This paper develops a new approach of large language model (LLM) enhanced reconfigurable intelligent surface (RIS), in a bid to achieve energy-efficient and reliable communication in 6G Internet of Vehicles (IoV). It is well known that RIS offers an innovative solution to improve signal quality by intelligently adjusting the propagation path of radio waves. However, configuring RIS in the dynamically changing vehicular environment remains a challenge. This study leverages the analytical capabilities of LLM, combined with key IoV data such as channel status, vehicle movement patterns, and quality of service requirements, to model and optimize RIS-based IoV communication systems. The work focuses on constructing a realtime model of the RIS-based IoV wireless transmission system and proposes an optimal strategy for wireless resource allocation. Through comprehensive simulation results, this paper justifies the significant performance advantages of LLM-enhanced RIS, demonstrating a viable technical pathway for the development of 6G IoV.

With advancements in artificial intelligence, wearable motion recognition systems based on flexible nanomaterial sensors exhibit excellent potential for harmonious human–machine interaction. However, the sensing stability and demand of large‐scale arrays limit the application of flexible nanomaterial sensors. Herein, a data glove system based on simple multiwalled carbon nanotube (MWCNT) sensors and a lightweight deep‐learning algorithm to achieve accurate gesture recognition is proposed. A regional‐crack mechanism is introduced through the microspine structure to enhance the strain sensitivity. Moreover, an efficient signal processing strategy based on an adaptive wavelet threshold function to improve the robustness and anti‐interference of signals obtained from MWCNT sensors, which exhibit strong generalization and can be used in other nanomaterial strain sensor. Based on the depth‐wise separable convolution, a novel hybrid convolutional neural network (CNN) long short‐term memory (LSTM) model for gesture recognition is constructed. The proposed model achieves an average accuracy of 97.5% and recognition accuracy of 30 gestures with an average recognition time of 2.173 ms based on only five sensors. The fabricated data glove is a promising platform for low‐cost and wearable human–machine interaction that can be directly interfaced in applications such as robotic hands, smart cars, and first‐person shooting games.

Continuous monitoring of physical motion, which can be successfully achieved via a wireless flexible wearable electronic device, is essential for people to ensure the appropriate level of exercise. Currently, most of the flexible LC pressure sensors have low sensitivity because of the high Young’s modulus of the dielectric properties (such as PDMS) and the inflexible polymer films (as the substrate of the sensors), which don’t have excellent stretchability to conform to arbitrarily curved and moving surfaces such as joints. In the LC sensing system, the metal rings, as the traditional readout device, are difficult to meet the needs of the portable readout device for the integrated and planar readout antenna. In order to improve the pressure sensitivity of the sensor, the Ecoflex microcolumn used as the dielectric of the capacitive pressure sensor was prepared by using a metal mold copying method. The Ecoflex elastomer substrates enhanced the levels of conformability, which offered improved capabilities to establish intimate contact with the curved and moving surfaces of the skin. The pressure was applied to the sensor by weights, and the resonance frequency curves of the sensor under different pressures were obtained by the readout device connected to the vector network analyzer. The experimental results show that resonant frequency decreases linearly with the increase of applied pressure in a range of 0–23,760 Pa with a high sensitivity of −2.2 MHz/KPa. We designed a coplanar waveguide-fed monopole antenna used to read the information of the LC sensor, which has the potential to be integrated with RF signal processing circuits as a portable readout device and a higher vertical readout distance (up to 4 cm) than the copper ring. The flexible LC pressure sensor can be attached to the skin conformally and is sensitive to limb bending and facial muscle movements. Therefore, it has the potential to be integrated as a body sensor network that can be used to monitor physical motion.

As a representative nanomaterial, C₆₀ and its derivatives have drawn much attention in the field of drug delivery over the past years, due to their unique geometric and electronic structures. Herein, the interactions of hydroxyurea (HU) drug with the pristine C₆₀ and heterofullerene MC₅₉ (M = B, Si, Al) were investigated using the density functional theory calculations. The geometric and electronic properties in terms of adsorption configuration, adsorption energy, Hirshfeld charge, frontier molecular orbitals, and charge density difference are calculated. In contrast to pristine C₆₀, it is found that HU molecule is chemisorbed on the BC₅₉, SiC₅₉, and AlC₅₉ molecules with moderate adsorption energy and apparent charge transfer. Therefore, heterofullerene BC₅₉, SiC₅₉, and AlC₅₉ are expected to be promising carriers for hydroxyurea drug delivery.