The development of the sixth generation of wireless communications technology requires terminals that can operate at frequencies above 100 GHz. For human-centric applications, these terminals should also be flexible and have low power. However, current flexible radio-frequency transistors typically have lower maximum frequencies, in part due to the poor thermal conductivity of flexible substrates. Here we report radio-frequency transistors that are based on aligned carbon nanotube arrays on flexible substrates, having current-gain cut-off frequencies (fT) and power-gain cut-off frequencies (fmax) above 100 GHz. This is achieved by using electrothermal co-design to improve the heat dissipation and radio-frequency performance of the devices. The transistors exhibit an on-state current of 0.947 mA µm−1, a transconductance of 0.728 mS µm−1, a peak extrinsic fT of 152 GHz, a peak extrinsic fmax of 102 GHz and a power consumption under 200 mW mm−1. We also show that the devices can be used to create flexible radio-frequency amplifiers with an output power of 64 mW mm−1 and an 11-dB power gain in the K band.

The rapid development of multimodal epidermal sensing requires scalable, energy-efficient data processing architectures capable of processing large volumes of raw data. Conventional systems suffer from high energy consumption and transmission latency due to the physical separation of sensors and processors. Here, we present an ultrathin flexible edge computing circuit based on carbon nanotube thin-film transistors (CNT-TFTs) and machine learning (ML)-assisted design. By incorporating substrate engineering, ML-derived device modeling, and industry-compatible design methodologies, we establish a complete toolchain from device to system. The ML model achieves 91.2% prediction accuracy, enabling simulation-guided optimization of logic gates. A CNT-based standard cell library enables the construction of flexible circuits with 361 transistors and 160 logic gates. Monolithic integration with an 8-channel tilt sensor achieves 62.5% data compression while maintaining functionality after undergoing 360° deformation. This work establishes an ML-assisted CNT circuit design framework for fully integrated flexible edge computing, enabling scalable wearable applications.

High quality factor and sensitivity are critical to wireless sensors targeting small perturbation detection. Although introducing parity-time symmetric dynamics promises enhanced performance, the symmetric scheme often yields limited sensing behaviors. In particular, its implementation is hindered by the inherent difficulties in decoupling capacitance- and coupling strength–induced responses, the requirements of delicately matching and/or tuning both gain and loss, and the need of strong coupling strength. Here, we report a concept of critical point (CP)–based wireless sensors that do not rely on balanced gain-loss configurations, delivering ultrahigh quality factor with an extended interrogation distance. Owing to a sharp and deep reflection dip, the CP-based scheme can resolve the change in coupling coefficient down to 1.92 × 10−4 and features frequency-independent responses. Furthermore, the CP-based scheme allows for identification of tiny asymmetric capacitive perturbations as small as 2.5 × 10−5 without requiring active tuning of the other parameters under weak coupling. Critical-point wireless sensors with unbalanced gain and loss enable reliable and sensitive detection under weak coupling.

Traditional polysomnography (PSG) systems are limited by cumbersome hardware, inefficient clinical workflows, and significant patient discomfort, hindering accurate characterization of natural sleep. Here, we present a wearable sleep-breathing monitoring system based on a printed electronic skin (E-skin) sensor that enables comfortable, high-fidelity, and home-viable respiratory assessment. The device employs a resistive eutectic gallium-indium-tin (EGaInSn) liquid-metal sensing layer screen-printed onto a flexible thermoplastic polyurethane (TPU) substrate, offering stable sensitivity over a broad dynamic range, mechanical robustness, and seamless skin conformability for long-term wear. A six-channel sensing network was implemented to capture thoracic and abdominal respiratory dynamics across diverse sleeping positions. Comprehensive clinical validation was conducted against gold-standard PSG, with respiratory events independently scored by Registered Polysomnographic Technologists (RPSGTs) under single-blind conditions. The system demonstrates high concordance with PSG in identifying obstructive and central sleep apnea, hypopnea, Cheyne–Stokes respiration, and respiratory rate abnormalities. By integrating flexible electronics and clinically aligned signal interpretation, this work advances wearable health technologies from conventional physiological monitoring toward credible diagnostic capability, providing a practical solution for continuous, accurate evaluation of sleep-related breathing disorders.

For biointegrated flexible systems that acquire and process electrophysiological signals, amplifying weak biosignals from their original low amplitudes (ranging from microvolt to millivolt) to volt-level is essential for subsequent processing. Achieving this level of amplification requires a high voltage gain (>105 or 100 decibels for microvolt signals). However, realizing such gain in flexible circuits remains highly challenging because of constrained integration scale and limits in feasible circuit topologies. Here, we report flexible amplifiers that achieve ultrahigh gain by leveraging intrinsic gain singularities induced by negative differential resistance (NDR) effect in carbon nanotube–based transistors. The NDR behavior is investigated under various factors, including contacts, gate structures, and channel lengths. Guided by insights into the correlations between NDR characteristics and device-level parameters, a device-circuit codesign approach is implemented to build a flexible amplifier achieving a record-high gain of 104 decibels among all reported flexible amplifiers, with successful demonstration of electroencephalogram signals amplification. A carbon nanotube–based flexible amplifier achieves >100-dB voltage gain, demonstrating EEG signal amplification.

Transistor-integrated flexible pressure sensors have received considerable interest in emerging fields such as humanoid robotics, prosthetics, and implantable electronics. However, existing designs for these integrated sensors often exhibit a trade-off between pressure response and operating voltage, thus significantly limiting their practical applications. In this letter, we report a unique device design of integrated pressure sensors based on deformable microstructured electrodes capacitively coupled with floating-gate carbon nanotube transistors. The microstructured electrodes can dramatically enhance the pressure-introduced electrostatic control of the transistor, enabling a substantial improvement in the transduced pressure response at low operating voltages. With this unique design, we achieve a high pressure response of $10^5$ and an ultrahigh sensitivity up to $10^4 \text kPa^\text - 1$ at a low operating voltage below 3 V, which holds great promise for the development of advanced functionalized flexible electronics.