Search Results (815)

Lignin used in this work was isolated from sapele (Entandrophragma cylindricum) wood through a hybrid pulping process using soda/ethanol as pulping liquor and denoted soda-oxyethylated lignin (SOL). SOL was mixed with a polyethylene glycol (PEG)–glycerol mixture (80/20 v/v) as liquefying solvent with 98% wt. sulfur acid as catalyst, and the mixture was taken to boil at 140 °C for 2, 2.5, and 3 h. Three bio-polyols LBP1, LBP2, and LBP3 were obtained, and each of them exhibited a high proportion of -OH groups. Lignin-based polyurethane foams (LBPUFs) were prepared using the bio-polyols obtained with a toluene diisocyanate (TDI) prepolymer by the one-shot method. Gel permeation chromatography (GPC), Fourier transform infrared spectroscopy (FTIR), and carbon-13 nuclear magnetic resonance spectroscopy (¹³C NMR) were used characterize lignin in order to determine viscosity, yield, and composition and to characterize their structure. The PEG-400–glycerol mixture was found to react with the lignin bio-polyols’ phenolic -OHs. The bio-polyols’ viscosity was found to increase as the liquefaction temperature increased, while simultaneously their molecular weights decreased. All the NCO groups were eliminated from the samples, which had high thermal stability as the liquefaction temperature increased, leading to a decrease in cell size, density, and crystallinity and an improvement in mechanical performance. Based on these properties, especially the presence of some aromatic rings in the bio-polyols, the foams produced can be useful in automotive applications and for floor carpets. Full article

Accurately predicting the compressive strength of high-performance concrete (HPC) is critical for ensuring structural integrity and promoting sustainable construction practices. However, HPC exhibits highly complex, nonlinear, and multi-factorial interactions among its constituents (such as cement, aggregates, admixtures, and curing conditions), which pose significant challenges to conventional predictive models. Traditional approaches often fail to adequately capture these intricate relationships, resulting in limited prediction accuracy and poor generalization. Moreover, the high dimensionality and noisy nature of HPC mix data increase the risk of model overfitting and convergence to local optima during optimization. To address these challenges, this study proposes a novel bio-inspired hybrid optimization model, AP-IVYPSO-BP, which is specifically designed to handle the nonlinear and complex nature of HPC strength prediction. The model integrates the ivy algorithm (IVYA) with particle swarm optimization (PSO) and incorporates an adaptive probability strategy based on fitness improvement to dynamically balance global exploration and local exploitation. This design effectively mitigates common issues such as premature convergence, slow convergence speed, and weak robustness in traditional metaheuristic algorithms when applied to complex engineering data. The AP-IVYPSO is employed to optimize the weights and biases of a backpropagation neural network (BPNN), thereby enhancing its predictive accuracy and robustness. The model was trained and validated on a dataset comprising 1,030 HPC mix samples. Experimental results show that AP-IVYPSO-BP significantly outperforms traditional BPNN, PSO-BP, GA-BP, and IVY-BP models across multiple evaluation metrics. Specifically, it achieved an R² of 0.9542, MAE of 3.0404, and RMSE of 3.7991 on the test set, demonstrating its high accuracy and reliability. These results confirm the potential of the proposed bio-inspired model in the prediction and optimization of concrete strength, offering practical value in civil engineering and materials design. Full article

Cyborg insects offer a biologically powered solution for locomotion control, but conventional methods typically rely on invasive electrical stimulation. Here, we introduce a noninvasive, phototaxis-based strategy to steer walking Endebius florensis beetles using light-emitting diode (LED) stimuli. Electroretinogram recordings revealed spectral sensitivity to blue, green, and yellow light, with reduced response to red. Behavioral assays demonstrated robust positive phototaxis to blue light and negative phototaxis to yellow. Using these findings, we built a wireless microcontroller-based backpack emitting directional blue light to induce steering. The beetles reliably turned toward the activated light, achieving angular deflections over 60° within seconds. This approach enables repeatable, trauma-free insect control and establishes a new paradigm for biohybrid locomotion systems. Full article

This work presents the experimental and numerical investigation of a novel acoustic metamaterial based on sustainable and biodegradable components: cork membranes and honeycomb cores made from treated aramid paper. The design exploits the principle of localized resonance induced by tensioned membranes coupled with subwavelength cavities, aiming to achieve high sound absorption at low (250–500 Hz) and mid frequencies (500–1400 Hz) with minimal thickness and environmental impact. Three configurations were analyzed, varying the number of membranes (one, two, and three) while keeping a constant core structure composed of three stacked honeycomb layers. Acoustic performance was measured using an impedance tube (Kundt’s tube), focusing on the normal-incidence sound absorption coefficient in the frequency range of 250–1400 Hz. The results demonstrate that increasing the number of membranes introduces multiple resonances and broadens the effective absorption bandwidth. Numerical simulations were performed to predict pressure field distributions. The numerical model showed good agreement with the experimental data, validating the underlying physical model of coupled mass–spring resonators. The proposed metamaterial offers a low-cost, modular, and fully recyclable solution for indoor sound control, combining acoustic performance and environmental sustainability. These findings offer promising perspectives for the application of bio-based metamaterials in architecture and eco-design. Further developments will address durability, high-frequency absorption, and integration in hybrid soundproofing systems. Full article

Recent advancements have been made in the precise prediction of protein structures within the Protein Folding Problem (PFP), particularly in relation to minimizing the energy function to achieve stable and biologically relevant protein structures. This problem is classified as NP-hard within computational theory, necessitating the development of various techniques and algorithms. Bio-inspired algorithms have proven effective in addressing NP-hard challenges in practical applications. This study introduces a novel hybrid algorithm, termed GRSABio, which integrates the strategies of Jumping Spider Algorithm (JSOA) with the Golden Ratio Simulated Annealing (GRSA) for peptide prediction. Furthermore, the GRSABio algorithm incorporates a Convolutional Neural Network for fragment prediction (FCNN), forms an enhanced methodology called GRSABio-FCNN. This integrated framework achieves improved structure refinement based on energy for protein prediction. The proposed enhanced GRSABio-FCNN approach was applied to a dataset of 60 peptides. The Wilcoxon and Friedman statistics test were employed to compare the GRSABio-FCNN results against recent state-of-the-art-approaches. The results of these tests indicate that the GRSABio-FCNN approach is competitive with state-of-the-art methods for peptides up to 50 amino acids in length and surpasses leading PFP algorithms for peptides with up to 30 amino acids. Full article

This Review examines up-to-date advancements in the integration of biomolecules and solar energy technologies, with a particular focus on biohybrid photovoltaic systems. Biomolecules have recently garnered increasing interest as functional components in a wide range of solar cell architectures, since they offer a huge variety of structural, optical, and electronic properties, useful to fulfill multiple roles within photovoltaic devices. These roles span from acting as light-harvesting sensitizers and charge transport mediators to serving as micro- and nanoscale structural scaffolds, rheological modifiers, and interfacial stabilizers. In this Review, a comprehensive overview of the state of the art about the integration of biomolecules across the various generations of photovoltaics is provided. The functional roles of pigments, DNA, proteins, and polysaccharides are critically reported improvements and limits associated with the use of biological molecules in optoelectronics. The molecular mechanisms underlying the interaction between biomolecules and semiconductors are also discussed as essential for a functional integration of biomolecules in solar cells. Finally, this Review shows the current state of the art, and the most significant results achieved in the use of biomolecules in solar cells, with the main scope of outlining some guidelines for future further developments in the field of biohybrid photovoltaics. Full article

Electroless nickel deposition (ELD) is an autocatalytic technique extensively used to impart conductive, protective, and mechanical functionalities to inherently non-conductive synthetic substrates. This review systematically explores the fundamental mechanisms of electroless nickel deposition, emphasising recent advancements in surface activation methods, solvent systems, and microstructural control. Critical analysis reveals that bio-inspired activation methods, such as polydopamine (PDA) and tannic acid (TA), significantly enhance coating adhesion and durability compared to traditional chemical etching and plasma treatments. Additionally, solvent engineering, particularly using polar aprotic solvents like dimethyl sulfoxide (DMSO) and ethanol-based systems, emerges as a key strategy for achieving uniform, dense, and flexible coatings, overcoming limitations associated with traditional aqueous baths. The review also highlights that microstructural tailoring, specifically the development of amorphous-nanocrystalline hybrid nickel coatings, effectively balances mechanical robustness (hardness exceeding 800 HV), flexibility, and corrosion resistance, making these coatings particularly suitable for wearable electronic textiles and smart materials. Furthermore, commercial examples demonstrate the real-world applicability and market readiness of nickel-coated synthetic fibres. Despite significant progress, persistent challenges remain, including reliable long-term adhesion, internal stress management, and environmental sustainability. Future research should prioritise environmentally benign plating baths, standardised surface activation protocols, and scalable deposition processes to fully realise the industrial potential of electroless nickel coatings. Full article

Underwater navigation remains constrained by technological limitations, driving the exploration of alternative approaches such as polarized light-based systems. This review systematically examines advances in polarized navigation from three perspectives. First, the principles of atmospheric polarization navigation are analyzed, with their operational mechanisms, advantages, and inherent constraints dissected. Second, innovations in bionic polarization multi-sensor fusion positioning are consolidated, highlighting progress beyond conventional heading-direction extraction. Third, emerging underwater polarization navigation techniques are critically evaluated, revealing that current methods predominantly adapt atmospheric frameworks enhanced by advanced filtering to mitigate underwater interference. A comprehensive synthesis of underwater polarization modeling methodologies is provided, categorizing physical, data-driven, and hybrid approaches. Through rigorous analysis of studies, three persistent barriers are identified: (1) inadequate polarization pattern modeling under dynamic cross-media conditions; (2) insufficient robustness against turbidity-induced noise; (3) immature integration of polarization vision with sonar/IMU (Inertial Measurement Unit) sensing. Targeted research directions are proposed, including adaptive deep learning models, multi-spectral polarization sensing, and bio-inspired sensor fusion architectures. These insights establish a roadmap for developing reliable underwater navigation systems that transcend current technological boundaries. Full article

Modern microgrids face critical challenges in maintaining stability and efficiency due to renewable energy intermittency and dynamic load demands. This paper proposes a novel real-time energy management framework that synergizes a bio-inspired T-Cell optimization algorithm with decentralized voltage-based droop control to address these challenges. A JADE-based multi-agent system (MAS) orchestrates coordination between the T-Cell optimizer and edge-level controllers, enabling scalable and fault-tolerant decision-making. The T-Cell algorithm, inspired by adaptive immune system dynamics, optimizes global power distribution through the MAS platform, while droop control ensures local voltage stability via autonomous adjustments by distributed energy resources (DERs). The framework is rigorously validated through Hardware-in-the-Loop (HIL) testing using OPAL-RT, which interfaces MATLAB/Simulink models with Raspberry Pi for real-time communication (MQTT/Modbus protocols). Experimental results demonstrate a 91% reduction in grid dependency, 70% mitigation of voltage fluctuations, and a 93% self-consumption rate, significantly enhancing power quality and resilience. By integrating centralized optimization with decentralized control through MAS coordination, the hybrid approach achieves scalable, self-organizing microgrid operation under variable generation and load conditions. This work advances the practical deployment of adaptive energy management systems, offering a robust solution for sustainable and resilient microgrids. Full article

Industrial robots are undergoing a transformative shift as Artificial Intelligence (AI)-driven and bio-inspired control strategies unlock new levels of precision, adaptability, and multi-dimensional sustainability aligned with Industry 5.0 (energy efficiency, material circularity, and life-cycle emissions). This systematic review analyzes 160 peer-reviewed industrial robotics control studies (2023–2025), including an expanded bio-inspired/human-centric subset, to evaluate: (1) the dominant and emerging control methodologies; (2) the transformative role of digital twins and 5G-enabled connectivity; and (3) the persistent technical, ethical, and environmental challenges. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, the study employs a rigorous methodology, focusing on adaptive control, deep reinforcement learning (DRL), human–robot collaboration (HRC), and quantum-inspired algorithms. The key findings highlight up to 30% latency reductions in real-time optimization, up to 22% efficiency gains through digital twins, and up to 25% energy savings from bio-inspired designs (all percentage ranges are reported relative to the comparator baselines specified in the cited sources). However, critical barriers remain, including scalability limitations (with up to 40% higher computational demands) and cybersecurity vulnerabilities (with up to 20% exposure rates). The convergence of AI, bio-inspired systems, and quantum computing is poised to enable sustainable, autonomous, and human-centric robotics, yet requires standardized safety frameworks and hybrid architectures to fully support the transition from Industry 4.0 to Industry 5.0. This review offers a strategic roadmap for future research and industrial adoption, emphasizing human-centric design, ethical frameworks, and circular-economy principles to address global manufacturing challenges. Full article

The advancement of next-generation lubricants is pivotal for enhancing energy efficiency and mitigating environmental impacts across diverse industrial applications. This review systematically examines recent developments in lubricant technologies, with a particular focus on sustainable strategies incorporating bio-based feedstocks, nanostructured additives, and hybrid formulations. These innovations are designed to reduce friction and wear, decrease energy consumption, and prolong the operational lifespan of mechanical systems. A critical assessment of tribological behavior, environmental compatibility, and functional performance is presented. Furthermore, the integration of artificial intelligence (AI) into lubricant formulation and performance prediction is explored, highlighting its potential to accelerate development cycles and enable application-specific optimization through data-driven approaches. The findings emphasize the strategic role of eco-innovative lubricants in supporting low-carbon technologies and facilitating the transition toward sustainable energy systems. Full article

Phosphorylated cellulose is proposed as a bio-resin for the removal of heavy metals, as a substitute for synthetic polymer-based materials. Phosphorylation is carried out using kraft pulp fibers as the cellulose source, with phosphate esters and urea as reactants to prevent significant fiber degradation. Herein, phosphorylated fibers, with three types of counterions (sodium, ammonium, or hydrogen), are used in adsorption trials involving four individual metals: nickel, copper, cadmium, and lead. The Langmuir isotherm model is applied to determine the maximum adsorption capacities at four different temperatures (10, 20, 30, and 50 °C), enabling the calculation of the Gibbs free energy (ΔG), entropy (ΔS), and enthalpy (ΔH) of adsorption. The results show that the adsorption capacity of phosphorylated fibers is equal or even higher than that of commercially available resins (1.7–2.9 vs. 2.4–2.6 mmol/g). However, the nature of the phosphate counterion plays an important role in the adsorption capacity, with the alkaline form showing a superior ion exchange capacity than the hybrid form and acid form (2.7–2.9 vs. 2.3–2.7 vs. 1.7–2.5 mmol/g). The thermodynamic analysis indicates the spontaneous (ΔG = (-)16–(-)30 kJ/mol) and endothermic nature of the adsorption process with positive changes in enthalpy (0.45–15.47 kJ/mol) and entropy (0.07–0.14 kJ/mol·K). These results confirm the high potential of phosphorylated lignocellulosic fibers for ion exchange applications, such as the removal of heavy metals from process or wastewaters. Full article

Nanomaterials’ special features enable their extensive application in chemical and biochemical nanosensors for food assays; food packaging; environmental, medicinal, and pharmaceutical applications; and photoelectronics. The analytical strategies based on novel nanomaterials have proved their pivotal role and increasing interest in the assay of key food components. The choice of transducer is pivotal for promoting the performance of electrochemical sensors. Electrochemical nano-transducers provide a large active surface area, enabling improved sensitivity, specificity, fast assay, precision, accuracy, and reproducibility, over the analytical range of interest, when compared to traditional sensors. Synthetic routes encompass physical techniques in general based on top–down approaches, chemical methods mainly relying on bottom–up approaches, or green technologies. Hybrid techniques such as electrochemical pathways or photochemical reduction are also applied. Electrochemical nanocomposite sensors relying on conducting polymers are amenable to performance improvement, achieved by integrating redox mediators, conductive hydrogels, and molecular imprinting polymers. Carbon-based or metal-based nanoparticles are used in combination with ionic liquids, enhancing conductivity and electron transfer. The composites may be prepared using a plethora of combinations of carbon-based, metal-based, or organic-based nanomaterials, promoting a high electrocatalytic response, and can accommodate biorecognition elements for increased specificity. Nanomaterials can function as pivotal components in electrochemical (bio)sensors applied to food assays, aiming at the analysis of bioactives, nutrients, food additives, and contaminants. Given the broad range of transducer types, detection modes, and targeted analytes, it is important to discuss the analytical performance and applicability of such nanosensors. Full article

Hydrogels have emerged as multifunctional biomaterials in cardiac surgery, offering promising solutions for myocardial regeneration, adhesion prevention, valve engineering, and localized drug and gene delivery. Their high water content, biocompatibility, and mechanical tunability enable close emulation of the cardiac extracellular matrix, supporting cellular viability and integration under dynamic physiological conditions. In myocardial repair, injectable and patch-forming hydrogels have been shown to be effective in reducing infarct size, promoting angiogenesis, and preserving contractile function. Hydrogel coatings and films have been designed as adhesion barriers to minimize pericardial adhesions after cardiotomy and improve reoperative safety. In heart valve and patch engineering, hydrogels contribute to scaffold design by providing bio-instructive, mechanically resilient, and printable matrices that are compatible with 3D fabrication. Furthermore, hydrogels serve as localized delivery platforms for small molecules, proteins, and nucleic acids, enabling sustained or stimuli-responsive release while minimizing systemic toxicity. Despite these advances, challenges such as mechanical durability, immune compatibility, and translational scalability persist. Ongoing innovations in smart polymer chemistry, hybrid composite design, and patient-specific manufacturing are addressing these limitations. This review aims to provide an integrated perspective on the application of hydrogels in cardiac surgery. The relevant literature was identified through a narrative search of PubMed, Scopus, Web of Science, Embase, and Google Scholar. Taken together, hydrogels offer a uniquely versatile and clinically translatable platform for addressing the multifaceted challenges of cardiac surgery. Hydrogels are poised to redefine clinical strategies in cardiac surgery by enabling tailored, bioresponsive, and functionally integrated therapies. Full article

This review critically examines the effects of moisture absorption on the mechanical and fatigue properties of natural fiber composites (NFCs), with a focus on tensile strength, elastic modulus, and long-term durability. Moisture uptake can cause reductions in tensile strength of up to 40% and in elastic modulus by 20–30% depending on fiber type, mass fraction (typically in the range of 30–60%), and surface treatments. The review highlights Ithat while surface modifications (e.g., alkaline and silane treatments) significantly mitigate moisture-induced degradation, their effectiveness is highly sensitive to the processing conditions. Additionally, hybridization strategies and optimized fiber orientations show promise in enhancing fatigue resistance under humid environments. Despite substantial progress, major challenges remain, including the lack of standardized testing protocols and the limited understanding of multiscale aging mechanisms. Future research directions include developing predictive models that couple moisture diffusion and mechanical deterioration, implementing advanced in situ monitoring of damage evolution, and exploring novel bio-based treatments. By addressing these gaps, NFCs can become more reliable and widely adopted as sustainable alternatives in structural applications. Full article