Evidence map›Paper›PMID 34805710›Full record

ArticleACS omega2021

Quantitative Correlation of Droplets on Galvanic-Coupled Arrays with Response Current by Image Processing.

Moataz Mekawy, Eiji Terada, Shinji Inoue, Yukihiro Sakamoto, Jin Kawakita

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

5 authors.

Moataz MekawyNational Institute for Materials Science, 1-1, Namiki, Tsukuba 305-0044, Japan.ORCID https://orcid.org/0000-0002-3516-7578
Eiji TeradaNational Institute for Materials Science, 1-1, Namiki, Tsukuba 305-0044, Japan.
Shinji InoueNational Institute for Materials Science, 1-1, Namiki, Tsukuba 305-0044, Japan.
Yukihiro SakamotoChiba Institute of Technology, 2-17-1, Tsudanuma, Chiba 275-0016, Japan.
Jin KawakitaNational Institute for Materials Science, 1-1, Namiki, Tsukuba 305-0044, Japan.ORCID https://orcid.org/0000-0002-4821-4150

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Evaluating the presence of a slight amount of water plays a crucial role in practical applications such as the advanced detection of dew condensation and the microdetermination of perspiration and transpiration. For this purpose, we have developed a configuration for the moisture sensor that consists of a microgalvanic cell composed of narrow metal arrays. It is inferred that the output response current arising from this sensor should depend on the geometric parameters (e.g., number, area, volume, etc.) of water droplets attaching on the sensor surface. In this study, the output current was recorded, while the microscopic images of the sensor surface were captured. The droplets on the sensor surface were analyzed manually and by computational image processing with deep learning and ImageJ. The deep learning technique shortened the processing time to 1/1000 of the manual one and was able to match 90-100% of the manual count. The results revealed that the response current increased with the total projected area of droplets bridging the galvanic-coupled arrays on the sensor surface. In addition, a straight line with relatively strong positive correlation was obtained between the response current and the total volume of the bridging droplets. These findings suggested that our sensor can be practically used to estimate the presence of a slight amount of water.

Identifiers

PMID34805710
PMCPMC8600636

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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.