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.

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.

Capacitive pressure sensors based on porous structures have been widely researched and applied to a variety of practical applications. To date, it remains a big challenge to develop a capacitive pressure sensor with a high sensitivity and good linearity over a wide pressure range. In this paper, a sensitive, flexible, porous capacitive pressure sensor was designed and manufactured by means of the “salt template method” and man-made grooves. To this aim, the size of the salt particles used for forming pores/air voids, time taken for thorough dissolution of salt particles, and the depth of the man-made groove by a pin were taken into consideration to achieve a better effect. With pores and the groove, the sensor is more liable be compressed, which will result in a dramatic decrease in distance between the two electrodes and a conspicuous increase of the effective dielectric constant. The optimize-designed sensor represents a sensitivity 6–8 times more than the sensor without the groove in the pressure range of 0–10 kPa, not to mention the sensor without pores or the groove, and it can keep good linearity within the measurement range (0–50 kPa). Besides, the sensor shows a low detection limit of 3.5 Pa and a fast response speed (≈50 ms), which makes it possible to detect a tiny applied pressure immediately. The fabricated sensor can be applied to wearable devices to monitor finger and wrist bending, and it can be used in the object identification of mechanical claws and object cutting of mechanical arms, and so on.

Flourishing in recent years, intelligent electronics is desirably pursued in many fields including bio-symbiotic, human physiology regulatory, robot operation, and human–computer interaction. To support this appealing vision, human-like tactile perception is urgently necessary for dexterous object manipulation. In particular, the real-time force perception with strength and orientation simultaneously is critical for intelligent electronic skin. However, it is still very challenging to achieve directional tactile sensing that has eminent properties, and at the same time, has the feasibility for scale expansion. Here, a fully soft capacitive omnidirectional tactile (ODT) sensor was developed based on the structure of MWCNTs coated stripe electrode and Ecoflex hemisphere array dielectric. The theoretical analysis of this structure was conducted for omnidirectional force detection by finite element simulation. Combined with the micro-spine and the hemispheric hills dielectric structure, this sensing structure could achieve omnidirectional detection with high sensitivity (0.306 ± 0.001 kPa⁻¹ under 10 kPa) and a wide response range (2.55 Pa to 160 kPa). Moreover, to overcome the inherent disunity in flexible sensor units due to nano-materials and polymer, machine learning approaches were introduced as a prospective technical routing to recognize various loading angles and finally performed more than 99% recognition accuracy. The practical validity of the design was demonstrated by the detection of human motion, physiological activities, and gripping of a cup, which was evident to have great potential for tactile e-skin for digital medical and soft robotics.

In this work, a conductive composite film composed of multi-walled carbon nanotubes (MWCNTs) and multi-layer Ti₃C₂Tx MXene nanosheets is used to construct a strain sensor on sandpaper Ecoflex substrate. The composite material forms a sophisticated conductive network with exceptional electrical conductivity, resulting in sensors with broad detection ranges and high sensitivities. The findings indicate that the strain sensing range of the Ecoflex/Ti₃C₂Tx/MWCNT strain sensor, when the mass ratio is set to 5:2, extends to 240%, with a gauge factor (GF) of 933 within the strain interval from 180% to 240%. The strain sensor has demonstrated its robustness by enduring more than 33,000 prolonged stretch-and-release cycles at 20% cyclic tensile strain. Moreover, a fast response time of 200 ms and detection limit of 0.05% are achieved. During application, the sensor effectively enables the detection of diverse physiological signals in the human body. More importantly, its application in a data glove that is coupled with machine learning and uses the Support Vector Machine (SVM) model trained on the collected gesture data results in an impressive recognition accuracy of 93.6%.