Projects per year
Abstract
Self-assembly of poly(styrene)-block-poly(isoprene)-block-poly(lactide)-block-poly(styrene) (PS-PI-PLA-PS′ or SILS′) tetrablock terpolymers, where the volume fractions of the first three blocks are nearly equivalent, was studied both experimentally and using the self-consistent field theory (SCFT). SCFT indicates that addition of the terminal PS′ chain to a low-molecular-mass, hexagonally packed cylinders forming, SIL precursor can produce a disordered state due to preferential mixing of the polystyrene end-blocks with the PI and PLA midblocks in the SILS′ tetrablock, alleviating the unfavorable contact between the highly incompatible PI and PLA segments. In contrast, SCFT predicts that higher-molar-mass triblock precursors will maintain an ordered morphology upon addition of the terminal PS′ block due to stronger overall segregation strengths. These predictions were tested using three sets of SILS′ polymers that were synthesized based on three precursor SIL triblock polymers differing in total molar mass (14, 30, and 47 kg mol-1) and varying the length of the terminal PS′ chain. In the lowest-molar-mass set of tetrablock polymers, the shift from order to disorder was observed in the materials at ambient temperature as the molar mass of the terminal PS′ block was increased, consistent with SCFT calculations. Disorder with longer S′ chain lengths was not found in the two higher-molar-mass polymer sets; the medium-molar-mass set showed both microphase separation and long-range order based on transmission electron microscopy (TEM) and small-angle X-ray scattering (SAXS), while the largest of these block polymers microphase separated but showed limited long-range order. The combination of the experimental and theoretical results presented in this work provides insights into the self-assembly of ABCA′-type polymers and highlights potential complications that arise from frustration in accessing well-ordered materials.
Original language | English (US) |
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Pages (from-to) | 10266-10275 |
Number of pages | 10 |
Journal | Journal of Physical Chemistry B |
Volume | 124 |
Issue number | 45 |
DOIs | |
State | Published - Nov 12 2020 |
Bibliographical note
Funding Information:This work was supported by the National Science Foundation (DMR-1333669 and DMR-1725272). During the bulk of this work, M.R.R. held the Camille and Henry Dreyfus Foundation Postdoctoral Fellowship in Environmental Chemistry. rac-Lactide was generously provided by Ortec. Parts of this work were carried out in the Characterization Facility, University of Minnesota, which receives partial support from NSF through the MRSEC program. SAXS data were obtained at the DuPont–Northwestern–Dow Collaborative Access Team (DND-CAT) located at Sector 5 of the Advanced Photon Source, a U. S. Department of Energy (DOE) Office of Science user facility operated for the DOE Office of Science by the Argonne National Laboratory under Contract No. DE-AC02-06CH11357. SCFT calculations were performed using the computational resources provided by the Minnesota Supercomputing Institute at the University of Minnesota.
MRSEC Support
- Shared
PubMed: MeSH publication types
- Journal Article
- Research Support, U.S. Gov't, Non-P.H.S.
Fingerprint
Dive into the research topics of 'Order and Disorder in ABCA′ Tetrablock Terpolymers'. Together they form a unique fingerprint.Projects
- 2 Finished
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MRSEC IRG-3: Hierarchical Multifunctional Macromolecular Materials
Reineke, T. M. (Coordinator), Bates, F. S. (Senior Investigator), Dorfman, K. (Senior Investigator), Dutcher, C. S. (Senior Investigator), Hillmyer, M. A. (Senior Investigator), Lodge, T. P. (Senior Investigator), Morse, D. C. (Senior Investigator), Siepmann, I. (Senior Investigator), Barreda, L. (Researcher) & Ganewatta, M. S. (Researcher)
11/1/14 → 10/31/20
Project: Research project
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Datasets
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Data for "Order and Disorder in ABCA′ Tetrablock Terpolymers"
Dorfman, K., Radlauer, M. R., Arora, A., Matta, M. E., Bates, F. S. & Hillmyer, M. A., Data Repository for the University of Minnesota, 2020
DOI: 10.13020/f0pj-0j55, http://hdl.handle.net/11299/217103
Dataset