Textile-based triboelectric nanogenerator (T-TENG) devices, particularly, narrow-gap mode, have been conceived and developed for obtaining energy harvesting and tactile sensing devices unaffected by the external environment.

With triboelectric nanogenerators (TENGs) increasingly being used as touch sensors, their recognition accuracy is critical for the practical application of TENG devices. Here, we propose a method to prepare polydimethylsiloxane (PDMS) films with nano-array structures on the surface using an anodised aluminium oxide (AAO) template to assemble a TENG device with a high static contact angle. The method can improve the TENG output effect and enhance the accuracy of material recognition. The prepared nano-array structures (na-PDMS) are a series of cylinders with a radius of 150 nm and a height of 500 nm. The results show that the N-TENG device assembled from na-PDMS produced strain in the nano-array layer of na-PDMS at an applied stress of 0.89–19.50 kPa; when the applied stress of 19.50–134.52 kPa exceeded the tolerance range of the nano-array layer of na-PDMS, the non-nano-array structured layer of na-PDMS produced strain. Furthermore, under the test conditions of 14.95 kPa, the maximum power density of the N-TENG was 3.96 mW m⁻² when the load resistance was 4 MΩ. In addition, the feature signals generated when the N-TENG was in contact with the material/separated from the material were combined with the CNN deep learning technique. The following could be observed: the fabricated N-TENG device could recognise eight different materials with 97.7% certainty, which is better than the conventional S-TENG device. Overall, the method proposed in this paper provides a feasible way to improve the triboelectric properties of TENGs and sheds light on the surface structure design of triboelectric materials.

Reported herein is the first Rh(III)-catalyzed carbenoid insertion C–H activation/cyclization of N-arylureas and α-diazo β-keto esters. The redox-neutral reaction has the following features: good to excellent yields, broad substrate/functional group tolerance, exclusive regioselectivity, and no need for additional oxidants or additives, which render this methodology as a more efficient and versatile alternative to the existing methods for the synthesis of 2,3-difunctionalized indoles.

Textile-based triboelectric nanogenerators (T-TENGs) have great potential in new-generation tactile sensors because their output signal is determined by the specific charge interaction between the target and triboelectric material. However, gesture recognition by existing T-TENG tactile sensors is very challenging because the electron affinity and polarity of the volunteer skin are similar. This study proposes a new structural sensor that integrates a Ni particle-encapsulated carbon tube (Ni@C tube) with polydimethylsiloxane (PDMS) by means of multiple strain mechanisms for improving recognition accuracy using a signal superposition technique. Interestingly, dynamic strain from multiple sources, i.e., PDMS layer strain (1.24–2.47 kPa), Ni@C tube crack (2.47–5.56 kPa), PDMS layer further strain (5.56–52.97 kPa), and Ni@C tube slip laterally (52.97–139.05 kPa), was established in the PDMS-coated Ni@C tube. In addition, the output signal of the as-fabricated T-TENG device was a combination of output signals from the PDMS/Ni@C tube and PDMS/contact object devices, which could fully reflect the stress field caused by the contact material pressing the surface and the surface characteristics, promoting the deep learning algorithm to be able to extract more hidden features of the identified materials. By integrating convolutional neural network (CNN) analysis based on the voltage signals of the T-TENG device, we explored a strategy that could enable the as-fabricated device to recognize 6 gestures with 99.3% recognition accuracy and 8 different materials with 99.8% recognition accuracy in the natural environment.