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001025659 0247_ $$2doi$$a10.48550/ARXIV.2403.08376
001025659 037__ $$aFZJ-2024-03048
001025659 1001_ $$0P:(DE-HGF)0$$aKoronaki, Eleni D.$$b0
001025659 245__ $$aNonlinear Manifold Learning Determines Microgel Size from Raman Spectroscopy
001025659 260__ $$barXiv$$c2024
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001025659 520__ $$aPolymer particle size constitutes a crucial characteristic of product quality in polymerization. Raman spectroscopy is an established and reliable process analytical technology for in-line concentration monitoring. Recent approaches and some theoretical considerations show a correlation between Raman signals and particle sizes but do not determine polymer size from Raman spectroscopic measurements accurately and reliably. With this in mind, we propose three alternative machine learning workflows to perform this task, all involving diffusion maps, a nonlinear manifold learning technique for dimensionality reduction: (i) directly from diffusion maps, (ii) alternating diffusion maps, and (iii) conformal autoencoder neural networks. We apply the workflows to a data set of Raman spectra with associated size measured via dynamic light scattering of 47 microgel (cross-linked polymer) samples in a diameter range of 208nm to 483 nm. The conformal autoencoders substantially outperform state-of-the-art methods and results for the first time in a promising prediction of polymer size from Raman spectra.
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001025659 650_7 $$2Other$$aMachine Learning (cs.LG)
001025659 650_7 $$2Other$$aSignal Processing (eess.SP)
001025659 650_7 $$2Other$$aFOS: Computer and information sciences
001025659 650_7 $$2Other$$aFOS: Electrical engineering, electronic engineering, information engineering
001025659 7001_ $$0P:(DE-HGF)0$$aKaven, Luise F.$$b1
001025659 7001_ $$0P:(DE-HGF)0$$aFaust, Johannes M. M.$$b2
001025659 7001_ $$0P:(DE-HGF)0$$aKevrekidis, Ioannis G.$$b3
001025659 7001_ $$0P:(DE-Juel1)172025$$aMitsos, Alexander$$b4$$eCorresponding author$$ufzj
001025659 773__ $$a10.48550/ARXIV.2403.08376
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001025659 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Johns Hopkins University$$b3
001025659 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)172025$$aForschungszentrum Jülich$$b4$$kFZJ
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001025659 9141_ $$y2024
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001025659 9201_ $$0I:(DE-Juel1)IEK-10-20170217$$kIEK-10$$lModellierung von Energiesystemen$$x0
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