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a conjunction of triboelectric effect and electrostatic induction. The high sensitivity of TENG toward tiny vibration, the cost-effective fabrication process, and the flexible device structure provide much possibilities for the smart and interesting applications, such as humidity sensor, [10] pressure sensor, [11] active acoustic sensor, [12] ultraviolet detector, [13] Keystroke dynamics identification, [14] handheld printer, [15] and so on.Machine learning (ML) [16,17] is usually referred to statistical models and algorithms used by computer systems to conduct a specific task without too much complex instructions. ML algorithms are widely used in computer vision, disease diagnosis, email filtering, and signal recognition. ML has close relationship with computational statistics focusing on making predictions using computers. Support vector machine (SVM) [18] is a supervised ML algorithm for linear and nonlinear classification. The SVM training algorithm builds a model based on functional margin, which generates a line or a hyperplane to separate the data into classes. In information theory and ML, by obtaining a set of principal variables, dimension reduction can reduce the dimension of random variables. t-distributed stochastic neighbor embedding (t-SNE) [19] is a approach to reduce high-dimensional datasets and widely used in speech processing, genomic data, natural language processing, and image processing. t-SNE starts by calculating the probability of high-dimensional space points similarity as well as the probability of the corresponding low-dimensional space points similarity to map the multi-dimensional data to a lower dimensional space.With the development of artificial intelligence, it is urgent to empower traditional electronics with the ability to "think," to "analyze," and to "advise." Here, a new product concept namely triboelectric nanogenerator (TENG) based smart electronics via the automatic machine learning data analysis algorithm is proposed. In this work, a simple water processing technique is used to fabricate porous polydimethylsiloxane, together with the weaving copper mesh, forming a high sensitivity flexible TENG. The as-prepared TENG presents high sensitivity for the voice signal and handwriting signal detection with ≈0.2 V amplitude in the common talking and writing condition. Three words' pronunciation are recorded and the ensemble method is used as the machine learning model for the voice signal recognition with a recognition accuracy of 93.3%. To further demonstrate the possibility of applying machine learning algorithm for automatic analysis and recognition, larger database is analyzed. Twenty-six letters' handwriting signals with total 520 samples are collected and a letter fingerprint library is established for further analysis. Hierarchical clustering and similarity matrix are used to study the intrinsic relationship between letters. "Medium Gaussian support vector machine" is used as machine learning model for the 26-letter fingerprint identification with recognition accuracy of...

High sensitivity, good signal repeatability, and facile fabrication of flexible surface enhanced Raman scattering (SERS) substrates are common pursuits of researchers for the detection of probe molecules in a complex environment. However, fragile adhesion between the noble-metal nanoparticles and substrate material, low selectivity, and complex fabrication process on a large scale limit SERS technology for wide-ranging applications. Herein, we propose a scalable and cost-effective strategy to a fabricate sensitive and mechanically stable flexible Ti 3 C 2 T x MXene@graphene oxide/Au nanoclusters (MG/AuNCs) fiber SERS substrate from wet spinning and subsequent in situ reduction processes. The use of MG fiber provides good flexibility (114 MPa) and charge transfer enhancement (chemical mechanism, CM) for a SERS sensor and allows further in situ growth of AuNCs on its surface to build highly sensitive hot spots (electromagnetic mechanism, EM), promoting the durability and SERS performance of the substrate in complex environments. Therefore, the formed flexible MG/AuNCs-1 fiber exhibits a low detection limit of 1 × 10 −11 M with a 2.01 × 10 9 enhancement factor (EF exp ), signal repeatability (RSD = 9.80%), and time retention (remains 75% after 90 days of storage) for R6G molecules. Furthermore, the L-cysteine-modified MG/AuNCs-1 fiber realized the trace and selective detection of trinitrotoluene (TNT) molecules (0.1 μM) via Meisenheimer complex formation, even by sampling the TNT molecules at a fingerprint or sample bag. These findings fill the gap in the large-scale fabrication of high-performance 2D materials/precious-metal particle composite SERS substrates, with the expectation of pushing flexible SERS sensors toward wider applications.

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