN and GAT, and immune-inspired attention mechanisms in transformer models. This manuscript reviews the typical types of data employed in DDI (pDDI) prediction studies and the evaluation methods employed, discussing the pros and cons of each. There are useful approaches outlined that reveal important points that require further research and suggest ways to improve the accuracy, usability, and understanding of DDI prediction models. Full article

Space exploration is a major global focus, advancing knowledge and exploiting new resources beyond Earth. Bioinspired design—drawing principles from nature—offers systematic pathways to increase the capability and intelligence of space robots. Prior reviews have emphasized on-orbit manipulators or lunar rovers, while a comprehensive treatment across application domains has been limited. This review synthesizes bioinspired capability and intelligence for space exploration under varied environmental constraints. We highlight four domains: adhesion and grasping for on-orbit servicing; terrain-adaptive mobility on granular and rocky surfaces; exploration intelligence that couples animal-like sensing with decision strategies; and design methodologies for translating biological functions into robotic implementations. Representative applications include gecko-like dry adhesives for debris capture, beetle-inspired climbers for truss operations, sand-moving quadrupeds and mole-inspired burrowers for granular regolith access, and insect flapping-wing robots for flight under Martian conditions. By linking biological analogues to quantitative performance metrics, this review highlights how bioinspired strategies can significantly improve on-orbit inspection, planetary mobility, subsurface access, and autonomous decision-making. Framed by capability and intelligence, bioinspired approaches reveal how biological analogues translate into tangible performance gains for on-orbit inspection, servicing, and long-range planetary exploration. Full article

While BioTRIZ is widely employed in biomimetic design to facilitate creative ideation and standardize workflows, accurately formulating domain conflicts and assessing design schemes during critical stages—such as initial concept development and scheme evaluation—remains a significant challenge. To address these issues, this study proposes an advanced BioTRIZ method. Firstly, the theory of technological evolution is integrated into the domain conflict identification stage, resulting in the development of a prompt framework based on patent analysis to guide large language models (LLMs) in verifying the laws of technological evolution (LTE). Building on these insights, domain conflicts encountered throughout the design process are formulated, and inventive principles with heuristic value, alongside standardized biological knowledge, are derived to generate conceptual solutions. Subsequently, a main parameter of value (MPV) model is constructed through mining user review data, and the evaluation of conceptual designs is systematically performed via the integration of orthogonal design and the fuzzy analytic hierarchy process to identify the optimal combination of component solutions. The optimization case study of a floor scrubber, along with the corresponding experimental results, demonstrates the efficacy and advancement of the proposed method. This study aims to reduce the operational difficulty associated with implementing BioTRIZ in product development processes, while simultaneously enhancing its accuracy. Full article

As conventional computing architectures face fundamental physical limitations and the von Neumann bottleneck constrains computational efficiency, neuromorphic systems have emerged as a promising paradigm for next-generation information processing. Memristive neurons, particularly third-order circuits operating near the edge of chaos, exhibit rich neuromorphic dynamics that closely mimic biological neural activities but present significant prediction challenges due to their complex nonlinear behavior. Current approaches typically require complete system state measurements, which is often impractical in real-world neuromorphic hardware implementations where only partial state information is accessible. This paper addresses this critical limitation by proposing an innovative hybrid machine learning framework that integrates a Modified Next-Generation Reservoir Computing (MNGRC) with XGBoost regression. The core novelty lies in its dual-path prediction architecture designed specifically for partial state observability scenarios. The primary path employs NGRC to capture and forecast the system’s temporal dynamics using available state variables and input stimuli, while the secondary path leverages XGBoost as an efficient state estimator to infer unobserved state variables from minimal measurements. This strategic combination enables accurate prediction of diverse neuromorphic patterns with significantly reduced sensor requirements. Experimentally, the framework demonstrates its capability to identify and predict the complex spectrum of neuromorphic behaviors exhibited by the third-order memristive neuron. This includes accurately capturing all 18 distinct neuronal patterns, which are theoretically grounded in Hopf bifurcation analysis near the edge of chaos. Additionally, the framework successfully addresses the inverse problem of input stimulus reconstruction. By achieving accurate prediction of complex dynamics from limited states, our approach represents a key breakthrough, where full state access is often impossible, thereby addressing a critical challenge in edge AI and brain-inspired computing. Full article

Bone regeneration remains difficult due to the complex bone microenvironment and the limited healing capacity of large defects. Biomimetic strategies offer promising solutions by using advanced 3D scaffolds guided by natural tissue cues. Recent advances in additive manufacturing, nanotechnology, and tissue engineering now allow the fabrication of hierarchical scaffolds that closely mimic native bone. Smart scaffold systems combine materials with biochemical and mechanical signals. These features improve vascularization, enhance tissue integration, and support better regenerative outcomes. Bio-inspired materials also help connect inert implants with living tissues by promoting vascular network formation and improving cell communication. Multiscale design approaches recreate bone nano- to macro-level structure and support both osteogenic activity and immune regulation. Intelligent and adaptive scaffolds are being developed to respond to physiological changes and enable personalized bone repair. This review discusses the current landscape of biomimetic scaffold design, fabrication techniques, material strategies, biological mechanisms, and translational considerations shaping next-generation bone regeneration technologies. Future directions focus on sustainable, clinically translatable biomimetic systems that can integrate with digital health tools for improved treatment planning. Full article

The symmetrical relationship between affective semantics and form bionics creates new possibilities for tea packaging. This study proposes a biologically inspired workflow for tea packaging design, effectively integrating natural forms, affective semantics, and sustainability assessment. First, ten natural forms suitable for bionic design were collected. The Affinity Diagram (AD) method was adopted based on evaluations from 20 consumers and tea merchants, yielding nine effective semantic and sustainability evaluation systems. Then, 10 domain experts scored the affective semantics, and the indicator weights were determined via the Precedence Chart (PC) method. The Quality Function Deployment (QFD) method was used to construct a relationship matrix between natural forms and affective semantics, identifying prioritized natural forms. Three biomimetic tea packaging designs were developed based on the three selected priority forms. Subsequently, the Criteria Importance Through Intercriteria Correlation (CRITIC) method calculated the objective weights of sustainability indicators. These weights were combined with Grey Relational Analysis (GRA) for comprehensive ranking to determine the optimal packaging scheme. The results show that stylish design (P1) has the highest weight among affective semantics, while low resource consumption (Q1) ranks first in sustainability evaluation indicators. Bamboo joint packaging was selected as the optimal design solution in the comprehensive ranking. This design process provides a methodological framework for tea packaging design, integrates biological bionics with affective semantics, and demonstrates potential for cross-category applications. Full article