Evidence map›Paper›PMID 41259526›Full record

ArticleScience advances2025

Directed self-assembly of 3D interconnected networks.

Mingchao Ma, Guillermo A Hernández-Mendoza, Baopu Zhang, Zehao Sun, Alfredo Alexander-Katz, Caroline A Ross

Abstract read
In one paragraph

Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Flow-Induced Microdomain Alignment During Block Copolymer Graphoepitaxy.Advanced materials (Deerfield Beach, Fla.) · 2026
    Article
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

6 authors.

Mingchao MaDepartment of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.ORCID 0000-0003-4563-7542
Guillermo A Hernández-MendozaDepartment of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.ORCID 0000-0003-0086-9382
Baopu ZhangDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.ORCID 0000-0001-6512-0474
Zehao SunDepartment of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.ORCID 0000-0002-9720-694X
Alfredo Alexander-KatzDepartment of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.ORCID 0000-0001-5554-1283
Caroline A RossDepartment of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.ORCID 0000-0003-2262-1249

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Directed self-assembly (DSA) of block copolymers (BCPs) has long been included in the semiconductor roadmap as a lithographic pathway to enable continued device scaling. Tremendous progress has been made in generating two-dimensional (2D) BCP patterns with device-relevant features and low defect density and in transferring these patterns to functional materials. Extension of pattern generation into 3D could markedly enhance the utility of BCPs in nanoscale manufacturing, but methods to template and synthesize well-ordered, nontrivial 3D structures are less well developed. Here, we demonstrate a hierarchical DSA method to generate cross-point structures with connected in-plane and out-of-plane segments and controlled orientation angle between the horizontal layers. Various highly ordered, 3D interconnected networks, including ladder and cross-point structures, are produced by combinations of surface modification, BCP periodicity, and topographic templates, expanding the capabilities of BCP-derived nanofabrication.

Identifiers

PMID41259526
PMCPMC13142751

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.