Evidence map›Paper›PMID 33681607›Full record

ArticleACS omega2021

Significant Capacitance Enhancement via In Situ Exfoliation of Quasi-One-Dimensional Graphene Nanostripes in Supercapacitor Electrodes.

Jacob D Bagley, Dorte R Danielsen, Nai-Chang Yeh

Abstract read
In one paragraph

Article in ACS omega, 2021. 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. 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

3 authors.

Jacob D BagleyDivision of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, United States.
Dorte R DanielsenDepartment of Physics, California Institute of Technology, Pasadena, California 91125, United States.
Nai-Chang YehDepartment of Physics, California Institute of Technology, Pasadena, California 91125, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Graphene has received much attention as a supercapacitor electrode material due to its chemical inertness in preventing reaction with electrolytes and the large surface area due to its two-dimensional nature. However, when graphene sheets are processed into electrodes, they tend to stack together and form a turbostratic graphite material with a much reduced surface area relative to the total surface area of individual graphene sheets. Separately, electrochemical exfoliation of graphite is one method of producing single-layer graphene, which is often used to produce graphene for supercapacitor electrodes, although such exfoliated graphene still leads to reduced surface areas due to stacking during electrode fabrication. To utilize the large surface area of graphene, graphene must be exfoliated in situ within a supercapacitor device after the device fabrication. However, graphitic electrodes are typically destroyed upon exfoliation, which is largely due to the loss of electrical connectivity among small exfoliated graphene flakes. Here, we report successful in situ exfoliation of graphene nanostripes, a type of quasi-one-dimensional graphene nanomaterial with large length-to-width aspect ratios, as the anode material in supercapacitors. We find that the in situ exfoliation leads to over 400% enhancement in capacitance as the result of retaining the electrical connectivity among exfoliated quasi-one-dimensional graphene nanostripes in addition to increasing the total surface area, paving ways to fully realizing the benefit of graphene electrodes in supercapacitor applications.

Identifiers

PMID33681607
PMCPMC7931431

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.