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W e present a scalable solution-processing method for fabricating high-quality graphene and graphene/1T-MoS 2 heterostructure films. The process begins with the synthesis of potassium-intercalated graphite (KC 8 ), which is exfoliated in tetrahydrofuran (THF) to produce stable dispersions of negatively charged (electron rich) graphene sheets. The graphene is subsequently transferred to water, forming a surfactant-free aqueous dispersion suitable for creating homogenous graphene films via vacuum filtration and stamping. Additionally, graphene is combined with 1T-MoS 2 nanosheets to fabricate graphene/1T-MoS 2 bulk heterostructure films. Comprehensive characterization, including X-ray diffraction (XRD), absorption spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy ( TEM), Raman spectroscopy, and X-ray photon emission spectroscopy (XPS), reveals that the heterostructure films exhibit enhanced optical and electronic properties, including improved light absorption, which could lead to novel photo-responsive devices. Raman spectroscopy shows significant changes in the graphene’s structural a nd electronic properties upon interaction with MoS 2 , indicating strong interlayer coupling and potential charge transfer between the layered components. The g raphene films demonstrate highly sensitive detection of dopamine (DA), while the graphene/1T-MoS 2 b ulk heterostructure films exhibit capacitance values up to 3 8.3 Fg − 1 at 5 mV/s in non-aqueous electrolytes. These results highlight the potential of these films for advanced applications in molecular sensing and energy storage. 

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.