搜索结果：Bioinspiration & biomimetics

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.

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.

Amino acid surfactants are widely used in daily life and food industry. Melanin-like amino acid surfactant (MLAAS) is a novel type surfactant. Herein, a robust nanozyme with tyrosinase mimetic activity was first developed by fabricating BSA@Cu3(PO4)2 organic-inorganic hybrid nanoflower (HNF) using bovine serum albumin and copper ions as an organic and inorganic components, respectively. HNF nanozyme biomimetically oxidized ethyl caffeate to o-quinone, achieving yield of 59.57% in organic solvents with Na2CO3·10H2O as essential cofactor. Michaelis constant was 15.05 mM and turnover number was 6.32 s-1. It exhibited broad substrate scope toward diphenols and outstanding stability in various organic solvents. o-Quinone was conjugated with leucine to produce MLAAS, which possessed excellent emulsifying and foaming capacities with a critical micelle concentration of 8.12 mM. It presents a novel strategy by combination of HNF and salt hydrate for production of amino acid surfactant with promising emulsification and foaming potential in food processing.

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.

The precise perception of unsteady flow environments is critical for realizing 'fly-by-feel' flight control in next-generation aircraft. However, existing artificial hair sensors typically operate in a rigid, low-Cauchy-number regime and rely on scalar transduction, limiting their ability to resolve the flow direction without complex, dense arrays. In this study, we present a bio-inspired sensing system based on flexible magnetic cilia fabricated from a soft elastomer matrix. These sensors achieve a low elastic modulus that places them in a high-Cauchy-number regime, mechanically mimicking the compliance of seal whiskers and bat hairs. By synergizing this mechanical compliance with vector-sensitive magnetic transduction, we demonstrate that a single cilium can simultaneously resolve both the magnitude and direction of the airflow. Experimental validation on a non-slender delta wing confirms the array's ability to capture critical aerodynamic features, including leading-edge vortex migration, flow separation, and reattachment. Unlike traditional isotropic designs, this approach provides directional sensitivity at the single-sensor level, offering a scalable pathway for distributed aerodynamic monitoring.

This review provides a thorough summary of recent progress in catalysts used for hydrogen production. It starts with an overview of the importance of hydrogen and its historical background, then looks into catalytic methods for the thermochemical transformation of fossil fuels and organic materials, with a specific emphasis on steam methane reforming (SMR) and ethanol steam reforming (ESR). The review discusses the fundamental processes and approaches to minimize CO2 emissions and tackle catalyst deactivation due to sulfur and coking. It also delves into dry reforming and the catalytic breakdown of methane. Furthermore, the review investigates water splitting through electrolysis and photolysis, clarifying key principles, types of catalysts, reaction pathways, and performance assessment criteria. A specific section focuses on biological, biomimetic, and bioinspired catalytic-driven methods for generating hydrogen, along with a brief overview of catalysts involved in hydrogen release from chemical hydrides. Ultimately, the review underscores the increasingly important role of computational techniques, such as density functional theory (DFT) and Artificial Intelligence/Machine Learning (AI/ML), in the rational design of innovative catalytic materials. While acknowledging the extensive field of hydrogen production, this review seeks to offer an insightful perspective on catalytic elements, highlighting current trends, challenges, and future opportunities in the pursuit of sustainable hydrogen generation. The final remarks stress the importance of developing practical and scalable catalysts, taking environmental impacts into account, standardizing the reporting of catalytic performance, and recognizing the significant role often played by catalyst supports.

Viruses, as naturally evolved gene delivery systems, exhibit exceptional efficiency in overcoming biological barriers, targeting specific cells, and inducing robust immune responses. Inspired by these characteristics, virus-mimicking nanocarriers have become a promising strategy for enhancing drug and gene delivery. This review provides a detailed exploration of virus-inspired nanodelivery systems, with particular emphasis on their structural and functional biomimetic properties. We first focus on natural viral delivery platforms, such as viral nanoparticles and virus-like particles. Then, we discuss the application of biomimetic strategies that mimic viral morphology and topological structure in overcoming multiple physiological barriers in the body. Particularly, we examine bioinspired strategies that mimic key viral infection functions, including overcoming physiological barriers, cell adhesion and surface recognition, membrane fusion and cellular internalization, environmental perception and endosomal escape, intracellular trafficking and organelle targeting, and immune modulation. Additionally, we highlight the integration of intelligent strategies to optimize delivery efficiency. Finally, we discuss the current challenges and future opportunities in the field, aiming to advance virus-inspired nanomedicine toward clinical applications.

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.

Biological cellular structures exhibit a high degree of systematic organization in both morphological configuration and functional coordination, providing important biomimetic insights for urban spatial organization. To address issues in traditional high-density residential areas, such as homogeneous spatial structures and insufficient accessibility of public spaces, this study proposes a planning method for five-minute living circle residential areas based on a biomimetic cellular structure within the framework of space syntax theory. Taking a residential area in Wuhan, China, as a case study, a cell-like spatial structure model was constructed. Convex space analysis, axial analysis, and visibility analysis were conducted using Depthmap software to quantitatively evaluate key syntactic indicators, including integration, connectivity, mean depth, and choice. The results show that, compared with the original planning scheme, the biomimetic cellular planning model significantly optimized the spatial structure of the residential area by relying on the functionally synergistic mechanisms of selective permeability of the cell membrane, whole-area permeation of the cytoplasm, central regulation of the nucleus, distributed coordination of organelles, and efficient transport through cellular microfilaments. In the sample living circle, the overall integration increased from 1.27 to 1.64, the mean depth decreased from 3.79 to 3.18, and spatial connectivity increased from 3.74 to 5.44. Meanwhile, the synergy of the road network increased from 0.44 to 0.86, indicating marked improvements in spatial accessibility, connectivity, and the degree of coordination within the spatial structure. In addition, the visibility analysis showed that the pedestrian aggregation capacity of the public core space was enhanced, and the spatial vitality of public activity spaces in the residential area was improved. The findings demonstrate that the spatial organization model based on biomimetic cellular principles can effectively enhance spatial efficiency and social vitality in five-minute living circle residential areas, providing a quantifiable design method and theoretical framework for bio-inspired urban planning.

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.

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.

Biomimetic spider silk achieves remarkable functionalities through hierarchical architectures with highly oriented crystalline domains, offering potential across multiple disciplines. However, achieving uniform alignment and spatial control of nanocrystalline domains remains a critical challenge, limiting the realization of structure-derived optical and mechanical functionalities in bioinspired systems. Here, we develop an ultrastrong, transparent photonic hydrogel composed of cellulose nanocrystals (CNCs), wherein a programmable five-stage stretching-pause process enables precise alignment of CNC domains without sacrificing their intrinsic chirality-unattainable in conventional flexible polymers. This strategy facilitates uniform nanocrystal reorientation (orientation factor = 0.91) and transforms the porous network into aligned nanofibril bundles, yielding optical transparency (>90%) with anisotropic polarization responses, superior mechanical strength (61.6 MPa), toughness (251.8 MJ·m-3), and fatigue resistance (226.7 kJ·m-2). The flexible hydrogel resists creasing and serves as a sustainable scattering polarizer for programmable polarized displays and secure information encryption, providing a versatile platform for advanced optical and electronic applications.

So far, space exploration has attracted increasing scientific interest due to the growth of missions promoted by private investment, such as SpaceX, Boeing, Blue Origin, and the recent attention generated by astronomical phenomena such as 3I/ATLAS. However, access to space experimentation remains limited and expensive. For this reason, new approaches to simulate space conditions on Earth are being developed to broaden research opportunities bio-inspired by plant responses to phototropism and geotropism. In this context, Betta Aerospace has continued the development of a microgravity simulation system consisting of a 3-axis clinostat powered by a single motor, continuous external electrical supply, and, in this project, a continuous external liquid supply. The proposed pioneer system was designed as a flexible platform manufactured through reinforced 3D printing, with an approximate size of 30 cm, an estimated payload of 30 kg, and a 24 V supply. Its main goal is to study the effects of simulated microgravity on aquatic organisms while enabling longer observation times in a controlled freshwater environment. Candidate biological samples include Ulva lactuca, Pyropia, Spirulina/Arthrospira, and Chlorella. Preliminary motion tests confirmed continuous operation at 10 rpm. In addition, a simplified static finite element analysis under a 294 N load yielded a maximum von Mises stress of 5.45 × 107 Pa and a maximum displacement of 1.73 mm.

RNA therapeutics offer transformative potential for neural repair. However, various delivery challenges continue to hinder the clinical translation of RNA therapeutics. Synthetic biology, as an interdisciplinary cutting-edge field, improves delivery systems by utilizing modular design and rational engineering. This approach leads to greater efficiency, precision, and programmability in these systems. This review addresses the application of synthetic biology-based bioinspired delivery systems for neural repair. First, this review outlines the challenges faced by RNA therapies in neural repair: systemic administration encounters challenges posed by the blood-brain barrier and blood-nerve barrier; local administration faces issues related to limited tissue penetration and diffusion; intranasal administration suffers from low efficiency; and clinical translation must also address safety concerns and the need for standardized production that complies with Good Manufacturing Practices. Second, this review describes bioinspired delivery strategies based on synthetic biology, which incorporate modular design, biomimetic synthesis, and biological engineering approaches. Third, this review introduces innovative applications of synthetic biology in drug delivery systems for neural repair, guided by the Design-Build-Test-Learn cycle. This cycle connects fundamental biological mechanisms with artificial intelligence-assisted computational tools to optimize formulations while managing the complexities of deploying biological circuits in the central nervous system. Fourth, this review evaluates the landscape of clinical translation by drawing on insights from commercial products and clinical trials. It considers important factors such as Chemistry, Manufacturing, and Controls requirements, platform-based regulatory pathways, and ethical issues related to engineered cell therapies. Finally, this review offers a perspective on the potential of synthetic biology-based RNA therapeutics in neural repair, emphasizing significant technical innovations. Three primary challenges that can be addressed are identified: (1) overcoming central nervous system delivery challenges through the design of synthetic biology; (2) translating mechanistic insights into practical applications using the Design-Build-Test-Learn framework; and (3) aligning new delivery methods with complex regulatory pathways. The primary contribution of this review is the creation of a system engineering framework that converts RNA delivery into programmable biological machines. This framework emphasizes the Design-Build-Test-Learn cycle and incorporates artificial intelligence-assisted tools, thereby advancing the field of central nervous system delivery technologies, particularly in neural repair.

Immobilized enzymes offer a sustainable, cost-effective alternative to soluble catalysis, but an adaptable platform preserving native conformation, stability, and broad enzyme/material compatibility remains elusive. We report a facile, general, jigsaw-designed platform via co-assembling silk fibroin (SF), protocatechualdehyde (PA), and enzymes. Mussel-inspired PA remodels SF into β-sheet-rich "nano-pockets" to conformationally lock bioenzymes, while its aldehyde and catechol moieties anchor enzymes and surfaces, yielding structurally and functionally adaptable, bioactive-biocatalytic self-assemblies. The platform conferred exceptional stability: enzymes withstood 20 cycles at 50 °C and retained 47.7% activity after one-year ambient storage. The assemblies accommodated diverse enzymes (ALP, GOx, HRP, SOD, CAT), adhered universally to solid surfaces, MOFs, and liquid metal microspheres, and were programmable into coatings, films, or hydrogels. Rational enzyme selection enabled biomimetic mineralization (ALP), antioxidation (SOD/CAT), and antibacterial activity (GOx). Spatial organization of cascade enzymes in core-shell nanoparticles enabled gated catalysis, while segregation on conductive supports facilitated enzyme-mediated electrochemical corrosion control. Moreover, these biointerfacing assemblies elicited favorable cell adhesion, biocompatibility, and anti-inflammatory/-infective effects in vitro/vivo. This modular platform provides a versatile blueprint for robust, catalytically bioactive materials tailored to advanced bioapplications and readily extendable to immobilize other functional biomolecules like peptides.

Insects can achieve rapid and precise collision detection despite having limited neural resources. This efficiency provides a vital reference for the development of artificial collision detection systems. Existing bio-inspired models typically include LGMD-based and correlation-based methods. Methods in the former category suffer from a non-linear dependency of warning time on the object's contrast against the background due to the strong reliance on inter-frame intensity differences. While the latter effectively describe motion perception by leveraging local motion information derived from a delay-and-correlation mechanism, they lack precise spatial boundaries, failing to isolate the actual moving target across irrelevant background dynamics. In this paper, we propose a bio-inspired visual system with a motion-contour-guided mechanism to suppress false-positive background movement while achieving contrast-independent looming warning generation. Specifically, the proposed visual system is composed of two synergistic pathways. The first pathway is designed to extract motion cues and spatial perception of motion via neuronal ensemble coding, whereas the second pathway is developed to extract the contour of the moving target by employing geometric contour evolution. By integrating this derived contour with localized motion cues, the system analyzes the dynamic evolution of the target's boundary to identify potential collision threats. Benefiting from this fusion of structure and motion, experimental results demonstrate that the proposed visual system is more robust than conventional bio-inspired models in collision detection across distinct contrast scenarios.

Multi-modal Magnetic Resonance Imaging (MRI) provides complementary information for clinical diagnosis, yet its large-scale storage, privacy sensitivity, and annotation cost pose significant challenges. Inspired by biological vision systems, which integrate multi-sensory inputs and compress experiences into compact memory representations, we propose a bio-inspired framework termed Contrast-Guided Multi-modal Dataset Distillation (CGMDD). In biological perception, different sensory channels observe the same environment from complementary perspectives, while hierarchical neural processing ensures perceptual consistency across modalities. Meanwhile, memory systems such as the associated medial temporal lobe structures consolidate redundant experiences into efficient representations for long-term storage. Motivated by these principles, CGMDD treats multi-modal MRI as multi-view perceptual signals and introduces a hierarchical cross-modal contrastive learning mechanism that enforces perceptual alignment across modalities, analogous to multi-level processing in the visual cortex. Furthermore, we design a dynamic dataset distillation strategy that mimics memory consolidation by compressing large-scale data into compact, informative synthetic representations through gradient-based optimization. The proposed framework jointly optimizes perceptual alignment and memory compression in an end-to-end manner, achieving a biologically plausible integration of perception and learning. Experimental results on two MRI datasets demonstrate that CGMDD can compress the original dataset to 5% of its size while maintaining competitive performance, even with only 30% of the labels. These findings highlight the effectiveness of bio-inspired mechanisms in building efficient, robust, and privacy-preserving computer vision systems.

Selecting suitable biological models remains one of the most challenging and least formalised steps in biomimetic design. Existing tools support searching for biological strategies and transferring principles into engineering concepts, but provide limited guidance on which organism to prioritise once multiple plausible candidates are identified. Here we present a phylogeny-informed, data-driven framework that structures model selection as an explicit comparison. The framework computes four module scores per candidate model: data sufficiency, innovativeness, phylogenetic characteristics, and an open-ended contextual module for project-specific constraints. Users assign weights to the modules to reflect resources, timelines, and design requirements, yielding an overall compatible score and a ranked shortlist. We demonstrate the framework in a microplastic-filtration case study using 35 suspension-feeding taxa and three archetypal user scenarios. Rankings reveal a compact set of candidates that remains competitive across scenarios, while a smaller subset shifts in response to changes in priorities, thereby distinguishing robust starting points from context-dependent opportunities. Overall, the framework fills an under-supported gap in current biomimetic workflows by providing a structured decision-support layer for biological model selection.

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.