Polymer particle sizing from Raman spectra by regression of hard model parameters

Abstract

The particle size of polymer colloids is a key characteristic. It directly affects Raman measurements due to light scattering by particles. This publication exploits the extent to which these spectral changes can be correlated to the particle sizes by using a spectral hard model and data‐driven model. To this end, spectra of aqueous polystyrene nanoparticles are decomposed into pure component spectral models by means of indirect hard modeling (IHM). Each pure component model is characterized by multiple peak‐shaped Voigt profiles. By fitting the model to spectra of polymer colloids, spectral changes due to light scattering by particles are incorporated in the model parameters. The resulting parameter values are used as input for data‐driven partial least squares (PLS) regression to extract particle sizes. The method is applied to Raman spectra of polystyrene nanoparticles of 23–60 nm diameter measured repeatedly at concentrations 0.17–1 wt%. The hybrid model predicts the particle size with R2 = 0.99. In contrast, purely data‐driven PLS regression of the spectral intensities with 3 latent PLS variables results in R2 from 0.78 to 0.83. Resulting PLS scores portray a clear differentiability of Raman scattering and light scattering by particles within IHM model parameters. PLS regression coefficients further allow identification of individual peak parameters that correlate with particle size, which substantiates previous findings on correlation between Raman signal and scattering by polymer colloids. Combining IHM and data‐driven PLS regression demonstrates more accurate prediction of particle size for extended usage of Raman spectroscopy as comprehensive process analytical technology to determine sample concentrations and particle data.

Recent advances in linear and nonlinear Raman spectroscopy. Part XIII

“…Meyer‐Kirschner and co‐workers determined polymer particle sizing from Raman spectra by regression of hard model parameters. Partial least squares regression coeficients allow identification of individual peak parameters that correlate with particle, which substantiates previous findings on correlation between Raman signal and scattering by polymer colloids …”

“…Partial least squares regression coeficients allow identification of individual peak parameters that correlate with particle, which substantiates previous findings on correlation between Raman signal and scattering by polymer colloids. [169] 15.5 | Glasses, ceramics, powders, alloys, and disordered materials Bajpai et al used temperature-dependent Raman spectroscopy as a probe of swift heavy ion induced material modifications in Ba 1−x Sr x TiO 3 ceramics. [170] Nayak and Desa examined the roles of iron and lithium in silicate glasses by Raman spectroscopy.…”

A Component Content Measurement Method Modified Using Indirect Hard Modeling for Polymer Blends Based on Raman Spectroscopy

“…At present, the IHM method has mainly been used for the determination of fuel gas compositions, 23 solute content, 24,25 monomer content, 26 and particle size. 27 Inspired by the previous research, this study would like to explore the possibility of combining a method modified using IHM method and Raman spectroscopy to realize the measurement of the component content of polymer blends. To achieve this aim, the collected Raman spectra firstly were resolved into the sum of several parameterized independent Voigt peaks.…”

“…At present, the IHM method has mainly been used for the determination of fuel gas compositions, 23 solute content, 24,25 monomer content, 26 and particle size. 27…”

Development of pH- and temperature-sensitive microgel particles: synthesis monitoring, characterisation and application as potential oral carriers.

“…The results show that the monitored data allow the determination of the duration of the inhibition period, the rates of eosin consumption in different stages of the reaction, and the rate of polymerisation. According to the authors, the developed method could be applied to analyse the dynamic morphology of a wide range of polymer networks.Recently, many works have been developed in the research group of Meyer-Kirschner and coworkers about the in-line monitoring of precipitation polymerisation using Raman spectroscopyVIELL, 2018aVIELL, , 2018b. They applied Raman to monitor monomer and polymer content of poly(Nvinylcaprolactam) microgels, and the monomer and polymer fraction during copolymerisation of N-vinylcaprolactam and N-isopropylacrylamide.…”