Search Results (554)

Hydroxychloroquine (HCQ), a cornerstone therapeutic agent for autoimmune diseases, requires precise serum concentration monitoring due to its narrow therapeutic window. Current HCQ monitoring methods such as HPLC and LC-MS/MS are sensitive but costly and complex. While electrochemical sensors offer rapid, cost-effective detection, their large chambers and high sample consumption hinder point-of-care use. To address these challenges, we developed a microfluidic electrochemical sensing platform based on a screen-printed carbon electrode (SPCE) modified with a hierarchical nanocomposite of gold nanoparticles (AuNPs), copper-based metal–organic frameworks (Cu-MOFs), and multi-walled carbon nanotubes (MWCNTs). The Cu-MOF provided high porosity and analyte enrichment, MWCNTs established a 3D conductive network to enhance electron transfer, and AuNPs further optimized catalytic activity through localized plasmonic effects. Structural characterization (SEM, XRD, FT-IR) confirmed the successful integration of these components via π-π stacking and metal–carboxylate coordination. Electrochemical analyses (CV, EIS, DPV) revealed exceptional performance, with a wide linear range (0.05–50 μM), a low detection limit (19 nM, S/N = 3), and a rapid response time (<5 min). The sensor exhibited outstanding selectivity against common interferents, high reproducibility (RSD = 3.15%), and long-term stability (98% signal retention after 15 days). By integrating the nanocomposite-modified SPCE into a microfluidic chip, we achieved accurate HCQ detection in 50 μL of serum, with recovery rates of 95.0–103.0%, meeting FDA validation criteria. This portable platform combines the synergistic advantages of nanomaterials with microfluidic miniaturization, offering a robust and practical tool for real-time therapeutic drug monitoring in clinical settings. Full article

A method for characterizing carbon nanotubes (CNTs) in powder form with different packing densities in the microwave regions is proposed. The CNTs were sandwiched between two dielectric walls in (Polyvinyl Chloride) PVC and put in a waveguide shim. We measured the transmission/reflection S-parameters of the waveguide using a Vector Network Analysis (VNA), and the impacts of the PVCs on the measured S-parameters were de-embedded by microwave network analysis. Then, the well-known Nicolson–Ross–Weir (NRW) method was processed to determine the complex permittivity and permeability of the CNTs. Furthermore, we pressed the PVC to increase the packing densities of the CNTs. The results of the characterization can be employed to design microwave devices using the CNTs. Full article

As one of the most widely used packaging materials, epoxy composite (EP) offers excellent insulation properties; however, its intrinsic low thermal conductivity (TC) limits its application in high-frequency and high-power devices. To enhance the TC of EP, six highly thermally conductive inorganic fillers, namely, Al₂O₃, MgO, ZnO, Si₃N₄, h-BN, and AlN, were incorporated into the EP matrix at varying contents (60–90 wt.%). The resulting epoxy molding compounds (EMCs) demonstrated significant improvement in thermal conductivity coefficient (λ) at high filler contents (90 wt.%), ranging from 0.67 W m⁻¹ K⁻¹ to 1.19 W m⁻¹ K⁻¹, compared to the pristine epoxy composite preform (ECP, 0.36 W m⁻¹ K⁻¹). However, it was found that the interfacial thermal resistance (ITR) between EP and filler materials is a major hindrance restricting TC improvement. In order to address this challenge, graphene nanosheets (GNSs) and carbon nanotubes (CNTs) were introduced as additives to reduce the ITR. The experimental results indicated that CNTs were effective in enhancing the TC, with the optimized EMC achieving a λ value of 1.14 W m⁻¹ K⁻¹ using 60 wt.% Si₃N₄ + 2 wt.% CNTs. Through the introduction of a small amount of CNT (2 wt.%), the inorganic filler content was significantly reduced from 90 wt.% to 60 wt.% while still maintaining high thermal conductivity (1.14 W m⁻¹ K⁻¹). We propose that the addition of CNTs helps in the construction of a partial heat conduction network within the EP matrix, thereby facilitating interfacial heat transfer. Full article

A uniformly dispersed carbon nanotubes (CNTs)/polyvinyl alcohol (PVA) nano-colloidal emulsion was synthesized by leveraging colloidal stability and interfacial chemical interactions. This study systematically investigated the influence of the CNTs/PVA nano-colloidal emulsion on the mechanical properties, drying shrinkage, capillary water absorption, and microstructure of cement-based materials, while elucidating the underlying reinforcement mechanisms. The experimental results demonstrated that different CNTs/PVA ratios enhanced the concrete properties: For instance, 0.3% CNTs and 1.0% PVA improved the 28-day compressive and flexural strengths by 15% and 10%, respectively, while 0.5% CNTs and 1.0% PVA reduced the drying shrinkage by 76%, 34%, 22%, and 21% at 7, 28, 180, and 360 days. Additionally, the 0.5% CNTs/1.0% PVA mixture achieved a 25.7% lower absorption rate (25.25 vs. 34.00 g·m⁻², *p* < 0.001) than plain concrete. A microstructural analysis revealed that the CNTs/PVA composite formed an interpenetrating network within the cement matrix, which correlated with the observed mechanical improvements and shrinkage reduction. These findings indicate that even minimal additions of CNTs/PVA could effectively enhance the tensile and flexural capacity of concrete while mitigating its susceptibility to drying shrinkage. Full article

The rapid advancement of sustainable materials has driven the need for high-performance polymer nanocomposites with superior mechanical, thermal, and structural properties. In this study, a novel RPET/PA-11/Joncryl^® nanocomposite reinforced with halloysite nanotubes (HNTs) is developed for the first time, marking a significant breakthrough in polymer engineering. Six different proportions of HNT (0, 1, 2, 3, 4, and 5 phr) are introduced to the blend of rPET/PA-11/Joncryl^® through a twin-screw extruder and injection moulding machine. The incorporation of HNTs into the RPET/PA-11 matrix, coupled with Joncryl^® as a compatibilizer, results in a synergistic enhancement of material properties through improved interfacial adhesion, load transfer efficiency, and nanoscale reinforcement. Comprehensive characterization reveals that the optimal formulation with 2 phr HNT (NCS-H2) achieves remarkable improvements in tensile strength (56.14 MPa), flexural strength (68.34 MPa), and Young’s modulus (895 MPa), far exceeding conventional polymer blends. Impact resistance reaches 243.46 J/m, demonstrating exceptional energy absorption and fracture toughness. Thermal analysis confirms enhanced stability, with an onset degradation temperature of 370 °C, attributing the improvement to effective matrix–filler interactions and restricted chain mobility. Morphological analysis through FESEM validates uniform HNT dispersion at optimal loading, eliminating agglomeration-induced stress concentrators and reinforcing the polymer network. The pioneering integration of HNT into RPET/PA-11/Joncryl^® nanocomposites not only bridges a critical gap in sustainable polymers but also establishes a new benchmark for polymer nanocomposites. This work presents an eco-friendly solution for engineering applications, offering mechanical robustness, thermal stability, and recyclability. The results form the basis for next-generation high-performance materials for industrial use in automotive, aerospace, and high-strength structural applications. Full article

The intrinsic trade-off among sensitivity, response speed, and measurement range continues to hinder the wider adoption of flexible pressure sensors in areas such as medical diagnostics and gesture recognition. In this work, we propose a grid-structured polycaprolactone/thermoplastic-polyurethane nanofiber pressure sensor decorated with multi-walled carbon nanotubes (PCL/TPU@MWCNTs). By introducing a gradient grid membrane, the strain distribution and reconstruction of the conductive network can be modulated, thereby alleviating the conflict between sensitivity, response speed, and operating range. First, static mechanical simulations were performed to compare the mechanical responses of planar and grid membranes, confirming that the grid architecture offers superior sensitivity. Next, PCL/TPU@MWCNT nanofiber membranes were fabricated via coaxial electrospinning followed by vacuum-filtration and assembled into three-layer planar and grid piezoresistive pressure sensors. Their sensing characteristics were evaluated by simple index-finger motions and slide the mouse wheel identified. Within 0–34 kPa, the sensitivities of the planar and grid sensors reached 1.80 kPa⁻¹ and 2.24 kPa⁻¹, respectively; in the 35–75 kPa range, they were 1.03 kPa⁻¹ and 1.27 kPa⁻¹. The rise/decay times of the output signals were 10.53 ms/11.20 ms for the planar sensor and 9.17 ms/9.65 ms for the grid sensor. Both sensors successfully distinguished active index-finger bending at 0–0.5 Hz. The dynamic range of the grid sensor during the extension motion of the index finger is 105 dB and, during the scrolling mouse motion, is 55 dB, affording higher measurement stability and a broader operating window, fully meeting the requirements for high-precision hand-motion recognition. Full article

Uneven local electric fields and limited nucleation sites at the reaction interface can lead to the formation of hazardous lithium (Li) dendrites, posing a significant safety risk and impeding the practical utilization of Li metal anodes (LMAs). Here, we present a method utilizing atomic layer deposition (ALD) to create lithiophilic titanium nitride (TiN) sites on carbon nanotubes (CNTs) surfaces, integrated with nanocellulose to form a lithiophilic interlayer (NFCP@TN). This interlayer, which is highly flexible and electrolyte-wettable, functions as a current collector and host material for LMAs. The uniform deposition of Li is facilitated by the synergistic interplay of the lithiophilic active sites TiN, the conductive CNT network, and excellent electrolyte wettability of nanocellulose. As a result, Li preferentially adsorbs on TiN sheaths with lower diffusion barriers, leading to controlled nucleation sites and dendrite-free Li deposition. Furthermore, the well-designed NFCP@TN interlayer exhibits exceptional electrochemical performance and significantly extended cycle life when paired LMA with high areal capacity NCM811 (5.0 mAh cm⁻²) electrodes. Full article

Multifunctional self-healing supramolecular structural toughened resins, formulated to counteract the insulating properties of epoxy polymers and integrating auto-repair mechanisms, are morphologically and spectroscopically characterized using Tunneling Atomic Force Microscopy (TUNA) and Fourier transform infrared spectroscopy (FT-IR), respectively. Specifically, the multifunctional resin comprises self-healing molecular fillers and electrically conductive carbon nanotubes (CNTs) embedded in the matrix. The selected self-healing molecules can form non-covalent bonds with the hydroxyl (OH) and carbonyl (C=O) groups of the toughened epoxy matrix through their H-bonding donor and acceptor sites. An FT-IR analysis has been conducted to evaluate the interactions that the barbiturate acid derivatives, serving as self-healing fillers, can form with the constituent parts of the toughened epoxy blend. Tunneling Atomic Force Microscopy (TUNA) highlights the morphological characteristics of CNTs, their dispersion within the polymeric matrix, and their affinity for the globular rubber domains. The TUNA technique maps the samples’ electrical conductivity at micro- and nanoscale spatial domains. Detecting electrical currents reveals supramolecular networks, determined by hydrogen bonds, within the samples, showcasing the morphological features of the sample containing an embedded conductive nanofiller in the hosting matrix. Full article

Compared with acute wounds, typical chronic wounds (infection, burn, and diabetic wounds) are susceptible to bacterial infection and hard to heal. As for the complexity of chronic wounds, biocompatible hydrogel dressings can be employed to regulate the microenvironment and accelerate wound healing with their controllable physical and chemical properties. Recently, various nanomaterials have been introduced into hydrogel networks to prepare functional nanocomposite hydrogels. Among them, carbon-based nanomaterials (CBNs) have attracted wide attention in the biomedical field due to their outstanding physicochemical properties. However, comprehensive reviews on the use of CBNs for multifunctional hydrogel wound dressings in the past 10 years are very scarce. This review focuses on the research progress on hydrogel dressings made with typical CBNs. Specifically, a series of CBNs (carbon dots, graphene quantum dots, fullerenes, nanodiamonds, carbon nanotubes, graphene, graphene oxide and reduced graphene oxide) employed in the preparation of hydrogels are described as well as carbon-based nanocomposite hydrogels (CBNHs) with versatility (conductivity, antibacterial, injectable and self-healing, anti-inflammatory and antioxidant properties, substance delivery, stimulus response and real-time monitoring). Moreover, applications of CBNHs in treating different chronic wounds are concretely discussed. This review may provide some new inspirations for the future development of CBNHs in wound care and tissue engineering. Full article

Geometric optimization of carbon nanotubes (CNTs) is a fundamental step in computational simulations, enabling precise studies of their properties for various applications. However, this process becomes computationally expensive as the molecular structure grows in complexity and size. To address this challenge, this study utilized three deep-learning-based neural network architectures: Multi-Layer Perceptron (MLP), Bidirectional Long Short-Term Memory (BiLSTM), and 1D Convolutional Neural Networks (1D-CNNs). Simulations were performed using the CASTEP module in Material Studio to generate datasets for training the neural networks. While the final geometric optimization calculations were completed within Material Studio, the neural networks effectively generated preoptimized CNT structures that served as starting points, significantly reducing computational time. The results showed that the 1D-CNN architecture performed best for CNTs with 28, 52, 76, and 156 atoms, while the MLP outperformed others for CNTs with 84, 124, 148, and 196 atoms. Across all cases, computational time was reduced by 39.68% to 90.62%. Although the BiLSTM also achieved reductions, its performance was less effective than the other two architectures. This work highlights the potential of integrating deep learning techniques into materials science; it also offers a transformative approach to reducing computational costs in optimizing CNTs and presents a way for accelerated research in molecular systems. Full article

A newly developed series of polylactide (PLA)-based composites filled with hybrid lignin–carbon nanotube (CNTs) particles were studied using thermal and dielectric techniques. The low CNT content (up to 3 wt%) aimed to create conductive networks while enhancing particle–polymer adhesion. For comparison, PLA composites based on lignin and CNTs were also examined. Although infrared spectroscopy showed no significant interactions, calorimetry and dielectric spectroscopy revealed a rigid interfacial PLA layer exhibiting restricted mobility. The interfacial polymer amount was found to increase monotonically with the particle content. The hybrid-filled PLA composites exhibited electrical conductivity, whereas PLA/Lignin and PLA/CNTs remained insulators. The result was indicative of a synergistic effect between lignin and CNTs, leading to lowering of the percolation threshold to 3 wt%, being almost ideal for sustainable conductive printing inks. Despite the addition of lignin and CNTs at different loadings, the glass transition temperature of PLA (60 °C) decreased slightly (softer composites) by 1–2 K in the composites, while the melting temperature remained stable at ~175 °C, favoring efficient processing. Regarding crystallization, which is typically slow in PLA, the hybrid lignin/CNT particles promoted crystal nucleation without increasing the total crystallizable fraction. Overall, these findings highlight the potential of eco-friendly conductive PLA composites for new-generation applications, such as printed electronics. Full article

Machining processes often face challenges such as elevated temperatures and wear, which traditional cutting fluids are insufficient to address. As a result, solutions involving nanoparticle additives are being explored to enhance cooling and lubrication performance. This study investigates the effect of thermal conductivity, an important property influenced by the densities of mono and hybrid nanofluids. To this end, various nanofluids were prepared by incorporating hexagonal boron nitride (hBN), zinc oxide (ZnO), multi-walled carbon nanotubes (MWCNTs), titanium dioxide (TiO₂), and aluminum oxide (Al₂O₃) nanoparticles into sunflower oil as the base fluid. Hybrid nanofluids were created by combining two nanoparticles, including ZnO + MWCNT, hBN + MWCNT, hBN + ZnO, hBN + TiO₂, hBN + Al₂O₃, and TiO₂ + Al₂O₃. A dataset consisting of 180 data points was generated by measuring the thermal conductivity and density of the prepared nanofluids at various temperatures (30–70 °C) in a laboratory setting. Conducting thermal conductivity measurements across different temperature ranges presents significant challenges, requiring considerable time and resources, and often resulting in high costs and potential inaccuracies. To address these issues, a feedforward artificial neural network (FFANN) method was proposed to predict thermal conductivity. Our multilayer FFANN model takes as input the temperature of the experimental environment where the measurement is made, the measured thermal conductivity of the relevant nanoparticle, and the relative density of the nanoparticle. The FFANN model predicts the thermal conductivity value linearly as output. The model demonstrated high predictive accuracy, with a reliability of R = 0.99628 and a coefficient of determination (R²) of 0.9999. The average mean absolute error (MAE) for all hybrid nanofluids was 0.001, and the mean squared error (MSE) was 1.76 × 10⁻⁶. The proposed FFANN model provides a State-of-the-Art approach for predicting thermal conductivity, offering valuable insights into selecting optimal hybrid nanofluids based on thermal conductivity values and nanoparticle density. Full article

Organic hybrid materials are gaining traction as electrode candidates for energy storage due to their structural tunability and environmental compatibility. This study investigates polyimide/carbon nanotube/polypyrrole (PI/CNTs/PPy) hybrid nanocomposites, focusing on the correlation between thermal imidization temperature, polypyrrole deposition time, and the resulting electrochemical properties. By modulating PI processing temperatures (90 °C, 180 °C, 250 °C) and PPy deposition durations (60–700 s), this research uncovers critical structure–function relationships governing charge storage behavior. Scanning electron microscopy and electrochemical impedance spectroscopy reveal that low-temperature imidization preserves porosity and enables ion-accessible pathways, while moderate PPy deposition enhances electrical conductivity without blocking pore networks. The optimized composite, processed at 90 °C with 60 s PPy deposition, demonstrates superior specific capacitance (850 F/g), high redox contribution (~70% of total charge), low charge transfer resistance, and enhanced energy/power density. In contrast, high-temperature processing and prolonged PPy deposition result in structural densification, increased resistance, and diminished performance. These findings highlight a synergistic design approach that leverages partial imidization and controlled doping to balance ionic diffusion, electron transport, and redox activity. The results provide a framework for developing scalable, high-performance, and sustainable electrode materials for next-generation lithium-ion batteries and supercapacitors. Full article

Flexible pressure sensors face the dual challenges of weak signal extraction and environmental noise suppression in wearable electronics and human-machine interfaces. This research proposes an intelligent pressure sensor utilizing chitosan/carbon nanotube/melamine sponge (CS/CNT/MS) composites, achieving high-performance sensing through a dual-stage noise reduction architecture that combines mechanical pre-filtration and electrical synergistic regulation. An innovative compressed-stitching encapsulation technique creates pressure sensors with equivalent mechanical low-pass filtering characteristics, actively eliminating interference signals below 3 kPa while maintaining linear response within the 3–20 kPa effective loading range (sensitivity: 0.053 kPa⁻¹). The synergistic effects of CS molecular cross-linking and CNTs’ three-dimensional conductive network endow the device with a 72 ms response time, 24 ms recovery speed, and over 3500-cycle compression stability. Successful applications in smart sport monitoring and tactile interactive interfaces demonstrate a material-structure-circuit co-design paradigm for mechanical perception in complex environments. Full article

Flexible pressure sensors have great potential for wearable applications such as human health monitoring and human–computer interaction, which require different trade-offs between the sensitivity and operating range. However, preparing washable and wide-range piezoresistive pressure sensors remains a great challenge. Here, we developed a porous flexible elastomer sponge based on a carbon nanotube composite network coating for pressure sensors with extremely high stability and washability over a wide range. Specifically, a sugar template was used to fabricate a homogeneous macroporous PDMS sponge as a substrate, and a dip-coated MCNT-PDMS composite was used as a conductive layer. The high degree of adhesion formed between the substrate and the conductive layer resulted in a sponge with greatly enhanced mechanical properties and stability, while improving the operating range. The pressure sensors exhibited a broad operating range of 0–650 kPa, demonstrating excellent sensitivity (0.0049 kPa⁻¹ in the range of 0–74 kPa, 0.0010 kPa⁻¹ in the range of 74–310 kPa, and 0.0004 kPa⁻¹ in the range of 310–650 kPa), as well as a fast response time of 143 ms and recovery time of 73 ms, long-term cycling stability of over 10,000 cycles, and excellent washable stability. Finally, we demonstrate that the sensors can be applied to gesture monitoring, human motion gait monitoring, and cycling pressure monitoring. Full article