Evidence map›Paper›PMID 41233347›Full record

ArticleNature communications2025

Film surface assemblies from chemically distinct block copolymer micelles.

Lieihn Tsaur, Luis A Nieves-Rosado, B P Prajwal, Qi Zhang, Yuk Mun Li, Fernando A Escobedo, Ulrich B Wiesner

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Lieihn TsaurDepartment of Materials Science and Engineering, Cornell University, Ithaca, NY, USA.ORCID http://orcid.org/0000-0002-7120-8453
Luis A Nieves-RosadoDepartment of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, USA.ORCID http://orcid.org/0009-0000-1029-2519
B P PrajwalDepartment of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, USA.
Qi ZhangDepartment of Materials Science and Engineering, Cornell University, Ithaca, NY, USA.
Yuk Mun LiDepartment of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, USA.
Fernando A EscobedoDepartment of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, USA.
Ulrich B WiesnerDepartment of Materials Science and Engineering, Cornell University, Ithaca, NY, USA. ubw1@cornell.edu.ORCID http://orcid.org/0000-0001-6934-3755

Funding

National Science Foundation (NSF) DMR-2307013
6 · The paper itself

Abstract

Block copolymer (BCP) self-assembly - neat, or in a homopolymer/second BCP containing molecular blend - enables complex nanostructures including spherical micelles, co-continuous cubic gyroids and Frank-Kasper phases. Emergent properties in metallic and high entropy alloys inspire a paradigm shift: treating chemically distinct BCP micelles as functional building blocks to pave a path for multicomponent assemblies with molecularly engineered property profiles. Slowing current progress are challenges associated with the structural characterization of such BCP assemblies with poor atomic contrast. Here, we demonstrate how film surface self-assemblies of binary and ternary BCP micelle alloys can be phase inverted to generate porosity. This, combined with machine-learning assisted image segmentation, allows component classification via routine scanning electron microscopy. Results are supported by mechanistic insights from Voronoi analysis, cluster analysis and Monte Carlo/Brownian Dynamics simulations, revealing rich and controllable non-equilibrium surface structural behavior. Such multicomponent materials are expected to enable emergent properties for a range of applications.

Identifiers

PMID41233347
PMCPMC12615628

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.