Evidence map›Paper›PMID 33050317›Full record

ArticleNanomaterials (Basel, Switzerland)2020

Design and Synthesis of Novel 2D Porous Zinc Oxide-Nickel Oxide Composite Nanosheets for Detecting Ethanol Vapor.

Yuan-Chang Liang, Yen-Cheng Chang, Wei-Cheng Zhao

Abstract read
In one paragraph

Article in Nanomaterials (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Mechanisms of NOSensors (Basel, Switzerland) · 2022
    Review
  2. 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

3 authors.

Yuan-Chang LiangDepartment of Optoelectronics and Materials Technology, National Taiwan Ocean University, Keelung 20224, Taiwan.
Yen-Cheng ChangDepartment of Optoelectronics and Materials Technology, National Taiwan Ocean University, Keelung 20224, Taiwan.
Wei-Cheng ZhaoDepartment of Optoelectronics and Materials Technology, National Taiwan Ocean University, Keelung 20224, Taiwan.

Funding

Ministry of Science and Technology, Taiwan MOST 108-2221-E-019-034-MY3
6 · The paper itself

Abstract

The porous zinc oxide-nickel oxide (ZnO-NiO) composite nanosheets were synthesized via sputtering deposition of NiO thin film on the porous ZnO nanosheet templates. Various NiO film coverage sizes on porous ZnO nanosheet templates were achieved by changing NiO sputtering duration in this study. The microstructures of the porous ZnO-NiO composite nanosheets were investigated herein. The rugged surface feature of the porous ZnO-NiO composite nanosheets were formed and thicker NiO coverage layer narrowed the pore size on the ZnO nanosheet template. The gas sensors based on the porous ZnO-NiO composite nanosheets displayed higher sensing responses to ethanol vapor in comparison with the pristine ZnO template at the given target gas concentrations. Furthermore, the porous ZnO-NiO composite nanosheets with the suitable NiO coverage content demonstrated superior gas-sensing performance towards 50-750 ppm ethanol vapor. The observed ethanol vapor-sensing performance might be attributed to suitable ZnO/NiO heterojunction numbers and unique porous nanosheet structure with a high specific surface area, providing abundant active sites on the surface and numerous gas diffusion channels for the ethanol vapor molecules. This study demonstrated that coating of NiO on the porous ZnO nanosheet template with a suitable coverage size via sputtering deposition is a promising route to fabricate porous ZnO-NiO composite nanosheets with a high ethanol vapor sensing ability.

Indexed as

2D nanoarchitecturecompositeshydrothermalsensing responsesputtering

Identifiers

PMID33050317
PMCPMC7599868

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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.