The indirect bandgap semiconductor tin selenide (SnSe) has been a research hotspot in the thermoelectric fields since a ZT (figure of merit) value of 2.6 at 923 K in SnSe single crystals along the b‐axis is reported. SnSe has also been extensively studied in the photovoltaic (PV) application for its extraordinary advantages including excellent optoelectronic properties, absence of toxicity, cheap raw materials, and relative abundance. Moreover, the thermoelectric and optoelectronic properties of SnSe can be regulated by the structural transformation and appropriate doping. Here, the studies in SnSe research, from its evolution to till now, are reviewed. The growth, characterization, and recent developments in SnSe research are discussed. The most popular growth techniques that have been used to prepare SnSe materials are discussed in detail with their recent progress. Important phenomena in the growth of SnSe as well as the problems remaining for future study are discussed. The applications of SnSe in the PV fields, Li‐ion batteries, and other emerging fields are also discussed.

This paper reviews the original achievements and advances regarding the field effect transistor (FET) fabricated from one of the most studied transition metal dichalcogenides: two-dimensional MoS2. Not like graphene, which is highlighted by a gapless Dirac cone band structure, Monolayer MoS2 is featured with a 1.9 eV gapped direct energy band thus facilitates convenient electronic and/or optoelectronic modulation of its physical properties in FET structure. Indeed, many MoS2 devices based on FET architecture such as phototransistors, memory devices, and sensors have been studied and extraordinary properties such as excellent mobility, ON/OFF ratio, and sensitivity of these devices have been exhibited. However, further developments in FET device applications depend a lot on if novel physics would be involved in them. In this review, an overview on advances and developments in the MoS2-based FETs are presented. Engineering of MoS2-based FETs will be discussed in details for understanding contact physics, formation of gate dielectric, and doping strategies. Also reported are demonstrations of device behaviors such as low-frequency noise and photoresponse in MoS2-based FETs, which is crucial for developing electronic and optoelectronic devices.

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The field of chiral plasmonics has registered considerable progress with machine-learning (ML)-mediated metamaterial prototyping, drawing from the success of ML frameworks in other applications such as pattern and image recognition. Here, we present an end-to-end functional bidirectional deep-learning (DL) model for three-dimensional chiral metamaterial design and optimization. This ML model utilizes multitask joint learning features to recognize, generalize, and explore in detail the nontrivial relationship between the metamaterials’ geometry and their chiroptical response, eliminating the need for auxiliary networks or equivalent approaches to stabilize the physically relevant output. Our model efficiently realizes both forward and inverse retrieval tasks with great precision, offering a promising tool for iterative computational design tasks in complex physical systems. Finally, we explore the behavior of a sample ML-optimized structure in a practical application, assisting the sensing of biomolecular enantiomers. Other potential applications of our metastructure include photodetectors, polarization-resolved imaging, and circular dichroism (CD) spectroscopy, with our ML framework being applicable to a wider range of physical problems.