An asymmetric fitting function for condensed-phase Raman spectroscopy

Korepanov, Vitaly I.; Sedlovets, Daria M.

doi:10.1039/c8an00710a

Cited by 39 publications

(23 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Instead, it shows distinct asymmetry that is attributed to the anharmonic interaction with acoustic phonons [38]. For the bulk crystal, the spectral profile can be well fitted with an asymmetric pseudo-Voigt function [41], however, the use of such line shape in PCM needs a solid justification. The spectral profile of the optical phonons in ZnO nanoparticles therefore is more complex than the PCM predictions.…”

Section: Resultsmentioning

confidence: 99%

Phonon confinement and size effect in Raman spectra of ZnO nanoparticles

Korepanov

Chan

Hsu

et al. 2019

Heliyon

Self Cite

View full text Add to dashboard Cite

We study Raman spectra of ZnO nanoparticles of 5–12 nm size in the whole range of the first-order phonon bands. We apply the 3D phonon confinement model (PCM) for the interpretation of the observed Raman spectra. It is found that PCM is well applicable to the acoustic modes as well as to the optical ones, despite the fact that PCM has been thought not to be suitable for acoustic phonons. We show that the asymptotic behavior of PCM for the small-size limit is more consistent with the observation than that of the elastic sphere model (ESM). Furthermore, PCM gives detailed information on the complex size-dependent shapes of the phonon bands.

show abstract

Section: Resultsmentioning

confidence: 99%

Phonon confinement and size effect in Raman spectra of ZnO nanoparticles

Korepanov

Chan

Hsu

et al. 2019

Heliyon

Self Cite

View full text Add to dashboard Cite

show abstract

“…For the latter, a multi‐peak fit of the experimental spectrum was done with individual peaks described as asymmetric pseudo‐Voigt functions. [ 12 ] This approach is illustrated in Figure S1.…”

Section: Methodsmentioning

confidence: 99%

Asymmetric least‐squares baseline algorithm with peak screening for automatic processing of the Raman spectra

Korepanov

2020

J Raman Spectroscopy

Self Cite

View full text Add to dashboard Cite

The signal in Raman spectroscopy has three components: vibrational bands, noise, and luminescence. Although the noise can be easily separated from the Raman bands either by a variety of algorithms or by adjusting the measurement conditions, to distinguish between the luminescence and the Raman peaks is a challenging task. Even though a number of algorithms have been proposed for this purpose, most of them show poor performance when applied to broad overlapping Raman bands with extended tails. A practical example of such a case can be found in many carbon-based nanoscale materials with some degree of disorder, where the two close-lying D and G bands dominate the spectrum. Conventional asymmetric least-squares algorithms generate a baseline with penalizing the second derivative of the baseline itself. Instead, the approach suggested in the present work is based on an assumption that the vibrational bands can be "detected" and "masked" by their first and second derivatives of the raw spectrum. This makes it possible to handle the broad tails and superimposed bands very efficiently. The proposed algorithm was compared to several state-of-the-art approaches including original and recently proposed ALS implementations, wavelet-transform, and morphological algorithms. The "quality" of different algorithms was quantitatively estimated by adding random smooth curves to the spectrum. It was found that among the tested approaches, the peak-screened ALS algorithm shows the best separation of the broad superimposed Raman peaks from the smooth random baselines. The Python code of the algorithm is provided.

show abstract

“…Moreover, Raman bands can be of different shapes except the symmetric bands. For this reason, we investigated the other two typical situations—asymmetric and shoulder bands . The result of asymmetric bands was shown in Figure a.…”

Section: Experiments and Discussionmentioning

confidence: 99%

Joint sparse representation and denoising method for Raman spectrum

Fang

Tao

Wang

et al. 2018

J Raman Spectroscopy

View full text Add to dashboard Cite

A new method based on joint sparse representation is developed to recover the peak information from high‐noise Raman signal. This method used the sparsity of Raman spectrum to recover the signals and preserve its useful peak information. The peak information is then reconstructed by using an orthogonal matching pursuit algorithm. The joint sparse representation method is found to be an effective approach to analyze the Raman spectrum, especially Raman spectrum that have high noise, thus improving the detection limit of Raman spectroscopy. Experimental results demonstrate that this approach is better than other approaches in case of low signal‐to‐noise ratios.

show abstract

An asymmetric fitting function for condensed-phase Raman spectroscopy

Cited by 39 publications

References 31 publications

Phonon confinement and size effect in Raman spectra of ZnO nanoparticles

Phonon confinement and size effect in Raman spectra of ZnO nanoparticles

Asymmetric least‐squares baseline algorithm with peak screening for automatic processing of the Raman spectra

Joint sparse representation and denoising method for Raman spectrum

Contact Info

Product

Resources

About