Raman scattering provides stable narrow-banded signals that potentially allow for multicolor microscopic imaging. The major obstacle for the applications of Raman spectroscopy and microscopy is the small cross section of Raman scattering that results in low sensitivity. Here, we report a new concept of azo-enhanced Raman scattering (AERS) by designing the intrinsic molecular structures using resonance Raman and concomitant fluorescence quenching strategies. Based on the selection of vibrational modes and the enhancing unit of azobenzenes, we obtained a library of AERS molecules with specific Raman signals in the fingerprint and silent frequency regions. The spectral characterization and molecular simulation revealed that the azobenzene unit conjugated to the vibrational modes significantly enhanced Raman signals due to the mechanism of extending the conjugation system, coupling the electronic–vibrational transitions, and improving the symmetry of vibrational modes. The nonradiative decay of azobenzene from the excited state quenched the commitment fluorescence, thus providing a clean background for identifying Raman scattering. The most sensitive AERS molecules produced Raman signals of more than 4 orders of magnitude compared to 5-ethynyl-2′-deoxyuridine (EdU). In addition, a frequency tunability of 10 distinct Raman bands was achieved by selecting different types of vibrational modes. This methodology of AERS allows for designing small-molecule Raman probes to visualize various entities in complex systems by multicolor spontaneous Raman imaging. It will open new prospects to explore innovative applications of AERS in interdisciplinary research fields.

PtSe ₂ /SiH type-II van der Waals heterostructure is a highly efficient photocatalyst for water splitting in visible light.

Noise and fluorescent background are two major problems for acquiring Raman spectra from samples, which blur Raman spectra and make Raman detection or imaging difficult. In this paper, a novel algorithm based on wavelet transform that contains denoising and baseline correction is presented to automatically extract Raman signals. For the denoising section, the improved conventional-scale correlation denoising method is proposed. The baseline correction section, which is performed after denoising, basically consists of five aspects: (1) detection of the peak position; (2) approximate second derivative calculation based on continuous wavelet transform is performed using the Haar wavelet function to find peaks and background areas; (3) the threshold is estimated from the peak intensive area for identification of peaks; (4) correction of endpoints, spectral peaks, and peak position; and (5) determine the endpoints of the peak after subtracting the background. We tested this algorithm for simulated and experimental Raman spectra, and a satisfactory denoising effect and a good capability to correct background are observed. It is noteworthy that this algorithm requires few human interventions, which enables automatic denoising and background removal.

The built-in electric field promotes GaAs/SiH as a high efficiency photocatalyst for water splitting in visible light.