Evidence map›Paper›PMID 35557680›Full record

ArticleACS omega2022

Hysteresis Dynamic Modeling and Analysis of Flexible Nano Silver-Polyvinyl Alcohol Humidity Sensor Based on the Microscopic Process and Langmuir-Fick Theory.

Jie Xia, Xuepei Wang, Xiang Wang, Krisztina Majer-Baranyi, Xiaoshuan Zhang

Abstract read
In one paragraph

Article in ACS omega, 2022. 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

5 authors.

Jie XiaCollege of Engineering, China Agricultural University, Beijing 100083, China.
Xuepei WangCollege of Engineering, China Agricultural University, Beijing 100083, China.
Xiang WangCollege of Engineering, China Agricultural University, Beijing 100083, China.
Krisztina Majer-BaranyiFood Science Research Group, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, Herman Ottó út 15, Budapest H-1022, Hungary.
Xiaoshuan ZhangCollege of Engineering, China Agricultural University, Beijing 100083, China.ORCID https://orcid.org/0000-0002-0395-928X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, advances in materials science and manufacturing technologies have facilitated the development of flexible sensors. However, there are still performance gaps between emerging flexible sensors and traditional silicon-based rigid sensors, especially lacking dynamic modeling and optimization analysis for addressing above challenges. This paper describes a hysteresis dynamic modeling method for flexible humidity sensors. Through inkjet printing and coating methods, the polyvinyl alcohol (PVA) sensitive layer and nano silver interdigital electrode are fabricated on flexible polyethylene naphthalate substrates. The performance characterization results show that the sensitivity and maximum hysteresis within the range of 12-98% relative humidity (RH) are -0.02167 MΩ/% RH and 2.7% RH, respectively. The sensor also has outstanding dynamic response ability and stability in a wide range of humidity variation. The hysteresis mechanism of flexible humidity sensors is theoretically analyzed from microscopic hysteresis processes, Langmuir monomolecular adsorption dynamic modeling, and Fick diffusion dynamic modeling. These hysteresis models provide a path for the hysteresis optimization of flexible PVA humidity sensors. Further exploration of the diffusion rate of water molecules and the proportion of PVA in ink represents promising hysteresis optimization directions of flexible humidity sensors based on PVA-sensitive material.

Identifiers

PMID35557680
PMCPMC9089386

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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.