Flexible pressure sensors with high sensitivity and good linearity are in high demand to meet the long-term and accurate detection requirements for pulse detection. In this study, we propose a composite membrane pressure sensor using polydimethylsiloxane (PDMS) and multiwalled carbon nanotubes (MWNTS) reinforced with isopropanol prepared by solution blending and a self-made 3D-printed mold. The device doped with isopropanol had a higher sensitivity and linearity owning to the construction of additional conductive paths. The optimal conditions for realizing a high-performance pressure sensor are a multiwalled carbon nanotube mass ratio of 7% and a composite membrane thickness of 490 μm. The membrane achieves a high linear sensitivity of −57.07 kΩ∙kPa⁻¹ and a linear fitting correlation coefficient of 98.78% in the 0.13~5.2 kPa pressure range corresponding to pulse detection. Clearly, this device has great potential for application in pulse detection.

Electronic fabrics exhibit desirable breathability, wearing comfort, and easy integration with garments. However, surficial deposition of electronically functional materials/compounds onto fabric substrates would consequentially alter their intrinsic properties (e.g., softness, permeability, biocompatibility, etc.). To address this issue, here, a strategy to innervate arbitrary commercial fabrics with unique spirally‐layered iontronic fibrous (SLIF) sensors is presented to realize both mechanical and thermal sensing functionalities without sacrificing the intrinsic fabric properties. The mechanical sensing function is realized via mechanically regulating the interfacial ionic supercapacitance between two perpendicular SLIF sensors, while the thermal sensing function is achieved based on thermally modulating the intrinsic ionic impedance in a single SLIF sensor. The resultant SLIF sensor‐innervated electronic fabrics exhibit high mechanical sensitivity of 81 N⁻¹, superior thermal sensitivity of 34,400 Ω °C⁻¹, and more importantly, greatly minimized mutual interference between the two sensing functions. As demonstrations, various smart garments are developed for the precise monitoring of diverse human physiological signals. Moreover, artificial intelligence‐assisted object recognition with high‐accuracy (97.8%) is demonstrated with a SLIF sensor‐innervated smart glove. This work opens up a new path toward the facile construction of versatile smart garments for wearable healthcare, human‐machine interfaces, and the Internet of Things.

For traditional capacitive pressure sensors, high nonlinearity and poor sensitivity greatly limited their sensing applications. Hence, an innovative design of capacitors based on spiral comb electrodes is proposed for high-sensitivity pressure detection in this work. Compared to traditional capacitive pressure sensors with straight plate comb electrodes, the proposed sensor with the spiral comb electrodes increases the overlap areas of electrodes sufficiently, the pressure sensitivity can thus be greatly improved. Moreover, the capacitance variation of the proposed sensor is dominated by the change of the overlap area of the electrodes rather than the electrode’s distance, the linearity can also thus be improved to higher than 0.99. Theoretical analysis and COMSOL-based finite element simulation have been implemented for principle verification and performance optimization. Simulation results show that the proposed design has a mechanical sensitivity of 1.5 × 10⁻⁴ m Pa⁻¹, capacitive sensitivity of 1.10 aF Pa⁻¹, and nonlinear error of 3.63%, respectively, at the pressure range from 0 to 30 kPa. An equivalent experiment has been further carried out for verification. Experimental results also show that both the sensitivity and linearity of capacitive pressure sensors with spiral comb electrodes are higher than those with straight comb electrodes. This work not only provides a new avenue for capacitor design, but also can be applied to high-sensitivity pressure detection.

Carbon black (CB) is a low-cost and excellent conductive material, and polyvinyl alcohol (PVA) is a non-conductive material with the advantages of easy processing and high mechanical stability. Here, we report a CB/PVA-based flexible conductive polymer film suitable for small strain detection and humidity detection. Thin film is formed by depositing the CB/PVA dispersion liquid droplets on a cleaned silicon/silicon dioxide (Si/SiO₂) substrate. Theoretically, CB/PVA films can be transferred or formed on other substrates, such as polydimethylsiloxane, which have the advantage of flexibility. The droplet deposition method not only enhances the controllability of the film thickness and wastage of materials, but also improves the sensitivity of the prepared film. The electrical conductivity of the CB/PVA composite film and the relationship between the resistance change and strain were measured by the four-point bending method, which showed a good gauge factor of 30 when the strain rate was 0.007%. In addition, the sensor also showed excellent sensing performance and repeatability at humidity levels ranging from 10% to 70% RH. These results demonstrate that the CB/PVA thin film prepared in this work has the advantages of a simple fabrication process, low-cost, multifunctional properties, and high device sensitivity, providing further insights for detecting minor strain and humidity.

This paper proposes a resistive humidity sensor that uses a carbon-black and polyvinyl alcohol composites thin film, fabricated with a unique film coating method for thinner thickness and higher sensitivity. Improving the sensitivity of sensing films is still of great importance in the research field of gas sensors. The humidity sensor devices with thin composite film and microelectrode structure are fabricated on the glass substrate for a low cost and a simple fabrication process. The sensor gives a rapid response for humidity levels from 10.9% relative humidity (RH) to 73.7% RH, and the response time is about 5.77 s. Experimental results reveal that the sensor has good sensitivity, reproducibility, fast reaction time, and wide range. In addition to humidity, the sensor also responds well to gases such as ethanol. The proposed gas sensor in this paper can be applied to the other combinations of polymers and nanoparticles to form new gas sensors, which have the potential to be used as a gas sensor array for detecting the composition of complex gases such as volatile organic components.

This study addresses the challenge of multi-dimensional and small gas sensor data classification using a gelatin–carbon black (CB-GE) composite film sensor, achieving 91.7% accuracy in differentiating gas types (ethanol, acetone, and air). Key techniques include Principal Component Analysis (PCA) for dimensionality reduction, the Synthetic Minority Over-sampling Technique (SMOTE) for data augmentation, and the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms for classification. PCA improved KNN and SVM classification, boosting the Area Under the Curve (AUC) scores by 15.7% and 25.2%, respectively. SMOTE increased KNN’s accuracy by 2.1%, preserving data structure better than polynomial fitting. The results demonstrate a scalable approach to enhancing classification accuracy under data constraints. This approach shows promise for expanding gas sensor applicability in fields where data limitations previously restricted reliability and effectiveness.