Evidence map›Paper›PMID 33637751›Full record

ArticleNature communications2021

Electrical switching of high-performance bioinspired nanocellulose nanocomposites.

Dejin Jiao, Francisco Lossada, Jiaqi Guo, Oliver Skarsetz, Daniel Hoenders, Jin Liu, Andreas Walther

Abstract read
In one paragraph

Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
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  3. Review
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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.

Dejin JiaoInstitute for Macromolecular Chemistry, University of Freiburg, Freiburg, Germany.
Francisco LossadaInstitute for Macromolecular Chemistry, University of Freiburg, Freiburg, Germany.
Jiaqi GuoInstitute for Macromolecular Chemistry, University of Freiburg, Freiburg, Germany.
Oliver SkarsetzInstitute for Macromolecular Chemistry, University of Freiburg, Freiburg, Germany.ORCID http://orcid.org/0000-0002-1540-1873
Daniel HoendersInstitute for Macromolecular Chemistry, University of Freiburg, Freiburg, Germany.
Jin LiuInstitute for Macromolecular Chemistry, University of Freiburg, Freiburg, Germany.
Andreas WaltherInstitute for Macromolecular Chemistry, University of Freiburg, Freiburg, Germany. andreas.walther@uni-mainz.de.ORCID http://orcid.org/0000-0003-2170-3306

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nature fascinates with living organisms showing mechanically adaptive behavior. In contrast to gels or elastomers, it is profoundly challenging to switch mechanical properties in stiff bioinspired nanocomposites as they contain high fractions of immobile reinforcements. Here, we introduce facile electrical switching to the field of bioinspired nanocomposites, and show how the mechanical properties adapt to low direct current (DC). This is realized for renewable cellulose nanofibrils/polymer nanopapers with tailor-made interactions by deposition of thin single-walled carbon nanotube electrode layers for Joule heating. Application of DC at specific voltages translates into significant electrothermal softening via dynamization and breakage of the thermo-reversible supramolecular bonds. The altered mechanical properties are reversibly switchable in power on/power off cycles. Furthermore, we showcase electricity-adaptive patterns and reconfiguration of deformation patterns using electrode patterning techniques. The simple and generic approach opens avenues for bioinspired nanocomposites for facile application in adaptive damping and structural materials, and soft robotics.

Identifiers

PMID33637751
PMCPMC7910463

What OpenQuestion holds

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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.