Optimized Stress Transfer Interfaces Enabled Wearable Nano-Electronics for Fatigue Driving Monitoring

Optimized Stress Transfer Interfaces Enabled Wearable Nano-Electronics for Fatigue Driving Monitoring | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Optimized Stress Transfer Interfaces Enabled Wearable Nano-Electronics for Fatigue Driving Monitoring Xuhui Sun, Hao Lei, Lingjie Xie, Xuan Qin, Guoxuan Sun, Peihao Huang, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6446990/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Mar, 2026 Read the published version in Microsystems & Nanoengineering → Version 1 posted 11 You are reading this latest preprint version Abstract Accurate detection of arterial pulse waves is crucial for wearable warning systems but faces challenges under non-close contact or pre-stress. Here, an interfacial engineered triboelectric sensor (IETS) has been proposed to improve the detection accuracy of pulse waves. It consists of a stress-transferring sensor-skin interface with piezo-frustums array and a gradient triboelectric interface with mountain-like microstructures. The mountain-like microstructures provide stress concentration points even under a pre-stress of 10 kPa with capturing all details of the pulse waves. Additionally, the incorporation of piezo-frustums array at the sensor-skin interface not only facilitates stress transfer but also generates piezoelectric charges. Such mechano-electric coupling effect endows IETS with a high sensitivity of 4.28 V/kPa. Integrated with machine learning, a wearable system based on IETS allows for drivers' health and fatigue assessment via pulse wave analysis, offering an effective approach to prevent road accidents caused by sudden cardiovascular diseases and fatigue driving. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Nanoscience and technology/Nanoscale devices/Sensors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Driver fatigue sudden cardiovascular diseases are the two leading causes of traffic fatalities 1, 2 . Effective monitoring and timely warnings of driver's physiological states are critical measures for reducing traffic accidents 3 . The non-invasive acquisition and analysis of pulse waves through wearable electronic devices can facilitate real-time management of cardiovascular health 4–7 . Mechanical sensor-based pulse wave measurement systems have recently attracted extensive interest, which are capable of directly mapping the pulse waveform by monitoring the mechanical stimuli of arterial pulsations, more accurately reflecting vital cardiovascular health information such as arterial blood pressure and the elasticity of blood vessel walls 8–10 . However, the mechanical energy of pulse beats is extremely subtle and the real-time acquisition of accurate pulse wave throughout the day poses great challenges to sensitivity and the sensor's power consumption 11–12 . Mechanical sensors based on piezoelectric and triboelectric principles, due to their zero power consumption and sensitive response to faint pressure signals, have been extensively used in real-time monitoring of pulse waves 13–18 . Recent work has found that triboelectric sensors exhibit a higher signal-to-noise ratio (SNR) when detecting faint mechanical physiological signals from the human body, such as heart sound 19 . Moreover, triboelectric sensors naturally can be made of a wide range of raw materials, thereby attracting extensive attention in recent years 20, 21 . To accurately reflect the details of the pulse wave, previous research primarily focused on constructing microstructures on the triboelectric layer surface to enhance the sensitivity of triboelectric sensors 22–24 . The contribution of microstructures to sensing performance mainly comes from two aspects: the increased surface area increases SNR and the stress concentration induced by microstructures enhances the sensitivity 25 . However, the regular microstructures are prone to reaching their deformation limits under high pressure, leading to a sharp decrease in sensor sensitivity at high pressures 26–30 . In real applications, a large pre-load stress is required in wearable electronics to make the close contact between the sensor and skin. The pre-stress from tightening the wristband or patch will significantly reduce the accuracy of the sensor in detecting pulse wave signals 31 . In addition, the gaps in the interface between the sensor and the skin could cause incomplete adhesion 32, 33 , which results in non-effective transformation of faint pressure to the sensor's active detection area, leading to a low SNR in the output signals. Therefore, improving the device's sensitivity to weak pulse wave via interface engineering is also an urgent demand 34, 35 . In this work, we enhance the detection precision of pulse waves by triboelectric sensor through optimizing the stress transfer path and distribution at both the triboelectric interface and the sensor-skin interface. The piezo-frustums are used to fill the gaps at the sensor-skin interface, providing more pathways for the stress generated by pulse beats and simultaneously generate the piezoelectric charges. Through the mechano-electric coupling enhancement effect, it endows the interfacial engineered triboelectric sensor (IETS) with a high sensitivity of 4.28 V/kPa. Additionally, the design of mountain-like microstructures on the contact-electrification interfaces offer multiple stress concentration points, broadening the high-sensitivity response range until 12 kPa. IETS can detect pressure changes caused by the accumulation of water droplet weight (about 0.05 g) under a pre-stress of 5 kPa and it is able to clearly detect the detailed characteristics of pulse waves under a pre-stress of 10 kPa as well. Moreover, the IETS has a low detection limit of 2 Pa, a quick response time of 70 ms and a wide detection range up to 110 kPa. For the applications in preventing road accidents caused by sudden cardiovascular diseases and fatigue driving, a wearable tribo-electronic system has been constructed to collect and analyze the pulse wave signals of drivers in real-time. With the assistance of machine learning algorithms, the physiological data obtained from IETS can be effectively employed to assess the driver’s health and fatigue status. 2. Results and discussion 2.1 Structure and working mechanisms of the interfacial engineering-based triboelectric sensor (IETS) During ventricular systole, the ejection of blood from the heart into the central and peripheral arteries induces a pulse wave. The real-time monitoring of mechanical information of pulse waves, such as amplitude and frequency, by using wearable sensors can enable applications such as early warning of cardiovascular diseases and assessment of fatigue states. As shown in Fig. 1 a and Supplementary Fig. 1, by integrating triboelectric sensors into the wristband of a watch, the mechanical signals of pulse beats can be directly converted into electrical signals for output. After signal processing and Bluetooth module on the front side of the wristband, high-quality pulse wave signals will be sent to a mobile APP for real-time display. Machine learning algorithms in the back-end, by extracting and calculating the characteristic values of the pulse wave signals, can achieve the monitoring of the user’s cardiovascular health and fatigue state. Figure 1 b illustrates the structure and equivalent circuit diagram of IETS. The device is composed of a triboelectric sensor with mountain-like triboelectric interface and a piezoelectric sensor with pillar-like sensor-skin interface. By sharing a common electrode to integrate these two kinds of sensors, it realizes aggregating charges on the common electrode from two sensors for amplifying the electrical signal. The working principle of the IETS is depicted in Supplementary Fig. 2. The triboelectric pressure sensor at the lower section is endowed with a specially designed mountain-like microstructures (Fig. 1 c), which is constructed by the partial stacking of two conical microstructures with varying heights. The working mechanism of the triboelectric sensor under different pressures is shown in Supplementary Fig. 3–5. Supplementary Note 1 provides a detailed discussion of the sensing mechanism of the triboelectric sensor under different pressures. The upper part of the IETS features a piezoelectric sensor whose primary component is a polymer-based piezoelectric material (PVDF) with frustums microstructure (Fig. 1 d). The lower electrode of the piezoelectric sensor also serves as the upper electrode of the triboelectric sensor and the electrical coupling enhances the output signals. To minimize the impact of external factors, such as body sweat and wear, on the device's performance, a polymer protective film has been applied to the surface of the piezoelectric sensor's upper electrode through a spray coating process. Figure 1 e presents a photograph of a wearable tribo-electronic system designed for driver fatigue and health monitoring. The exterior of the wristband is equipped with a flexible circuit board for signal processing and data transmission, while the interior of the wristband is secured with IETS. From a micro perspective, the human wrist is not an absolute plane. When the sensor contacts with the wrist, the traditional sensor with a smooth outer surface encounters recessed regions, preventing it from closely adhering to the skin, as depicted in left panel of Fig. 1 f. The weak pulsation cannot effectively transfer mechanical energy to the device at these recessed interfaces, resulting in a low signal-to-noise ratio for the sensor. Additionally, the triboelectric interface with a regular structure tends to quickly reach its deformation limit under the action of external forces, leading to a rapid decline in the sensitivity of the triboelectric sensor in high pressure range. This work optimizes the transfer and distribution of stress through interface microstructures (right panel of Fig. 1 f), achieving precise detection of pulse waves by IETS even under a pre-stress. The incorporation of the piezoelectric sensor not only enhances the output signal but also makes a significant positive contribution in terms of mechanical properties. Pillar-like microstructures, fabricated by a laser demolding technique, can closely adhere to the skin under the pre-tension of the wristband (Supplementary Fig. 5). These microstructures efficiently transfer the faint mechanical energy from pulse beats to the main body of IETS, generating a distinct electrical signal. Moreover, the piezoelectric microstructure themselves generate charge through the piezoelectric effect, serving dual roles in stress transfer and electrical signal enhancement. Figure 1 g clearly illustrates the stress distribution of the device with and without the piezo-frustums microstructures. In areas where the wrist is recessed, stress cannot be effectively transmitted to the triboelectric sensor. However, with the presence of piezo-frustums microstructures, stress is efficiently conveyed to the sensor. For the triboelectric sensor, the larger the effective contact area, the higher for the output signal. Depressions at the skin-device interface leads to a reduction in the effective working area of the triboelectric sensor. The introduction of piezo-frustums microstructures allows for the effective transmission of pulse-induced stress at recessed areas, thereby increasing the effective working area of the triboelectric sensor. The triboelectric interface with mountain-like microstructures endows the sensor with high sensitivity under high pressure through multiple stress concentration points. Under a low pressure, the deformation is predominantly localized in the taller peak, which ensures the device's high sensitivity for tiny pressure. With the application of a preload, the deformation capacity of the taller peaks is constrained after compression. The tips of the secondary peaks can provide the necessary deformation under this condition, ensuring the device's high sensitivity under a greater pressure (Fig. S7, Supporting Information). 2.2 Mechanism of the laser-based microstructure fabrication method In this work, we propose a method for preparing surface microstructures on the interface of polymer materials by using a CO 2 laser to pattern etch PMMA substrate. Herein, the formation mechanism of the microstructures is further explained. The microscopic process of the CO 2 laser acting on the PMMA substrate is depicted in Fig. 2 a. According to the keyhole model, when the energy of the laser is absorbed by PMMA, a high temperature is instantly generated internally, as illustrated in Supplementary Note 2. When the temperature is higher than the vaporization point of PMMA, the intermediate PMMA vaporizes and evaporates, leaving cavities in the substrate 36–38 . Around the cavity, since the temperature is lower than the vaporization point of PMMA, the PMMA becomes a liquid molten pool. When the laser is turned off or moves away, the molten pool cools and becomes solid PMMA, leaving the middle hole structure (Supplementary Movie 1). The energy of the light beam emitted by the laser generally obeys a Gaussian distribution; thus the energy absorbed by PMMA also shows a Gaussian distribution in the irradiated area by the laser. Figure 2 b shows the simulation and experimental results of the cavity depth in the PMMA substrate under different laser power conditions. When the distance between the laser head and the PMMA is fixed, the higher the laser power is, the deeper the cavity depth in the substrate. Figure 2 c depicts the simulated temperature distribution in the substrate due to the Gaussian distribution of laser energy, resulting in a conical cavity morphology in PMMA. This enables indirect control of cone microstructure sizes by adjusting laser power to manipulate the temperature field. Figure 2 d presents the relationship between the laser power and height of the conical microstructures. The higher the laser power is, the higher the height of the conical microstructures. Additionally, the conical microstructures created by laser etching are highly consistent due to the constancy of the laser power, as shown in Supplementary Fig. 8. Herein, a triboelectric layer with cone microstructures (width of 500 µm and height of 600 µm) is chosen as an example to analyze the deformation process under pressure. Supplementary Fig. 9 shows the deformation process of microstructures under certain pressures through simulation and experimental pressing tests. As the pressure increases, the vertical compression deformation of the cone microstructures increases. Due to the specific stress concentrations of the microstructures, the device deforms greatly, even under a low pressure. Different microstructures possess distinct stress concentration effects, thereby endowing the sensor with diverse sensing capabilities. By designing various etching patterns, microstructures with different morphologies can be obtained. As depicted in Fig. 2 e and Supplementary Fig. 10, when the laser etching pattern consists of two intersecting circles with one circle etched at a low power, mountain-like cavities with two apexes can be attained in the PMMA mold. In the subsequent replica molding process, the cavities can be filled with silicone rubber, thereby obtaining a mountain-shaped surface microstructure on the cured silicone rubber layer. By adjusting the laser power and the distance between the laser head and substrate, the mountain-like microstructure can exhibit the same height as the conical microstructure (Supplementary Fig. 11). Figure 2 f presents a comparison of the displacement of the two types of microstructures under the same pressure through simulation. The mountain-like microstructure exhibits approximately 150 µm deformation under the same pressure, which is greater than that of the conical microstructure with same bottom diameter and same height. The outcome is mainly from the smaller volume of the mountain-like microstructure comparing to the conical microstructures of the same size to resist the applied external force. In addition, it has two peak structures at the top, which has a stronger stress concentration effect than the conical structure. This characteristic allows the microstructure to keep a large deformation capacity under high pressure without quickly reaching deformation saturation. Having greater deformation under high pressure implies that sensors with mountain-like microstructures maintain high sensitivity even under a pre-load stress. Figure 2 g shows the potential for the large area fabrication of the mountain-like microstructures based on laser-etching processing, which also exhibits the capability for mass production of the IETS (Supplementary Figs. 12 and 13). Figure 2 h shows the stress‒strain curves of different microstructures, further illustrating that the mountain-like microstructure can experience relatively great deformation under a large pressure. This result provides a mechanical basis for improving the sensitivities of triboelectric sensors under a prestress. To investigate the contribution of various interface structures to the output signal, the charge output of three kinds of devices is shown in Fig. 2 i. A smooth acrylic plate was selected as the contact interface to avoid measurement errors caused by surface roughness. The test results indicate that the output signal is primarily provided by the TENG in the lower half of IETS. Concurrently, the piezoelectric micro-columns in the upper half also offer a portion of the electrical output, which is coupled with the signal of the TENG to enhance the signal-to-noise ratio. 2.3 Sensing performance of IETS In order to authentically simulate the device's response to pressure in a wearable scenario, the surface of a silicone rubber pad is etched using a laser to mimic the irregular curvature and roughness characteristic of the skin surface. For the testing procedure, the rough side of the silicone rubber pad is brought into close contact with the sensor, while a pressure gauge is utilized to exert force on the smooth side of the silicone rubber pad. As shown in Fig. 3 a, the sensitivities of sensor with piezo-frustums microstructures are higher than those of devices with mountain-like and cone microstructures but without piezo-frustums. This is because the outer surface of the sensor without piezo-frustums is a smooth plane, when in contact with the roughness silicone pad, stress can be only transmitted through points of partial contact. This results in a smaller effective contact area at the triboelectric interface, thereby reducing the sensitivity of the triboelectric sensor. For IETS with both piezo-frustums and mountain-like microstructure, it exhibits the highest sensitivity of 4.28 V/kPa within the pressure range of 0–12 kPa and retains a high sensitivity of 0.18 V/kPa even under the pressure over 100 kPa. The high sensitivity of IETS can be attributed to two aspects: the optimization of interfacial stress and the enhanced output signals by the coupling of piezoelectric and triboelectric effects. Interestingly, among devices with piezo-frustums, those with a mountain-like microstructure at the triboelectric interface exhibit higher sensitivity and a broader detection range under low pressure compared to those with conical structures. This is due to the stress concentration effect of the peak-like microstructures, which can produce large deformation, resulting in significant changes in contact area and gap distance. Overall, IETS with piezo-frustums and mountain-like microstructures have higher sensitivity and wider detection range, making it more suitable in the application that pre-load stress is required. As shown in Fig. 3 b, as the pressure increases, the output voltage of the IETS also increases, showing a good correlation. Supplementary Fig. 14 shows the output response of device with mountain-like microstructures but without piezo-frustums under an increasing pressure. It also proves the ability of triboelectric sensor to distinguish pressure amplitude. To test the limit of detection (LOD) of the IETS at low pressures, sand paper with different sizes are used as pressure sources on the surface of the sensor. As shown in Fig. 3 c, the test results show that when the sand paper weight is reduced to 4.19 mg, the IETS reaches its response limit, corresponding to an LOD of 2 Pa. To further test the device response to low pressures, different numbers of water drops (around 50 mg of each drop) are continuously dripped on a weight surface with 5 kPa at the same height. Figure 3 d shows that the IETS is highly sensitive to low pressures even under a pre-load stress of 5 kPa and it can clearly detect the dripping and accumulation of water drops on the weight surface with a fast response time of 70 ms (Supplementary Fig. 15). In addition, the durability of the sensor is an important indicator affecting its actual service life. As shown in Supplementary Fig. 16, when a cyclic pressure of 2 kPa is applied to the surface of the sensor, the device does not show obvious signal attenuation after 5000 cycles, which indicates that the device has excellent durability. Moreover, the IETS can monitor static pressure of 2 kPa in real time and the static drift within 45 min is less than 15%, as shown in Supplementary Fig. 17. Upon comprehensive analysis, the interfacial stress engineering at the sensor-skin interface and the triboelectric interface significantly enhances sensing performance. Figure 3 e presents a performance comparison between IETS and recently reported triboelectric and piezoelectric sensors in terms of sensitivity and detection range 39–43 . The reported IETS in this work possesses both high sensitivity and a broad detection range within a pressure range of less than 12 kPa. Although some previously reported triboelectric sensors have achieved higher sensitivity through stress concentration strategies, their high-sensitivity detection range is limited to below 7 kPa. By designing a mountain-like microstructure, the sensitivity and detection range of the sensor in this work are effectively ensured. The pulse wave signal is an important indicator for cardiovascular health status and driver fatigue monitoring. The signal quality of the pulse wave directly determines the accuracy of the diagnosis. As shown in Fig. 3 f, real-time monitoring characteristics of pulse waves from the same volunteer under a preload of 10 kPa during the same period by sensors with different microstructure types are compared. The results show that the IETS responds sensitively to pulse waves, and its output pulse wave signal has three obvious characteristic peaks. In contrast, the sensor with mountain-like microstructures but without piezo-frustums can just detect two characteristic peaks of the pulse wave. The device with 600 µm conical microstructures can just detect the main peak of the pulse wave, while the device without microstructures cannot effectively detect the pulse wave signal. The results for pulse wave monitoring demonstrate that the piezo-frustums and mountain-shaped microstructures can effectively improve the sensitivity of the triboelectric sensor and thus the IETS with piezo-frustums and mountain-shaped microstructures are selected in subsequent actual applications. 2.4 Applications of IETS in monitoring driver physiological signals and behaviors To achieve real-time monitoring of driver’s health and fatigue state, the system has to obtain the pulse wave parameter of the driver in real time. Herein, the IETS is integrated into the strap of the smart watch and connected to the driver mobile terminal via a Bluetooth module to acquire and process pulse wave data. As shown in Fig. 4 a, the sensor is integrated into a specific part of the strap so that the sensor is in close contact with the wrist artery when the watch is properly worn. When blood flow causes pulse beats in the artery, the highly sensitive sensor can capture these low-pressure signals in real time and convert them into electrical signals. The edge data processing module and Bluetooth module can process and send pulse wave data to the user mobile phone in real time. Figure 4 b illustrates the schematic diagram of the real-time wireless pulse wave monitoring system based on IETS. Due to the low amplitude and frequency (< 5 Hz) values of pulsation signals, this system is designed with unique amplification and filtering circuits. The pulse wave signal with a high signal-to-noise ratio is subsequently collected by an analog-to-digital converter (ADC) for initial shaping and feature extraction. The signal is then transmitted via Bluetooth to a mobile device for further analysis. Figure 4 c presents the physical components of the hardware section of the system. Electronic components are integrated on a flexible circuit board, enabling the hardware system to bend and accommodate the users with various wrist shapes. When the complete hardware system is affixed to the surface of a cylindrical object with a radius of 2.5 cm, as shown in Fig. 4 d, the system can continue to function as intended. Figure 4 e displays the waveform of the pulse wave signal detected by IETS after undergoing analog signal processing. Evidently, the signal exhibits a high signal-to-noise ratio and can distinctly reflect information from multiple characteristic peaks. Figure 4 f shows the Bluetooth connection interface on the mobile phone to prove the feasibility of the communication module. After the mobile phone and the sensor module successfully connect via Bluetooth, the sensor module can send preprocessed data to the mobile phone in real time and display the pulse waveform on the APP interface. This process provides a solid foundation for implementing subsequent eigenvalue extraction, frequency domain signal conversion and algorithms process. Figure 4 g depicts the physical layout of the hardware system after the wristwatch strap is removed. Apparently, the sensor and flexible circuit board are installed on opposite sides of the strap that are interconnected by wires. Once the sensor and flexible circuit board are integrated with the wristwatch strap, the real-time collection of the driver pulse wave signal can be achieved, as shown in Fig. 4 h. Assessing the driver fatigue level based on the pulse wave signal typically relies on indicators of heart rate variability (HRV). Figure 4 i shows a conceptual diagram for calculating HRV. The core of HRV calculation is to obtain the time difference between two consecutive pulse beats. In practical measurement scenarios, the real-time collected pulse wave signal is a temporal signal. To effectively extract HRV parameters, the pulse wave signal is generally transformed into the frequency domain through fast Fourier transformation (FFT), as shown in Fig. 4 j. In the frequency domain signal, the system can acquire parameters for each characteristic frequency and determine the volunteer fatigue state based on the proportion of these parameters 44–46 . As depicted in Fig. S18, noticeable distinctions in characteristic frequency parameters are observed for the same volunteer between wakeful morning and drowsy states. Additionally, based on the feature values in the temporal signal of the pulse wave, the system can assess the user cardiovascular health status. On this basis, we develop a driver fatigue and health status monitoring system, as demonstrated in Fig. 4 k and Supplementary Fig. 19. By processing and analyzing the temporal and frequency domain signals of raw data, this system enables real-time monitoring and early warning of driver fatigue and physical health status (Supplementary Movie 2). In addition, when a driver is tired, they may display irregular behaviors such as frequent abrupt braking and yawning due to distractions. The wearable sensors can be utilized to monitor these driver behaviors, providing an alternative method for assessing fatigue. On this basis, IETSs are wore on the driver face or attached on driving cab components, such as the accelerator and brake pedals, for the real-time collection of driver behaviors, as shown in Supplementary Fig. 20. Due to the high sensitivity of the IETS in a wide pressure range, the sensor can accurately detect both low pressure signals, such as eye movements, and large pressure signals, such as stepping on the brake pedals and the driver leaving the seat. As shown in Supplementary Fig. 21a, when a IETS is attached to the corner of the eye, blinking causes slight pressure changes due to contraction of the eye muscles. The sensor will clearly capture the driver blinking action. In a normal state, the frequency of driver blinking is maintained near a fixed value. In a state of fatigue, the driver usually experiences a period of first unblinking and then rapid blinking. The IETS can clearly monitor the driver blinking signal for the evaluation of the driver fatigue state. Similarly, the sensor can be attached to the driver’s mouth corner to monitor yawning. As shown in Supplementary Fig. 21b, when the driver is in a state of fatigue, yawning causes the mouth corner muscles to contract for usually 2–5 seconds, which is significantly different from normal talking and chewing actions. Therefore, monitoring driver yawning behaviors through the sensors can assist in evaluating the state of driver fatigue. In addition, installing sensors on the accelerator and brake pedals can also monitor driver actions in real time. As shown in Supplementary Fig. 21c, the driver’s behavioral data acquired by the IETS installed on the accelerator pedal show that when the vehicle starts, the output voltage of the sensor first slowly increases and then remains stable. When overtaking is needed, the force applied by the driver foot increases, the output voltage of the sensor increases as well. Similarly, the IETS attached to the brake pedal can provide real-time feedback on the changes in the force applied by the driver foot when braking. When the driver is in a state of fatigue, emergencies cause the driver to brake frequently and suddenly, and the sensor outputs sudden braking signals, as shown in Supplementary Fig. 21d. Regarding to driver safety, IETSs can be integrated into seat belt buckles or cushions to determine whether the driver has fastened the seat belt or left the seat, respectively, by detecting changes in pressure (Supplementary Movie 3). As shown in Supplementary Fig. 21e, when the seat belt is fastened, the sensor in the buckle receives compression and outputs a continuous output signal. When the seat belt is released, the signal returns to the initial state. Supplementary Fig. S21f shows the output signal of the sensor under the seat cushion after being pressed by the driver weight. Upon the parallel integration of 12 IETSs to form an array device, the quantity of sensors under compression can be detected by assessing the magnitude of the output signal. This method allows for determination of the driver's seating posture (Supplementary Fig. 22). The IETS can accurately detect whether the driver has seat or left the seat. These applications show that the sensor has an ultrawide detection range from low pressures, such as pulse waves, to large pressures, such as body weight. 2.5 Deep learning-enabled cardiovascular monitoring and driver fatigue monitoring Biological signs can reliably indicate early fatigue and prevent accidents from occurring. Cardiovascular signals, such as electrocardiography (ECG) and photoplethysmography (PPG), can accurately monitor fatigue status. However, due to the limited viability of invasive sensors for real-time driver wear and the low sensitivity of noninvasive sensors influenced by humans or the environment, normal driver behavior and fatigue monitoring accuracy are compromised. Due to the high sensitivity in detecting imperceptible low pressures, the IETS can be used to monitor weak pulse waves to continuously assess cardiovascular and fatigue conditions. Moreover, a combination of artificial intelligence (AI), such as deep learning, can improve the accuracy and actionability of new sensing devices, ultimately facilitating real-time personal identification and driver fatigue (Fig. 5 a). By comparing the voltage signal obtained by IETS with the typical arterial pulse wave in Fig. 5 b, we find that the voltage signal matches the typical arterial pulse waveform and can detect low intra-arterial blood pressure oscillations with systolic, reflected and diastolic peaks (P 1 , P 2 and P 3 ). Therefore, the cardiovascular condition and degree of arteriosclerosis can be assessed by comparing the measured results with references. Figure 5 c and Supplementary Fig. 17 show the reflected wave transit time (RWTT), systolic–diastolic time (PPT), upstroke time (UT) and left ventricular ejection time (LVET) from the continuous 20 voltage signal cycles, all of which are correlated with the reported reference values of a healthy individual. Cardiovascular conditions are related to heart rate (HR) and HRV. Figure 5 d reflects a mapped scatter points plot of pulse interval (Ti) to reflect the HRV when the subject is nonfatigued (HRV = 8.9) and fatigued (HRV = 5.4); HRV is a valuable predictor of sudden cardiac death and arrhythmic events. The lower the HRV value is, the more likely the subject is to suffer from acute myocardial infarction and arrhythmias, and the stronger the need to relax and rest in a timely manner. Moreover, the frequency–time distribution chart (Fig. 5 e) illustrates that when the subject is fatigued, the frequency change over time is unstable, aiding in demonstrating the instability of heart rate variability during fatigue. Heart rate decreases significantly in the fatigued state of the subject, as shown in Fig. 5 f–g and i–j. The heart rate and HRV index in pulse wave signals are crucial physiological indicators for evaluating fatigue. However, this method requires long-term pulse wave data, which cannot monitor and assess the driver fatigue status and alert the driver in real time. Thus, in this study, the pulse wave data are divided into time lengths of 750 ms per sample, and the short-term signals are classified by a one-dimensional (1d) convolutional neural network (CNN)-based method for recognizing driver behavior and fatigue. To reduce the effects of noise, baseline drift and environment, the collected data are preprocessed (wavelet noise reduction, R-peak splitting and normalization, respectively). The detailed framework and parameters used to construct the CNN model are labeled in Supplementary Table 1. Supplementary Fig. 18 supplies the typical pulse signals of the 5 different subjects. Through data collection and signal preprocessing, 625 sample data points with each 750 data points constitute dataset 1 (80% training set and 20% test set). The average recognition accuracy is 94% (Supplementary Fig. 19a), providing great potential for high-accuracy behavioral recognition based on deep learning (DL) prediction. After training in the 1d-CNN model with 80 training epochs, the maximum accuracy is achieved, and the dropout layer avoids overfitting, as shown in Supplementary Fig. 19b. In contrast to unprocessed data and support vector machine (SVM) models, which require pre-extracted features, the end-to-end CNN model exhibits increased accuracy and reduced overall complexity in Supplementary Fig. 19c. Pulse signal-based identification technology avoids the drawbacks of traditional identity authentication and is less susceptible to copying and counterfeiting. Combining it with deep learning networks increases the convenience and effectiveness of identification. As depicted in Fig. 5 h and k, the morphology of the pulse changes after fatigue, with reflected and diastolic peaks decreasing relative to the systolic peaks. By merging the data of the different states for several days, dataset 2 is trained and validated by the deep learning network described above. The average accuracy reaches 98% for one subject, as shown in Fig. 5 l. Fatigue classification based on short-time pulse signals can achieve the real-time and accurate detection of driver fatigue, providing a certain guarantee for safe driving. 3. Discussion In conclusion, this work presents an interfacial engineering-based triboelectric sensor (IETS) to realize the precise pulse wave detection. The integration of piezo-frustums at the sensor-skin interface not only provides the pathways for stress transfer but also facilitates the generation of piezoelectric charges. Through the mechano-electric coupling effect, the sensitivity of IETS has been improved by 5 times compared to devices without piezo-frustums, reaching 4.28 V/kPa. Additionally, the construction of mountain-like microstructures at triboelectric interface has further expanded the sensor's high-sensitivity response range to 12 kPa, enabling the clear detection of pulse wave characteristics under a pre-stress of 10 kPa. Simultaneously, IETS has a low detection limit of 2 Pa, a quick response time of 70 ms and a wide detection range reaching 110 kPa. Furthermore, the development of a wearable tribo-electronic system, supported by machine learning algorithms, allows for real-time collection and analysis of driver's pulse wave signals, providing a robust tool for assessing health and fatigue status, thereby contributing to the prevention of road accidents associated with cardiovascular diseases and fatigue driving. 4. Methods 4.1 Fabrication of the interfacial engineering-based triboelectric sensor (IETS) The fabricated IETS consists of a piezoelectric nanogenerator-based sensor (PENG) and a triboelectric nanogenerator-based sensor (TENG). The TENG is composed of upper and bottom electrodes and a microstructured triboelectric layer. The bottom electrode is formed by screen printing silver paste (01 L-2211D, Sryed Paste) on a polyethylene terephthalate (PET) substrate. Specifically, through the extrusion of the scraper, the silver paste passes through the customized pattern mesh plate to form a conductive circuit on the PET surface and is then cured at 120°C to fabricate flexible electrodes. To obtain the microstructured triboelectric layer, components A and B of Ecoflex (00–30, Smooth-On) are mixed evenly in a 1:1 ratio and spin-coated on the PMMA mold. Then the layer is left in a vacuum drying oven for approximately 2 h until it is fully cured. The PMMA mold uses the Gaussian distribution of laser energy to form a microstructure pattern inside the PMMA. By adjusting the power and motion modes of the laser machine, microstructures with different sizes and shapes can be obtained. By sequentially bonding the lower electrode, triboelectric layer and upper electrode with 3 M double-sided tape (3MVHB4905, 3 M), a triboelectric pressure sensor can be obtained. Similarly, triboelectric sensors with different shapes and sizes can be prepared by designing different electrode patterns. The piezo-pillar microstructures are also fabricated using a demolding method. To acquire high-quality microstructures, a PMMA mold with a cylindrical structure is first etched using a laser. Subsequently, a second mold is formed by casting PDMS over the PMMA mold. PVDF (Polyk-Piezo) powder is dissolved in acetone at a weight ratio of 3:7, then poured into the PDMS mold to replicate the cylindrical piezoelectric microstructures. Silver electrodes are fabricated on their base using a screen printing method. The piezoelectric performance of the material is enhanced through high-voltage polarization, followed by the sputtering of a gold electrode layer on the upper surface using a magnetron sputtering process. Finally, after the integration with the triboelectric sensor, a polymer layer is sprayed as a protective coating. 4.2 Performance characterization For the electrical output measurement of the IETS, an external contact force is applied by a commercial linear mechanical motor (Winnemotor, WMUC512075-06-X) and the applied force is detected by digital force measurement (Chatillon, DFS Ⅱ). A programmable electrometer (Keithley model 6514) is used to test the electrical output signal. The triboelectric potential distribution simulation and mechanical deformation are conducted by COMSOL Multiphysics software. Declarations Data and materials availability All data supporting this study and its findings are available within the article, its Supplementary Information and associated files. The source data are available from the corresponding author upon reasonable request. Competing interests The authors declare no competing interests. Author contributions H. L. and Z. W. conceived the idea, H. L., X. Q., and G. S. analyzed the data and contributed to writing the paper. P. H. and G.S. designed the microstructure of the sensors. W. W., J. Y. and L. X. developed and fabricated the circuit board. L. X., and B. L. constructed the app and machine learning algorithm. H. L., X. Q., and G. S. conducted the experiments to fabricate the sensors. H. L., Y. L., E. G. L. and X. T. created and optimized the figures, tables, and videos. C. Z., X. S. and Z. W. revised the manuscript. All authors collectively analyzed the results and implications and provided comments on the manuscript at all stages. Acknowledgments This work was supported by the National Natural Science Foundation of China (No. 62174115, No. 62273247, No. U21A20147), Natural Science Foundation of the Jiangsu Higher Education Institutions of China Program (No. 19KJB510059), Suzhou Science and Technology Development Planning Project: Key Industrial Technology Innovation (No. SYG202009, No. SYG201924), Jiangsu Key Laboratory for Carbon-based Functional Materials & Devices, Soochow University (No. KJS2157), XJTLU Research Development Fund (No. RDF-17-01-13, No. RDF-21-02-068 and No. RDF-22-01-110) and SIP AI innovation platform (No.YZCXPT2022103). This work was partially supported by the Collaborative Innovation Center of Suzhou Nano Science & Technology, 111 Project, Joint International Research Laboratory of Carbon-Based Functional Materials and Devices, XJTLU AI University Research Centre, Jiangsu Province Engineering Research Centre of Data Science and Cognitive Computation at XJTLU. References Feng, S., Sun, H., Yan, X. et al. Dense reinforcement learning for safety validation of autonomous vehicles. Nature 615 , 620–627 (2023). Spatz, E. S., Ginsburg, G. S., Rumsfeld, J. S. & Turakhia, M. P. Wearable Digital Health Technologies for Monitoring in Cardiovascular Medicine. N. Engl. J. Med. 390 , 346–356 (2024). Pham, T., Lau, Z. J., Chen, S. H. A. & Makowski, D. Heart rate variability in psychology: A review of HRV indices and an analysis tutorial. Sensors 21 , 3998 (2021). Bayoumy, K. et al. Smart wearable devices in cardiovascular care: where we are and how to move forward. Nat. Rev. Cardiol. 18 , 581–599 (2021). Ates, H. C. et al. End-to-end design of wearable sensors. Nat. Rev. Mater. 7 , 887–907 (2022). Kim, S. et al. Wearable and implantable bioelectronics as eco‐friendly and patient‐friendly integrated nanoarchitectonics for next‐generation smart healthcare technology. EcoMat 5 , e12356 (2023). Lee, H. et al. Toward all-day wearable health monitoring: An ultralow-power, reflective organic pulse oximetry sensing patch. Sci. Adv. 4 , eaas9530 (2018). Chen, S. et al. Flexible Wearable Sensors for Cardiovascular Health Monitoring. Adv. Healthc. Mater. 10 , 2100116 (2021). Zhao, L. et al. Emerging sensing and modeling technologies for wearable and cuffless blood pressure monitoring. npj Digit. Med. 6 , 93 (2023). Iqbal, S. M. A., Mahgoub, I., Du, E., Leavitt, M. A. & Asghar, W. Advances in healthcare wearable devices. npj Flex. Electron. 5 , 9 (2021). Meng, K. et al. Wearable Pressure Sensors for Pulse Wave Monitoring (Adv. Mater. 21/2022). Adv. Mater. 34 , 2270158 (2022). Guan, Y.-S. et al. Elastic electronics based on micromesh-structured rubbery semiconductor films. Nat. Electron. 5 , 881–892 (2022). Li, J. et al. Thin, soft, wearable system for continuous wireless monitoring of artery blood pressure. Nat. Commun. 14 , 5009 (2023). Tian, G. et al. Hierarchical Piezoelectric Composites for Noninvasive Continuous Cardiovascular Monitoring. Adv. Mater. 36 , 2313612 (2024). Yi, Z. et al. Piezoelectric Dynamics of Arterial Pulse for Wearable Continuous Blood Pressure Monitoring. Adv. Mater. 34 , 2110291 (2022). Chorsi, M. T. et al. Highly piezoelectric, biodegradable, and flexible amino acid nanofibers for medical applications. Sci. Adv. 9 , eadg6075 (2023). Yao, C. et al. Personalized Machine Learning-Coupled Nanopillar Triboelectric Pulse Sensor for Cuffless Blood Pressure Continuous Monitoring. ACS Nano 17 , 24242–24258 (2023). Peng, X. et al. A breathable, biodegradable, antibacterial, and self-powered electronic skin based on all-nanofiber triboelectric nanogenerators. Sci. Adv. 6 , eaba9624 (2020). Hui, X. et al. Acoustically Enhanced Triboelectric Stethoscope for Ultrasensitive Cardiac Sounds Sensing and Disease Diagnosis. Adv. Mater. 36 , 2401508 (2024). Fan, W. et al. Machine-knitted washable sensor array textile for precise epidermal physiological signal monitoring. Sci. Adv. 6 , eaay2840 (2020). Khandelwal, G. & Dahiya, R. Self‐Powered Active Sensing Based on Triboelectric Generators. Adv. Mater. 34 , 2200724 (2022). Lee, S. & Park, J.-W. Fingerprint-inspired triboelectric nanogenerator with a geometrically asymmetric electrode design for a self-powered dynamic pressure sensor. Nano Energy 101 , 107546 (2022). Liu, W. et al. Touchless interactive teaching of soft robots through flexible bimodal sensory interfaces. Nat. Commun. 13 , 5030 (2022). Meng, K. et al. Kirigami‐Inspired Pressure Sensors for Wearable Dynamic Cardiovascular Monitoring. Adv. Mater. 34 , 2202478 (2022). Zhang, Z. et al. Artificial intelligence of toilet (AI-Toilet) for an integrated health monitoring system (IHMS) using smart triboelectric pressure sensors and image sensor. Nano Energy 90 , 106517 (2021). Yang, Y. et al. Triboelectric Nanogenerator Enabled Wearable Sensors and Electronics for Sustainable Internet of Things Integrated Green Earth. Adv. Energy Mater. 13 , 2203040 (2023). Sun, Z., Zhu, M., Shan, X. & Lee, C. Augmented tactile-perception and haptic-feedback rings as human-machine interfaces aiming for immersive interactions. Nat. Commun. 13 , 5224 (2022). Bai, N. et al. A robotic sensory system with high spatiotemporal resolution for texture recognition. Nat. Commun. 14 , 7121 (2023). Liu, Y. et al. Electronic skin as wireless human-machine interfaces for robotic VR. Sci. Adv. 8 , eabl6700 (2022). Shi, J. et al. Embedment of sensing elements for robust, highly sensitive, and cross-talk–free iontronic skins for robotics applications. Sci. Adv. 9 , eadf8831 (2023). Fang, Y. et al. Ambulatory Cardiovascular Monitoring Via a Machine‐Learning‐Assisted Textile Triboelectric Sensor. Adv. Mater. 33 , 2104178 (2021). Liu, X. et al. Bioinspired, Microstructured Silk Fibroin Adhesives for Flexible Skin Sensors. ACS Appl. Mater. Interfaces 12 , 5601–5609 (2020). Tang, L., Shang, J. & Jiang, X. Multilayered electronic transfer tattoo that can enable the crease amplification effect. Sci. Adv. 7 , eabe3778 (2021). Min, S. et al. Clinical Validation of a Wearable Piezoelectric Blood‐Pressure Sensor for Continuous Health Monitoring. Adv. Mater. 35 , 2301627 (2023). Park, J. et al. Frequency-selective acoustic and haptic smart skin for dual-mode dynamic/static human-machine interface. Sci. Adv. 8 , eabj9220 (2022). Cao, X., Wallace, W., Poon, C. & Immarigeon, J.-P. Research and Progress in Laser Welding of Wrought Aluminum Alloys. I. Laser Welding Processes. Mater. Manuf. Process. 18 , 1–22 (2003). Svenungsson, J., Choquet, I. & Kaplan, A. F. H. Laser Welding Process – A Review of Keyhole Welding Modelling. Phys. Procedia 78 , 182–191 (2015). Yang, R. et al. Author Correction: Iontronic pressure sensor with high sensitivity over ultra-broad linear range enabled by laser-induced gradient micro-pyramids. Nat. Commun. 14 , 3913 (2023). Zu, L. et al. Multiangle, self-powered sensor array for monitoring head impacts. Sci. Adv. 9 , eadg5152 (2023). Xie, L. et al. Ultrasensitive Wearable Pressure Sensors with Stress‐Concentrated Tip‐Array Design for Long‐Term Bimodal Identification. Adv. Mater. 2406235 (2024). Ha, M. et al. Skin-Inspired Hierarchical Polymer Architectures with Gradient Stiffness for Spacer-Free, Ultrathin, and Highly Sensitive Triboelectric Sensors. ACS Nano 12 , 3964–3974 (2018). Lu, J. et al. A Biodegradable and Recyclable Piezoelectric Sensor Based on a Molecular Ferroelectric Embedded in a Bacterial Cellulose Hydrogel. ACS Nano 16 , 3744–3755 (2022). Park, Y. et al. Ferroelectric Multilayer Nanocomposites with Polarization and Stress Concentration Structures for Enhanced Triboelectric Performances. ACS Nano 14 , 7101–7110 (2020). Pham, T., Lau, Z. J., Chen, S. H. A. & Makowski, D. Heart Rate Variability in Psychology: A Review of HRV Indices and an Analysis Tutorial. Sensors 21 , 3998 (2021). Gao, R. et al. Study on the nonfatigue and fatigue states of orchard workers based on electrocardiogram signal analysis. Sci. Rep. 12 , 4858 (2022). Ahmed, S. et al. Noncontact assessment for fatigue based on heart rate variability using IR-UWB radar. Sci. Rep. 12 , 14211 (2022). 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6446990","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":450837854,"identity":"3b8f8c06-ba87-4efe-835d-a57fbf57feae","order_by":0,"name":"Xuhui Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYDACCRBRAcQHgJiHeC1nSNbC2EaKFoPb7c+kC+fVJfYdP8D44G0bg7w5IS2Sc86YSc/cdjhx5pkEZsO5bQyGOxsIaOGXyGGT5t12IHHDgQQgo40hweAAAS1sEunPpHnn1CVuOP+A/TdRWvglEsykeRuYEzfcSGBjJkqL5IwcY2ueY4eNZ9542Cw555yE4QZCWgxupD+8zVNTJ9t3PvnghzdlNvIEbUECjA0M0JgdBaNgFIyCUUApAAA2kD9HtRHy/wAAAABJRU5ErkJggg==","orcid":"","institution":"Soochow University","correspondingAuthor":true,"prefix":"","firstName":"Xuhui","middleName":"","lastName":"Sun","suffix":""},{"id":450837855,"identity":"1fd552d2-c8ae-407c-b6ee-e7c63151265e","order_by":1,"name":"Hao Lei","email":"","orcid":"","institution":"Xi'an Jiaotong-Liverpool University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Lei","suffix":""},{"id":450837856,"identity":"c2665f79-094d-401e-884b-92142e091b0b","order_by":2,"name":"Lingjie Xie","email":"","orcid":"","institution":"Xi'an Jiaotong-Liverpool University","correspondingAuthor":false,"prefix":"","firstName":"Lingjie","middleName":"","lastName":"Xie","suffix":""},{"id":450837857,"identity":"bfb89534-2f53-486c-b8ee-efc9d27addce","order_by":3,"name":"Xuan Qin","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Qin","suffix":""},{"id":450837858,"identity":"0445cf06-4660-4c8f-aada-c6487061fad7","order_by":4,"name":"Guoxuan Sun","email":"","orcid":"","institution":"Xi'an Jiaotong-Liverpool University","correspondingAuthor":false,"prefix":"","firstName":"Guoxuan","middleName":"","lastName":"Sun","suffix":""},{"id":450837859,"identity":"735ac912-7c2d-4be9-a397-e70aaae90196","order_by":5,"name":"Peihao Huang","email":"","orcid":"","institution":"Xi'an Jiaotong-Liverpool University","correspondingAuthor":false,"prefix":"","firstName":"Peihao","middleName":"","lastName":"Huang","suffix":""},{"id":450837860,"identity":"59e6a028-6fe2-48e6-ae5e-834247b5c999","order_by":6,"name":"Weinuo Wang","email":"","orcid":"","institution":"Xi'an Jiaotong-Liverpool University","correspondingAuthor":false,"prefix":"","firstName":"Weinuo","middleName":"","lastName":"Wang","suffix":""},{"id":450837861,"identity":"9383041f-5963-4874-9b5f-231be7bf6b46","order_by":7,"name":"Bohan Lu","email":"","orcid":"","institution":"Xi'an Jiaotong-Liverpool University","correspondingAuthor":false,"prefix":"","firstName":"Bohan","middleName":"","lastName":"Lu","suffix":""},{"id":450837862,"identity":"30be5b16-68b8-4b06-8040-ac5b4f03c8e0","order_by":8,"name":"Jiawei Yan","email":"","orcid":"","institution":"Xi'an Jiaotong-Liverpool University","correspondingAuthor":false,"prefix":"","firstName":"Jiawei","middleName":"","lastName":"Yan","suffix":""},{"id":450837863,"identity":"321bb59f-79d6-49d8-a895-11b83350579f","order_by":9,"name":"Yuxi Wang","email":"","orcid":"","institution":"Xi'an Jiaotong-Liverpool University","correspondingAuthor":false,"prefix":"","firstName":"Yuxi","middleName":"","lastName":"Wang","suffix":""},{"id":450837864,"identity":"108c6817-1431-4514-9f60-984f0e8ccc43","order_by":10,"name":"Yina Liu","email":"","orcid":"https://orcid.org/0000-0002-4908-9355","institution":"Xi'an Jiaotong-Liverpool University","correspondingAuthor":false,"prefix":"","firstName":"Yina","middleName":"","lastName":"Liu","suffix":""},{"id":450837865,"identity":"c505a639-d16d-4294-9b58-e2f7341ca1f0","order_by":11,"name":"Eng Lim","email":"","orcid":"","institution":"Xi'an Jiaotong - 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The right panel illustrates the working mechanism of the interfacial stress engineering. \u003cstrong\u003eg\u003c/strong\u003e Simulation results of sensor with and without interfacial stress optimization microstructures.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6446990/v1/e0827b7f4da3445c1431f313.jpeg"},{"id":82136294,"identity":"acd17cd9-cb5a-4c54-a89d-7aebffc9249e","added_by":"auto","created_at":"2025-05-07 06:12:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":933217,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFabrication mechanisms of cone and mountain-like microstructures.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e Cone structure formation in a Poly (methyl methacrylate) (PMMA) substrate through a CO\u003csub\u003e2\u003c/sub\u003e laser beam. \u003cstrong\u003eb\u003c/strong\u003e Simulation and fabrication results of cone structure formation in a PMMA substrate with different laser powers. The scale bar is 200 µm. \u003cstrong\u003ec\u003c/strong\u003e Simulated results of the laser energy distribution. \u003cstrong\u003ed\u003c/strong\u003e Correlation between cone height and laser power. \u003cstrong\u003ee\u003c/strong\u003e Fabrication mechanisms of mountain-like microstructures. The scale bar is 200 µm. \u003cstrong\u003ef\u003c/strong\u003e Simulation results of two kinds of microstructures deformation with an increasing pressure. \u003cstrong\u003eg\u003c/strong\u003e Large-area dielectric layer with mountain-like microstructures. The scale bar is 6 cm. The inset shows the microscopy picture of mountain-like microstructures. The scale bar is 500 µm. \u003cstrong\u003eh\u003c/strong\u003e Mechanical performance comparison of dielectric layers with different types of microstructures. \u003cstrong\u003ei\u003c/strong\u003e Output performance comparison of different types of nanogenerators.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6446990/v1/73312aba2870e8340b8a0a99.png"},{"id":82134542,"identity":"6e80d071-ae1b-491a-b57d-7128c6065607","added_by":"auto","created_at":"2025-05-07 06:04:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":630245,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSensing performance characteristics of IETS.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e Sensitivity curves of the fabricated sensors with different structural features. \u003cstrong\u003eb\u003c/strong\u003e Correlation between the applied pressure and output voltage values of the IETS. \u003cstrong\u003ec\u003c/strong\u003e Limit of detection (LOD) value of the IETS. \u003cstrong\u003ed\u003c/strong\u003e Real-time signal change in the IETS generated by several successive water drops under a pre-load stress of 5 kPa. \u003cstrong\u003ee\u003c/strong\u003e Sensing performance comparison with recent works. \u003cstrong\u003ef\u003c/strong\u003e Responses of devices with different structural characteristics to a low pulse pressure under a preload of 10 kPa. The pulse waves detected by IETS feature more necessary physiological details.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6446990/v1/9418d79a2233e66422313bb5.png"},{"id":82136295,"identity":"20c85c06-e75f-4ac5-853f-7a3c6d2c88f8","added_by":"auto","created_at":"2025-05-07 06:12:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1648459,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWireless driver fatigue monitoring system based on IETS. a\u003c/strong\u003e Schematic illustration of a smart strap with the IETS as a driver state monitor. \u003cstrong\u003eb\u003c/strong\u003eSchematic diagram of sensor signal acquisition and processing. \u003cstrong\u003ec\u003c/strong\u003e Photograph of the smart strap based on the IETS during the testing process. \u003cstrong\u003ed\u003c/strong\u003e Photograph of the smart strap during the bending state. \u003cstrong\u003ee\u003c/strong\u003e Real-time pulse wave detected by the oscilloscope to demonstrate the feasibility of the circuit during the bending state. \u003cstrong\u003ef\u003c/strong\u003e Photographs of the app for the Bluetooth module to verify the function of wireless communication. \u003cstrong\u003eg\u003c/strong\u003e Photograph of the IETS and the signal processing circuit without the strap. \u003cstrong\u003eh\u003c/strong\u003eReal testing scenario based on the proposed smart strap for driver fatigue monitoring. \u003cstrong\u003ei\u003c/strong\u003e Schematic illustration of HRV. \u003cstrong\u003ej\u003c/strong\u003e Frequency domain information of the real-time pulse wave signals monitored by the smart strap. \u003cstrong\u003ek\u003c/strong\u003e Photographs of the customized app for driver fatigue and health state monitoring.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6446990/v1/14db6e34707c81c5544f920a.png"},{"id":82136298,"identity":"e6386f2f-2072-4e07-9fc8-e2182d4534c5","added_by":"auto","created_at":"2025-05-07 06:12:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1177722,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDemonstrations of the IETS in cardiovascular and driver fatigue monitoring processes.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e Illustration of deep learning-enabled personal identification, driver fatigue monitoring and pulse monitoring, with the detailed framework of 1D-CNN analytics. \u003cstrong\u003eb\u003c/strong\u003e Schematic diagram of a typical pulse wave. \u003cstrong\u003ec\u003c/strong\u003e RWTT and PPT acquired from a continuous pulse signal during 20 cycles.\u003cstrong\u003e d\u003c/strong\u003e Poincare plot for the nonfatigue and fatigue states of a healthy person. \u003cstrong\u003ee\u003c/strong\u003e Frequency domain distribution chart under nonfatigue and fatigue states. \u003cstrong\u003ef–h\u003c/strong\u003e Heart rate distribution and real-time output of IETS in a regular state. \u003cstrong\u003ei–k\u003c/strong\u003e Heart rate distribution and real-time output of IETS when the wearer is fatigued. \u003cstrong\u003el\u003c/strong\u003eConfusion map for the 1D-CNN outcome of 2 states from one subject.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6446990/v1/ba20baec9e795afd675a69cb.png"},{"id":104777870,"identity":"e4d2e369-e151-4e5b-9c04-a4c7095ed617","added_by":"auto","created_at":"2026-03-17 07:14:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5585240,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6446990/v1/603e35af-345a-4534-ae50-65ddf14a507d.pdf"},{"id":82134499,"identity":"3e233e1c-5036-4f51-a7ca-0dd2b2b76055","added_by":"auto","created_at":"2025-05-07 06:04:03","extension":"avi","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1784712,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Movie 1\u003c/p\u003e","description":"","filename":"SupplementaryMovie1.avi","url":"https://assets-eu.researchsquare.com/files/rs-6446990/v1/7c4790da27b946e93ebc5a63.avi"},{"id":82134496,"identity":"986a4cbc-f799-417b-8fe7-7b29442120a1","added_by":"auto","created_at":"2025-05-07 06:04:03","extension":"avi","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2222724,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Movie 2\u003c/p\u003e","description":"","filename":"SupplementaryMovie2.avi","url":"https://assets-eu.researchsquare.com/files/rs-6446990/v1/7aa905e30473333fd1985422.avi"},{"id":82134543,"identity":"379c6349-9807-4315-a154-45c2b2bc52f3","added_by":"auto","created_at":"2025-05-07 06:04:04","extension":"avi","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":4371610,"visible":true,"origin":"","legend":"Supplementary Movie 3","description":"","filename":"SupplementaryMovie3.avi","url":"https://assets-eu.researchsquare.com/files/rs-6446990/v1/5e641b91ec6465b7f5383a83.avi"},{"id":82134546,"identity":"a46b48eb-65fa-470b-9541-40413c4e5c7f","added_by":"auto","created_at":"2025-05-07 06:04:04","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":5802182,"visible":true,"origin":"","legend":"Supporting Information","description":"","filename":"SupportingInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6446990/v1/15a3f185c2d747384ad5c2b8.docx"}],"financialInterests":"There is no conflict of interest","formattedTitle":"Optimized Stress Transfer Interfaces Enabled Wearable Nano-Electronics for Fatigue Driving Monitoring","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDriver fatigue sudden cardiovascular diseases are the two leading causes of traffic fatalities\u003csup\u003e1, 2\u003c/sup\u003e. Effective monitoring and timely warnings of driver's physiological states are critical measures for reducing traffic accidents\u003csup\u003e3\u003c/sup\u003e. The non-invasive acquisition and analysis of pulse waves through wearable electronic devices can facilitate real-time management of cardiovascular health\u003csup\u003e4\u0026ndash;7\u003c/sup\u003e. Mechanical sensor-based pulse wave measurement systems have recently attracted extensive interest, which are capable of directly mapping the pulse waveform by monitoring the mechanical stimuli of arterial pulsations, more accurately reflecting vital cardiovascular health information such as arterial blood pressure and the elasticity of blood vessel walls\u003csup\u003e8\u0026ndash;10\u003c/sup\u003e. However, the mechanical energy of pulse beats is extremely subtle and the real-time acquisition of accurate pulse wave throughout the day poses great challenges to sensitivity and the sensor's power consumption\u003csup\u003e11\u0026ndash;12\u003c/sup\u003e. Mechanical sensors based on piezoelectric and triboelectric principles, due to their zero power consumption and sensitive response to faint pressure signals, have been extensively used in real-time monitoring of pulse waves\u003csup\u003e13\u0026ndash;18\u003c/sup\u003e. Recent work has found that triboelectric sensors exhibit a higher signal-to-noise ratio (SNR) when detecting faint mechanical physiological signals from the human body, such as heart sound\u003csup\u003e19\u003c/sup\u003e. Moreover, triboelectric sensors naturally can be made of a wide range of raw materials, thereby attracting extensive attention in recent years\u003csup\u003e20, 21\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo accurately reflect the details of the pulse wave, previous research primarily focused on constructing microstructures on the triboelectric layer surface to enhance the sensitivity of triboelectric sensors\u003csup\u003e22\u0026ndash;24\u003c/sup\u003e. The contribution of microstructures to sensing performance mainly comes from two aspects: the increased surface area increases SNR and the stress concentration induced by microstructures enhances the sensitivity\u003csup\u003e25\u003c/sup\u003e. However, the regular microstructures are prone to reaching their deformation limits under high pressure, leading to a sharp decrease in sensor sensitivity at high pressures\u003csup\u003e26\u0026ndash;30\u003c/sup\u003e. In real applications, a large pre-load stress is required in wearable electronics to make the close contact between the sensor and skin. The pre-stress from tightening the wristband or patch will significantly reduce the accuracy of the sensor in detecting pulse wave signals\u003csup\u003e31\u003c/sup\u003e. In addition, the gaps in the interface between the sensor and the skin could cause incomplete adhesion\u003csup\u003e32, 33\u003c/sup\u003e, which results in non-effective transformation of faint pressure to the sensor's active detection area, leading to a low SNR in the output signals. Therefore, improving the device's sensitivity to weak pulse wave via interface engineering is also an urgent demand\u003csup\u003e34, 35\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this work, we enhance the detection precision of pulse waves by triboelectric sensor through optimizing the stress transfer path and distribution at both the triboelectric interface and the sensor-skin interface. The piezo-frustums are used to fill the gaps at the sensor-skin interface, providing more pathways for the stress generated by pulse beats and simultaneously generate the piezoelectric charges. Through the mechano-electric coupling enhancement effect, it endows the interfacial engineered triboelectric sensor (IETS) with a high sensitivity of 4.28 V/kPa. Additionally, the design of mountain-like microstructures on the contact-electrification interfaces offer multiple stress concentration points, broadening the high-sensitivity response range until 12 kPa. IETS can detect pressure changes caused by the accumulation of water droplet weight (about 0.05 g) under a pre-stress of 5 kPa and it is able to clearly detect the detailed characteristics of pulse waves under a pre-stress of 10 kPa as well. Moreover, the IETS has a low detection limit of 2 Pa, a quick response time of 70 ms and a wide detection range up to 110 kPa. For the applications in preventing road accidents caused by sudden cardiovascular diseases and fatigue driving, a wearable tribo-electronic system has been constructed to collect and analyze the pulse wave signals of drivers in real-time. With the assistance of machine learning algorithms, the physiological data obtained from IETS can be effectively employed to assess the driver\u0026rsquo;s health and fatigue status.\u003c/p\u003e"},{"header":"2. Results and discussion","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Structure and working mechanisms of the interfacial engineering-based triboelectric sensor (IETS)\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDuring ventricular systole, the ejection of blood from the heart into the central and peripheral arteries induces a pulse wave. The real-time monitoring of mechanical information of pulse waves, such as amplitude and frequency, by using wearable sensors can enable applications such as early warning of cardiovascular diseases and assessment of fatigue states. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and Supplementary Fig.\u0026nbsp;1, by integrating triboelectric sensors into the wristband of a watch, the mechanical signals of pulse beats can be directly converted into electrical signals for output. After signal processing and Bluetooth module on the front side of the wristband, high-quality pulse wave signals will be sent to a mobile APP for real-time display. Machine learning algorithms in the back-end, by extracting and calculating the characteristic values of the pulse wave signals, can achieve the monitoring of the user\u0026rsquo;s cardiovascular health and fatigue state. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb illustrates the structure and equivalent circuit diagram of IETS. The device is composed of a triboelectric sensor with mountain-like triboelectric interface and a piezoelectric sensor with pillar-like sensor-skin interface. By sharing a common electrode to integrate these two kinds of sensors, it realizes aggregating charges on the common electrode from two sensors for amplifying the electrical signal. The working principle of the IETS is depicted in Supplementary Fig.\u0026nbsp;2. The triboelectric pressure sensor at the lower section is endowed with a specially designed mountain-like microstructures (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec), which is constructed by the partial stacking of two conical microstructures with varying heights. The working mechanism of the triboelectric sensor under different pressures is shown in Supplementary Fig.\u0026nbsp;3\u0026ndash;5. Supplementary Note 1 provides a detailed discussion of the sensing mechanism of the triboelectric sensor under different pressures. The upper part of the IETS features a piezoelectric sensor whose primary component is a polymer-based piezoelectric material (PVDF) with frustums microstructure (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). The lower electrode of the piezoelectric sensor also serves as the upper electrode of the triboelectric sensor and the electrical coupling enhances the output signals. To minimize the impact of external factors, such as body sweat and wear, on the device's performance, a polymer protective film has been applied to the surface of the piezoelectric sensor's upper electrode through a spray coating process.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee presents a photograph of a wearable tribo-electronic system designed for driver fatigue and health monitoring. The exterior of the wristband is equipped with a flexible circuit board for signal processing and data transmission, while the interior of the wristband is secured with IETS. From a micro perspective, the human wrist is not an absolute plane. When the sensor contacts with the wrist, the traditional sensor with a smooth outer surface encounters recessed regions, preventing it from closely adhering to the skin, as depicted in left panel of Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef. The weak pulsation cannot effectively transfer mechanical energy to the device at these recessed interfaces, resulting in a low signal-to-noise ratio for the sensor. Additionally, the triboelectric interface with a regular structure tends to quickly reach its deformation limit under the action of external forces, leading to a rapid decline in the sensitivity of the triboelectric sensor in high pressure range. This work optimizes the transfer and distribution of stress through interface microstructures (right panel of Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef), achieving precise detection of pulse waves by IETS even under a pre-stress. The incorporation of the piezoelectric sensor not only enhances the output signal but also makes a significant positive contribution in terms of mechanical properties. Pillar-like microstructures, fabricated by a laser demolding technique, can closely adhere to the skin under the pre-tension of the wristband (Supplementary Fig.\u0026nbsp;5). These microstructures efficiently transfer the faint mechanical energy from pulse beats to the main body of IETS, generating a distinct electrical signal. Moreover, the piezoelectric microstructure themselves generate charge through the piezoelectric effect, serving dual roles in stress transfer and electrical signal enhancement. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg clearly illustrates the stress distribution of the device with and without the piezo-frustums microstructures. In areas where the wrist is recessed, stress cannot be effectively transmitted to the triboelectric sensor. However, with the presence of piezo-frustums microstructures, stress is efficiently conveyed to the sensor. For the triboelectric sensor, the larger the effective contact area, the higher for the output signal. Depressions at the skin-device interface leads to a reduction in the effective working area of the triboelectric sensor. The introduction of piezo-frustums microstructures allows for the effective transmission of pulse-induced stress at recessed areas, thereby increasing the effective working area of the triboelectric sensor. The triboelectric interface with mountain-like microstructures endows the sensor with high sensitivity under high pressure through multiple stress concentration points. Under a low pressure, the deformation is predominantly localized in the taller peak, which ensures the device's high sensitivity for tiny pressure. With the application of a preload, the deformation capacity of the taller peaks is constrained after compression. The tips of the secondary peaks can provide the necessary deformation under this condition, ensuring the device's high sensitivity under a greater pressure (Fig. S7, Supporting Information).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Mechanism of the laser-based microstructure fabrication method\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn this work, we propose a method for preparing surface microstructures on the interface of polymer materials by using a CO\u003csub\u003e2\u003c/sub\u003e laser to pattern etch PMMA substrate. Herein, the formation mechanism of the microstructures is further explained. The microscopic process of the CO\u003csub\u003e2\u003c/sub\u003e laser acting on the PMMA substrate is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea. According to the keyhole model, when the energy of the laser is absorbed by PMMA, a high temperature is instantly generated internally, as illustrated in Supplementary Note 2. When the temperature is higher than the vaporization point of PMMA, the intermediate PMMA vaporizes and evaporates, leaving cavities in the substrate\u003csup\u003e36\u0026ndash;38\u003c/sup\u003e. Around the cavity, since the temperature is lower than the vaporization point of PMMA, the PMMA becomes a liquid molten pool. When the laser is turned off or moves away, the molten pool cools and becomes solid PMMA, leaving the middle hole structure (Supplementary Movie 1). The energy of the light beam emitted by the laser generally obeys a Gaussian distribution; thus the energy absorbed by PMMA also shows a Gaussian distribution in the irradiated area by the laser. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb shows the simulation and experimental results of the cavity depth in the PMMA substrate under different laser power conditions. When the distance between the laser head and the PMMA is fixed, the higher the laser power is, the deeper the cavity depth in the substrate. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec depicts the simulated temperature distribution in the substrate due to the Gaussian distribution of laser energy, resulting in a conical cavity morphology in PMMA. This enables indirect control of cone microstructure sizes by adjusting laser power to manipulate the temperature field. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed presents the relationship between the laser power and height of the conical microstructures. The higher the laser power is, the higher the height of the conical microstructures. Additionally, the conical microstructures created by laser etching are highly consistent due to the constancy of the laser power, as shown in Supplementary Fig.\u0026nbsp;8. Herein, a triboelectric layer with cone microstructures (width of 500 \u0026micro;m and height of 600 \u0026micro;m) is chosen as an example to analyze the deformation process under pressure. Supplementary Fig.\u0026nbsp;9 shows the deformation process of microstructures under certain pressures through simulation and experimental pressing tests. As the pressure increases, the vertical compression deformation of the cone microstructures increases. Due to the specific stress concentrations of the microstructures, the device deforms greatly, even under a low pressure. Different microstructures possess distinct stress concentration effects, thereby endowing the sensor with diverse sensing capabilities.\u003c/p\u003e \u003cp\u003eBy designing various etching patterns, microstructures with different morphologies can be obtained. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee and Supplementary Fig.\u0026nbsp;10, when the laser etching pattern consists of two intersecting circles with one circle etched at a low power, mountain-like cavities with two apexes can be attained in the PMMA mold. In the subsequent replica molding process, the cavities can be filled with silicone rubber, thereby obtaining a mountain-shaped surface microstructure on the cured silicone rubber layer. By adjusting the laser power and the distance between the laser head and substrate, the mountain-like microstructure can exhibit the same height as the conical microstructure (Supplementary Fig.\u0026nbsp;11). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef presents a comparison of the displacement of the two types of microstructures under the same pressure through simulation. The mountain-like microstructure exhibits approximately 150 \u0026micro;m deformation under the same pressure, which is greater than that of the conical microstructure with same bottom diameter and same height. The outcome is mainly from the smaller volume of the mountain-like microstructure comparing to the conical microstructures of the same size to resist the applied external force. In addition, it has two peak structures at the top, which has a stronger stress concentration effect than the conical structure. This characteristic allows the microstructure to keep a large deformation capacity under high pressure without quickly reaching deformation saturation. Having greater deformation under high pressure implies that sensors with mountain-like microstructures maintain high sensitivity even under a pre-load stress. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg shows the potential for the large area fabrication of the mountain-like microstructures based on laser-etching processing, which also exhibits the capability for mass production of the IETS (Supplementary Figs.\u0026nbsp;12 and 13). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh shows the stress‒strain curves of different microstructures, further illustrating that the mountain-like microstructure can experience relatively great deformation under a large pressure. This result provides a mechanical basis for improving the sensitivities of triboelectric sensors under a prestress. To investigate the contribution of various interface structures to the output signal, the charge output of three kinds of devices is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ei. A smooth acrylic plate was selected as the contact interface to avoid measurement errors caused by surface roughness. The test results indicate that the output signal is primarily provided by the TENG in the lower half of IETS. Concurrently, the piezoelectric micro-columns in the upper half also offer a portion of the electrical output, which is coupled with the signal of the TENG to enhance the signal-to-noise ratio.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Sensing performance of IETS\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn order to authentically simulate the device's response to pressure in a wearable scenario, the surface of a silicone rubber pad is etched using a laser to mimic the irregular curvature and roughness characteristic of the skin surface. For the testing procedure, the rough side of the silicone rubber pad is brought into close contact with the sensor, while a pressure gauge is utilized to exert force on the smooth side of the silicone rubber pad. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, the sensitivities of sensor with piezo-frustums microstructures are higher than those of devices with mountain-like and cone microstructures but without piezo-frustums. This is because the outer surface of the sensor without piezo-frustums is a smooth plane, when in contact with the roughness silicone pad, stress can be only transmitted through points of partial contact. This results in a smaller effective contact area at the triboelectric interface, thereby reducing the sensitivity of the triboelectric sensor. For IETS with both piezo-frustums and mountain-like microstructure, it exhibits the highest sensitivity of 4.28 V/kPa within the pressure range of 0\u0026ndash;12 kPa and retains a high sensitivity of 0.18 V/kPa even under the pressure over 100 kPa. The high sensitivity of IETS can be attributed to two aspects: the optimization of interfacial stress and the enhanced output signals by the coupling of piezoelectric and triboelectric effects. Interestingly, among devices with piezo-frustums, those with a mountain-like microstructure at the triboelectric interface exhibit higher sensitivity and a broader detection range under low pressure compared to those with conical structures. This is due to the stress concentration effect of the peak-like microstructures, which can produce large deformation, resulting in significant changes in contact area and gap distance. Overall, IETS with piezo-frustums and mountain-like microstructures have higher sensitivity and wider detection range, making it more suitable in the application that pre-load stress is required.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, as the pressure increases, the output voltage of the IETS also increases, showing a good correlation. Supplementary Fig.\u0026nbsp;14 shows the output response of device with mountain-like microstructures but without piezo-frustums under an increasing pressure. It also proves the ability of triboelectric sensor to distinguish pressure amplitude. To test the limit of detection (LOD) of the IETS at low pressures, sand paper with different sizes are used as pressure sources on the surface of the sensor. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, the test results show that when the sand paper weight is reduced to 4.19 mg, the IETS reaches its response limit, corresponding to an LOD of 2 Pa. To further test the device response to low pressures, different numbers of water drops (around 50 mg of each drop) are continuously dripped on a weight surface with 5 kPa at the same height. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed shows that the IETS is highly sensitive to low pressures even under a pre-load stress of 5 kPa and it can clearly detect the dripping and accumulation of water drops on the weight surface with a fast response time of 70 ms (Supplementary Fig.\u0026nbsp;15). In addition, the durability of the sensor is an important indicator affecting its actual service life. As shown in Supplementary Fig.\u0026nbsp;16, when a cyclic pressure of 2 kPa is applied to the surface of the sensor, the device does not show obvious signal attenuation after 5000 cycles, which indicates that the device has excellent durability. Moreover, the IETS can monitor static pressure of 2 kPa in real time and the static drift within 45 min is less than 15%, as shown in Supplementary Fig.\u0026nbsp;17. Upon comprehensive analysis, the interfacial stress engineering at the sensor-skin interface and the triboelectric interface significantly enhances sensing performance. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee presents a performance comparison between IETS and recently reported triboelectric and piezoelectric sensors in terms of sensitivity and detection range\u003csup\u003e39\u0026ndash;43\u003c/sup\u003e. The reported IETS in this work possesses both high sensitivity and a broad detection range within a pressure range of less than 12 kPa. Although some previously reported triboelectric sensors have achieved higher sensitivity through stress concentration strategies, their high-sensitivity detection range is limited to below 7 kPa. By designing a mountain-like microstructure, the sensitivity and detection range of the sensor in this work are effectively ensured.\u003c/p\u003e \u003cp\u003eThe pulse wave signal is an important indicator for cardiovascular health status and driver fatigue monitoring. The signal quality of the pulse wave directly determines the accuracy of the diagnosis. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef, real-time monitoring characteristics of pulse waves from the same volunteer under a preload of 10 kPa during the same period by sensors with different microstructure types are compared. The results show that the IETS responds sensitively to pulse waves, and its output pulse wave signal has three obvious characteristic peaks. In contrast, the sensor with mountain-like microstructures but without piezo-frustums can just detect two characteristic peaks of the pulse wave. The device with 600 \u0026micro;m conical microstructures can just detect the main peak of the pulse wave, while the device without microstructures cannot effectively detect the pulse wave signal. The results for pulse wave monitoring demonstrate that the piezo-frustums and mountain-shaped microstructures can effectively improve the sensitivity of the triboelectric sensor and thus the IETS with piezo-frustums and mountain-shaped microstructures are selected in subsequent actual applications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Applications of IETS in monitoring driver physiological signals and behaviors\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo achieve real-time monitoring of driver\u0026rsquo;s health and fatigue state, the system has to obtain the pulse wave parameter of the driver in real time. Herein, the IETS is integrated into the strap of the smart watch and connected to the driver mobile terminal via a Bluetooth module to acquire and process pulse wave data. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, the sensor is integrated into a specific part of the strap so that the sensor is in close contact with the wrist artery when the watch is properly worn. When blood flow causes pulse beats in the artery, the highly sensitive sensor can capture these low-pressure signals in real time and convert them into electrical signals. The edge data processing module and Bluetooth module can process and send pulse wave data to the user mobile phone in real time. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb illustrates the schematic diagram of the real-time wireless pulse wave monitoring system based on IETS. Due to the low amplitude and frequency (\u0026lt;\u0026thinsp;5 Hz) values of pulsation signals, this system is designed with unique amplification and filtering circuits. The pulse wave signal with a high signal-to-noise ratio is subsequently collected by an analog-to-digital converter (ADC) for initial shaping and feature extraction. The signal is then transmitted via Bluetooth to a mobile device for further analysis. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec presents the physical components of the hardware section of the system. Electronic components are integrated on a flexible circuit board, enabling the hardware system to bend and accommodate the users with various wrist shapes. When the complete hardware system is affixed to the surface of a cylindrical object with a radius of 2.5 cm, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed, the system can continue to function as intended. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee displays the waveform of the pulse wave signal detected by IETS after undergoing analog signal processing. Evidently, the signal exhibits a high signal-to-noise ratio and can distinctly reflect information from multiple characteristic peaks. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef shows the Bluetooth connection interface on the mobile phone to prove the feasibility of the communication module. After the mobile phone and the sensor module successfully connect via Bluetooth, the sensor module can send preprocessed data to the mobile phone in real time and display the pulse waveform on the APP interface. This process provides a solid foundation for implementing subsequent eigenvalue extraction, frequency domain signal conversion and algorithms process. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg depicts the physical layout of the hardware system after the wristwatch strap is removed. Apparently, the sensor and flexible circuit board are installed on opposite sides of the strap that are interconnected by wires. Once the sensor and flexible circuit board are integrated with the wristwatch strap, the real-time collection of the driver pulse wave signal can be achieved, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eh. Assessing the driver fatigue level based on the pulse wave signal typically relies on indicators of heart rate variability (HRV). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ei shows a conceptual diagram for calculating HRV. The core of HRV calculation is to obtain the time difference between two consecutive pulse beats. In practical measurement scenarios, the real-time collected pulse wave signal is a temporal signal. To effectively extract HRV parameters, the pulse wave signal is generally transformed into the frequency domain through fast Fourier transformation (FFT), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ej. In the frequency domain signal, the system can acquire parameters for each characteristic frequency and determine the volunteer fatigue state based on the proportion of these parameters\u003csup\u003e44\u0026ndash;46\u003c/sup\u003e. As depicted in Fig. S18, noticeable distinctions in characteristic frequency parameters are observed for the same volunteer between wakeful morning and drowsy states. Additionally, based on the feature values in the temporal signal of the pulse wave, the system can assess the user cardiovascular health status. On this basis, we develop a driver fatigue and health status monitoring system, as demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ek and Supplementary Fig.\u0026nbsp;19. By processing and analyzing the temporal and frequency domain signals of raw data, this system enables real-time monitoring and early warning of driver fatigue and physical health status (Supplementary Movie 2).\u003c/p\u003e \u003cp\u003eIn addition, when a driver is tired, they may display irregular behaviors such as frequent abrupt braking and yawning due to distractions. The wearable sensors can be utilized to monitor these driver behaviors, providing an alternative method for assessing fatigue. On this basis, IETSs are wore on the driver face or attached on driving cab components, such as the accelerator and brake pedals, for the real-time collection of driver behaviors, as shown in Supplementary Fig.\u0026nbsp;20. Due to the high sensitivity of the IETS in a wide pressure range, the sensor can accurately detect both low pressure signals, such as eye movements, and large pressure signals, such as stepping on the brake pedals and the driver leaving the seat. As shown in Supplementary Fig.\u0026nbsp;21a, when a IETS is attached to the corner of the eye, blinking causes slight pressure changes due to contraction of the eye muscles. The sensor will clearly capture the driver blinking action. In a normal state, the frequency of driver blinking is maintained near a fixed value. In a state of fatigue, the driver usually experiences a period of first unblinking and then rapid blinking. The IETS can clearly monitor the driver blinking signal for the evaluation of the driver fatigue state. Similarly, the sensor can be attached to the driver\u0026rsquo;s mouth corner to monitor yawning. As shown in Supplementary Fig.\u0026nbsp;21b, when the driver is in a state of fatigue, yawning causes the mouth corner muscles to contract for usually 2\u0026ndash;5 seconds, which is significantly different from normal talking and chewing actions. Therefore, monitoring driver yawning behaviors through the sensors can assist in evaluating the state of driver fatigue. In addition, installing sensors on the accelerator and brake pedals can also monitor driver actions in real time. As shown in Supplementary Fig.\u0026nbsp;21c, the driver\u0026rsquo;s behavioral data acquired by the IETS installed on the accelerator pedal show that when the vehicle starts, the output voltage of the sensor first slowly increases and then remains stable. When overtaking is needed, the force applied by the driver foot increases, the output voltage of the sensor increases as well. Similarly, the IETS attached to the brake pedal can provide real-time feedback on the changes in the force applied by the driver foot when braking. When the driver is in a state of fatigue, emergencies cause the driver to brake frequently and suddenly, and the sensor outputs sudden braking signals, as shown in Supplementary Fig.\u0026nbsp;21d.\u003c/p\u003e \u003cp\u003eRegarding to driver safety, IETSs can be integrated into seat belt buckles or cushions to determine whether the driver has fastened the seat belt or left the seat, respectively, by detecting changes in pressure (Supplementary Movie 3). As shown in Supplementary Fig.\u0026nbsp;21e, when the seat belt is fastened, the sensor in the buckle receives compression and outputs a continuous output signal. When the seat belt is released, the signal returns to the initial state. Supplementary Fig. S21f shows the output signal of the sensor under the seat cushion after being pressed by the driver weight. Upon the parallel integration of 12 IETSs to form an array device, the quantity of sensors under compression can be detected by assessing the magnitude of the output signal. This method allows for determination of the driver's seating posture (Supplementary Fig.\u0026nbsp;22). The IETS can accurately detect whether the driver has seat or left the seat. These applications show that the sensor has an ultrawide detection range from low pressures, such as pulse waves, to large pressures, such as body weight.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Deep learning-enabled cardiovascular monitoring and driver fatigue monitoring\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBiological signs can reliably indicate early fatigue and prevent accidents from occurring. Cardiovascular signals, such as electrocardiography (ECG) and photoplethysmography (PPG), can accurately monitor fatigue status. However, due to the limited viability of invasive sensors for real-time driver wear and the low sensitivity of noninvasive sensors influenced by humans or the environment, normal driver behavior and fatigue monitoring accuracy are compromised. Due to the high sensitivity in detecting imperceptible low pressures, the IETS can be used to monitor weak pulse waves to continuously assess cardiovascular and fatigue conditions. Moreover, a combination of artificial intelligence (AI), such as deep learning, can improve the accuracy and actionability of new sensing devices, ultimately facilitating real-time personal identification and driver fatigue (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). By comparing the voltage signal obtained by IETS with the typical arterial pulse wave in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, we find that the voltage signal matches the typical arterial pulse waveform and can detect low intra-arterial blood pressure oscillations with systolic, reflected and diastolic peaks (P\u003csub\u003e1\u003c/sub\u003e, P\u003csub\u003e2\u003c/sub\u003e and P\u003csub\u003e3\u003c/sub\u003e). Therefore, the cardiovascular condition and degree of arteriosclerosis can be assessed by comparing the measured results with references.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec and Supplementary Fig.\u0026nbsp;17 show the reflected wave transit time (RWTT), systolic\u0026ndash;diastolic time (PPT), upstroke time (UT) and left ventricular ejection time (LVET) from the continuous 20 voltage signal cycles, all of which are correlated with the reported reference values of a healthy individual. Cardiovascular conditions are related to heart rate (HR) and HRV. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed reflects a mapped scatter points plot of pulse interval (Ti) to reflect the HRV when the subject is nonfatigued (HRV\u0026thinsp;=\u0026thinsp;8.9) and fatigued (HRV\u0026thinsp;=\u0026thinsp;5.4); HRV is a valuable predictor of sudden cardiac death and arrhythmic events. The lower the HRV value is, the more likely the subject is to suffer from acute myocardial infarction and arrhythmias, and the stronger the need to relax and rest in a timely manner. Moreover, the frequency\u0026ndash;time distribution chart (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee) illustrates that when the subject is fatigued, the frequency change over time is unstable, aiding in demonstrating the instability of heart rate variability during fatigue. Heart rate decreases significantly in the fatigued state of the subject, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef\u0026ndash;g and i\u0026ndash;j. The heart rate and HRV index in pulse wave signals are crucial physiological indicators for evaluating fatigue. However, this method requires long-term pulse wave data, which cannot monitor and assess the driver fatigue status and alert the driver in real time. Thus, in this study, the pulse wave data are divided into time lengths of 750 ms per sample, and the short-term signals are classified by a one-dimensional (1d) convolutional neural network (CNN)-based method for recognizing driver behavior and fatigue. To reduce the effects of noise, baseline drift and environment, the collected data are preprocessed (wavelet noise reduction, R-peak splitting and normalization, respectively). The detailed framework and parameters used to construct the CNN model are labeled in Supplementary Table\u0026nbsp;1. Supplementary Fig.\u0026nbsp;18 supplies the typical pulse signals of the 5 different subjects. Through data collection and signal preprocessing, 625 sample data points with each 750 data points constitute dataset 1 (80% training set and 20% test set). The average recognition accuracy is 94% (Supplementary Fig.\u0026nbsp;19a), providing great potential for high-accuracy behavioral recognition based on deep learning (DL) prediction. After training in the 1d-CNN model with 80 training epochs, the maximum accuracy is achieved, and the dropout layer avoids overfitting, as shown in Supplementary Fig.\u0026nbsp;19b. In contrast to unprocessed data and support vector machine (SVM) models, which require pre-extracted features, the end-to-end CNN model exhibits increased accuracy and reduced overall complexity in Supplementary Fig.\u0026nbsp;19c.\u003c/p\u003e \u003cp\u003ePulse signal-based identification technology avoids the drawbacks of traditional identity authentication and is less susceptible to copying and counterfeiting. Combining it with deep learning networks increases the convenience and effectiveness of identification. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eh and k, the morphology of the pulse changes after fatigue, with reflected and diastolic peaks decreasing relative to the systolic peaks. By merging the data of the different states for several days, dataset 2 is trained and validated by the deep learning network described above. The average accuracy reaches 98% for one subject, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003el. Fatigue classification based on short-time pulse signals can achieve the real-time and accurate detection of driver fatigue, providing a certain guarantee for safe driving.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eIn conclusion, this work presents an interfacial engineering-based triboelectric sensor (IETS) to realize the precise pulse wave detection. The integration of piezo-frustums at the sensor-skin interface not only provides the pathways for stress transfer but also facilitates the generation of piezoelectric charges. Through the mechano-electric coupling effect, the sensitivity of IETS has been improved by 5 times compared to devices without piezo-frustums, reaching 4.28 V/kPa. Additionally, the construction of mountain-like microstructures at triboelectric interface has further expanded the sensor's high-sensitivity response range to 12 kPa, enabling the clear detection of pulse wave characteristics under a pre-stress of 10 kPa. Simultaneously, IETS has a low detection limit of 2 Pa, a quick response time of 70 ms and a wide detection range reaching 110 kPa. Furthermore, the development of a wearable tribo-electronic system, supported by machine learning algorithms, allows for real-time collection and analysis of driver's pulse wave signals, providing a robust tool for assessing health and fatigue status, thereby contributing to the prevention of road accidents associated with cardiovascular diseases and fatigue driving.\u003c/p\u003e"},{"header":"4. Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Fabrication of the interfacial engineering-based triboelectric sensor (IETS)\u003c/h2\u003e \u003cp\u003eThe fabricated IETS consists of a piezoelectric nanogenerator-based sensor (PENG) and a triboelectric nanogenerator-based sensor (TENG). The TENG is composed of upper and bottom electrodes and a microstructured triboelectric layer. The bottom electrode is formed by screen printing silver paste (01 L-2211D, Sryed Paste) on a polyethylene terephthalate (PET) substrate. Specifically, through the extrusion of the scraper, the silver paste passes through the customized pattern mesh plate to form a conductive circuit on the PET surface and is then cured at 120\u0026deg;C to fabricate flexible electrodes. To obtain the microstructured triboelectric layer, components A and B of Ecoflex (00\u0026ndash;30, Smooth-On) are mixed evenly in a 1:1 ratio and spin-coated on the PMMA mold. Then the layer is left in a vacuum drying oven for approximately 2 h until it is fully cured. The PMMA mold uses the Gaussian distribution of laser energy to form a microstructure pattern inside the PMMA. By adjusting the power and motion modes of the laser machine, microstructures with different sizes and shapes can be obtained. By sequentially bonding the lower electrode, triboelectric layer and upper electrode with 3 M double-sided tape (3MVHB4905, 3 M), a triboelectric pressure sensor can be obtained. Similarly, triboelectric sensors with different shapes and sizes can be prepared by designing different electrode patterns.\u003c/p\u003e \u003cp\u003eThe piezo-pillar microstructures are also fabricated using a demolding method. To acquire high-quality microstructures, a PMMA mold with a cylindrical structure is first etched using a laser. Subsequently, a second mold is formed by casting PDMS over the PMMA mold. PVDF (Polyk-Piezo) powder is dissolved in acetone at a weight ratio of 3:7, then poured into the PDMS mold to replicate the cylindrical piezoelectric microstructures. Silver electrodes are fabricated on their base using a screen printing method. The piezoelectric performance of the material is enhanced through high-voltage polarization, followed by the sputtering of a gold electrode layer on the upper surface using a magnetron sputtering process. Finally, after the integration with the triboelectric sensor, a polymer layer is sprayed as a protective coating.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Performance characterization\u003c/h2\u003e \u003cp\u003eFor the electrical output measurement of the IETS, an external contact force is applied by a commercial linear mechanical motor (Winnemotor, WMUC512075-06-X) and the applied force is detected by digital force measurement (Chatillon, DFS Ⅱ). A programmable electrometer (Keithley model 6514) is used to test the electrical output signal. The triboelectric potential distribution simulation and mechanical deformation are conducted by COMSOL Multiphysics software.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eData and materials availability\u003c/p\u003e\n\u003cp\u003eAll data supporting this study and its findings are available within the article, its Supplementary Information and associated files. The source data are available from the corresponding author upon reasonable request.\u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eH. L. and Z. W. conceived the idea, H. L., X. Q., and G. S. analyzed the data and contributed to writing the paper. P. H. and G.S. designed the microstructure of the sensors. W. W., J. Y. and L. X. developed and fabricated the circuit board. L. X., and B. L. constructed the app and machine learning algorithm. H. L., X. Q., and G. S. conducted the experiments to fabricate the sensors. H. L., Y. L., E. G. L. and X. T. created and optimized the figures, tables, and videos. C. Z., X. S. and Z. W. revised the manuscript. All authors collectively analyzed the results and implications and provided comments on the manuscript at all stages.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Natural Science Foundation of China (No. 62174115, No. 62273247, No. U21A20147), Natural Science Foundation of the Jiangsu Higher Education Institutions of China Program (No. 19KJB510059), Suzhou Science and Technology Development Planning Project: Key Industrial Technology Innovation (No. SYG202009, No. SYG201924), Jiangsu Key Laboratory for Carbon-based Functional Materials \u0026amp; Devices, Soochow University (No. KJS2157), XJTLU Research Development Fund (No. RDF-17-01-13, No. RDF-21-02-068 and No. RDF-22-01-110) and SIP AI innovation platform (No.YZCXPT2022103). This work was partially supported by the Collaborative Innovation Center of Suzhou Nano Science \u0026amp; Technology, 111 Project, Joint International Research Laboratory of Carbon-Based Functional Materials and Devices, XJTLU AI University Research Centre, Jiangsu Province Engineering Research Centre of Data Science and Cognitive Computation at XJTLU.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFeng, S., Sun, H., Yan, X. et al. Dense reinforcement learning for safety validation of autonomous vehicles. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e615\u003c/strong\u003e, 620\u0026ndash;627 (2023).\u003c/li\u003e\n\u003cli\u003eSpatz, E. S., Ginsburg, G. S., Rumsfeld, J. S. \u0026amp; Turakhia, M. P. Wearable Digital Health Technologies for Monitoring in Cardiovascular Medicine. \u003cem\u003eN. Engl. J. Med.\u003c/em\u003e \u003cstrong\u003e390\u003c/strong\u003e, 346\u0026ndash;356 (2024).\u003c/li\u003e\n\u003cli\u003ePham, T., Lau, Z. J., Chen, S. H. A. \u0026amp; Makowski, D. Heart rate variability in psychology: A review of HRV indices and an analysis tutorial. \u003cem\u003eSensors\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 3998 (2021).\u003c/li\u003e\n\u003cli\u003eBayoumy, K. \u003cem\u003eet al.\u003c/em\u003e Smart wearable devices in cardiovascular care: where we are and how to move forward. \u003cem\u003eNat. Rev. Cardiol.\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 581\u0026ndash;599 (2021).\u003c/li\u003e\n\u003cli\u003eAtes, H. C. \u003cem\u003eet al.\u003c/em\u003e End-to-end design of wearable sensors. \u003cem\u003eNat. Rev. Mater.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 887\u0026ndash;907 (2022).\u003c/li\u003e\n\u003cli\u003eKim, S. \u003cem\u003eet al.\u003c/em\u003e Wearable and implantable bioelectronics as eco‐friendly and patient‐friendly integrated nanoarchitectonics for next‐generation smart healthcare technology. \u003cem\u003eEcoMat\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, e12356 (2023).\u003c/li\u003e\n\u003cli\u003eLee, H. \u003cem\u003eet al.\u003c/em\u003e Toward all-day wearable health monitoring: An ultralow-power, reflective organic pulse oximetry sensing patch. \u003cem\u003eSci. Adv.\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, eaas9530 (2018).\u003c/li\u003e\n\u003cli\u003eChen, S. \u003cem\u003eet al.\u003c/em\u003e Flexible Wearable Sensors for Cardiovascular Health Monitoring. \u003cem\u003eAdv. Healthc. Mater.\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 2100116 (2021).\u003c/li\u003e\n\u003cli\u003eZhao, L. \u003cem\u003eet al.\u003c/em\u003e Emerging sensing and modeling technologies for wearable and cuffless blood pressure monitoring. \u003cem\u003enpj Digit. Med.\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 93 (2023).\u003c/li\u003e\n\u003cli\u003eIqbal, S. M. A., Mahgoub, I., Du, E., Leavitt, M. A. \u0026amp; Asghar, W. Advances in healthcare wearable devices. \u003cem\u003enpj Flex. Electron.\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 9 (2021).\u003c/li\u003e\n\u003cli\u003eMeng, K. \u003cem\u003eet al.\u003c/em\u003e Wearable Pressure Sensors for Pulse Wave Monitoring (Adv. Mater. 21/2022). \u003cem\u003eAdv. Mater.\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 2270158 (2022).\u003c/li\u003e\n\u003cli\u003eGuan, Y.-S. \u003cem\u003eet al.\u003c/em\u003e Elastic electronics based on micromesh-structured rubbery semiconductor films. \u003cem\u003eNat. Electron.\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 881\u0026ndash;892 (2022).\u003c/li\u003e\n\u003cli\u003eLi, J. \u003cem\u003eet al.\u003c/em\u003e Thin, soft, wearable system for continuous wireless monitoring of artery blood pressure. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 5009 (2023).\u003c/li\u003e\n\u003cli\u003eTian, G. \u003cem\u003eet al.\u003c/em\u003e Hierarchical Piezoelectric Composites for Noninvasive Continuous Cardiovascular Monitoring. \u003cem\u003eAdv. Mater.\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 2313612 (2024).\u003c/li\u003e\n\u003cli\u003eYi, Z. \u003cem\u003eet al.\u003c/em\u003e Piezoelectric Dynamics of Arterial Pulse for Wearable Continuous Blood Pressure Monitoring. \u003cem\u003eAdv. Mater.\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 2110291 (2022).\u003c/li\u003e\n\u003cli\u003eChorsi, M. T. \u003cem\u003eet al.\u003c/em\u003e Highly piezoelectric, biodegradable, and flexible amino acid nanofibers for medical applications. \u003cem\u003eSci. Adv.\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, eadg6075 (2023).\u003c/li\u003e\n\u003cli\u003eYao, C. \u003cem\u003eet al.\u003c/em\u003e Personalized Machine Learning-Coupled Nanopillar Triboelectric Pulse Sensor for Cuffless Blood Pressure Continuous Monitoring. \u003cem\u003eACS Nano\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 24242\u0026ndash;24258 (2023).\u003c/li\u003e\n\u003cli\u003ePeng, X. \u003cem\u003eet al.\u003c/em\u003e A breathable, biodegradable, antibacterial, and self-powered electronic skin based on all-nanofiber triboelectric nanogenerators. \u003cem\u003eSci. Adv.\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, eaba9624 (2020).\u003c/li\u003e\n\u003cli\u003eHui, X. \u003cem\u003eet al.\u003c/em\u003e Acoustically Enhanced Triboelectric Stethoscope for Ultrasensitive Cardiac Sounds Sensing and Disease Diagnosis. \u003cem\u003eAdv. Mater.\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 2401508 (2024).\u003c/li\u003e\n\u003cli\u003eFan, W. \u003cem\u003eet al.\u003c/em\u003e Machine-knitted washable sensor array textile for precise epidermal physiological signal monitoring. \u003cem\u003eSci. Adv.\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, eaay2840 (2020).\u003c/li\u003e\n\u003cli\u003eKhandelwal, G. \u0026amp; Dahiya, R. Self‐Powered Active Sensing Based on Triboelectric Generators. \u003cem\u003eAdv. Mater.\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 2200724 (2022).\u003c/li\u003e\n\u003cli\u003eLee, S. \u0026amp; Park, J.-W. Fingerprint-inspired triboelectric nanogenerator with a geometrically asymmetric electrode design for a self-powered dynamic pressure sensor. \u003cem\u003eNano Energy\u003c/em\u003e \u003cstrong\u003e101\u003c/strong\u003e, 107546 (2022).\u003c/li\u003e\n\u003cli\u003eLiu, W. \u003cem\u003eet al.\u003c/em\u003e Touchless interactive teaching of soft robots through flexible bimodal sensory interfaces. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 5030 (2022).\u003c/li\u003e\n\u003cli\u003eMeng, K. \u003cem\u003eet al.\u003c/em\u003e Kirigami‐Inspired Pressure Sensors for Wearable Dynamic Cardiovascular Monitoring. \u003cem\u003eAdv. Mater.\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 2202478 (2022).\u003c/li\u003e\n\u003cli\u003eZhang, Z. \u003cem\u003eet al.\u003c/em\u003e Artificial intelligence of toilet (AI-Toilet) for an integrated health monitoring system (IHMS) using smart triboelectric pressure sensors and image sensor. \u003cem\u003eNano Energy\u003c/em\u003e \u003cstrong\u003e90\u003c/strong\u003e, 106517 (2021).\u003c/li\u003e\n\u003cli\u003eYang, Y. \u003cem\u003eet al.\u003c/em\u003e Triboelectric Nanogenerator Enabled Wearable Sensors and Electronics for Sustainable Internet of Things Integrated Green Earth. \u003cem\u003eAdv. Energy Mater.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 2203040 (2023).\u003c/li\u003e\n\u003cli\u003eSun, Z., Zhu, M., Shan, X. \u0026amp; Lee, C. Augmented tactile-perception and haptic-feedback rings as human-machine interfaces aiming for immersive interactions. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 5224 (2022).\u003c/li\u003e\n\u003cli\u003eBai, N. \u003cem\u003eet al.\u003c/em\u003e A robotic sensory system with high spatiotemporal resolution for texture recognition. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 7121 (2023).\u003c/li\u003e\n\u003cli\u003eLiu, Y. \u003cem\u003eet al.\u003c/em\u003e Electronic skin as wireless human-machine interfaces for robotic VR. \u003cem\u003eSci. Adv.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, eabl6700 (2022).\u003c/li\u003e\n\u003cli\u003eShi, J. \u003cem\u003eet al.\u003c/em\u003e Embedment of sensing elements for robust, highly sensitive, and cross-talk\u0026ndash;free iontronic skins for robotics applications. \u003cem\u003eSci. Adv.\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, eadf8831 (2023).\u003c/li\u003e\n\u003cli\u003eFang, Y. \u003cem\u003eet al.\u003c/em\u003e Ambulatory Cardiovascular Monitoring Via a Machine‐Learning‐Assisted Textile Triboelectric Sensor. \u003cem\u003eAdv. Mater.\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 2104178 (2021).\u003c/li\u003e\n\u003cli\u003eLiu, X. \u003cem\u003eet al.\u003c/em\u003e Bioinspired, Microstructured Silk Fibroin Adhesives for Flexible Skin Sensors. \u003cem\u003eACS Appl. Mater. Interfaces\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 5601\u0026ndash;5609 (2020).\u003c/li\u003e\n\u003cli\u003eTang, L., Shang, J. \u0026amp; Jiang, X. Multilayered electronic transfer tattoo that can enable the crease amplification effect. \u003cem\u003eSci. Adv.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, eabe3778 (2021).\u003c/li\u003e\n\u003cli\u003eMin, S. \u003cem\u003eet al.\u003c/em\u003e Clinical Validation of a Wearable Piezoelectric Blood‐Pressure Sensor for Continuous Health Monitoring. \u003cem\u003eAdv. Mater.\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, 2301627 (2023).\u003c/li\u003e\n\u003cli\u003ePark, J. \u003cem\u003eet al.\u003c/em\u003e Frequency-selective acoustic and haptic smart skin for dual-mode dynamic/static human-machine interface. \u003cem\u003eSci. Adv.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, eabj9220 (2022).\u003c/li\u003e\n\u003cli\u003eCao, X., Wallace, W., Poon, C. \u0026amp; Immarigeon, J.-P. Research and Progress in Laser Welding of Wrought Aluminum Alloys. I. Laser Welding Processes. \u003cem\u003eMater. Manuf. Process.\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 1\u0026ndash;22 (2003).\u003c/li\u003e\n\u003cli\u003eSvenungsson, J., Choquet, I. \u0026amp; Kaplan, A. F. H. Laser Welding Process \u0026ndash; A Review of Keyhole Welding Modelling. \u003cem\u003ePhys. Procedia\u003c/em\u003e \u003cstrong\u003e78\u003c/strong\u003e, 182\u0026ndash;191 (2015).\u003c/li\u003e\n\u003cli\u003eYang, R. \u003cem\u003eet al.\u003c/em\u003e Author Correction: Iontronic pressure sensor with high sensitivity over ultra-broad linear range enabled by laser-induced gradient micro-pyramids. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 3913 (2023).\u003c/li\u003e\n\u003cli\u003eZu, L. \u003cem\u003eet al.\u003c/em\u003e Multiangle, self-powered sensor array for monitoring head impacts. \u003cem\u003eSci. Adv.\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, eadg5152 (2023).\u003c/li\u003e\n\u003cli\u003eXie, L. \u003cem\u003eet al.\u003c/em\u003e Ultrasensitive Wearable Pressure Sensors with Stress‐Concentrated Tip‐Array Design for Long‐Term Bimodal Identification. \u003cem\u003eAdv. Mater.\u003c/em\u003e 2406235 (2024).\u003c/li\u003e\n\u003cli\u003eHa, M. \u003cem\u003eet al.\u003c/em\u003e Skin-Inspired Hierarchical Polymer Architectures with Gradient Stiffness for Spacer-Free, Ultrathin, and Highly Sensitive Triboelectric Sensors. \u003cem\u003eACS Nano\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 3964\u0026ndash;3974 (2018).\u003c/li\u003e\n\u003cli\u003eLu, J. \u003cem\u003eet al.\u003c/em\u003e A Biodegradable and Recyclable Piezoelectric Sensor Based on a Molecular Ferroelectric Embedded in a Bacterial Cellulose Hydrogel. \u003cem\u003eACS Nano\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 3744\u0026ndash;3755 (2022).\u003c/li\u003e\n\u003cli\u003ePark, Y. \u003cem\u003eet al.\u003c/em\u003e Ferroelectric Multilayer Nanocomposites with Polarization and Stress Concentration Structures for Enhanced Triboelectric Performances. \u003cem\u003eACS Nano\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 7101\u0026ndash;7110 (2020).\u003c/li\u003e\n\u003cli\u003ePham, T., Lau, Z. J., Chen, S. H. A. \u0026amp; Makowski, D. Heart Rate Variability in Psychology: A Review of HRV Indices and an Analysis Tutorial. \u003cem\u003eSensors\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 3998 (2021).\u003c/li\u003e\n\u003cli\u003eGao, R. \u003cem\u003eet al.\u003c/em\u003e Study on the nonfatigue and fatigue states of orchard workers based on electrocardiogram signal analysis. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 4858 (2022).\u003c/li\u003e\n\u003cli\u003eAhmed, S. \u003cem\u003eet al.\u003c/em\u003e Noncontact assessment for fatigue based on heart rate variability using IR-UWB radar. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 14211 (2022).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"microsystems-and-nanoengineering","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"micronano","sideBox":"Learn more about [Microsystems \u0026 Nanoengineering](http://www.nature.com/micronano/)","snPcode":"41378","submissionUrl":"https://mts-micronano.nature.com/","title":"Microsystems \u0026 Nanoengineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6446990/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6446990/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate detection of arterial pulse waves is crucial for wearable warning systems but faces challenges under non-close contact or pre-stress. Here, an interfacial engineered triboelectric sensor (IETS) has been proposed to improve the detection accuracy of pulse waves. It consists of a stress-transferring sensor-skin interface with piezo-frustums array and a gradient triboelectric interface with mountain-like microstructures. The mountain-like microstructures provide stress concentration points even under a pre-stress of 10 kPa with capturing all details of the pulse waves. Additionally, the incorporation of piezo-frustums array at the sensor-skin interface not only facilitates stress transfer but also generates piezoelectric charges. Such mechano-electric coupling effect endows IETS with a high sensitivity of 4.28 V/kPa. Integrated with machine learning, a wearable system based on IETS allows for drivers' health and fatigue assessment via pulse wave analysis, offering an effective approach to prevent road accidents caused by sudden cardiovascular diseases and fatigue driving.\u003c/p\u003e","manuscriptTitle":"Optimized Stress Transfer Interfaces Enabled Wearable Nano-Electronics for Fatigue Driving Monitoring","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 06:03:59","doi":"10.21203/rs.3.rs-6446990/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-06-09T03:10:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-06-06T14:59:50+00:00","index":3,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-06-02T06:18:44+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-05-26T03:49:30+00:00","index":3,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-05-07T11:07:24+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-05-02T08:50:17+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-05-02T02:44:05+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-05-02T02:17:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-17T07:13:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-14T14:32:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Microsystems \u0026 Nanoengineering","date":"2025-04-14T14:32:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"microsystems-and-nanoengineering","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"micronano","sideBox":"Learn more about [Microsystems \u0026 Nanoengineering](http://www.nature.com/micronano/)","snPcode":"41378","submissionUrl":"https://mts-micronano.nature.com/","title":"Microsystems \u0026 Nanoengineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bc0e5aca-b951-4311-86fe-16c3ad3b4f03","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47966349,"name":"Physical sciences/Engineering/Electrical and electronic engineering"},{"id":47966350,"name":"Physical sciences/Nanoscience and technology/Nanoscale devices/Sensors"}],"tags":[],"updatedAt":"2026-03-17T07:14:50+00:00","versionOfRecord":{"articleIdentity":"rs-6446990","link":"https://doi.org/10.1038/s41378-025-01107-x","journal":{"identity":"microsystems-and-nanoengineering","isVorOnly":false,"title":"Microsystems \u0026 Nanoengineering"},"publishedOn":"2026-03-15 04:00:00","publishedOnDateReadable":"March 15th, 2026"},"versionCreatedAt":"2025-05-07 06:03:59","video":"","vorDoi":"10.1038/s41378-025-01107-x","vorDoiUrl":"https://doi.org/10.1038/s41378-025-01107-x","workflowStages":[]},"version":"v1","identity":"rs-6446990","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6446990","identity":"rs-6446990","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}