Search Results (236)

Mesoporous silica nanoparticles (MSNs) are among the most adaptable nanocarriers in modern pharmaceutics, characterized by a high surface area, tunable pore size, controllable morphology, and excellent biocompatibility. These qualities enable effective encapsulation, protection, and the delivery of drugs in a specific area and, therefore, MSNs are powerful platforms for the targeted and controlled delivery of drugs and theragnostic agents. Over the past ten years and within the 2021–2025 period, the advancement of MSN design has led to the creation of hybrid nanostructures into polymers, lipids, metals, and biomolecules that have yielded multifunctional carriers with enhanced stability, responsiveness, and biological activities. The current review provides a review of the synthesis methods, surface functionalization techniques, and physicochemical characterization techniques that define the next-generation MSN-based delivery systems. The particular focus is put on stimuli-responsive systems, such as redox, pH, enzyme-activated, and light-activated systems, that enable delivering drugs in a controlled and localized manner. We further provide a summary of the biomedical use of MSNs and their hybrids such as in cancer chemotherapy, gene and nucleic acid delivery, antimicrobial and vaccine delivery, and central nervous system targeting, supported by recent in vivo and in vitro studies. Important evaluations of biocompatibility, immunogenicity, degradation, and biodistribution in vivo are also provided with a focus on safety in addition to the regulatory impediments to clinical translation. The review concludes by saying that there are still limitations such as large-scale reproducibility, long-term toxicity, and standardization by the regulators, and that directions are being taken in the future in the fields of smart programmable nanocarriers, green synthesis, and sustainable manufacture. Overall, mesoporous silica and hybrid nanoparticles represent a breakthrough technology in the nanomedicine sector with potentials that are unrivaled in relation to targeted, controlled, and personalized therapeutic interventions. Full article

In this study, the integration of SnO₂ with a perfluorinated Zn(II) porphyrin derivative, namely ZnTPPF₂₀CN, was explored as a strategy to enhance the performance of chemoresistive sensors toward gaseous acetone detection. The ZnTPPF₂₀CN molecule was specifically designed with an ethynylphenyl-cyanoacrylic anchoring group and a benzothiadiazole (BTD) spacer, enabling its chemisorption onto the SnO₂ surface. Hybrid materials containing three different ZnTPPF₂₀CN-to-SnO₂ ratios (1:4, 1:32, 1:64) were fabricated and tested for acetone detection at 120 °C, both under dark conditions and LED illumination. The sensing behavior of these hybrids was compared with that of previously studied SnO₂ composites, incorporating physisorbed, unsubstituted ZnTPPF₂₀. Among the tested ratios, the 1:32 ZnTPPF₂₀CN/SnO₂ demonstrated superior acetone sensitivity compared to its unmodified counterpart, despite showing a lower intrinsic conductivity in air and a reduced electron transfer efficiency. Density functional theory (DFT) calculations provided insights into the possible anchoring modes and interfacial electronic interactions, helping to rationalize this counterintuitive observation. The enhanced sensing response was attributed to a more favorable balance between charge injection and the availability of SnO₂ electronic states, facilitated by the chemisorbed anchoring of ZnTPPF₂₀CN. Overall, our findings highlight the importance of molecular engineering, particularly in terms of molecular design, loading ratio, and anchoring mechanism, in modulating charge dynamics and optimizing the sensing efficiency of porphyrin/SnO₂ nanocomposites. Full article

Smart vesicle therapeutics represent a transformative frontier in nanomedicine, offering precise, biocompatible, and adaptable platforms for drug delivery and theranostic applications. This review explores recent advances in the design and engineering of liposomes, niosomes, polymersomes, and extracellular vesicles (EVs), emphasizing their capacity to integrate therapeutic and diagnostic functions within a single nanoscale system. By tailoring vesicle size, composition, and surface chemistry, researchers have achieved improved pharmacokinetics, reduced immunogenicity, and fine-tuned control of drug release. Stimuli-responsive vesicles activated by pH, temperature, and redox gradients, or external fields enable spatiotemporal regulation of therapeutic action, while hybrid bio-inspired systems merge synthetic stability with natural targeting and biocompatibility. Theranostic vesicles further enhance precision medicine by allowing real-time imaging, monitoring, and adaptive control of treatment efficacy. Despite these advances, challenges in large-scale production, reproducibility, and regulatory standardization still limit clinical translation. Emerging solutions—such as microfluidic manufacturing, artificial intelligence-guided optimization, and multimodal imaging integration—are accelerating the development of personalized, high-performance vesicular therapeutics. Altogether, smart vesicle platforms exemplify the convergence of nanotechnology, biotechnology, and clinical science, driving the next generation of precision therapies that are safer, more effective, and tailored to individual patient needs. Full article

In response to the personalized and precise rehabilitation needs for motor injuries and stroke associated with population aging, this study proposes a design method for an intelligent rehabilitation trainer that integrates Bayesian information gain (BIG) and axis matching techniques. Grounded in the biomechanical characteristics of the human ankle joint, the design fully draws upon biomimetic principles, constructing a 3-PUU-R hybrid serial–parallel bionic mechanism. By mimicking the dynamic variation of the ankle’s instantaneous motion axis and its balance between stiffness and compliance, a three-dimensional digital model was developed, and multi-posture human factor simulations were conducted, thereby achieving a rehabilitation process more consistent with natural human movement patterns. Natural randomized disability grade experimental data were collected for 100 people to verify the validity of the design results. On this basis, a Bayesian information gain framework was established by quantifying the reduction of uncertainty in rehabilitation outcomes through characteristic parameters, enabling the dynamic optimization of training strategies for personalized and precise ankle rehabilitation. The rehabilitation process was modeled as a problem of uncertainty quantification and information gain optimization. Prior distributions were constructed using surface EMG (electromyography) signals and motion trajectory errors, and mutual information was used to drive the dynamic adjustment of training strategies, ultimately forming a closed-loop control architecture of “demand perception–strategy optimization–execution adaptation.” This innovative integration of probabilistic modeling and cross-joint bionic design overcomes the limitations of single-joint rehabilitation and provides a new paradigm for the development of intelligent rehabilitation devices. The deep integration mechanism-based dynamic axis matching and Bayesian information gain holds significant theoretical value and engineering application prospects for enhancing the effectiveness of neural plasticity training. Full article

Hyperparameters allow metaheuristics to be tuned to a wide range of problems. However, even though formalized tuning of metaheuristic parameters can affect the quality of the solution, it is rarely performed. The empirical selection method and the trial-and-error method are the primary conventional parameter selection techniques for optimization heuristics. Both require a priori knowledge of the problem and involve multiple experiments requiring significant time and effort, yet neither guarantees the attainment of optimum parameter values. Of the studies that perform formal parameter tuning, experimental design is the most commonly used method. Although experimental design is feasible for systematic experimentation, it is also time-consuming and requires extensive effort for large optimization problems. The computational effort in this study refers to the number of experimental runs required for hyperparameter tuning, not the computational time for each run. This study proposes a simpler, faster method based on an optimized Latin hypercube sampling (OLHS) technique augmented with response surface methodology for estimating the best hyperparameter settings for a hybrid simulated annealing algorithm. The method is applied to solve the aircraft landing problem with time windows (ALPTW), a combinatorial optimization problem that seeks to determine the optimal landing sequence within a predetermined time window while maintaining minimum separation criteria. The results showed that the proposed method improves sampling efficiency, providing better coverage and higher accuracy with 70% fewer sample points and only 30% of the total runs compared to full factorial design. Full article

It is crucial to investigate methods for improving the stiffness performance of machine tools according to their specific dynamic working conditions. This paper presents a complete computer-aided workflow for structural transient topology optimization (TO) design, which is applied to the structural design issue of the JH31-250 press machine (Zhejiang Weili Forging Machinery Co., Ltd., Shaoxing, China). The stiffness influenced by the shape of the press frame under long-term dynamic impact load is analyzed, and an optimal design for the frame structure of the press machine is explored. In order to reduce the iteration time of the dynamic analysis, we also proposed a way to simplify the physical structure of the machine tool into a thin-walled structure model with artificial pseudo-density and introduced the hybrid cellular automata (HCA) criterion to obtain the topological iteration direction. This simplified model can be transformed back into 3D solid design of the press. The maximum relative displacement of the worktable in this optimized press model is 0.4896 mm, which is reduced by 31.02% compared to the original press model, which shows that the transient dynamic stiffness of the press machine frame is improved. This work presents a topological optimization method and path, which can be used for the optimization of dynamic stiffness in forging machine tools, and proves the correctness and effectiveness of the design for the transient dynamic stiffness of the frame. Full article

Shape memory polymers (SMPs) are a class of smart materials that exhibit unique shape-fixing and recovery abilities, attracting wide attention for applications in electronics, aerospace, and biomedical engineering. Chitosan (CS) as a renewable biopolymer, possessing good biocompatibility, biodegradability, and antimicrobial properties; its use as a matrix enhances the environmental compatibility and bio-adaptability of SMPs. MXene, as a novel two-dimensional material, is characterized by high electrical conductivity, abundant surface functional groups and good hydrophilicity, showing potential in energy storage, electromagnetic shielding and sensing. In this work, CS and poly (vinyl alcohol) (PVA) were used as the polymer matrix, and carbon nanotubes (CNTs) together with MXene were introduced as co-fillers to construct multifunctional composites. The effect of the CNTs/MXene hybrid fillers on mechanical properties, electromagnetic shielding and multi-stimuli-responsive shape memory behavior was systematically investigated. After ratio optimization, the composites showed excellent comprehensive performance: tensile strength reached up to 20.0 MPa, Young’s modulus up to 292.2 MPa, and maximum elongation at break of 23.2%; electromagnetic interference shielding effectiveness (SE_T) in the X-band (8.2–12.4 GHz) reached a maximum of 10.6 dB; shape fixation rates exceeded 90%; under thermal stimulation, a shape recovery ratio of 98.3% was achieved within 41.7 s; light-driven recovery rate reached 86.5% with a minimal recovery time of 82.3 s; under electrical stimulation the highest recovery rate was 94.1% with a shortest recovery time of 30 s. This study successfully prepared functional multi-stimuli-responsive shape memory composite films and provided a new strategy for the design of green smart materials. Full article

In the present experiment, the abrasive water jet machining parameters, such as water pressure, standoff distance, and traverse speed, are selected to study the effect of each parameter on the kerf taper angle and surface roughness during the machining of glass, jute, and carbon hybrid composite. The other machining parameters are kept constant. For each parameter, three levels are fixed on the basis of previous literature reviews. The Response Surface Methodology is used to design the required number of experiments and to optimize the machining parameters to obtain the minimum kerf taper angle and surface roughness. The levels selected for water pressure are 150, 220, and 250 MPa; traverse speeds are 20, 40, and 60 mm/min; and, similarly, stand-off distances are 2, 5, and 8 mm. Experimental results confirm that the parameter inversely affects both kerf angle and surface roughness. On the other hand, parameters traverse speed and stand-off distance, directly affecting both outputs. According to RSM optimization, to obtain the minimum kerf taper angle and surface roughness, we should fix the pressure at a higher level and other parameters at a lower level. For the considered range, the obtained minimum kerf angle and roughness values are 1.4982 radians and 2.0920 μm. Full article

With the comprehensive digitalization and electrification of aircraft, electromechanical actuation systems (EAS) have been increasingly applied. However, EAS are affected by various nonlinear factors, such as friction and mechanical backlash, which can compromise system stability and control accuracy, thereby reducing the operational lifespan of the EAS. This study focuses on these two nonlinear factors and proposes a hybrid control approach to mitigate their effects. In the speed loop of the EAS, a Super-Twisting sliding mode controller combined with a generalized proportional–integral observer (GPIO) is designed, while in the position loop, a hybrid controller integrating a radial basis function (RBF) neural network with sliding mode control is implemented. Leveraging the advantages of numerical analysis in SIMULINK and dynamic simulation in ADAMS, a co-simulation framework is established to evaluate the hybrid control algorithm under nonlinear effects. Furthermore, a control test bench for the control surface transmission system is constructed to analyze the dynamic and static performance of the system under different control strategies and input commands. The experimental results show that, compared with the PID control, the hybrid control method reduces the steady-state error and vibration amplitude of the step response displacement by 51% and 75%, respectively, and decreases the amplitude of speed fluctuations by 75%. For the sinusoidal response, the displacement lag is reduced by 76%, and the amplitude of speed fluctuations is reduced by 50%. Full article

Surface-enhanced Raman spectroscopy (SERS) has rapidly emerged as a powerful analytical technique for the sensitive and selective detection of organophosphorus pesticides (OPPs) in complex food matrices. This review summarizes recent advances in substrate engineering, emphasizing structure–performance relationships between nanomaterial design and molecular enhancement mechanisms. Functional groups such as P=O, P=S, and aromatic rings are highlighted as key determinants of Raman activity through combined chemical and electromagnetic effects. State-of-the-art substrates, including noble metals, carbon-based materials, bimetallic hybrids, MOF-derived systems, and emerging liquid metals, are critically evaluated with respect to sensitivity, stability, and applicability in typical matrices such as fruit and vegetable surfaces, juices, grains, and agricultural waters. Reported performance commonly achieves sub-μg L⁻¹ to low μg L⁻¹ detection limits in liquids and 10⁻³ to 10 μg cm⁻² on surfaces, with reproducibility often in the 5–15% RSD range under optimized conditions. Persistent challenges are also emphasized, including substrate variability, quantitative accuracy under matrix interference, and limited portability for real-world applications. Structure–response correlation models and data-driven strategies are discussed as tools to improve substrate predictability. Although AI and machine learning show promise for automated spectral interpretation and high-throughput screening, current applications remain primarily proof-of-concept rather than routine workflows. Future priorities include standardized fabrication protocols, portable detection systems, and computation-guided multidimensional designs to accelerate translation from laboratory research to practical deployment in food safety and environmental surveillance. Full article

To enhance the effectiveness and precision of design and to produce more low-carbon and high-performance Engineered Cementitious Composites (ECCs), novel hybrid fiber-reinforced high-ductility cementitious composites developed by incorporating a combination of ultra-high-molecular-weight polyethylene fibers (UHMWPE) and basalt fibers (BFs) into the cementitious matrix. Building upon the Box–Behnken design model from Response Surface Methodology (RSM), this study investigates the effects of different water-to-binder ratios and fiber contents on the mechanical properties of hybrid fiber-reinforced ECC. Analysis of variance (ANOVA) was used to validate the regression models. Furthermore, multi-objective optimization of the ECC mix proportion was achieved by employing the NSGA-II algorithm in conjunction with the TOPSIS comprehensive evaluation method. The results indicate that UHMWPE and BFs exhibited a significant positive hybrid effect. The order of factor significance was as follows: The content of ultra-high-molecular-weight polyethylene is greater than that of basalt fiber, and the content of basalt fiber is greater than that of the water–binder ratio. The results of variance analysis show that the regression model has high fitting accuracy. Furthermore, the algorithmic optimization yielded an optimal mix proportion: a water-to-binder ratio of 0.21, UHMWPE fiber content of 1.51%, and BF content of 0.85%. This study provides a valuable reference for the multi-objective optimization design of ECC mix proportions targeting diverse strength and toughness requirements. Full article

Additive manufacturing is driving a significant change in industry, extending beyond prototyping to the inclusion of printed parts in final designs. Stereolithography (SLA) is a polymerization technique valued for producing highly detailed parts with smooth surface finishes. This study presents a hybrid intelligent framework for modeling and optimizing the SLA 3D printer process’s parameters for Acrylonitrile Butadiene Styrene (ABS) photopolymer parts. The nonlinear relationships between the process’s parameters (Orientation, Lifting Speed, Lifting Distance, Exposure Time) and multiple performance characteristics (ultimate tensile strength, yield strength, modulus of elasticity, Shore D hardness, and surface roughness), which represent complex relationships, were investigated. A Taguchi design of the experiment with an L18 orthogonal array was employed as an efficient experimental design. A novel hybrid fuzzy logic–Particle Swarm Optimization (PSO) algorithm, ARGOS (Adaptive Rule Generation with Optimized Structure), was developed to automatically generate high-accuracy Mamdani-type fuzzy inference systems (FISs) from experimental data. The algorithm starts by customizing Modified Learn From Example (MLFE) to create an initial FIS. Subsequently, the generated FIS is tuned using PSO to develop and enhance predictive accuracy. The ARGOS models provided excellent performances, achieving correlation coefficients (R²) exceeding 0.9999 for all five output responses. Once the FISs were tuned, a multi-objective optimization was carried out based on the weighted sum method. This step helped to identify a well-balanced set of parameters that optimizes the key qualities of the printed parts, ensuring that the results are not just mathematically ideal, but also genuinely helpful for real-world manufacturing. The results showed that the proposed hybrid approach is a robust and highly accurate method for the modeling and multi-objective optimization of the SLA 3D process. Full article

The thermal behavior of 3D-printed polyvinylidene fluoride (PVDF)-based composites enhanced with carbon nanotubes (CNTs), graphene nanoplatelets (GNPs), and their hybrid formulations was investigated under Joule heating at applied voltages of 2, 3, and 4 V. The influence of filler type and weight fraction on both electrical and thermal conductivity was systematically assessed using a Design of Experiments (DoE) approach. Response Surface Methodology (RSM) was employed to derive an analytical relationship linking conductivity values to filler loading, revealing clear trends and interaction effects. Among all tested formulations, the composite containing 6 wt% of GNPs exhibited the highest performance in terms of thermal response and electrical conductivity, reaching a steady-state temperature of 88.1 °C under an applied voltage of just 4 V. This optimal formulation was further analyzed through multiphysics simulations, validated against experimental data and theoretical predictions, to evaluate its effectiveness for potential practical applications—particularly in de-icing systems leveraging Joule heating. The integrated experimental–theoretical–numerical workflow proposed herein offers a robust strategy for guiding the development and optimization of next-generation polymer nanocomposites for thermal management technologies. Full article

The persistent contamination of water bodies by organic compounds, heavy metals, and pathogenic microorganisms represents a critical environmental and public health concern worldwide. In this context, polymer composite materials have emerged as promising multifunctional platforms for advanced water purification. These materials combine the structural versatility of natural and synthetic polymers with the enhanced physicochemical functionalities of inorganic fillers, such as metal oxides and clay minerals. This review comprehensively analyzes recent developments in polymer composites designed to remove organic, inorganic, and biological pollutants from water systems. Emphasis is placed on key removal mechanisms, adsorption, ion exchange, photocatalysis, and antimicrobial action, alongside relevant synthesis strategies and material properties that influence performance, such as surface area, porosity, functional group availability, and mechanical stability. Representative studies are examined to illustrate contaminant-specific composite designs and removal efficiencies. Despite significant advancements, challenges remain regarding scalability, material regeneration, and the environmental safety of nanostructured components. Future perspectives highlight the potential of bio-based and stimuli-responsive polymers, hybrid systems, and AI-assisted material design in promoting sustainable, efficient, and targeted water purification technologies. Full article

Zinc oxide (ZnO) nanomaterials have emerged as promising candidates for gas sensing applications due to their high sensitivity, fast response–recovery cycles, thermal and chemical stability, and low fabrication cost. However, the performance of pristine ZnO remains limited by high operating temperatures, poor selectivity, and suboptimal detection at low gas concentrations. To address these limitations, significant research efforts have focused on dopant incorporation and polymer hybridization. This review summarizes recent advances in dopant engineering using elements such as Al, Ga, Mg, In, Sn, and transition metals (Co, Ni, Cu), which modulate ZnO’s crystal structure, defect density, carrier concentration, and surface activity—resulting in enhanced gas adsorption and electron transport. Furthermore, ZnO–polymer nanocomposites (e.g., with polyaniline, polypyrrole, PEG, and chitosan) exhibit improved flexibility, surface functionality, and room-temperature responsiveness due to the presence of active functional groups and tunable porosity. The synergistic combination of dopants and polymers facilitates enhanced charge transfer, increased surface area, and stronger gas–molecule interactions. Where applicable, sol–gel-based studies are explicitly highlighted and contrasted with non-sol–gel routes to show how synthesis controls defect chemistry, morphology, and sensing metrics. This review provides a comprehensive understanding of the structure–function relationships in doped ZnO and ZnO–polymer hybrids and offers guidelines for the rational design of next-generation, low-power, and selective gas sensors for environmental and industrial applications. Full article