Evidence map›Paper›PMID 32523076›Full record

ArticleScientific reports2020

Nano-engineering the material structure of preferentially oriented nano-graphitic carbon for making high-performance electrochemical micro-sensors.

Edoardo Cuniberto, Abdullah Alharbi, Ting Wu, Zhujun Huang, Kasra Sardashti, Kae-Dyi You, Kim Kisslinger, Takashi Taniguchi, Kenji Watanabe, Roozbeh Kiani and 1 more

Abstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

11 authors.

Edoardo Cuniberto *Electrical and Computer Engineering, New York University, Brooklyn, NY, 11201, USA.
Abdullah Alharbi *Electrical and Computer Engineering, New York University, Brooklyn, NY, 11201, USA.
Ting WuCenter for Neural Science, New York University, New York, NY, 10003, USA.
Zhujun HuangElectrical and Computer Engineering, New York University, Brooklyn, NY, 11201, USA.
Kasra SardashtiCenter for Quantum Phenomena, Physics Department, New York University, New York, NY, 10003, USA.
Kae-Dyi YouElectrical and Computer Engineering, New York University, Brooklyn, NY, 11201, USA.
Kim KisslingerCenter for Functional Nanomaterials, Brookhaven National Laboratory, Upton, NY, 11973, USA.
Takashi TaniguchiNational Institute of Materials Science, 1-1 Namiki Tsukuba, Ibaraki, 305-0044, Japan.
Kenji WatanabeNational Institute of Materials Science, 1-1 Namiki Tsukuba, Ibaraki, 305-0044, Japan.
Roozbeh KianiCenter for Neural Science, New York University, New York, NY, 10003, USA.
Davood ShahrjerdiElectrical and Computer Engineering, New York University, Brooklyn, NY, 11201, USA. davood@nyu.edu.

Funding

CRCNS: Neural coding and computation in large ensembles in prefrontal cortexR01MH109180 · NIMH · NEW YORK UNIVERSITY · PI KIANI, ROOZBEH · 2015 to 2018
$939k
NIMH NIH HHS R01 MH109180
6 · The paper itself

Abstract

Direct synthesis of thin-film carbon nanomaterials on oxide-coated silicon substrates provides a viable pathway for building a dense array of miniaturized (micron-scale) electrochemical sensors with high performance. However, material synthesis generally involves many parameters, making material engineering based on trial and error highly inefficient. Here, we report a two-pronged strategy for producing engineered thin-film carbon nanomaterials that have a nano-graphitic structure. First, we introduce a variant of the metal-induced graphitization technique that generates micron-scale islands of nano-graphitic carbon materials directly on oxide-coated silicon substrates. A novel feature of our material synthesis is that, through substrate engineering, the orientation of graphitic planes within the film aligns preferentially with the silicon substrate. This feature allows us to use the Raman spectroscopy for quantifying structural properties of the sensor surface, where the electrochemical processes occur. Second, we find phenomenological models for predicting the amplitudes of the redox current and the sensor capacitance from the material structure, quantified by Raman. Our results indicate that the key to achieving high-performance micro-sensors from nano-graphitic carbon is to increase both the density of point defects and the size of the graphitic crystallites. Our study offers a viable strategy for building planar electrochemical micro-sensors with high-performance.

Identifiers

PMID32523076
PMCPMC7286892

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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.