Talk: Bottom-up approach for biodegradable polymers used in additive manufacturing: building computational tools to bridge the gaps

Bacova, Petra; Christofi, Eleftherios; Harmandaris, Vagelis; Molina, Sergio I.

doi:10.5281/zenodo.17290333

Talk: Bottom-up approach for biodegradable polymers used in additive manufacturing: building computational tools to bridge the gaps

Bacova, Petra; Christofi, Eleftherios; Harmandaris, Vagelis; Molina, Sergio I.

Publisher: Zenodo
DOI: 10.5281/zenodo.17290333
Repository: zenodo.org
Language: en

Abstract

Oral contribution presented at Annual European Rheology Conference 2024 (https://rheology-esr.org/aercs/aerc-2024/welcome/) which took place from 9th to 12th of April 2024 in Leeds, UK. Abstract: In the family of biodegradable polymers, poly(lactic acid) (PLA) occupies a special place among those most popular, mainly due to its versatile usage in additive manufacturing. Despite being commercially available at low price, its usage as a full replacement of the synthetic polymers is hindered by its poor mechanical properties and thermal stability. In order to be able to tackle fundamental problems related to the structure-properties-performance relationship, we present a systematic simulation study of PLA of a wide range of molecular weights and stereochemistry. More specifically, we analyze the basic structural and dynamical properties at atomistic level and build a chemistry-specific coarse-grained (CG) model to extend the time and length scales to those relevant in the experimental studies. In addition, to close the loop, we implement a machine-learning based methodology to reinsert the atomistic details into CG models of different stereochemistry [1]. Since the computational techniques are considered to be a more sustainable alternative to the experimental characterization, we aim to extend the simulation practices commonly used for synthetic polymers to more complex bio-based polymers. By combining different computational techniques, we provide a consistent set of open-access tools [2,3] with the ultimate goal to facilitate the usage of multiscale computational analysis in the fast-growing field of biodegradable materials and additive manufacturing. [1] A Physics-informed Deep Learning Approach for Re-introducing Atomic Detail in Coarse-Grained Configurations of Multiple Poly(lactic Acid) Stereoisomers, E. Christofi, P. Bačová, V. Harmandaris, Journal of Chemical Information and Modeling, 2024 64 (6), 1853-1867 [2] https://github.com/SimEA-ERA/PLA-BackMap-CG [3] https://github.com/pbacova/PLA_analysis_tools.git

Full text

Bottom-up approach for biodegradable polymers used in additive manufacturing: building computational tools to bridge the gaps Petra Baˇ cová, Eleftherios Christofi, Vagelis Harmandaris, Sergio I. Molina Departamento de Ciencia de los Materiales e Ingeniería Metalúrgica y Química Inorgánica, Facultad de Ciencias, IMEYMAT, Campus Universitario Río San Pedro s/n., Puerto Real, Cádiz 11510, Spain PITS3D [1,2] * [1] https://adicork.es/index.php/sectores/; [2] Created with BioRender.com PITS3D Poly(lactic acid) in additive manufacturing ✓widely popular, easy to print ✓low melting point, no heated tray ✘sensitive to sunlight and high temperatures ✘commercial samples of unknown composition ➺Lvs. Dstereoisomer ➺additives, polydisperse samples ➺empirical approach to nanocomposite design PITS3D Rheology of poly(lactic acid) Ïeffect of the increasing D-content Ï100k, 423K, 10%, 25%, 50%, 100% 101 102 103 104 105 106 107 108 100101102103104105106107 %D dynamic moduli (G’,G’’)bT (Pa) angular frequency ωαΤ (rad/s) G’, D100 G’’, D100 G’, L50D50 G’’, L50D50 G’, L75D25 G’’, L75D25 G’, L90D10 G’’, L90D10 Othman, Norhayani, PhD thesis, 2012 PITS3D Rheology of poly(lactic acid) Ïeffect of tacticity Ï≈150k, 50% D, syndiotactic vs. atactic Othman, Norhayani, PhD thesis, 2012 L.-E. Chile, P. Mehrkhodavandi, S. G. Hatzikiriakos, Macromolecules (2016) 49 (3), 909-919 PITS3D Systematic bottom-up simulation approach Computational methods: (A) quantum: force field (B) atomistic: chemistry-specific interactions (C) coarse-grained: trends at mesoscale (D) continuum (A) (B) (C) (D) time length Multiscale: ✓quantitative predictions of macroscopic properties ✘time and resourse intensive [1] Guseva, D. V.; Glagolev, M. K.; Lazutin, A. A.; Vasilevskaya, V. V.; Polym. Rev. 2023, 0, 1–39 PITS3D Challenges and motivation ✘biodegradable polymers similar to proteins: slow structural rearrangements ✘H-bonds: atomistic detail ✘chirality: random Land Dcontent ✘lack of open-access data for data-driven approaches ✓extensive atomistic data set which can be used for systematic bottom-up and/or data-driven approaches ✘lack of experimental reference, monodisperse and well-defined systems ✓theoretical and experimental rheological data on reference systems to determine printability PITS3D Systems under investigation Ïmolecular dynamics simulations: atomistic and coarse-grained Ïnon-entangled melts Ï500K, 1 atm Label Mw Microstructure [g/mol] PLLA PDLA PLLA100 7.2 k 100%0% PDLA100 7.2 k 0%100% Copo100 7.2 k 45%55% PLLA30 2.2 k 100%0% PDLA30 2.2 k 0%100% Copo30 2.2 k 84%16% PITS3D Backmapping procedure (A) atomistic dataset, 5000 frames per system, training set: PLLA100, PDLA100 and Copo100 (B) encoding and learning ➺convolutional neural network ➺versatile: bond vectors and lengths ➺local: no intermonomeric and intermolecular information (C) coarse-graining, monomer-like representation (D) backmapping ➺verify the model ➺testing the transferability (A) (B) (C) (D)