Evidence map›Paper›PMID 33376793›Full record

ArticleACS central science2020

Selectively Micro-Patternable Fibers via In-Fiber Photolithography.

Youngbin Lee, Andres Canales, Gabriel Loke, Mehmet Kanik, Yoel Fink, Polina Anikeeva

Abstract read
In one paragraph

Article in ACS central science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Magnetically Actuated Fiber-Based Soft Robots.Advanced materials (Deerfield Beach, Fla.) · 2023
    Article
  5. Review
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.

Youngbin LeeDepartment of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
Andres CanalesDepartment of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
Gabriel LokeDepartment of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
Mehmet KanikResearch Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
Yoel FinkDepartment of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
Polina AnikeevaDepartment of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.

Funding

Fiber Inspired Neural Probes for the Multifunctional Dynamic Brain MappingR01NS086804 · NINDS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI ANIKEEVA, POLINA O · 2015 to 2019
$1.7M
NINDS NIH HHS R01 NS086804
6 · The paper itself

Abstract

Multimaterial fibers engineered to integrate glasses, metals, semiconductors, and composites found applications in ubiquitous sensing, biomedicine, and robotics. The longitudinal symmetry typical of fibers, however, limits the density of functional interfaces with fiber-based devices. Here, thermal drawing and photolithography are combined to produce a scalable method for deterministically breaking axial symmetry within multimaterial fibers. Our approach harnesses a two-step polymerization in thiol-epoxy and thiol-ene photopolymer networks to create a photoresist compatible with high-throughput thermal drawing in atmospheric conditions. This, in turn, delivers meters of fiber that can be patterned along the length increasing the density of functional points. This approach may advance applications of fiber-based devices in distributed sensors, large area optoelectronic devices, and smart textiles.

Identifiers

PMID33376793
PMCPMC7760470

What OpenQuestion holds

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Registered trials

None linked

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.