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Engineering electronic bandgaps is crucial for applications in information technology, sensing, and renewable energy. Transition metal dichalcogenides (TMDCs) offer a versatile platform for bandgap modulation through alloying, doping, and heterostructure formation. Here, the synthesis of a 2D Mo_xW_1‐xS₂graded alloy is reported, featuring a Mo‐rich center that transitions to W‐rich edges, achieving a tunable bandgap of 1.85 to 1.95 eV when moving from the center to the edge of the flake. Aberration‐corrected high‐angle annular dark‐field scanning transmission electron microscopy showed the presence of sulfur monovacancy, V_S, whose concentration varied across the graded Mo_xW_1‐xS₂layer as a function of Mo content with the highest value in the Mo‐rich center region. Optical spectroscopy measurements supported by ab initio calculations reveal a doublet electronic state of V_S, which is split due to the spin‐orbit interaction, with energy levels close to the conduction band or deep in the bandgap depending on whether the vacancy is surrounded by W atoms or Mo atoms. This unique electronic configuration of V_Sin the alloy gave rise to four spin‐allowed optical transitions between the V_Slevels and the valence bands. The study demonstrates the potential of defect and optical engineering in 2D monolayers for advanced device applications.

 

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.