Bio-inspired Method Research Articles

Phyto-mediated synthesis of ZnO/attapulgite nanocomposites using C. limon peel extract for enhancing the resistance against multidrug-resistant bacteria and fungi

ZnO have great potential in combating multidrug-resistant (MDR) bacteria, however, green synthesis and activity regulation still face challenges. In this study, we designed a green bioinspired method mediated by C. limon peel extract to synthesise a series of attapulgite (APT) supported ZnO nanocomposites for enhancing the resistance against MDR pathogenic bacterial and fungal, in which the structure and morphology of ZnO was regulated by APT inducing heterogeneous nucleation of ZnO, thus enhancing the antibacterial activity of the nanocomposites. XRD and TEM analysis demonstrated that ZnO/APT with a unique peg-top-like morphology and well-defined crystal structure was successfully synthesized via the regulation of Zn2+ ions concentration. XPS results confirmed that the oxygen vacancy increased significantly compared with the pure ZnO (has a cubic shape) due to the structural modulation of ZnO induced by APT. The obtained ZnO/APT nanocomposites exhibited excellent biocompatibility and enhanced antibacterial efficacy with a complete inhibition at a concentration of 0.125 mg/mL for MRSA, a high inhibitory rate of 94.68 ± 1.43 % at a concentration of 0.5 mg/mL for ESBLs-E. coli, and the antifungal rate against C. albicans was found to be 95.59 ± 1.08 % at a concentration of 0.25 mg/mL. In addition, the ZnO/APT had a good hemostatic effect and enhanced biocompatibility compared to pure ZnO. The study provides a green way to design efficient ZnO antibacterial nanomaterials with remarkable antimicrobial efficacy and lower cytotoxicity, and the ZnO/APT nanocomposite is potential for clinical applications.

ViCo: Human visual perception-based contour extraction of Chinese Tang dynasty textile patterns

The study of ancient textile patterns not only enriches historical material but also inspires modern costume art design. Recent developments in computer vision technology provide novel analytical methods for the study of ancient textile patterns. As contours are one of the most significant features in computer vision tasks, contour extraction serves as a prerequisite to accomplishing intelligent understanding and automatic design of textile patterns. In this paper, a perception-consistent contour extraction method (ViCo) that simulates the human visual system from the retina, the lateral geniculate nucleus (LGN), to the V1 area in the cortex is proposed, along with an ancient textile pattern dataset represented by TuanKe in the Chinese Tang dynasty. Experiments were conducted both qualitatively and quantitatively to verify the effectiveness of the proposed method on the TuanKe dataset. Analysis of the results implies the effectiveness of ViCo because the retinal and LGN filter enhances edge information and restrains tiny pixel changes, and the cortex simulation is direction selective. This study is among the initial efforts to employ computer vision technology for ancient textile pattern studies. The study presents a TuanKe dataset and a novel bio-inspired contour extraction method, which has significant historical research importance, fabric art design inspiration, and computer application value.

Firefly forest: A swarm iteration-free swarm intelligence clustering algorithm

The Firefly Forest algorithm is a novel bio-inspired clustering method designed to address key challenges in traditional clustering techniques, such as the need to set a fixed number of neighbors, predefine cluster numbers, and rely on computationally intensive swarm iterative processes. The algorithm begins by using an adaptive neighbor estimation, refined to filter outliers, to determine the brightness of each firefly. This brightness guides the formation of firefly trees, which are then merged into cohesive firefly forests, completing the clustering process. This approach allows the algorithm to dynamically capture both local and global patterns, eliminate the need for predefined cluster numbers, and operate with low computational complexity. Experiments involving 14 established clustering algorithms across 19 diverse datasets, using 8 evaluative metrics, demonstrate the Firefly Forest algorithm’s superior accuracy and robustness. These results highlight its potential as a powerful tool for real-world clustering applications. Our code is available at: https://github.com/firesaku/FireflyForest.

Deep Learning Enhanced CNN with Bio-Inspired Techniques and BCE For Effective Lung Nodules Detection & Classification For Accurate Diagnosis

Objectives: To propose a new hybrid deep learning model to boost lung nodule detection and classification in combination with a bioinspired method for efficient hyper-parameter optimization to maximize true positive and nodule discrimination rates. Methods: In order to extract the intrinsic and complex features from different lung nodules, deep learning-based enhanced CNN (ECNN) is used. Segmentation masks are generated using Mask-RCNN to isolate and capture the ROIs, which serve as input to CNN. Prior to segmentation and extraction, data cleaning, transformation, and augmentation is done. Hyper-parameter optimization is done using a bio-inspired differential evolution method, which helps to identify the learning rate and number of layers to achieve optimal performance. We utilize BCE to quantify the performance of classification tasks. A large-scale CT-DICOM image dataset with 25,1135 images is used for this research work, collected from the cancer imaging archive, which has a clinically proven record of 355 instances with detailed image analysis, tumor location, and bounding boxes with a resolution of 512x512 pixels. 175794 images are used for training, and 75341 images are used for testing and validation. The performance of the new system is assessed using MATLAB, where results are compared with existing models such as SVM-WSS, GCPSO-PNN, and 3D-DLCNN models. Findings: The newly suggested ECNN with DE Bio-inspired model boosts the performance of lung nodule detection and classification with 94.7% accuracy, 93.8% sensitivity, 94.5% specificity, 93.4% F1 score, 92.4% dice coefficient, 0.1 Log Loss and AUC-ROC with 0.93 TPR and 0.07 FPR. Novelty: The novel method presents an advanced computational model using deep learning and bio-inspired algorithms for robust classification of lung nodules, which significantly improves the early diagnosis. This CAD model overcomes the limitations of the existing approaches SVM-WSS, GCPSO-PNN, and 3D-DLCNN in terms of segmentation, classification, error detection, and hyper-parameter tuning. Keywords: Lung Nodules, Deep Learning, Disease Classification, Image Processing, ECNN- DE Classifier, CT-DICOM Dataset

Bio-inspired Approaches for G-protein coupled receptors identification using Chou’s PseAAC

Background: G–protein coupled receptors (GPCRs) are key factors in cell-to-cell communication. GPCR activation is necessary for normal physiology of all organisms while dysfunction of GPCR signalling is responsible for many of the diseases. Consequently, GPCRs have a fundamental role in pharmacological research and are targets for many drugs. Objective: The problem is that many GPCRs remain orphans (have unknown function), they are not classified correctly, and new bioinformatics approaches are needed to address this issue. In our work, we focus on bio-inspired approaches, which are increasingly used in recent years because of their interesting inspirations from biological systems mechanisms and their good performances in many research areas. Methods: In this article, we use categories of bio-inspired well-known methods to identify GPCR function, which are swarm-based approaches and immunological computing. The proposed classifiers based on three popular swarm intelligence approaches are Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and PSO/ACO hybridization. The classification results are compared with these of the proposed immunological classifier based on the Artificial Immune Recognition System (AIRS), in order to identify the best bio-inspired method for the given problem. Results: The immune classifier (AIRS2) provided better results than swarm-based classifiers, specifically at the first levels (superfamily and families) Conclusion: It is interesting to adapt the bio-inspired algorithms in order to increase predictive accuracy at all GPCR hierarchical levels

Molecular Communication-Based Intelligent Dopamine Rate Modulator for Parkinson's Disease Treatment.

Parkinson's disease (PD) is a progressive neurodegenerative disease, and it is caused by the loss of dopaminergic neurons in the basal ganglia (BG). Currently, there is no definite cure for PD, and available treatments mainly aim to alleviate its symptoms. Due to impaired neurotransmitter-based information transmission in PD, molecular communication-based approaches can be employed as potential solutions to address this issue. Molecular Communications (MC) is a bio-inspired communication method utilizing molecules to carry information. This mode of communication stands out for developing biocompatible nanomachines for diagnosing and treating, particularly in addressing neurodegenerative diseases like PD, due to its compatibility with biological systems. This study presents a novel treatment method that introduces an Intelligent Dopamine Rate Modulator (IDRM), which is located in the synaptic gap between the substantia nigra pars compacta (SNc) and striatum to compensate for insufficiency dopamine release in BG caused by PD. For storing dopamine in the IDRM, dopamine compound (DAC) is swallowed and crossed through the digestive system, blood circulatory system, blood-brain barrier (BBB), and brain extracellular matrix uptakes with IDRMs. Here, the DAC concentration is calculated in these regions, revealing that the required exogenous dopamine consistently reaches IDRM. Therefore, the perpetual dopamine insufficiency in BG associated with PD can be compensated. This method reduces drug side effects because dopamine is not released in other brain regions. Unlike other treatments, this approach targets the root cause of PD rather than just reducing symptoms.

A Bio-Inspired Sliding Mode Method for Autonomous Cooperative Formation Control of Underactuated USVs with Ocean Environment Disturbances

In this paper, a bio-inspired sliding mode control (bio-SMC) and minimal learning parameter (MLP) are proposed to achieve the cooperative formation control of underactuated unmanned surface vehicles (USVs) with external environmental disturbances and model uncertainties. Firstly, the desired trajectory of the follower USV is generated by the leader USV’s position information based on the leader–follower framework, and the problem of cooperative formation control is transformed into a trajectory tracking error stabilization problem. Besides, the USV position errors are stabilized by a backstepping approach, then the virtual longitudinal and virtual lateral velocities can be designed. To alleviate the system oscillation and reduce the computational complexity of the controller, a sliding mode control with a bio-inspired model is designed to avoid the problem of differential explosion caused by repeated derivation. A radial basis function neural network (RBFNN) is adopted for estimating and compensating for the environmental disturbances and model uncertainties, where the MLP algorithm is utilized to substitute for online weight learning in a single-parameter form. Finally, the proposed method is proved to be uniformly and ultimately bounded through the Lyapunov stability theory, and the validity of the method is also verified by simulation experiments.

Robust AI Based Bio Inspired Protocol using GANs for Secure and Efficient Data Transmission in IoT to Minimize Data Loss

Objectives: To propose an AI-based protocol to enhance the reliability and security of IoT data transmission in a robust manner. Deep learning with bio-inspired methods is employed to address real-time challenges such as route optimization, data integrity, error recovery, and end-to-end delay in order to minimize data loss and maximize the transmission rate. Methods: Generative Adversarial Networks (GANs) are used to enhance the robustness of the protocol in combination with a bio-inspired Artificial Immune System (AIS) to detect IoT network anomalies and responds to malicious activities by using the adaptive learning capabilities of IS. Hybrid Automatic Repeat Request (HARQ), an error recovery method, is utilized to detect network errors in order to correct and retransmit data to the destination during error-prone network conditions. Queue learning action sets are used to discover the finest path for efficient data transmission. The OMNeT++ simulator is used to assess the performance of the proposed BIP-GANs protocol. The performance results are compared with the prevailing data transmission protocols, such as EAP-IFBA, DNN-CSO, and ALO-DHT. Findings: The suggested BIP-GANs data transmission protocol outperforms with promising results, with an energy consumption rate of 6%, 8 seconds data transmission speed, 6 second less delay rate, 98% robustness (security and confidentiality), 98.5% alive nodes, and 99% network life span, which is higher than the prevailing methods. Novelty: This research study provides a comprehensive solution to secure and efficient data transmission by integrating the AI-based bio-inspired BIP-GANs method to address the security and data transmission challenges of existing methods such as EAP-IFBA, DNN-CSO, and ALO-DHT in terms of energy consumption rate, transmission speed, delay rate, life span of the network, robustness, and number of alive nodes. Keywords: Artificial Intelligence, Generative Adversarial Networks, Error Recovery, Secured Data Transmission, Deep Learning, Bio­Inspired Algorithm

Bio-inspired target detection method of active sonar based on multi-ping perception

Abstract The performance of the active sonar may degrade severely in real ocean environments, influenced by strong reverberation and multiple clutters. Inspired by the observation that dolphins always utilize a train of clicks when detecting underwater, we proposed an active target detection method by making full use of the multi-ping echo information which encompasses the differences between targets and reverberation/clutters. The detection performance of the click trains with different parameters were analysed first to give an insight on signal choice. Then we achieved apparent reverberation/clutter suppression result by utilizing the correlations and differences between multi-ping echoes. Further, the relative motion status between the target and the sonar platform could be perceived within multiple echoes, which strengthened the detection ability of weak target. We proposed two weighting strategies for multi-ping co-utilization. Either equal weights or unequal weights based on the different envelopes of dolphin echolocation click trains were exerted on multiple echoes, which can achieve different perception results for the relative target motion status. Both simulation and lake trials were conducted to verify the performance of the proposed method. The proposed method effectively suppress reveberaton and noise background by around 2-3 dB and achieve bionic detection of weak targets compared to traditional active sonar signal processing method.

Passive flow control of a Francis turbine operating in sand-laden rivers for mitigating sediment erosion

In a typical Francis turbine operating in sand-laden rivers, owing to its complicated geometry and variable operating conditions, vortex structures appear and cause severe erosion damage to turbine components. Here, we present a bioinspired method to mitigate severe sediment erosion on Francis turbines. The proposed method includes a passive flow control strategy using biomimetic convex domes for the inter-blade vortex, a major contributor to severe sediment erosion on the turbine runner. The effects of biomimetic convex domes on sediment erosion are investigated through numerical simulations and experiments. The results indicate that biomimetic convex domes significantly reduce the impact velocity and accretion rate of the particles, eventually reducing sediment erosion by at least 50 %. The mechanism underlying the effect of convex domes on sediment erosion is their inhibition of the development of the inter-blade vortex. The convex domes induce small-scale vortices from the blade boundary layer. When located in the nascent region of the inter-blade vortex, the small-scale vortex effectively inhibits its formation. Moreover, convex domes placed in severe erosion areas can accelerate the dissipation process of the inter-blade vortex.

Parallel PSO for Efficient Neural Network Training Using GPGPU and Apache Spark in Edge Computing Sets

The training phase of a deep learning neural network (DLNN) is a computationally demanding process, particularly for models comprising multiple layers of intermediate neurons.This paper presents a novel approach to accelerating DLNN training using the particle swarm optimisation (PSO) algorithm, which exploits the GPGPU architecture and the Apache Spark analytics engine for large-scale data processing tasks. PSO is a bio-inspired stochastic optimisation method whose objective is to iteratively enhance the solution to a (usually complex) problem by approximating a given objective. The expensive fitness evaluation and updating of particle positions can be supported more effectively by parallel processing. Nevertheless, the parallelisation of an efficient PSO is not a simple process due to the complexity of the computations performed on the swarm of particles and the iterative execution of the algorithm until a solution close to the objective with minimal error is achieved. In this study, two forms of parallelisation have been developed for the PSO algorithm, both of which are designed for execution in a distributed execution environment. The synchronous parallel PSO implementation guarantees consistency but may result in idle time due to global synchronisation. In contrast, the asynchronous parallel PSO approach reduces the necessity for global synchronization, thereby enhancing execution time and making it more appropriate for large datasets and distributed environments such as Apache Spark. The two variants of PSO have been implemented with the objective of distributing the computational load supported by the algorithm across the different executor nodes of the Spark cluster to effectively achieve coarse-grained parallelism. The result is a significant performance improvement over current sequential variants of PSO.

Smoothing RRT Path for Mobile Robot Navigation Using Bio-inspired Optimization Method

Smoothing RRT Path for Mobile Robot Navigation Using Bio-inspired Optimization Method

Advances in Structural Health Monitoring: Bio-Inspired Optimization Techniques and Vision-Based Monitoring System for Damage Detection Using Natural Frequency

This paper introduces the improvements in natural-frequency-based SHM by applying bio-inspired optimization methods and a vision-based monitoring system for effective damage detection. This paper proposes a natural frequency extraction method using a motion magnification-based vision monitoring system with bio-inspired optimization techniques to estimate the damage location and depth in a cantilever beam. The proposed optimization techniques are inspired by natural processes and biological evolution including genetic algorithms, particle swarm optimization, sea lion optimization, and coral reefs optimization. To verify the performance of each bio-inspired optimization method, the eigenvalues of a two-bay truss structure are used for estimating the damaged elements. Then, using the proposed video motion magnification method, the natural frequency for each undamaged and damaged cantilever beam is extracted and compared with the LDV sensor to verify the proposed vision-based monitoring system. The performance of each bio-inspired optimizer in damage detection is compared. As a result, coral reefs optimization shows the lowest average error, around 1%, in damage detection using the natural frequency.

Constructing Multifunctional Composite Single Crystals via Polymer Gel Incorporation.

The non-uniformity of a single crystal can sometimes be found in biominerals, where surrounding biomacromolecules are incorporated into the growing crystals. This unique composite structure, combining heterogeneity and long-range ordering, enables the functionalization of single crystals. Polymer gel media are often used to prepare composite single crystals, in which the growing crystals incorporate gel networks and form a bi-continuous interpenetrating structure without any disruption to single crystallinity. Moreover, dyes and many kinds of nanoparticles can be occluded into single crystals under the guidance of gel incorporation. On this basis, the bio-inspired method has been applied in crystal morphology control, crystal dyeing, mechanical reinforcement, and organic bulk heterojunction-based optoelectronics. In this paper, the composite structure, the incorporation mechanisms, and the multiple functions of gel-incorporated single crystals are reviewed.

Physicochemical characterization of novel okra mucilage/hyaluronic acid-based oral disintegrating films for functional food applications

Oral disintegrating films (ODFs) offer a patient-friendly approach with enhanced convenience and rapid onset of action over various health benefits. ODFs are fabricated for geriatric, pediatric, and individuals facing swallowing challenges. The present work aims to fabricate and characterize ODFs mainly composed of okra mucilage (OM), hyaluronic acid (HA), vitamin-C-loaded bioactive glass nanoparticles (VBG NPs), and clove essential oil. A bio-inspired method was employed to synthesize VBG NPs using fructose template. The nutrient analysis of OM depicted that it is a rich source of protein, carbohydrates, magnesium, and flavonoids (quercetin), accounting for its antioxidant activity. The physicochemical characteristics of the ODFs studied using contact angle measurement, surface pH, opacity, and in vitro disintegration time revealed that ODFs disintegrated rapidly in simulated saliva. The neutral surface pH of ODFs indicates their non-irritant behaviour to the oral mucosa. VBG NPs and essential oil (EO) addition enhance the thermal and mechanical properties. Further, EO infusion in the film matrix resulted in the porous and antibacterial nature of the functional film as revealed by FE-SEM micrographs and antibacterial disk diffusion assay respectively. The obtained novel nutrient-rich ODF is hemocompatible with a hemolysis rate (HR%) <5 % and suitable for functional food applications.

Impact of design strategy favoring powder removal on mechanical performance of bio-inspired porous architectures for laser-based powder-bed fusion additive manufacturing

Mass reduction is a concern both in mechanical engineering and living organisms to reduce energy and materials consumption. Lately, transfer of knowledge from biology to engineering has become easier thanks to additive manufacturing. Trabecular bone for example optimally adapts to the mechanical stress it undergoes. Mass reduction methods bio-inspired from trabecular architecture, proposed in the literature, are not always applicable for laser-based powder-bed fusion. In particular, bio-inspired mass reduction methods based on a material removal principle generate porosities throughout a solid initial part which can result in enclosed porosities that trap un-melted powders. Here, we propose a 3D depowderable bio-inspired mass reduction method. Powder removal was controlled in different samples through weighing and X-ray computed microtomography and the impact of the design strategy that ensures correct powder removal on mechanical performance (stiffness) was quantified. Mean performance difference is 10.35 % (between 1.82 % and 26.47 %). For stiff and light parts loaded in bending, the proposed method outperforms parts made of bulk steel alloy by leveraging an architecture bio-inspired from trabecular bone.

Extracting Geometry and Topology of Orange Pericarps for the Design of Bioinspired Energy Absorbing Materials.

As a result of evolution, many biological materials have developed irregular structures that lead to outstanding mechanical performances, like high stiffness-to-weight ratios and good energy absorption. However, reproducing these irregular biological structures in synthetic materials remains a complex design and fabrication challenge. Here, a bioinspired material design method is presented that characterizes the irregular structure as a network of building blocks, also known as tiles, and rules to connect them. Rather than replicating the biological structure one-to-one, synthetic materials are generated with the same distributions of tiles and connectivity rules as the biological material and it is shown that these equivalent materials have structure-to-property relationships similar to the biological ones. To demonstrate the method, the pericarp of the orange, a member of the citrus family known for its protective, energy-absorbing capabilities is studied. Polymer samples are generated and characterized under quasistatic and dynamic compression and display spatially-varying stiffness and good energy absorption, as seen in the biological materials. By quantifying which tiles and connectivity rules locally deform in response to loading, it is also determined how to spatially control the stiffness and energy absorption.

Inactivation of listerial biofilm on the food-contact surface using thymol and bio-modulated yeast microcarriers

Decontamination of bacterial biofilms formed on food-contact surfaces is a serious challenge in the food industry. In this study, a food-grade antimicrobial system was developed using yeast-based microcarriers and essential oil (EOs; thymol) to inactivate bacterial biofilms on food-contact surfaces. A bio-inspired method that involves pre-treatment of yeast microcarriers with simulated intestinal fluid (SIF) was employed to modulate the release of the encapsulated EOs. The effectiveness of the developed antimicrobial delivery system was evaluated based on physicochemical characterization, antimicrobial activities against planktonic bacterial cells in the presence of organic matter, affinity to bind bacterial biofilms, and co-incubation and residual antimicrobial activities against bacterial biofilms. The results illustrate that pre-treatment of yeast-based microcarriers with SIF significantly (p < 0.05) enhanced the release of the encapsulated EOs without influencing the encapsulation yield and binding of yeast-based microcarriers with bacterial biofilms. Strong antibiofilm activities were exhibited by the thymol encapsulated in the pre-digested yeast cells (pdYC@Thymol) compared to free thymol or thymol encapsulated in the undigested yeast microcarriers. Populations of Listeria innocua in biofilms (ca. 7.54 log CFU/cm2) decreased to ca. 2.63 log CFU/cm2 after 1 h of co-incubation with the pdYC@Thymol suspensions and further decreased to ca. 0.85 log CFU/cm2 after removal of the loosely bound pdYC@Thymol particles. Overall, this study suggests that the combined effects of the targeted delivery of the EOs via encapsulation in yeast-based microcarriers and facilitated release of the encapsulated EOs upon binding with bacterial biofilms can effectively inactivate the bacterial biofilms formed on food-contact surfaces, thereby enhancing the microbiological safety of the food-related environments.

Prediction of Crop Leaf Health by MCCM and Histogram Learning Model Using Leaf Region

This study introduces a model called the crop leaf health prediction model (CLHPM) that utilizes a bio-inspired method to accurately identify the leaf region. This approach enhances the process of learning important features and overcomes the challenges posed by the hindrance from the chromatic and structural diversity of each leaf. To train the learning model, a modified co-occurrence matrix (MCCM) in texture analysis is used to overcome the limitations of the leaf region, and a histogram method is also deployed for color analysis. The experiment is conducted on a real dataset of tomato crop leaves. It is observed that the average accuracy has increased by 3.50%. The existing MobileNetV2 model presents an accuracy of 95.73%, and the proposed CLHPM model renders 99.23%. Moreover, an enhancement of 3.72 in the F-measure is also noticed.

Wool fiber as an In situ reducing agent towards ZnO-based surface modified fabric development: Durability assessment in the realm of textile applications

The development of a bio-inspired method to fabricate ZnO nanostructure-modified functional wool fabric represents a substantial challenge in the textile field. This article reports for the first time the use of wool as a reducing agent for the synthesis of ZnO nanoparticles. While previous studies have employed alternative natural reducing agents for surface modification of host materials, this investigation uniquely utilizes the host material, wool, as the reducing agent for in-situ ZnO nanoparticle synthesis. In addition to demonstrating the use of wool as a reducing agent, this literature uniquely reports the durability of the modified fabric, achieved through the use of trimethoxy methyl silane. This silane compound acts as a bridging agent, forming a strong interface between the wool fibers and the synthesized ZnO nanoparticles. This innovative approach enhances the adhesion and stability of the ZnO nanoparticles on the wool fabric, a key aspect that contributes to the overall durability and performance of the modified material. The in situ surface modification process employed a metal oxide precursor (zinc acetate dihydrate), capping agents (hexamethylene tetraamine, sodium carbonate, and sodium sulfate), and a coupling agent (trimethyl methoxy silane). The capping agents were used to control the reaction conditions, while the coupling agent was employed to improve the durability of the surface modification. The impact of three different capping agents on the corresponding synthesis process was assessed, and the findings revealed that the choice of capping agent helped to form ZnO nanoparticles with distinct morphologies. Analytical techniques, such as X-ray diffraction (XRD), scanning electron microscopy (SEM), dynamic light scattering (DLS), and attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy, were utilized to characterize the synthesized particles and the treated fabric. The XRD analysis coupled with SEM analysis confirmed the successful formation of ZnO nanoparticles. The ATR-FTIR spectroscopy provided evidence of the formation of Si-O-Zn and Si-O-C bonds, observed at around 920 cm−1 and 1040 cm−1 respectively. These spectroscopic findings indicate that the surface modification involving the silane compound has enhanced the durability of the ZnO-modified fabric. The covalent linkages between the ZnO nanoparticles, the silane compound, and the wool fibers, confirmed by SEM analysis before and after washing cycles, have enabled the durable attachment of the nanoparticles to the fabric surface, even after multiple washing cycles. The results suggest a novel pathway to use wool as a reducing agent for the surface modification of the same material. Detailed mechanistic insights into the surface modification process and the corresponding chemical interactions were also discussed.