Electroporation ablation, as an innovative cancer treatment, not only preserves the structure and function of affected organs but also significantly reduces surgical risks, offers patients a safer and more effective therapeutic option, and demonstrates immense potential in the field of oncology. This paper presents the innovative design of a high-voltage nanosecond pulse generator triggered by a silicon carbide (SiC) photoconductive switch. The generator is capable of stably outputting adjustable voltages ranging from 10 kV to 15 kV, with pulse widths precisely controlled between 10 and 15 nanoseconds, and an operating frequency adjustable from 1 Hz to 10 Hz. This device enables instant activation and deactivation of the pulse generator during ablation, enhancing the efficiency of strong electric field applications and preventing overtreatment due to delayed shutdown. This paper introduces the structure and basic principles of this novel SiC photoconductive switch-triggered pulse device and reports on the impact of device-related pulse parameters on the ablation effect of hepatocellular carcinoma cells through cell experiments. Under optimal ablation parameters, the CCK8 results show that the number of viable cells is only 0.7% of that in the untreated control group after 12 h of subculture following ablation. These findings hold significant importance for expanding the application areas of SiC devices.

Enzyme-free flexible glucose sensors hold great promise in the field of wearable health monitoring. However, their performance is limited by the balance between the catalytic interface activity and stability. This paper reports a strategy for interface gradient roughening of screen-printed carbon electrodes (SPCE) via controllable electrochemical exfoliation (EE). It systematically reveals the inherent relationships among the degree of EE treatment, electrode morphology, surface chemistry, and electrochemical performance. On this basis, the deposition of gold nanoparticles (AuNPs) with high density and uniform distribution is achieved, and a high-performance flexible enzyme-free glucose sensor is constructed. The study finds that EE treatment can significantly increase the true surface area of the electrode and introduce abundant oxygen-containing functional groups, thus effectively reducing the charge transfer resistance. Nevertheless, excessive exfoliation leads to the degradation of the conductive network, indicating the existence of a critical “performance window”. The EE-SPCE optimized with 150 cycles has both a high active area and good electrical conductivity, providing an ideal deposition substrate for AuNPs, increasing their distribution density by approximately 158% and reducing the average particle size to 125 nm. The fabricated AuNPs/EE-SPCE sensor exhibits excellent performance in glucose detection: it has a high sensitivity of 550.766 μA·mM⁻¹·cm⁻² in the range of 0.1–3 mM, a detection limit of 0.0998 mM, a wide linear range, excellent selectivity, long-term stability, and good mechanical flexibility. This research not only develops an efficient and scalable method for constructing flexible sensing interfaces but also clarifies the trade-off relationship among “roughening–conductivity–catalytic performance” at the mechanistic level, providing an important theoretical basis and a general strategy for rationally designing high-performance flexible electrochemical devices.

During flight, pilots are exposed to unique environmental factors such as low air pressure, acceleration, and psychological stress, which can lead to altered glucose metabolism that poses a threat to flight safety. Traditional fingerstick blood glucose monitoring has drawbacks such as the need for intermittent readings, time-consuming procedures, and passive monitoring. Conventional continuous glucose monitoring (CGM) can only display historical and real-time glucose data and lacks predictive capabilities. In the present study, an artificial intelligence (AI)-driven blood glucose prediction system for pilots based on CGM and a multimodal transformer was developed with the aim of achieving proactive early warning of blood glucose risks among pilots. Sixty healthy male volunteers aged 25–50 years were recruited. Subjects completed 180 simulated flight experiments, during which multimodal data such as CGM glucose level, heart rate, blood oxygen saturation, and physical activity levels, were simultaneously collected to establish a scenario-specific dataset in aviation. A multimodal transformer model was developed to predict future blood glucose levels at 15, 30, and 60 min, and its performance was compared with baseline models such as linear extrapolation, long short-term memory (LSTM), and gated recurrent unit (GRU).The results showed that the mean absolute percentage error (MAPE) of the model for the prediction of blood glucose at 30 min was 7.2%, and the root mean square error (RMSE) was 9.4 mg/dL, which indicated a significantly superior prediction performance compared with the baseline models. For hypoglycemia event prediction, sensitivity was 92.5%, specificity was 89.7%, and the area under the receiver operating characteristic (ROC) curve was 0.94. A Clarke error grid analysis revealed that 98.7% of the predicted points fell within the clinically accurate region, which could potentially provide pilots with approximately 32 min of lead time for intervention. This system proved capable of accurately predicting blood glucose fluctuations during flight. Therefore, it may aid in shifting the metabolic health management of pilots from passive monitoring to proactive prevention, and serve as a novel tool for assuring aviation medical safety.