Summary of Nonlinear Manifold Learning Determines Microgel Size From Raman Spectroscopy, by Eleni D. Koronaki et al.
Nonlinear Manifold Learning Determines Microgel Size from Raman Spectroscopy
by Eleni D. Koronaki, Luise F. Kaven, Johannes M. M. Faust, Ioannis G. Kevrekidis, Alexander Mitsos
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach is proposed to accurately predict polymer particle sizes using Raman spectroscopy and machine learning techniques. Building on previous work that showed a correlation between Raman signals and particle sizes, three alternative workflows are developed: directly from diffusion maps, alternating diffusion maps, and conformal autoencoder neural networks. These methods are applied to a dataset of 47 microgel samples with size measurements via dynamic light scattering, yielding a promising prediction of polymer size from Raman spectra. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists have been trying to figure out how to measure the size of tiny particles in polymers using Raman spectroscopy. This technique is good for checking if there’s enough material, but it’s not great at telling us the actual size of the particles. To solve this problem, researchers developed three new ways to use machine learning and diffusion maps to predict particle sizes from Raman signals. They tested these methods on a bunch of samples and found that one approach was really good at getting the size right. |
Keywords
* Artificial intelligence * Autoencoder * Diffusion * Machine learning
