Although high-performance flexible pressure sensors have been extensively investigated in recent years owing to their diverse applications in biomedical and information technologies, fabricating ultrasensitive sensors with high pixel density based on current transduction mechanisms still remains great challenging. Herein, we demonstrate a design idea based on Fowler-Nordheim tunnelling effect for fabrication of pressure sensors with ultrahigh sensitivity and sensing density by spin-coating extremely low urchin-like hollow carbon spheres (less than 1.5 wt.%) dispersed in polydimethylsiloxane, which is distinct from the current transduction mechanisms. This sensor exhibits an ultrahigh sensitivity of 260.3 kPa⁻¹ at 1 Pa, a proof-of-concept demonstration of a high sensing density of 400 cm⁻², high transparency and temperature noninterference. In addition, it can be fabricated by an industrially viable and scalable spin-coating method, providing an efficient avenue for realizing large-scale production and application of ultrahigh sensitivity flexible pressure sensors on various surfaces and in in vivo environments.

While accurate mapping of strain distribution is crucial for assessing stress concentration and estimating fatigue life in engineering applications, conventional strain sensor arrays face a great challenge in balancing sensitivity and sensing density for effective strain mapping. In this study, we present a Fowler-Nordheim tunneling effect of monodispersed spiky carbon nanosphere array on polydimethylsiloxane as strain sensor arrays to achieve a sensitivity up to 70,000, a sensing density of 100 pixel cm⁻², and logarithmic linearity over 99% within a wide strain range of 0% to 60%. The highly ordered assembly of spiky carbon nanospheres in each unit also ensures high inter-unit consistency (standard deviation ≤3.82%). Furthermore, this sensor array can conformally cover diverse surfaces, enabling accurate acquisition of strain distributions. The sensing array offers a convenient approach for mapping strain fields in various applications such as flexible electronics, soft robotics, biomechanics, and structure health monitoring.

Nowadays, cardiovascular disease is still one of the primary diseases that limit life expectation of humans. To address this challenge, this work reports an Internet of Medical Things (IoMT)-based cardiovascular healthcare system with cross-layer optimization from sensing patch to cloud platform. A wearable ECG patch with a custom System-on-Chip (SoC) features a miniaturized footprint, low power consumption, and embedded signal processing capability. The patch also integrates wireless connectivity with mobile devices and cloud platform for optimizing the complete system. On the big picture, a "wearable patch-mobile-cloud" hybrid computing framework is proposed with cross-layer optimization for performance-power trade-off in embedded-computing. The measurement results demonstrate that the on-patch compression ratio of the raw ECG signal can reach 12.07 yielding a percentage root mean square variation of 2.29%. In the test with the MIT-BIH database, the average improvement of signal to noise ratio and mean square error are 12.63 dB and 94.47%, respectively. The average accuracy of disease prediction operation executed in cloud platform is 97%.

With the burgeoning developments in artificial intelligence, hardware implementation of artificial neural network is also gaining pace. In this pursuit, ferroelectric devices (i.e., tunneling junctions and transistors) with voltage thresholds were recently proposed as suitable candidates. However, their development is hindered by the inherent integration issues of inorganic ferroelectrics, as well as poor properties of conventional organic ferroelectrics. In contrast to the conventional ferroelectric synapses, here we demonstrated a two-terminal ferroelectric synaptic device using a molecular ferroelectric (MF)/semiconductor interface. The interfacial resistance can be tuned via the polarization-controlled blocking effect of the semiconductor, owing to the high ferroelectricity and field amplification effect of the MF. Typical synaptic features including spike timing-dependent plasticity are substantiated. The introduction of the semiconductor also enables the attributes of optoelectronic synapse and in-sensor computing with high image recognition accuracies. Such interfaces may pave the way for the hardware implementation of multifunctional neuromorphic devices.

Flexible and stretchable dry active electrodes for multi bio-potentials sensing based on Ag flakes / polydimethylsiloxane (PDMS) electrically conductive composite (ECC) are developed and characterized. The proposed dry active electrode consists of soft substrate, electrode, and simple circuits with an amplifier and a capacitor. The soft substrate is made of silicone with a molding process, while the electrode is fabricated by bar coating the ECC on the patterned substrate. Furthermore, circuits interconnect, soldering of chip and other components, and connection with flexible PCB (FPC) are all implemented with ECC directly, which simplifies the fabrication process. A portable bio-potential sensing system is also designed and implemented to work with the proposed electrodes. Various experiments were carried out to verify the proposed electrodes as well as the whole sensing system. The connectivity and proper functionality of the electrodes and cables remain stable during the stretching test. The system and the sensors yield good signal quality for multiple bio-potentials. Compared with conventional Ag/AgCl wet passive electrodes in electrocardiogram (ECG) sensing, the proposed dry active electrodes showed comparable noise floor and less sensitivity to power line interferences and motion artifacts. In electroencephalogram (EEG) sensing, the observed alpha rhythm was 12.66 dB higher than the baseline in the eye-close state. The proposed sensors exhibited potential applications in wearable systems in the electromyogram (EMG) grip force measurement and classification test. Overall, the proposed sensors could provide better comforts during long-time wearing according to its better matching modulus with skin and can meet the requirements of high quality multi bio-potential sensing.

Despite that in-sensor processing has been proposed to remove the latency and energy consumption during the inevitable data transfer between spatial-separated sensors, memories and processors in traditional computer vision, its hardware implementation for artificial neural networks (ANNs) with all-in-one device arrays remains a challenge, especially for organic-based ANNs. With the advantages of biocompatibility, low cost, easy fabrication and flexibility, here we implement a self-powered in-sensor ANN using molecular ferroelectric (MF)-based photomemristor arrays. Tunable ferroelectric depolarization was intentionally introduced into the ANN, which enables reconfigurable conductance and photoresponse. Treating photoresponsivity as synaptic weight, the MF-based in-sensor ANN can operate analog convolutional computation, and successfully conduct perception and recognition of white-light letter images in experiments, with low processing energy consumption. Handwritten Chinese digits are also recognized and regressed by a large-scale array, demonstrating its scalability and potential for low-power processing and the applications in MF-based in-situ artificial retina.