Surface segregation in blended polymer films has attracted much interest in fundamental research as well as for practical applications. A variety of methodologies have been proposed for controlling surface segregation. They often require long annealing times, however, to achieve thermodynamic equilibrium. Here, a strategy and proof‐of‐principle experiments are described to control surface segregation of thin block‐copolymer (BCP) layers on top of a homopolymer in a single casting step from blended BCP/homopolymer solutions. The surface coverage by the minor constituent BCP (2–10 wt%) is accomplished despite almost identical surface energies of BCP and homopolymer constituents. Immersing this casted solution into water for nonsolvent induced phase separation (NIPS), a nonequilibrium process, affords solidified bilayer ultrafiltration membranes composed of a thin porous surface layer of self‐assembled BCP atop an asymmetric porous homopolymer substructure. Key to successful BCP surface segregation is the choice of a binary solvent system based on careful considerations of solvent surface energies and polymer‐solvent interaction parameters. Furthermore, stabilizing the BCP micellar structure by a divalent metal additive is also essential. The approach provides a cost‐effective method for fabricating bilayer‐type asymmetric ultrafiltration membranes with uniform BCP self‐assembly based selective top surface pore layers in a single casting step.
Directed self‐assembly of block copolymers is a key enabler for nanofabrication of devices with sub‐10 nm feature sizes, allowing patterning far below the resolution limit of conventional photolithography. Among all the process steps involved in block copolymer self‐assembly, solvent annealing plays a dominant role in determining the film morphology and pattern quality, yet the interplay of the multiple parameters during solvent annealing, including the initial thickness, swelling, time, and solvent ratio, makes it difficult to predict and control the resultant self‐assembled pattern. Here, machine learning tools are applied to analyze the solvent annealing process and predict the effect of process parameters on morphology and defectivity. Two neural networks are constructed and trained, yielding accurate prediction of the final morphology in agreement with experimental data. A ridge regression model is constructed to identify the critical parameters that determine the quality of line/space patterns. These results illustrate the potential of machine learning to inform nanomanufacturing processes.
more » « less- NSF-PAR ID:
- 10454333
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Materials
- Volume:
- 32
- Issue:
- 52
- ISSN:
- 0935-9648
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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