Graphene has attracted large interest in photonic applications owing to its promising optical properties, especially its ability to absorb light over a broad wavelength range, which has lead to several studies on pure monolayer graphene-based photodetectors. However, the maximum responsivity of these photodetectors is below 10 mA W⁻¹, which significantly limits their potential for applications. Here we report high photoresponsivity (with high photoconductive gain) of 8.61 A W⁻¹ in pure monolayer graphene photodetectors, about three orders of magnitude higher than those reported in the literature, by introducing electron trapping centres and by creating a bandgap in graphene through band structure engineering. In addition, broadband photoresponse with high photoresponsivity from the visible to the mid-infrared is experimentally demonstrated. To the best of our knowledge, this work demonstrates the broadest photoresponse with high photoresponsivity from pure monolayer graphene photodetectors, proving the potential of graphene as a promising material for efficient optoelectronic devices.

The interest in mid-infrared technologies surrounds plenty of important optoelectronic applications ranging from optical communications, biomedical imaging to night vision cameras, and so on. Although narrow bandgap semiconductors, such as Mercury Cadmium Telluride and Indium Antimonide, and quantum superlattices based on inter-subband transitions in wide bandgap semiconductors, have been employed for mid-infrared applications, it remains a daunting challenge to search for other materials that possess suitable bandgaps in this wavelength range. Here, we demonstrate experimentally for the first time that two-dimensional (2D) atomically thin PtSe₂ has a variable bandgap in the mid-infrared via layer and defect engineering. Here, we show that bilayer PtSe₂ combined with defects modulation possesses strong light absorption in the mid-infrared region, and we realize a mid-infrared photoconductive detector operating in a broadband mid-infrared range. Our results pave the way for atomically thin 2D noble metal dichalcogenides to be employed in high-performance mid-infrared optoelectronic devices.

The analysis of infrared spectroscopy of substances is a non-invasive measurement technique that can be used in analytics. Although the main objective of this study is to provide a review of machine learning (ML) algorithms that have been reported for analyzing near-infrared (NIR) spectroscopy from traditional machine learning methods to deep network architectures, we also provide different NIR measurement modes, instruments, signal preprocessing methods, etc. Firstly, four different measurement modes available in NIR are reviewed, different types of NIR instruments are compared, and a summary of NIR data analysis methods is provided. Secondly, the public NIR spectroscopy datasets are briefly discussed, with links provided. Thirdly, the widely used data preprocessing and feature selection algorithms that have been reported for NIR spectroscopy are presented. Then, the majority of the traditional machine learning methods and deep network architectures that are commonly employed are covered. Finally, we conclude that developing the integration of a variety of machine learning algorithms in an efficient and lightweight manner is a significant future research direction.