Semiconductor technology, which is rapidly evolving, is poised to enter a new era for which revolutionary innovations are needed to address fundamental limitations on material and working principle level. 2D semiconductors inherently holding novel properties at the atomic limit show great promise to tackle challenges imposed by traditional bulk semiconductor materials. Synergistic combination of 2D semiconductors with functional ferroelectrics further offers new working principles, and is expected to deliver massively enhanced device performance for existing complementary metal–oxide–semiconductor (CMOS) technologies and add unprecedented applications for next‐generation electronics. Herein, recent demonstrations of novel device concepts based on 2D semiconductor/ferroelectric heterostructures are critically reviewed covering their working mechanisms, device construction, applications, and challenges. In particular, emerging opportunities of CMOS‐process‐compatible 2D semiconductor/ferroelectric transistor structure devices for the development of a rich variety of applications are discussed, including beyond‐Boltzmann transistors, nonvolatile memories, neuromorphic devices, and reconfigurable nanodevices such as p–n homojunctions and self‐powered photodetectors. It is concluded that 2D semiconductor/ferroelectric heterostructures, as an emergent heterogeneous platform, could drive many more exciting innovations for modern electronics, beyond the capability of ubiquitous silicon systems.
Electrocatalytic nitrogen reduction reaction (NRR) represents a highly promising process to ammonia synthesis for artificial N2 fixation. However, the yield rate for NH3 production and Faradaic efficiency (FE) are still low, which greatly hinder its widespread applications. Until now, although a variety of catalysts, including single-atom catalysts, have been developed for NRR in the pursuit of suppressing hydrogen evolution reaction (HER) and the corresponding higher FE, the limited NH3 yield rate makes them uncompetitive for the synthesis of ammonia. Herein, we report a Fe single-atom catalyst anchoring on a nitrogen-doped carbon substrate (Fe SAC/N–C) as a highly efficient NRR catalyst. The catalyst achieves a high FE of 39.6% in 0.1 M KOH under room temperature, particularly a dramatically enhanced NH3 yield rate of 53.12 μgNH3 h–1 mgcat –1. The isotopic labeling (15N2) experiment confirms that the NH3 production completely originates from N2 reduction. Meanwhile, theoretical calculations and X-ray fine structure analysis reveal that the Fe–N3 coordination of Fe SAC/N–C is indeed responsible for the suppression of HER, particularly resulting in a maximum activation of NRR intermediates to produce ammonia with a high yield rate.
Amid the ever-accelerated development of wireless communication technology, we have become increasingly demanding for location-based service; thus, passive indoor positioning has gained widespread attention. Channel State Information (CSI), as it can provide more detailed and fine-grained information, has been followed by researchers. Existing indoor positioning methods, however, are vulnerable to the environment and thus fail to fully reflect all the position features, due to limited accuracy of the fingerprint. As a solution, a CSI-based passive indoor positioning method was proposed, Wavelet Domain Denoising (WDD) was adopted to deal with the collected CSI amplitude, and the CSI phase information was unwound and transformed linearly in the offline phase. The post-processed amplitude and phase were taken as fingerprint data to build a fingerprint database, correlating with reference point position information. Results of experimental data analyzed under two different environments show that the present method boasts lower positioning error and higher stability than similar methods and can offer decimeter-level positioning accuracy.
In recent years, with the development of wireless sensing technology and the widespread popularity of WiFi devices, human perception based on WiFi has become possible, and gesture recognition has become an active topic in the field of human-computer interaction. As a kind of gesture, sign language is widely used in life. The establishment of an effective sign language recognition system can help people with aphasia and hearing impairment to better interact with the computer and facilitate their daily life. For this reason, this paper proposes a contactless fine-grained gesture recognition method using Channel State Information (CSI), namely Wi-SL. This method uses a commercial WiFi device to establish the correlation mapping between the amplitude and phase difference information of the subcarrier level in the wireless signal and the sign language action, without requiring the user to wear any device. We combine an efficient denoising method to filter environmental interference with an effective selection of optimal subcarriers to reduce the computational cost of the system. We also use K-means combined with a Bagging algorithm to optimize the Support Vector Machine (SVM) classification (KSB) model to enhance the classification of sign language action data. We implemented the algorithms and evaluated them for three different scenarios. The experimental results show that the average accuracy of Wi-SL gesture recognition can reach 95.8%, which realizes device-free, non-invasive, high-precision sign language gesture recognition.
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