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Osteoarthritis (OA), a prevalent degenerative joint disease, significantly affects the well-being of afflicted individuals and compromises the standard functionality of human joints. The emerging biomarker, Cartilage acidic protein 1 (CRTAC1), intricately associates with OA initiation and serves as a prognostic indicator for the trajectory toward joint replacement. However, existing diagnostic methods for CRTAC1 are hampered by the limited abundance, thus restricting the precision and specificity. Herein, a novel approach utilizing a single-walled carbon nanotube field-effect transistor (SWCNTs FET) biosensor is reported for the direct label-free detection of CRTAC1. High-purity semiconducting carbon nanotube films, functionalized with antibodies of CRTAC1, provide excellent electrical and sensing properties. The SWCNTs FET biosensor exhibits high sensitivity, notable reproducibility, and a wide linear detection range (1 fg/mL to 100 ng/mL) for CRTAC1 with a theoretical limit of detection (LOD) of 0.2 fg/mL. Moreover, the SWCNTs FET biosensor is capable of directly detecting human serum samples, showing excellent sensing performance in differentiating clinical samples from OA patients and healthy populations. Comparative analysis with traditional enzyme-linked immunosorbent assay (ELISA) reveals that the proposed biosensor demonstrates faster detection speeds, higher sensitivity/accuracy, and lower errors, indicating high potential for the early OA diagnosis. Furthermore, the SWCNTs FET biosensor has good scalability for the combined diagnosis and measurement of multiple disease markers, thereby significantly expanding the application of SWCNTs FETs in biosensing and clinical diagnostics.

The mixing process of battery production contains a variety of monitoring sensors. These sensors generate a large number of multivariate time series during production, which reflect the potential operating conditions of the production equipment. By accurately detecting anomalies in the equipment, battery quality can be improved. However, due to the diverse types of anomalies in the mixing process and the large number of subtle anomalies, it is challenging to construct a robust and accurate anomaly detection model. In this article, we propose TPAD, a multivariate time series anomaly detection model based on temporal pattern, which learns serial temporal-pattern-based on attention mechanism and detects anomalies by temporal pattern discrepancy, making the model adaptive to detect multiple types of anomalies. Moreover, the model improves the accuracy of detecting subtle anomalies through an adversarial training reconstruction method. Finally, we conducted experiments on two public datasets and a battery mixing process dataset from a battery factory, and the experimental results outperformed the baseline methods, with an average F1 score of over 92% on the three datasets.

A semiconductor photoelectrochemical (PEC) aptamer sensor has been widely researched in recent years because of its broad application prospects. However, a universal PEC sensor has not been achieved, and its sensing mechanism based on a photogenerated carrier transfer process has yet to be elucidated. Herein, a novel hydrogen-treated TiO2nanorod array one-dimensional (1D)/Ti2COXMXene two-dimensional (2D) (H-TiO2/Ti2COX) PEC aptamer sensor is presented, which achieved a record detection range of 10–9–103μg/L and a limit of detection (LOD) of 1 fg/L for microcystic toxins-LR detection. Besides, the PEC sensor can also test serotonin (5-HT), aflatoxin-B1, and prostate-specific antigen (PSA) with high performance by changing the aptamers, exhibiting favorable application universality. Furthermore, a new phenomenon of a switchable enhanced/suppressed photocurrent detection signal was discovered from H-TiO2/Ti2COXPEC aptamer sensors through the variation of the length of the TiO2nanorod. Meanwhile, it reveals that the steric hindrance effect determines the photogenerated hole transfer and depolarization processes, which is proposed for the first time as the predominant mechanism of the switchable enhanced/suppressed photocurrent signal for PEC sensors, giving possibilities to develop PEC sensors with higher efficiency.

Multimodal electronic skin devices capable of detecting multimodal signals provide the possibility for health monitoring. Sensing and memory for temperature and deformation by human skin are of great significance for the perception and monitoring of physiological changes of the human body. Electronic skin is highly expected to have similar functions as human skin. Here, by implementing intrinsically stretchable neuromorphic transistors with mechanoreceptors and thermoreceptors in an array, we have realized stretchable temperature-responsive multimodal neuromorphic electronic skin (STRM-NES) with both sensory and memory functions, in which synaptic plasticity can be modulated by multiple modalities, in situtemperature variations, and stretching deformations. Temperature-responsive functions, spontaneous recovery, and temperature-dependent multitrial learning are proposed. Furthermore, a stretchable temperature neuromorphic array composed of multiple fully functional subcells is demonstrated to identify temperature distributions and variations at different regions and conditions after various strains of skin. The STRM-NES has temperature- and strain-responsive neuromorphic functions, excellent self-healing, and reusable capability, showing similar abilities as human skin to sense, transmit, memory, and recovery from external stimuli. It is expected to facilitate the development of wearable electronics, intelligent robotics, and prosthetic applications.