ABSTRACT The rising incidence of amphetamine misuse, particularly in the context of attention deficit hyperactivity disorder treatment, underscores the urgent need for sensitive and effective detection methods. This review examines innovative nanomaterial‐based approaches for AMP detection, emphasizing their advantages over conventional analytical techniques. Various nanomaterials, including carbon nanotubes, graphene and metal nanoparticles, have been utilized to enhance the sensitivity and selectivity of detection methods such as electrochemical sensors, surface‐enhanced Raman spectroscopy and fluorescence‐based assays. The distinctive properties of nanomaterials, including high surface area, conductivity and biocompatibility, enable the development of rapid and reliable detection systems. This paper discusses recent advancements in nanomaterial synthesis, functionalization and integration into detection platforms, along with the challenges and future directions in this field. By harnessing the potential of nanotechnology, these innovative approaches aim to improve the accuracy and efficiency of amphetamine detection, thereby enhancing the monitoring and management of substance abuse, particularly in individuals with attention deficit hyperactivity disorder.

Temporal modulation of cuproptosis and autophagy mediates nanographene-driven pulmonary fibrosis progression.

This work examines how curvature, nonlinear radiation, wall heating conditions, and the permeability of porous material impact flow and heat transfer of nanofluids over a cylindrical surface. We studied three different types of nanoparticles: Ferrous Sulfate, copper, and graphene. This showed how momentum and energy transfer are influenced by thermophysical properties. We changed the governing equations into dimensionless form and solved them with numerical methods. This method helped us understand how velocity, temperature, skin friction, and Nusselt number react across a wide range of parameters. The results show that increasing the power-law index noticeably improves skin friction and heat transfer rates due to stronger convective acceleration. Larger curvature values reduce the thickness of both the velocity and thermal boundary layers, which improves transport efficiency. In contrast, higher wall-to-ambient temperature ratios cause more energy to accumulate near the surface. Heat transfer has decreased, and the thermal boundary layer is thickened consequently. Higher shear stresses and sharper velocity gradients result from this. Graphene showed better performance than other nanoparticles used, as its superconductivity provides better thermal performance. The obtained results uncover that graphene-based nanofluid attains the highest skin friction and Nusselt number coefficients, surpassing ferrous sulfate and copper by about 18–25% under similar conditions.

The properties of carbon-based materials with nanometric size support their use in numerous applications, such as optoelectronics and energy devices, bioimaging, photodetectors, and sensors. Among the various nanostructure fabrication methods, pulsed laser ablation in liquids (PLA) is widely recognized for its simplicity and rapid processing. It is considered an environmentally friendly synthesis, as it enables nanostructure fabrication in pure liquids without chemical reagents, activators, or vacuum systems, in line with the increasing interest in sustainable and green nanotechnologies. A great challenge of PLA is the reproducibility of the size and shape of the produced structure. This can be accomplished by selection of the proper laser parameters and characteristics of the used liquid. This study is focused on the comparison of the synthesis of graphene-based nanostructures by electric-field-assisted pulsed laser ablation of a graphite target immersed in distilled water and deionized water, used as separate liquid media, without the use of chemical reagents. This is an innovative and environmentally friendly approach for the production of graphene nanoparticles. The laser parameters were kept constant throughout the experiments, while different voltage values were applied between the electrodes immersed in the liquid medium. The applied electric field significantly influences plasma dynamics, cavitation bubble evolution, and post-ablation nanoparticle growth processes, enabling controlled tuning of nanoparticle size and morphology. The optical properties of the obtained suspensions were evaluated by UV–Vis and FTIR spectroscopies. Atomic force microscopy revealed the composition, morphology, and quality of the formed structures.

ABSTRACT The present study investigates the effect of octadecyl‐trichlorosilane functionalization of chopped glass strand mat (CGSM), Graphene nano particles (GNP) reinforcement on shear and vibration behavior of glass fiber–reinforced polymer (GFRP) composite single lap joints (SLJs) fabricated through co‐cure approach. The experimental results revealed that both the type of reinforcement and the surface functionalization play a crucial role in enhancing the shear strength of the SLJ. Specifically, the incorporation of functionalized graphene nano particles (FGNP) and functionalized chopped glass strand mat (FCGSM) reinforcement has increased the load carrying behavior of composite joint under loading. It shows significant improvement in shear strength enhancement by 97.06%, 77.33%, 67.9%, 5.96%, 31.66% and 8.19% compared to Plain, CGSM, CGSM+GNP, FGNP, FCGSM and FGNP+CGSM respectively. This enhancement has been attributed to functionalization of reinforcements that improve the interfacial bonding behavior between adhesive and adherends. Field Emission Scanning Electron Microscopy analysis has confirmed that CGSM reinforcement without surface modification has exhibited smooth surfaces with poor adhesion, while the incorporation of non‐functionalized GNP has led to agglomeration and crack propagation. Failure analysis demonstrated that functionalized dual‐scale reinforcements effectively suppress the agglomeration, alter fracture morphology, and significantly improve the structural integrity and durability of adhesive joints. Vibration analysis revealed that FGNP with CGSM reinforced SLJ has achieved the highest fundamental natural frequency.

Abstract Silicon-tin (Si-Sn) nanocomposites are viable anode substitutes for lithium-ion batteries (LIBs) as they have the high theoretical storage capacities of lithium (4200 mAh g -1 (Si) and 994 mAh g -1 (Sn)), which are significantly more than those of graphite (372 mAh g -1 ). The Si and Sn cannot be used widely due to their extreme volume expansion during cycling, which results in limited structural stability and quick capacity deterioration. Herein, a nanocomposite based on reduced Graphene Oxide and nanoparticles containing silicon and tin species (SnSi9@rGO) is presented as a potential solution to mitigate these challenges by incorporating rGO to enhance the electrochemical performance. This nanocomposite demonstrates a high discharge capacity of 1506.91 mAh g -1 at a current density of 0.55 A g -1 after 205 cycles. In contrast, the SnSi9 nanoparticles only kept 125.85 mAh g -1 after the same number of cycles. SnSi9@rGO nanocomposite improved electrochemical performance due to the conductive network provided by rGO, which adapts to volume variations during cycling and facilitates charge transfer. Results from galvanostatic testing, cyclic voltammetry, and electrochemical impedance spectroscopy support the idea that SnSi9@rGO nanocomposites could be a high-performance anode material for next-generation LIBs.

The main purpose of this research is the impact of graphene nanoparticle reinforcement on ballistic protection of composites and to investigate the ballistic and mechanical properties of these products. In this context nanocomposite ballistic plates were obtained by reinforcing different amounts of graphene to aramid-based composite plates after some processes. To characterize the graphene, several analyses were performed. Characteristic peaks were obtained by performing FTIR (Fourier Transform InFrared). Scanning Electron Microscopy (SEM) and Raman analyzes on graphene nanoplatelets used in the experimental study. Ballistic plates reinforced with graphene nanoplatelets and non-reinforced plates were subjected to shooting tests in the ballistic test laboratory in accordance with the NIJ (National Institute of Justice) standard and the test results were compared. As a result of the shooting tests, successful results were not achieved with ballistic plates reinforced with graphene and epoxy resin, except for one plate. This outcome was attributed to the negative impact of epoxy resin on the flexibility and energy absorption properties of the aramid layers. From a mechanical properties perspective, it was observed that the ultimate tensile strength of the samples with 0.5% and 1% by weight graphene increased.

Introduction. Hybrid metal matrix composites (HMMCs) are increasingly used in the aviation and automotive industries due to their low density, high stiffness, and exceptional specific strength. Among aluminum MMCs, Al7075-based composites are gaining wider acceptance. Continuous research and development in this field focuses on improving the durability and performance of these advanced materials. Purpose of the work. Machinability of Al7075 is a significant challenge because the abrasive reinforcement phase causes rapid tool deterioration, increased machining forces, and a poor surface finish. Moreover, the industrial focus on green manufacturing has led to a shift from traditional coolant-based machining to sustainable alternatives. In this context, researchers have optimized machining performance using advanced technological advancements and techniques. However, limited work is reported on modeling the machining performance of Al7075 nanocomposites during turning under compressed air cooling. Methods of investigation. Manufacturers can gain a better understanding of increasing the effectiveness of turning processes for Al7075 nanocomposites by creating a comprehensive model. Therefore, this work models the machining performance of hybrid Al7075 nanocomposites during turning under compressed air-cooling conditions with an artificial neuro-fuzzy inference system (ANFIS) to predict tool wear (TW), surface roughness (Ra), and cutting force (Fc) as a function of process parameters. Results and discussion. In this work, an ANFIS model was developed to predict the machining performance considering the effect of process parameters such as cutting speed, feed rate, and depth of cut for different Al7075-based nanocomposites. These nanocomposites were prepared using silicon carbide (30–50 nm) and graphene (5–10 nm) nanoparticles as reinforcements by the stir casting process. Reinforcement materials affect the mechanical and physical properties of composites. For engineering applications, SiC and graphene are preferred reinforcements with distinctive features. ANFIS models were developed to predict Ra, Fc, and TW based on the experimental results. The Sugino method was used to represent fuzzy rules and membership functions, as it utilizes weighted averages in the defuzzification process and offers better processing efficiency. The MATLAB ANFIS toolbox was used to design and tune fuzzy inference systems. The developed ANFIS model predicts machining responses effectively and offers a practical approach for optimizing process parameters with high reliability. The results of this research show good agreement between the experimental results and the predicted ANFIS outcomes, with an average prediction error below 8%. Specifically, the ANFIS model yielded errors of 5.1% for Ra, 13.45% for Fc, and 7.92% for TW. The model exhibited excellent agreement with experimental data, demonstrating high prediction accuracy and generalization capability. 3-D graphs are plotted for a better understanding of the effect of process parameters on Fc, Ra, and TW for different nanocomposites. The findings affirm the efficacy of compressed air cooling in improving machinability while minimizing environmental impact. Furthermore, the developed ANFIS model serves as a reliable tool for optimizing turning parameters for Al7075 composites, supporting the advancement of green manufacturing strategies. This research warrants further investigation into the application of ANFIS in machining processes, specifically exploring various metal matrix composite types and rigorously assessing the long-term effects of compressed air cooling on both environmental sustainability and tool life.

Abstract Aim: Tricalcium silicate (TCS) cements are central to endodontic practice; however, they often exhibit inadequate mechanical performance and prolonged durations for setting. Incorporating graphene nanoparticles (GNPs) has emerged as a potential method to strengthen these materials, yet the available data remain unsystematically consolidated. This systematic review compiles the current in vitro findings on the influence of GNP incorporation on the physical, chemical, and biological attributes of TCS-based dental cements. Materials and Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework and a registered International Prospective Register of Systematic Reviews protocol (CRD420251012596), comprehensive searches were executed across five electronic databases. In vitro investigations evaluating compressive strength, hardness, setting time, calcium ion release, antimicrobial potential, and cytocompatibility were selected. Data extraction and quality appraisal were performed according to the Checklist for Reporting In Vitro Studies. Results: Out of 353 initially identified articles, six fulfilled the eligibility criteria. Overall evidence quality was limited: five studies were graded as “fair” and one as “poor,” primarily due to absent randomization and blinding. Synthesized findings revealed that GNP inclusion generally enhanced hardness and compressive strength while shortening the setting time. Alterations in pH and calcium ion release were inconsistent across studies. All reports demonstrated improved antimicrobial action and positive outcomes for cellular proliferation and viability. Conclusion: Although the available in vitro data are methodologically constrained, they consistently indicate that introducing GNPs into TCS matrices can augment mechanical strength, antimicrobial performance, and biocompatibility. Nevertheless, owing to heterogeneity in experimental design and modest evidence quality, these observations should be regarded as preliminary. Well-designed, standardized in vitro and subsequent in vivo experiments are required to confirm these promising outcomes for clinical adaptation.

Nanofluids offer superior heat transfer compared to conventional automotive coolants, with further improvements achievable through particle hybridization and particle shape effects. This study numerically investigates the thermal performance of a ruffled-fin radiator using ternary hybrid nanofluids composed of ZnO, Al2O3, TiO2, MWCNTs, graphene, Fe, Cu, and Ag nanoparticles at varying volume fractions. Combinations such as Fe/Cu/Ag, Fe/Cu/ZnO, and Ag/Al2O3/TiO2 enhanced convective heat transfer, as reflected in increased Nusselt numbers and overall heat transfer coefficients. Increasing the volume fraction reduced outlet coolant temperature, with an optimal nanofluid achieving a 44.6% temperature drop at the engine outlet. The performance index, defined as a composite measure of heat transfer and cooling efficiency, decreased by 5.8–11.7% across the eight ternary nanofluids, with non-metallic oxides exhibiting the smallest reduction. These results demonstrate the role of ternary hybrid nanofluids in improving radiator cooling while indicating efficiency trade-offs at higher volume fractions.

Aim: Peripheral nerve injuries (PNIs) often result in a diminished quality of life for those affected and are the most common nervous system injury, with limited treatment options. Regenerative medicine presents novel biomaterial and cell-based therapies to repair the damaged tissue. Graphene oxide (GO), and mesenchymal stem cells (MSCs) have the potential to serve as components to treat PNI. This study evaluates the systemic toxicity of GO and xenogenic human MSCs by analyzing the peripheral blood immune phenotype when a novel nerve guidance conduit (NGC) is implanted in a rat model for six months. Methods: A 10-mm long sciatic nerve defect model was created in 8–10-week-old Lewis rats. Four treatment groups were generated: autograft (positive control), poly (lactic-co-glycolic acid) (PLGA) NGC, PLGA NGC with 0.25% GO, and PLGA/GO NGC seeded with 1 × 106 human adipose-derived MSCs. Tail blood was collected before surgery, and at 24 hours, 2 weeks, 2, 3, 5, and 6 months after surgery. Hematological analyses were carried out to evaluate systemic changes, if any, in peripheral immune cell types, namely, T lymphocytes, B lymphocytes, natural killer cells, and macrophages. The treated and contralateral sciatic nerves were excised, paraffin embedded, sectioned, and H&E stained, to identify any local foreign body rejection. Results: Treatment groups with GO and MSCs displayed percent total values of peripheral immune cells equivalent to the autograft at each time point. There was no evidence of an inflammatory response in the histological samples. Conclusions: The lack of changes in immune phenotype demonstrates a lack of nanotoxicity of the graphene nanoparticles and no evidence of adverse effects due to the MSCs. This was further supported by a lack of local foreign body response at the site of implantation. Overall, the PLGA/GO NGC + MSCs construct is biocompatible for six months in a rat PNI model, exhibiting a potential for clinical translation.

The influence of prolonged ultraviolet (UV) irradiation on the structural and functional properties of graphene deposited on copper, silicon, and poly(butyl methacrylate) (PnBMA) substrates has been investigated. Using Raman spectroscopy, it was shown that UV exposure induces various types of defects, the nature of which is determined by both the substrate type and the number of graphene layers. It was established that for the transferred PnBMA/Gr1 and PnBMA/Gr2 coatings, a significant increase in specific surface resistance is observed after irradiation, with more pronounced degradation of conductivity being characteristic of the PnBMA/Gr2 sample with fewer graphene layers. It is important to note that despite the increase in resistance, its values for all studied "graphene-PnBMA" coatings remained within the antistatic range (10 4 –10 12 Ω/sq) throughout the experiment. The incorporation of commercial graphene nanoparticles into the PnBMA matrix (NP-Gr/PBMA) significantly enhances stability: the composite maintained antistatic properties (resistance of 2–3 kΩ/sq) even after 168 h of irradiation. A critical effect of UV exposure is the transition of the coating surfaces from a hydrophobic to a hydrophilic state due to the photo-oxidation of graphene, which was particularly pronounced in the PnBMA/Gr1 sample. The results demonstrate that the stability of graphene-containing coatings under UV irradiation is determined by the number of graphene layers and the properties of the substrate.

A multi-layer fiberglass-reinforced nanocomposite consisting of graphene nanoparticles, polyester resin, along fiberglass reinforcements have been fabricated using a standard templated layup approach. The composite structure is composed of three layers of graphene-reinforced polyester resin, with variable graphene concentrations of 2%, 4%, 6%, and 8% by weight, and two layers of fiberglass to support the specimen structure. The research aimed to study the physical and mechanical behavior of this novel composite material over a combination of experimental testing and numerical simulation. Tensile and compressive tests have been conducted to describe the composite's response under different mechanical loading conditions. The experimental results have shown a significant improvement in tensile strength with the incorporation of graphene nanoparticles and fiberglass into the polyester matrix. Particularly, the sample with 4% wt of graphene content exhibited a 78% increase in tensile strength compared to the unreinforced polyester resin. In contrast, the compressive strength has shown a gradual reduction with increasing graphene content, which was attributed to the inherently brittle behavior of the composite structure under compressive loading. To further validate the experimental findings and assess the overall mechanical performance, a numerical simulation was performed using the finite element analysis software ABAQUS. Numerical simulation has confirmed the trends observed in the experiments and provided insights into the stress distribution and failure mechanisms within the composite. These results have proposed that the hybrid integration of graphene nanoparticles and fiberglass into polyester matrices has offered a promising route for enhancing the tensile performance of polymer-based composites, though considerations must be made for compressive load-bearing applications.

This study investigates the performance of soybean oil-based nano-lubricants with CuO, graphene, and CuO/graphene hybrids under MQL-assisted CNC milling of AISI 1045 steel. The research aims to evaluate the thermophysical, rheological, and tribological properties of various lubricant formulations, including pure soybean oil and soybean oil with individual or hybrid nanoparticle additives. Nanoparticles were characterized by SEM, XRD, and FTIR, and fluid samples were evaluated for density, viscosity, thermal conductivity, sedimentation stability, and rheological behavior. Machining performance was assessed through tool wear, surface roughness, cutting temperature, wear debris morphology, and chip color analysis. Results showed that adding graphene nanoparticles significantly improved machining performance, achieving a surface roughness of 1.033 µm, tool wear of 0.0493 mm, and a cutting temperature of 46.1 °C, outperforming both conventional and alternative nanofluid formulations. Among all formulations, the graphene-based nanofluid delivered the lowest cutting temperature, surface roughness, and flank wear under MQL. The CuO/graphene hybrid improved performance relative to the base fluids but did not surpass the graphene formulation, indicating limited synergistic benefits under the present soybean oil-based-MQL conditions.

Ensuring food safety and quality has become increasingly critical due to the complexities introduced by globalization, industrialization, and extended supply chains. Traditional analytical methods for food quality control, such as chromatography and mass spectrometry, while accurate, face limitations including high costs, lengthy analysis times, and limited suitability for on-site rapid monitoring. Electrochemical sensors integrated with molecularly imprinted polymers (MIPs) have emerged as promising alternatives, combining high selectivity and sensitivity with portability and affordability. MIPs, often termed ‘plastic antibodies,’ are synthetic receptors capable of selective molecular recognition, tailored specifically for target analytes. This review comprehensively discusses recent advancements in MIP-based electrochemical sensing platforms, highlighting their applications in detecting various food quality markers. It particularly emphasizes the detection of antioxidants—both natural (e.g., vitamins, phenolics) and synthetic (e.g., BHA, TBHQ), artificial sweeteners (e.g., aspartame, acesulfame-K), colorants (e.g., azo dyes, anthocyanins), traditional contaminants (e.g., pesticides, heavy metals), and toxicants such as mycotoxins (e.g., aflatoxins, ochratoxins). The synthesis methods, including bulk, precipitation, surface imprinting, sol–gel polymerization, and electropolymerization (EP), are critically evaluated for their effectiveness in creating highly selective binding sites. Furthermore, the integration of advanced nanomaterials, such as graphene, carbon nanotubes, and metallic nanoparticles, into these platforms to enhance sensitivity, selectivity, and stability is examined. Practical challenges, including sensor reusability, regeneration strategies, and adaptability to complex food matrices, are addressed. Finally, the review provides an outlook on future developments and practical considerations necessary to transition these innovative MIP electrochemical sensors from laboratory research to widespread adoption in industry and regulatory settings, ultimately ensuring comprehensive food safety and consumer protection.

Abstract Inconel 718 (IN718) is a nickel (Ni)-based superalloy that is extremely valuable for its vital uses in spacecraft, rocket engines, and nuclear reactors. It is notoriously hard to machine; as a result, a possible substitute is provided by electric discharge machining (EDM), which erodes the material by repeatedly releasing sparks in a dielectric fluid, usually kerosene oil (KO). Since KO releases toxic fumes during EDM, scientists have investigated different biodegradable oils to cut down on using KO while still getting good results. This study investigates the combinations of graphene nanoparticles (GNPs), waste oil blend (WOB), and surfactant (Tween80) to get better machining results. For the experiments, the Taguchi L18 design of experiments was employed. Significant processing parameters have undergone analysis of variance (ANOVA). Following that, a model of an artificial neural network (ANN) was used to accurately predict the response measures. Following the modelling process, the nondominated sorting genetic algorithm (NSGA-II), which is based on machine learning (ML), was used to determine the ideal set of parameters. The combined influence of peak current (I P ) and surfactant concentration (S C ) shows that the magnitudes of material removal rate (MRR), surface roughness (SR), electrode wear rate (EWR), and accuracy index (AI) improved by 5.93 times, 1.27 times, 1.08 times, and 1.55%, respectively, when comparing lower and higher parametric states. In the confirmatory experiment, SR and EWR decreased by 9.80% and 16.14%, respectively, while MRR and AI increased by 90.83% and 1.95%, according to the NSGA-II results. As a result, energy savings using WOB in the EDM process are computed, and the potential for CO 2 reduction is then evaluated. It has been found that the percentage reduction in CO 2 emissions for WOB has been recorded as 77.60 ± 3.48% in comparison to the KO. Therefore, WOB dielectric can be an ideal alternative to KO in EDM applications.

Biosensors are innovative analytical devices that combine biological recognition elements such as enzymes, antibodies, nucleic acids, or cells with physical transducers to detect and quantify specific substances. In the pharmaceutical industry, biosensors have gained significant attention due to their ability to provide rapid, accurate, and cost-effective results compared to traditional laboratory techniques. Recent technological advancements have revolutionized biosensor applications through the integration of nanomaterials, microfluidic systems, artificial intelligence (AI), and the Internet of Things (IoT). Nanomaterials like graphene, carbon nanotubes, and gold nanoparticles enhance sensitivity and stability, while microfluidic lab-on-a-chip devices enable miniaturized, high-throughput testing with minimal sample volumes. Moreover, AI- and IoT-enabled biosensors allow real-time data collection, analysis, and personalized therapeutic monitoring. These developments have expanded biosensor use in drug discovery, quality control, vaccine development, and therapeutic drug monitoring. Wearable and implantable biosensors further contribute to personalized medicine by providing continuous, non-invasive health monitoring. Despite challenges such as cost, stability, and regulatory compliance, ongoing innovations continue to improve biosensor efficiency, scalability, and reliability. Therefore, biosensors are emerging as indispensable tools in modern pharmaceutical research, ensuring safer, faster, and more effective healthcare solutions.

Concrete reinforced with steel has made remarkable progress in the construction industry since it was introduced as a structural material. By adding nanoparticles, cementitious materials become self-healing, more durable, stronger, easier to clean, rapid compaction and fire-proof. Single nanoparticle focuses on certain attributes, such as greater strength, durability in cementitious materials. A single nanoparticle does not acquire all the favorable properties required for a particular purpose. In many real-world applications, it is required to cop-out between several characteristics, and hybrid nanofluids arise from there. Hybrid nanofluids combine two or more than two different types of nanoparticles. In cementitious materials, ternary nanoparticles enhanced the mechanical strength and other desired properties of cementitious materials more than hybrid nanoparticles and single nanoparticles. Ternary nanoparticles have three different nanoparticles. The problem is modeled in terms of partial differential equations for ternary nanofluids in a porous medium with the combined effect of heat and mass transfer. The derived governing equations for the present flow regime are generalized by means of Caputo–Fabrizio time fractional derivatives. Exact solutions are obtained using the Laplace transform technique. The effect of various physical parameters involved in this study on velocity, temperature and concentration distributions is shown graphically and discussed. For instance, the effect of the fractional parameter [Formula: see text] on velocity, temperature, and concentration profiles shows variation. Unlike the classical model ([Formula: see text], the fractional model is more general and provides multiple solutions ([Formula: see text]. This effect is called the memory effect. It is worth noting that skin friction is increased by 11.69%, 11.62%, and 11.59%, respectively, by the addition of graphene nanoparticles, silver nanoparticles, and copper nanoparticles. In cementitious materials, higher skin friction improves adhesion and shear transfer, strengthening the connection between surfaces.

This study examines the thermophysical properties of ethylene glycol–glycerol (60:40 v/v) hybrid nanofluids containing graphene nanoplatelets (GNPs) and silver nanoparticles (Ag) at concentrations of 0.1–0.5 vol.%. The nanofluids were synthesized using a two-step method with Tween-80 surfactant to enhance dispersion stability. High-resolution transmission electron microscopy (TEM) and Raman spectroscopy confirmed the morphology, lateral size, few-layer structure of GNPs, and the attachment of Ag nanoparticles. The addition of surfactant increased the zeta potential from 15.7 mV to 35.2 mV for the 0.1 vol.% GNPs/Ag formulation, indicating a marked improvement in colloidal stability. Thermal conductivity enhancement reached 102.85% at 0.1 vol.% with only a 19.84% viscosity increase. Higher nanoparticle loadings improved conductivity further but caused significant viscosity increases and reduced stability. Specific heat capacity decreased by up to 46.45%, potentially benefiting rapid thermal response applications but limiting heat storage capacity. Comparison with recent literature showed that the present formulation outperforms several similar Ag- and GNP-based nanofluids in thermal conductivity enhancement while maintaining manageable viscosity. This study is the first to report such high conductivity improvement in an EG–GLY-based hybrid nanofluid at ultra-low loading, achieved through optimized surfactant use, validated structural characterization, and benchmarking against literature. Low-concentration GNPs/Ag hybrid nanofluids, particularly at 0.1 vol.%, offer strong potential for thermal management applications where high heat transfer performance and acceptable pumping requirements are critical. However, stability limitations at higher concentrations and viscosity–conductivity trade-offs highlight the need for further optimization before large-scale deployment.

Stroke is a leading cause of long-term disability worldwide, with post-stroke aphasia significantly impairing communication and social interaction. Traditional rehabilitation devices are often bulky, expensive, and impractical for daily use, particularly in speech recovery, where accessible and effective solutions remain limited. To address this challenge, this study introduces a portable and wearable sensor system for stroke-induced aphasia rehabilitation. The proposed sensor integrates a flexible, ultrasensitive, and durable dual-sensor system comprising an Ag-MnO2-based sea-urchin-like nanoparticle pressure sensor to detect high-frequency vocal vibrations and a vertical graphene/polydimethylsiloxane (VGr/PDMS) strain sensor to capture low-frequency muscular movements. The sensors, integrated into a flexible circuit, employ an encoder-cycle-consistent generative adversarial networks (CycleGAN)model that recognizes users' intent and recovers voice, significantly reducing dependency on large-scale labelled datasets. Experimental results demonstrate accurate intent recognition with accuracies for certain commands exceeding 95%. The reconstructed speech exhibits improved naturalness based on objective and perceptual evaluations, highlighting potential clinical utility in enhancing daily communication and interaction for stroke survivors.