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Graphene oxide (GO) doping and reduction allow for physicochemical property modification to suit practical application needs. Herein, the challenge of simultaneous low‐thermal‐budget heteroatom doping of GO and its reduction in ambient air is addressed through the synthesis of B‐doped reduced GO (B@rGO) by flash irradiation of boric acid loaded onto a GO support with intense pulsed light (IPL). The effects of light power and number of shots on the in‐depth sequential doping and reduction mechanisms are investigated by ex situ X‐ray photoelectron spectroscopy and direct millisecond‐scale temperature measurements (temperature >1600 °C, < 10‐millisecond duration, ramping rate of 5.3 × 105 °C s−1). Single‐flash IPL allows the large‐scale synthesis of substantially doped B@rGO (≈3.60 at% B) to be realized with a thermal budget 106‐fold lower than that of conventional thermal methods, and the prepared material with abundant B active sites is employed for highly sensitive and selective room‐temperature NO2 sensing. Thus, this work showcases the great potential of optical annealing for millisecond‐scale ultrafast reduction and heteroatom doping of GO in ambient air, which allows the tuning of multiple physicochemical GO properties. [ABSTRACT FROM AUTHOR]

Abstract: Edges of 2D transition metal dichalcogenides (TMDs) are well known as highly reactive sites, thus researchers have attempted to maximize the edge site density of 2D TMDs. In this work, metal‐organic framework (MOF) templates are introduced to synthesize few‐layered WS2 nanoplates (a lateral dimension of ≈10 nm) confined in Co, N‐doped hollow carbon nanocages (WS2_Co‐N‐HCNCs), for highly sensitive NO2 gas sensors. WS2 precursors are assembled in the surface cavity of Co‐based zeolite imidazole framework (ZIF‐67) and subsequent pyrolysis produced WS2_Co‐N‐HCNCs. During the pyrolysis, the carbonized ZIF‐67 are doped by Co and N elements, and the growth of WS2 is effectively suppressed, creating few‐layered WS2 nanoplates functionalized Co‐N‐HCNCs. The WS2_Co‐N‐HCNCs exhibit outstanding NO2 sensing characteristics at room temperature, in terms of response (48.2% to 5 ppm), selectivity, response and recovery speed, and detection limit (100 ppb). These results are attributed to the enhanced adsorption and desorption kinetics of NO2 on abundant WS2 edges, confined in the gas permeable HCNCs. This work opens up an efficient way for the facile synthesis of edge abundant few‐layered TMDs combined with porous carbon matrix via MOF templating route, for applications relying on highly active sites. [ABSTRACT FROM AUTHOR]

Electrolyte‐gated synaptic transistors (EGSTs) have attracted considerable attention as synaptic devices owing to their adjustable conductance, low power consumption, and multi‐state storage capabilities. To demonstrate high‐density EGST arrays, 2D materials are recommended owing to their excellent electrical properties and ultrathin profile. However, widespread implementation of 2D‐based EGSTs has challenges in achieving large‐area channel growth and finding compatible nanoscale solid electrolytes. This study demonstrates large‐scale process‐compatible, all‐solid‐state EGSTs utilizing molybdenum disulfide (MoS2) channels grown through chemical vapor deposition (CVD) and sub‐30 nm organic‐inorganic hybrid electrolyte polymers synthesized via initiated chemical vapor deposition (iCVD). The iCVD technique enables precise modulation of the hydroxyl group density in the hybrid matrix, allowing the modulation of proton conduction, resulting in adjustable synaptic performance. By leveraging the tunable iCVD‐based hybrid electrolyte, the fabricated EGSTs achieve remarkable attributes: a wide on/off ratio of 109, state retention exceeding 103, and linear conductance updates. Additionally, the device exhibits endurance surpassing 5 × 104 cycles, while maintaining a low energy consumption of 200 fJ/spike. To evaluate the practicality of these EGSTs, a subset of devices is employed in system‐level simulations of MNIST handwritten digit recognition, yielding a recognition rate of 93.2%. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) technology has attracted tremendous interest in the medical community, from image analysis to lesion diagnosis. However, progress in medical AI is hampered by a lack of available medical image datasets and labor‐intensive labeling processes. Here, it is demonstrated that a large number of annotated, realistic chest X‐ray images can be generated using a state‐of‐the‐art generative adversarial network (GAN) that exploits noise produced by stochastic in‐memory computing of memristor crossbar arrays. Memristors based on polymer film with high thermal resistance can increase the stochasticity of the tunneling distance for randomly ruptured conductive filaments via excessive Joule heating, thus generating true random numbers required for creating naturally diverse images in GAN. Using StyleGAN2‐adaptive discriminator augmentation (ADA), high‐quality chest X‐ray images with and without pneumothorax are successfully augmented while maintaining a good Frechet inception distance score. The results provide a cost‐effective solution for preparing privacy‐sensitive medical images and labeling to develop innovative medical AI algorithms. [ABSTRACT FROM AUTHOR]

In an article published in Advanced Functional Materials, Sung‐Yool Choi, Byung Chul Jang, and their colleagues present a method for generating realistic chest X-ray images using a generative adversarial network (GAN) and stochastic in-memory computing of memristor crossbar arrays. The researchers demonstrate that this cost-effective solution has the potential to generate privacy-sensitive medical images and aid in the development of innovative medical AI algorithms. The article provides a large number of annotated chest X-ray images and highlights the benefits of their approach for machine learning-based medical diagnosis. [Extracted from the article]