Abstract
Raman spectroscopy has emerged as a promising tool for its noninvasive and nondestructive characterization of local chemical structures. However, spectrally overlapping components prevent the specific identification of hyperfine molecular information of different substances, because of limitations in the spectral resolving power. The challenge is to find a way of preserving scattered photons and retrieving hidden/buried Raman signatures to take full advantage of its chemical specificity. Here, we demonstrate a multichannel acquisition framework based on shift-excitation and slit-modulation, followed by mathematical post-processing, which enables a significant improvement in the spectral specificity of Raman characterization. The present technique, termed shift-excitation blind super-resolution Raman spectroscopy (SEBSR), uses multiple degraded spectra to beat the dispersion-loss trade-off and facilitate high-resolution applications. It overcomes a fundamental problem that has previously plagued high-resolution Raman spectroscopy: fine spectral resolution requires large dispersion, which is accompanied by extreme optical loss. Applicability is demonstrated by the perfect recovery of fine structure of the C-Cl bending mode as well as the clear discrimination of different polymorphs of mannitol. Due to its enhanced discrimination capability, this method offers a feasible route at encouraging a broader range of applications in analytical chemistry, materials and biomedicine.
Highlights
It falls beneath the single-shot noise level
As a demonstration of our technique, we first show a scenario of an experiment working with synthetically blurred data to evaluate the quality of high resolution spectra for different SNRs, qualitatively and quantitatively
The last two experiments demonstrate that SEBSR retrieves high spectral specificity from degraded and often noisy spectra, which would otherwise not be possible with a normal deconvolution method in the originally recorded spectra
Summary
It falls beneath the single-shot noise level. In turn, this drastically deteriorates the quality of the Raman spectra with signal-to-noise (SNR) decreases, and the results can be unstable. Raman signatures become overlapped during acquisition as data pass through the dispersion and transmission processes because of the broadening effect of the instrumental response function Because this degradation can be typically modeled by convolution with a blur kernel, deconvolution is recognized as being useful for spectroscopy to upgrade the spectral resolution[6,8,9,10,11,12,13,14,15,16,17,18]. We attempted to find solutions to these problems that both techniques encounter This strongly ill-posed property of current deconvolution methods should be addressed as a typical underdetermined inverse problem, where we have more unknowns (spectrum and blur) than equations, and it is highly sensitive to noise and minor perturbations and prone to numerical inaccuracy for large spectral data (see Supplementary Note 1.1). Multiple Raman acquisitions based on slit-modulation and shift-excitation are implemented in our experiments, illustrated in Fig. 1(b), where an optical frequency comb (OFC) is utilized to calibrate the excitation shifts to achieve the precise recovery of high resolution spectra
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