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Rapid identification of newly emerging or circulating viruses is an important first step toward managing the public health response to potential outbreaks. A portable virus capture device, coupled with label-free Raman spectroscopy, holds the promise of fast detection by rapidly obtaining the Raman signature of a virus followed by a machine learning (ML) approach applied to recognize the virus based on its Raman spectrum, which is used as a fingerprint. We present such an ML approach for analyzing Raman spectra of human and avian viruses. A convolutional neural network (CNN) classifier specifically designed for spectral data achieves very high accuracy for a variety of virus type or subtype identification tasks. In particular, it achieves 99% accuracy for classifying influenza virus type A versus type B, 96% accuracy for classifying four subtypes of influenza A, 95% accuracy for differentiating enveloped and nonenveloped viruses, and 99% accuracy for differentiating avian coronavirus (infectious bronchitis virus [IBV]) from other avian viruses. Furthermore, interpretation of neural net responses in the trained CNN model using a full-gradient algorithm highlights Raman spectral ranges that are most important to virus identification. By correlating ML-selected salient Raman ranges with the signature ranges of known biomolecules and chemical functional groups—for example, amide, amino acid, and carboxylic acid—we verify that our ML model effectively recognizes the Raman signatures of proteins, lipids, and other vital functional groups present in different viruses and uses a weighted combination of these signatures to identify viruses. 

Individual atomic defects in 2D materials impact their macroscopic functionality. Correlating the interplay is challenging, however, intelligent hyperspectral scanning tunneling spectroscopy (STS) mapping provides a feasible solution to this technically difficult and time consuming problem. Here, dense spectroscopic volume is collected autonomously via Gaussian process regression, where convolutional neural networks are used in tandem for spectral identification. Acquired data enable defect segmentation, and a workflow is provided for machine-driven decision making during experimentation with capability for user customization. We provide a means towards autonomous experimentation for the benefit of both enhanced reproducibility and user-accessibility. Hyperspectral investigations on WS₂sulfur vacancy sites are explored, which is combined with local density of states confirmation on the Au{111} herringbone reconstruction. Chalcogen vacancies, pristine WS₂, Au face-centered cubic, and Au hexagonal close-packed regions are examined and detected by machine learning methods to demonstrate the potential of artificial intelligence for hyperspectral STS mapping.

 

The field of photovoltaics is revolutionized in recent years by the development of two–dimensional (2D) type‐II heterostructures. These heterostructures are made up of two different materials with different electronic properties, which allows for the capture of a broader spectrum of solar energy than traditional photovoltaic devices. In this study, the potential of vanadium (V)‐doped WS₂is investigated, hereafter labeled V‐WS₂, in combination with air‐stable Bi₂O₂Se for use in high‐performance photovoltaic devices. Various techniques are used to confirm the charge transfer of these heterostructures, including photoluminescence (PL) and Raman spectroscopy, along with Kelvin probe force microscopy (KPFM). The results show that the PL is quenched by 40%, 95%, and 97% for WS₂/Bi₂O₂Se, 0.4 at.% V‐WS₂/Bi₂O₂Se, and 2 at.% V‐WS₂/Bi₂O₂Se, respectively, indicating a superior charge transfer in V‐WS₂/Bi₂O₂Se compared to pristine WS₂/Bi₂O₂Se. The exciton binding energies for WS₂/Bi₂O₂Se, 0.4 at.% V‐WS₂/Bi₂O₂Se and 2 at.% V‐WS₂/Bi₂O₂Se heterostructures are estimated to be ≈130, 100, and 80 meV, respectively, which is much lower than that for monolayer WS₂. These findings confirm that by incorporating V‐doped WS₂, charge transfer in WS₂/Bi₂O₂Se heterostructures can be tuned, providing a novel light‐harvesting technique for the development of the next generation of photovoltaic devices based on V‐doped transition metal dichalcogenides (TMDCs)/Bi₂O₂Se.

 

The ability to control the density and spatial distribution of substitutional dopants in semiconductors is crucial for achieving desired physicochemical properties. Substitutional doping with adjustable doping levels has been previously demonstrated in 2D transition metal dichalcogenides (TMDs); however, the spatial control of dopant distribution remains an open field. In this work, edge termination is demonstrated as an important characteristic of 2D TMD monocrystals that affects the distribution of substitutional dopants. Particularly, in chemical vapor deposition (CVD)‐grown monolayer WS₂, it is found that a higher density of transition metal dopants is always incorporated in sulfur‐terminated domains when compared to tungsten‐terminated domains. Two representative examples demonstrate this spatial distribution control, including hexagonal iron‐ and vanadium‐doped WS₂monolayers. Density functional theory (DFT) calculations are further performed, indicating that the edge‐dependent dopant distribution is due to a strong binding of tungsten atoms at tungsten‐zigzag edges, resulting in the formation of open sites at sulfur‐zigzag edges that enable preferential dopant incorporation. Based on these results, it is envisioned that edge termination in crystalline TMD monolayers can be utilized as a novel and effective knob for engineering the spatial distribution of substitutional dopants, leading to in‐plane hetero‐/multi‐junctions that display fascinating electronic, optoelectronic, and magnetic properties.