QSPR studije karbonilnih, hidroksilnih, polienskih indeksa i prosječne molekulske težine polimera pod fotostabilizacijom pristupom ANN i MLR

Author:

Maouz Hadjira1,Laidi Maamar2,Hamadache Mabrouk3,Ammi Yamina4,Hanini Salah3,Khaouane Latifa3

Affiliation:

1. Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, Quartier Aïn d'Heb, 26000, Algeria

2. Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa

3. Faculty of Technology, University of Médéa, LBMPT Laboratory

4. a Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, 26000 Médéa, Algeria; b Department of Chemical Engineering, University Center Ahmed Zabana Relizane, 48000 Relizane, Algeria

Abstract

One of the main disadvantages of the use of synthetic or semi-synthetic polymeric materials is their degradation and aging. The purpose of this study was to use artificial neural networks (ANN) and multiple linear regressions (MLR) to predict the carbonyl, hydroxyl, and polyene indices (ICO, IOH, and IOP), and viscosity average molecular weight (MV) of poly(vinyl chloride), polystyrene, and poly(methyl methacrylate). These physicochemical properties are considered fundamental during the study of photostabilization of polymers. From the five repeating units of monomers, the structure of the polymer studied is shown. Quantitative structure-property relationship (QSPR) models obtained by using relevant descriptors showed good predictability. Internal validation {R2, RMSE, and Q2LOO}, external validation {R2, RMSE, Q2pred, rm2, Δrm2, k, and k’}, and applicability domain were used to validate these models. The comparison of the results shows that the ANN models are more efficient than those of the MLR models. Accordingly, the QSPR model developed in this study provides excellent predictions, and can be used to predict ICO, IOH, IOP, and MV of polymers, particularly for those that have not been tested.

Publisher

Croatian Society of Chemical Engineers/HDKI

Subject

General Chemical Engineering,General Chemistry

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