Here we demonstrate a magnetic resonance coupling based wireless triboelectric nanogenerator (TENG) and fully self-powered wireless sensors. By integrating a microswitch and an inductor with the TENG, the pulsed voltage output is converted into a sinusoidal voltage signal with a fixed frequency. This can be transmitted wirelessly from the transmit coil to the resonant-coupled receiver coil with an efficiency of 73% for a 5 cm distance between the two coils (10 cm diameter). Analytic models of the oscillating and coupled voltage signals for the wireless energy transfer are developed, showing excellent agreement with the experimental results. A TENG of 40 × 50 mm² can wirelessly light up 70 LEDs or charge up a 15 μF capacitor to 12.5 V in ~90 s. The system is further utilized for two types of fully self-powered wireless chipless sensors with no microelectronic components. The technologies demonstrate an innovative strategy for a wireless ‘green’ power source and sensing.

As a reliable indicator of human physiological health, respiratory rate has been utilized in more and more cases for prediction and diagnosis of potential respiratory diseases and the respiratory dysfunction caused by cystic fibrosis. However, compared with smart mobile electronics, traditional clinical respiration monitoring systems is not convenient to work as a household wearable device for real-time respiration monitoring in daily life due to its cumbersome structure, complex operability, and reliance on external power sources. Thus, we propose a wearable wireless respiration sensor based on lateral sliding mode triboelectric nanogenerator (TENG) to monitor respiratory rates by sensing the variation of the abdominal circumference. In this paper, we validate the possibility of the device as a respiration monitoring sensor via an established theoretical model and investigate the output performance of the sensor via a series of mechanical tests. Furtherly, the applications of the respiration sensor in different individuals, different breathing rhythms, different active states, and wireless transmission have been verified by a lot of volunteer tests. All the results demonstrate the potential of the proposed wearable sensor as a new alternative for detecting and monitoring real-time respiratory rates with general applicability and sensitivity.

A flexible surface acoustic wave (SAW) strain sensor in the frequency range of 162–325 MHz was developed based on a single crystalline LiNbO₃ thin film with dual resonance modes, namely, the Rayleigh mode and the thickness shear mode (TSM). This SAW sensor could handle a wide strain range up to ±3500 $\mu \mathrm{\epsilon }$ owing to its excellent flexibility, which is nearly six times the detecting range of bulk piezoelectric substrate based SAW strain sensors. The sensor exhibited a high sensitivity of 193 Hz/$\mu \mathrm{\epsilon }$ with a maximum hysteresis less than 1.5%. The temperature coefficients of frequency, for Rayleigh and TSM modes, were −85 and −59 ppm/$°\mathrm{C}$ , respectively. No visible deterioration was observed after cyclic bending for hundreds of times, showing its desirable stability and reliability. By utilizing the dual modes, the strain sensor with a self-temperature calibrated capability can be achieved. The results demonstrate that the sensor is an excellent candidate for strain sensing.

With the increase of operating frequency above 4 GHz, the required resonator size is reduced to hundreds of $\mu \text{m}^{{2}}$ , which is proportional to 1/ ${f}^{\,{2}}$ . In traditional, the size reduction of the resonator will induce its parallel resonant frequency decrease, thus resulting in the decrease of its effective coupling coefficient ( ${k}_{\,{t}}^{\,{2}}$ ). Besides the parallel resonant frequency, we found that the serial resonant frequency of resonators working above 3 GHz also varies with its area size. However, this effect is not taken into account in conventional circuit models, leading to the failure of high-performance filter design. To address this issue, a modified Mason model of bulk acoustic wave (BAW) resonators was proposed in this work. Using the modified model, a high-performance N79 band (4.8–4.96 GHz) filter based on Al0.904Sc $_{{0.096}}\text{N}$ piezoelectric film was designed and fabricated, which achieved a minimum insertion loss of −0.93 dB and a bandwidth of 240 MHz. The power capacity and wire-bond package of BAW filters were further studied. The whole process of the Mason model modification, filter design, and fabrication pave the road for high-frequency filter applications.

The accurate monitoring of vital physiological parameters, exemplified by heart rate, respiratory rate, and intracranial pressure (ICP), is of paramount importance, particularly for managing severe cranial injuries. Despite the rapid development of implantable ICP sensing systems over the past decades, they still suffer from, for example, wire connection, low sensitivity, poor resolution, and the inability to monitor multiple variables simultaneously. Here, we propose an ultrasensitive multimodal biotelemetric system that amalgamates an iontronic pressure transducer with exceptional point (EP) operation for the monitoring of ICP signals. The proposed system can exhibit extraordinary performance regarding the detection of minuscule ICP fluctuation, demonstrated by the sensitivity of 115.95 kHz/mmHg and the sensing resolution down to 0.003 mmHg. Our system excels not only in the accurate quantification of ICP levels but also in distinguishing respiration and cardiac activities from ICP signals, thereby achieving the multimodal monitoring of ICP, respiratory, and heart rates within a single system. Our work may provide a pragmatic avenue for the real-time wireless monitoring of ICP and thus hold great potential to be extended to the monitoring of other vital physiological indicators.

The phonocardiogram (PCG) is an important analysis method for the diagnosis of cardiovascular disease, which is usually performed by experienced medical experts. Due to the high ratio of patients to doctors, there is a pressing need for a real-time automated phonocardiogram classification system for the diagnosis of cardiovascular disease. This paper proposes a deep neural-network structure based on a one-dimensional convolutional neural network (1D-CNN) and a long short-term memory network (LSTM), which can directly classify unsegmented PCG to identify abnormal signal. The PCG data were filtered and put into the model for analysis. A total of 3099 pieces of heart-sound recordings were used, while another 100 patients’ heart-sound data collected by our group and diagnosed by doctors were used to test and verify the model. Results show that the CNN-LSTM model provided a good overall balanced accuracy of 0.86 ± 0.01 with a sensitivity of 0.87 ± 0.02, and specificity of 0.89 ± 0.02. The F1-score was 0.91 ± 0.01, and the receiver-operating characteristic (ROC) plot produced an area under the curve (AUC) value of 0.92 ± 0.01. The sensitivity, specificity and accuracy of the 100 patients’ data were 0.83 ± 0.02, 0.80 ± 0.02 and 0.85 ± 0.03, respectively. The proposed model does not require feature engineering and heart-sound segmentation, which possesses reliable performance in classification of abnormal PCG; and is fast and suitable for real-time diagnosis application.