Sequence-Driven Phase and Conformational Transitions in Copolymers: From Molecular Thermodynamics to Biomimetic Materials

Living cells organize their internal chemistry into separate compartments, each with a specific function. Some compartments form when certain proteins naturally group together and separate from the rest of the cell. The exact order of a protein’s building blocks, known as its primary sequence, plays an important role in controlling whether this separation occurs. This project explores a similar process using synthetic polymer molecules to better understand the rules that govern how molecular sequence influences self-organization in materials. This work supports advanced manufacturing by contributing knowledge to design and create new materials with improved, targeted properties. It develops new design principles for smart materials that can self-organize in controlled and programmable ways, with potential uses in areas such as targeted drug delivery, artificial cells, and advanced manufacturing. The project also provides interdisciplinary training in chemistry, engineering, and physics through student mentorship, and it engages K–12 students through animated visualizations and interactive demonstrations at public outreach events. This project pursues a comprehensive theoretical and computational investigation of how monomer primary sequence controls the phase behavior and single-chain conformational transitions of non-ionic AB copolymers. Two main directions are explored: first, it tests the hypothesis that compositionally identical copolymers differing only in sequence blockiness can phase-separate into distinct macroscopic coacervate phases, providing a minimal physical model for sequence-driven intracellular compartmentalization; second, it examines whether monomer sequence governs the position, width, and cooperativity of the coil–globule transition in dilute solution, and whether specific sequence designs can produce internally structured globules (such as core–shell or striped morphologies) that resist aggregation. The methodology combines polymer field theory—specifically the Random Phase Approximation (RPA) with Gaussian fluctuation corrections—with large-scale coarse-grained simulations using dissipative particle dynamics (DPD) and molecular dynamics (MD). The RPA framework is extended to compute effective incompatibility parameters for non-ionic copolymers with arbitrary sequences, enabling prediction of macroscopic and microphase separation binodals. A range of systems, including regular block-alternating, random, and gradient sequences, is systematically studied across melts, concentrated solutions, and dilute solutions. Overall, the project develops a validated quantitative framework that links sequence metrics to phase behavior and single-chain conformations, advancing polymer physics beyond mean-field approximations and enabling rational sequence design for functional soft materials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2546055 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Artem Rumyantsev | Institution: North Carolina State University, RALEIGH, NC | Award Amount: $400,000 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2546055 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2546055.html