Polyimide (PI) films with excellent heat resistance and outstanding mechanical properties have been widely researched in microelectronics and aerospace fields. However, most PI films can only be used under ordinary conditions due to their instability of dimension. The fabrication of multifunctional PI films for harsh conditions is still a challenge. Herein, flexible, low coefficient of thermal expansion (CTE) and improved mechanical properties films modified by carboxylated carbon nanotube (C-CNT) were fabricated. Acid treatment was adapted to adjust the surface characteristics by using a mixture of concentrated H₂SO₄/HNO₃ solution to introduce carboxyl groups on the surface and improve the interfacial performance between the CNT and matrix. Moreover, different C-CNT concentrations of 0, 1, 3, 5, 7, and 9 wt.% were synthesized to use for the PI film fabrication. The results demonstrated that the 9 wt.% and 5 wt.% C-CNT/PI films possessed the lowest CTE value and the highest mechanical properties. In addition, the thermal stability of the C-CNT/PI films was improved, making them promising applications in precise and harsh environments.

In smart transportation, assisted driving relies on data integration from various sensors, notably LiDAR and cameras. However, their optical performance can degrade under adverse weather conditions, potentially compromising vehicle safety. Millimeter-wave radar, which can overcome these issues more economically, has been re-evaluated. Despite this, developing an accurate detection model is challenging due to significant noise interference and limited semantic information. To address these practical challenges, this paper presents the TC–Radar model, a novel approach that synergistically integrates the strengths of transformer and the convolutional neural network (CNN) to optimize the sensing potential of millimeter-wave radar in smart transportation systems. The rationale for this integration lies in the complementary nature of CNNs, which are adept at capturing local spatial features, and transformers, which excel at modeling long-range dependencies and global context within data. This hybrid approach allows for a more robust and accurate representation of radar signals, leading to enhanced detection performance. A key innovation of our approach is the introduction of the Cross-Attention (CA) module, which facilitates efficient and dynamic information exchange between the encoder and decoder stages of the network. This CA mechanism ensures that critical features are accurately captured and transferred, thereby significantly improving the overall network performance. In addition, the model contains the dense information fusion block (DIFB) to further enrich the feature representation by integrating different high-frequency local features. This integration process ensures thorough incorporation of key data points. Extensive tests conducted on the CRUW and CARRADA datasets validate the strengths of this method, with the model achieving an average precision (AP) of 83.99% and a mean intersection over union (mIoU) of 45.2%, demonstrating robust radar sensing capabilities.

Multifunctional flexible pressure sensors with high sensitivity and a wide sensing range can meet the requirements of various complex applications. Herein, metal (Au or Ag) nanoparticles (NPs) are introduced onto a MXene-coated nonwoven fabric (MNWF) using a self-reduction strategy to build a “brick–mortar” hierarchical structure (AuNPs or AgNPs@MNWF). This method is green and environmentally friendly because the self-reduction properties of MXene endow noble metal NPs with uniform sizes and controlled densities to be introduced without using reducing agents or surfactants. The introduction of metal NPs and the construction of composites considerably improve the piezoresistive properties of MNWF, resulting in a 4-fold increase in sensor sensitivity (a linear sensitivity of 24.5 kPa⁻¹ at 0–100 kPa). Moreover, the excellent electrical conductivity and unique plasmonic effect of metal NPs also contribute toward enhancing the heating performance and expanding the antibacterial applications of MNWF. In particular, compared to MNWF, AgNPs@MNWF as a dual energy-driven flexible wearable heater exhibits an electrothermal enhancement of 25.3 °C (at 7.5 V) and a photothermal enhancement of 5.6 °C (at 100 mW cm⁻²). Notably, the antibacterial efficiency of AgNPs@MNWF is as high as 99.9%, indicating the potential of this material for applications in the medical and health field, such as in intelligent mattresses. Overall, noble metal NPs can enhance the sensing performance as well as the Joule heating, and photothermal and antibacterial properties of MNWF. Thus, this work extends the applicability of the self-reduction strategy, providing a general strategy for expanding the applications of multifunctional fabric-based flexible sensors.