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Abstract Hydrocarbons and carbon oxides are typical dissolved gases in transformer oil that reflect the latent pitfalls, on‐line monitoring of their concentrations can effectively evaluate the operating status of the power transformer. However, these low‐concentration targets (especially for CH4) show high chemical inertness at room temperature, challenging the sensitive performance of current commonly used chemiresistive gas sensors. Herein, a strategy by combining traditional inorganic semiconductors to carbon nanofiber (CNF) via electrospinning–annealing route is described. Three optimized 3D mosaic films, CNFs scaffold incorporated with WO3, SnO2 and MoS2 nanoparticles, are obtained. Due to the large specific surface area of the 3D network, and the synergic and heterojunction effects between nanoparticles and CNFs, all three sensors exhibit high response to CH4 at room temperature, and also record distinguishable signals toward H2, C2H4, CO and CO2, revealing the three sensors are cross‐sensitive to the five analytes. Accordingly, preliminary discrimination of five dissolved gases is realized by principle component analysis. This study provides an effective and extendable solution of preparing room‐temperature chemiresistive sensors for the detection of CH4 and other gases, and offers a strategy for the construction of sensor array to achieve a high discrimination capability.

Electric vertical takeoff and landing (eVTOL) aircraft have attracted tremendous attention nowadays due to their flexible maneuverability, precise control, cost efficiency, and low noise. The optimal takeoff trajectory design is a key component of cost-effective and passenger-friendly eVTOL systems. However, conventional design optimization is typically computationally prohibitive due to the adoption of high-fidelity simulation models in an iterative manner. Machine learning (ML) allows rapid decision making; however, new ML surrogate modeling architectures and strategies are still desired to address large-scale problems. Therefore, we showcase a novel regression generative adversarial network (regGAN) surrogate for fast interactive optimal takeoff trajectory predictions of eVTOL aircraft. The regGAN leverages generative adversarial network architectures for regression tasks with a combined loss function of a mean squared error (MSE) loss and an adversarial binary cross-entropy (BC) loss. Moreover, we introduce a surrogate-based inverse mapping concept into eVTOL optimal trajectory designs for the first time. In particular, an inverse-mapping surrogate takes design requirements (including design constraints and flight condition parameters) as input and directly predicts optimal trajectory designs, with no need to run design optimizations once trained. We demonstrated the regGAN on optimal takeoff trajectory designs for the Airbus A3 Vahana. The results revealed that regGAN outperformed reference surrogate strategies, including multi-output Gaussian processes and conditional generative adversarial network surrogates, by matching simulation-based ground truth with 99.6% relative testing accuracy using 1000 training samples. A parametric study showed that a regGAN surrogate with an MSE weight of one and a BC weight of 0.01 consistently achieved over 99.5% accuracy (denoting negligible predictive errors) using 400 training samples, while other regGAN models require at least 800 samples.

With the innovation of wearable technology and the rapid development of biosensors, wearable biosensors based on flexible textile materials have become a hot topic. Such textile-based wearable biosensors promote the development of health monitoring, motion detection and medical management, and they have become an important support tool for human healthcare monitoring. Textile-based wearable biosensors not only non-invasively monitor various physiological indicators of the human body in real time, but they also provide accurate feedback of individual health information. This review examines the recent research progress of fabric-based wearable biosensors. Moreover, materials, detection principles and fabrication methods for textile-based wearable biosensors are introduced. In addition, the applications of biosensors in monitoring vital signs and detecting body fluids are also presented. Finally, we also discuss several challenges faced by textile-based wearable biosensors and the direction of future development.

Biodegradable electronics have great potential to reduce the environmental footprint of electronic devices and to avoid secondary removal of implantable health monitors and therapeutic electronics. Benefiting from the intensive innovation on biodegradable nanomaterials, current transient electronics can realize full components’ degradability. However, design of materials with tissue-comparable flexibility, desired dielectric properties, suitable biocompatibility and programmable biodegradability will always be a challenge to explore the subtle trade-offs between these parameters. In this review, we firstly discuss the general chemical structure and degradation behavior of polymeric biodegradable materials that have been widely studied for various applications. Then, specific properties of different degradable polymer materials such as biocompatibility, biodegradability, and flexibility were compared and evaluated for real-life applications. Complex biodegradable electronics and related strategies with enhanced functionality aimed for different components including substrates, insulators, conductors and semiconductors in complex biodegradable electronics are further researched and discussed. Finally, typical applications of biodegradable electronics in sensing, therapeutic drug delivery, energy storage and integrated electronic systems are highlighted. This paper critically reviews the significant progress made in the field and highlights the future prospects.

The monitoring of ethylene is of great importance to fruit and vegetable quality, yet routine techniques rely on manual and complex operation. Herein, a chemiresistive ethylene sensor based on reduced graphene oxide (rGO)/tungsten diselenide (WSe2)/Pd heterojunctions was designed for room-temperature (RT) ethylene detection. The sensor exhibited high sensitivity and quick p-type response/recovery (33/13 s) to 10–100 ppm ethylene at RT, and full reversibility and excellent selectivity to ethylene were also achieved. Such excellent ethylene sensing behaviors could be attributed to the synergistic effects of ethylene adsorption abilities derived from the negative adsorption energy and the promoted electron transfer across the WSe2/Pd and rGO/WSe2 interfaces through band energy alignment. Furthermore, its application feasibility to banana ripeness detection was verified by comparison with routine technique through simulation experiments. This work provides a feasible methodology toward designing and fabricating RT ethylene sensors, and may greatly push forward the development of modernized intelligent agriculture.