Evidence map›Paper›PMID 35267326›Full record

ArticleFoods (Basel, Switzerland)2022

A New Perspective to Tribocharging: Could Tribocharging Lead to the Development of a Non-Destructive Approach for Process Monitoring and Quality Control of Powders?

Hadi Mehrtash, Dinara Konakbayeva, Solmaz Tabtabaei, Seshasai Srinivasan, Amin Reza Rajabzadeh

Abstract read
In one paragraph

Article in Foods (Basel, Switzerland), 2022. 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.

Hadi MehrtashDepartment of Mechanical Engineering, McMaster University, Hamilton, ON L8S 3L8, Canada.
Dinara KonakbayevaDepartment of Chemical Engineering, Howard University, Washington, DC 20060, USA.
Solmaz TabtabaeiDepartment of Chemical Engineering, Howard University, Washington, DC 20060, USA.
Seshasai SrinivasanDepartment of Mechanical Engineering, McMaster University, Hamilton, ON L8S 3L8, Canada.ORCID 0000-0002-3765-3772
Amin Reza RajabzadehDepartment of Mechanical Engineering, McMaster University, Hamilton, ON L8S 3L8, Canada.ORCID 0000-0002-5371-3245

Funding

Natural Sciences and Engineering Research Council NSERC DG 2019
6 · The paper itself

Abstract

This study explores a new perspective on triboelectrification that could potentially lead to the development of a non-destructive approach for the rapid characterization of powders. Sieved yellow pea powders at various particle sizes and protein contents were used as a model system for the experimental charge measurements of the triboelectrified powders. A tribocharging model based on the prominent condenser model was combined with a Eulerian-Lagrangian computational fluid dynamics (CFD) model to simulate particle tribocharging in particle-laden flows. Further, an artificial neural network model was developed to predict particle-wall collision numbers based on a database obtained through CFD simulations. The tribocharging and CFD models were coupled with the experimental tribocharging data to estimate the contact potential difference of powders, which is a function of contact surfaces' work functions and depends on the chemical composition of powders. The experimentally measured charge-to-mass ratios were linearly related to the calculated contact potential differences for samples with different protein contents, indicating a potential approach for the chemical characterization of powders.

Indexed as

artificial neural network (ANN)computational fluid dynamics (CFD)rapid characterizationtribocharging

Identifiers

PMID35267326
PMCPMC8909115

What OpenQuestion holds

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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.