(338ad) Enhancing PLA/PBAT Blends with Sustainable Nanomaterials
AIChE Annual Meeting
2023
2023 AIChE Annual Meeting
Materials Engineering and Sciences Division
Poster Session: Materials Engineering & Sciences (08A - Polymers)
Tuesday, November 7, 2023 - 3:30pm to 5:00pm
In this study, PLA (PLE 005-1, NaturePlast, France) is enhanced by a) blending it with polybutylene adipate terephthalate (PBAT), a biodegradable rubbery biopolyester (PBE-006 NaturePlast, France), at selected ratios, b) adding a selected coupling agent (Lotader AX-8900, Terpolymer: ethylene-methyl acrylate-glycidyl methacrylate), and c) dosing a combination of cellulose nanofibrils (Freeze-dried experimental grade powder, University of Maine, CNF) and graphitic carbon nitride (in-house synthesized g-C3N4) [4] at chosen concentrations determined by a detailed design of experiment (DOE). The blends and compounding were conducted by high-speed extrusion using a PA11 Thermofisher Scientific Twin-screw Extruder (11-mm in diameter and L/D ratio of 40) at optimized processing conditions and screw configuration (i.e., maximum specific energy of mixing).
The neat PLA (our control, tested dried as molded, ASTM D638 Type V) exhibited a Youngâs modulus (YM), an ultimate tensile strength (UTS), an elongation-at-break (EB), and a toughness value of 3.955±0.398 GPa, 75.97±5.38 MPa, 2.93±0.26%, and 1.380±0.19Ã106 J/m3, respectively. Interestingly, at an 80/20 PLA/PBAT ratio, the blends displayed an EB and toughness value of 125.80±16.08%, and 65.05±9.13Ã106 J/m3, respectively. These values correspond to about a 40-fold and 50-fold increase with respect to the controlâs properties, respectively. In addition, this blend ratio retained virtually all the stiffness and strength of the pure PLA. Significant phase separation occurred between the PLA and PBAT domains as observed by SEM, which may be responsible for the effective toughening effect attained [5].
The next stage of this investigation will include the addition of the coupling agent to engineer the polymeric domainsâ interphase and that of the nanoparticles to achieve compatibility and functionality in the blends. The experimental methodology for this study will be carried out following a detailed DOE that will first analyze the responses using an optimal custom response surface methodology (RSM) with a point exchange algorithm [6] and I-optimality criterion (I-OC) [6, 7, 8]. Secondly, the influence of g-C3N4 and CNF nanoparticles will be studied by DOE optimal blend point using a combined optimal custom design of mixture and process to predict synergia as well as the individual impact of the nanoparticles. Thus, by using I-OC [6, 7] with point exchange algorithm, the optimal g-C3N4/CNF ratios and concentration levels for the mechanical, thermal, microstructural and biodegradability responses will be predicted. In this opportunity, we intend to present the latest work on this processing approach for the enhancement of PLA biopolymer.
References
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(2) Russo, D. Acierno and G. Filippone, "Mechanical performance of polylactic based formulations," in Biocomposites: Design and Mechanical Performance, Woodhead Publishing Series in Composites Science and Engineering: Number 61, 2015, pp. 19-20.
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(4) Majdoub, A. Amedlous, Z. Anfar and A. Jada, "Engineering of H-Bonding Interactions in PVA/g-C3N4 Hybrids for Enhanced Structural, Thermal, and Mechanical Properties: Toward Water-Responsive Shape Memory Nanocomposites," Advanced Materials Interfaces, pp. 1-13, 2022.
(5) Lai, J. Li, P. Liu, L. Wu, S. J. Severtson and W.-J. Wang, "Mechanically reinforced biodegradable Poly(butylene adipate-co-terephthalate) with interactive nanoinclusions," Polymer, pp. 1-7, 2020.
(6) C. MONTGOMERY, Design and Analysis of Experiments, 9 ed., John Wiley & Sons, 2007, pp. 550-554.
(7) H. Myers, D. C. Montgomery and C. M. Anderson-Cook, Response Surface Methodology: Process and Product Optimization Using Designed Experiments. THIRD EDITION, WILEY SERIES IN PROBABILITY AND STATISTICS, 2009, pp. 369-371.
(8) J. Anderson and P. J. Whitcomb, RSM Simplified: Optimizing Processes Using Response Surface Methods for Design of Experiments, Second Edition 2nd Edition, Second ed., Productivity Press, 2016, pp. 137-152.