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.

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.

Inspired by traditional Chinese medicine pulse diagnosis, a wireless pulse diagnostic device, based on a Micro Electro Mechanical Systems (MEMS) pressure sensor array, was designed to detect sleep apnea. The arrayed MEMS pressure sensor configuration within a silica gel casing, capturing multidimensional pulse signals at the radial artery positions. Pulse signals were collected from volunteers during normal breathing and breath-holding scenarios to establish a comprehensive sleep apnea recognition database. A feature vector incorporating the pulse contour characteristic K-value, Reflection Index (RI), Power Ratio (PR), and Heart Rate (HR) was created for classification model training using a Support Vector Machine (SVM). Experimental results showed that the accuracy of pulse wave signal feature vector classification is 99.33% and the sensitivity is 98%. Overall, this device offers innovative technical support and significant application potential to advance modernized traditional Chinese diagnostic methods and intelligent medical devices.

Hot embossing is a manufacturing technique used to create microchannels on polymer substrates. In recent years, microchannel fabrication technology based on hot embossing has attracted considerable attention due to its convenience and low cost. A new evaluation method of microchannels, as well as an approach to obtaining optimal hot embossing conditions based on the Taguchi method, is proposed in this paper to fabricate precise microchannels for a flexible proton-exchange membrane fuel cell (PEMFC). Our self-made hot embossing system can be used to fabricate assorted types of micro-channel structures on polymer substrates according to various applications, whose bottom width, top width, height and cross-sectional area vary in the aims of different situations. In order to obtain a high effective filling ratio, a new evaluation method is presented based on the four parameters of channel structures, and the Taguchi method is utilized to arrange three main factors (temperature, force and time) affecting the hot embossing in orthogonal arrays, quickly finding the optimal condition for the embossing process. The evaluation method for microchannels proposed in this paper, compared to traditional evaluation methods, incorporates the area factor, providing a more comprehensive assessment of the fabrication completeness of the microchannels. Additionally, it allows for the quick and simple identification of optimal conditions. The experimental results indicate that after determining the optimal embossing temperature, pressure and time using the Taguchi method, the effective filling rate remains above 95%, thereby enhancing the power density. Through variance analysis, it was found that temperature is the most significant factor affecting the hot embossing of microchannels. The high filling rate makes the process suitable for PEMFCs. The results demonstrate that under optimized process conditions, a self-made hot embossing system can effectively fabricate columnar structure microchannels for PEMFCs.

In the fields of aerospace dynamic navigation and high overload shock monitoring, the detection of acceleration shock and atmospheric pressure plays a crucial role. This work proposes a synchronous sensing mechanism of acceleration shock and atmospheric pressure with high sensitivity utilizing a graphene nanodrum with a suspended proof mass for the first time. The proposed sensing mechanism can convert inertial forces and atmospheric pressure loads into nanodrum deflection, which is then transduced into electrical signals via the piezoresistive effect. Signal separation of acceleration shock (rapidly varying signal) and atmospheric pressure (slowly varying signal) signals is further achieved using a discrete wavelet transform with Sym8 mother wavelet. The finite element simulation results indicate that the designed 2D nanodrum with suspended proof mass has an acceleration sensitivity of up to 1.01192×10^-6%/g with linearity of 0.9993, and an atmospheric pressure sensitivity of up to 4.20422×10^-4%/mbar with linearity of 0.9755. More importantly, after signal separation processing, the measuring accuracy of the acceleration shock and atmospheric pressure signal is up to 93.07% and 99.5% respectively. Macroscopic equivalent experiments were further carried out to verify the feasibility of a sensing mechanism. Experimental results indicate that both acceleration shock and atmospheric pressure can be detected with high linearities of 0.9993 and 0.9755, respectively. The mechanism proposed in this work is expected to provide a solution for synchronous high-sensitivity measurement of acceleration and air pressure in high dynamic environments.

Thermal sensors are widely used in medical, industrial and other fields, where the requirements for high sensitivity and portability continues to increase. Here we propose a suspended bridge structure fabricated using MEMS, which effectively shrinks the size and reduces heat loss. This study reviews current sensor-related theories of heat conduction, convective heat transfer and thermal radiation. Heat loss models for suspended and non-suspended bridge structures are established, and finite element analysis is conducted to evaluate their thermal performance. The thermal performance of the suspended bridge structure is further validated through infrared temperature measurements on the manufactured sensor device. Theoretical calculations demonstrate that the proposed suspension bridge structure reduces heat loss by 88.64% compared with traditional designs. Benefiting from this improved heat retention, which was also confirmed by infrared thermography, the thermal sensor fabricated based on the suspension bridge structure achieves an ultra-high sensitivity of 0.38 V/W and a fast response time of less than 200 ms, indicating a high accuracy in thermal characterization. The correlation coefficient obtained for the sensor output voltage and input power of the sensor is approximately 1.0. Based on this design, multiple microfluidic channels with suspended bridge structures can be integrated to realize multi-component detection, which is important for the development of multifunctional biomedical detection.