Evidence map›Paper›PMID 35708874›Full record

ArticleJournal of molecular modeling2022

Interfacial interaction-driven rheological properties of quartz nanofluids from molecular dynamics simulations and density functional theory calculations.

Zhaoyang Lou, Chen Cheng, Yingqi Cui, Hao Tian

Abstract read
In one paragraph

Article in Journal of molecular modeling, 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

4 authors.

Zhaoyang LouDepartment of Radiation Oncology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China. louslove@163.com.ORCID http://orcid.org/0000-0002-1336-7261
Chen ChengDepartment of Strategy and Healthcare Development, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China.
Yingqi CuiInstitute of Atomic and Molecular Physics, Sichuan University, Chengdu, China.
Hao TianInstitute of Atomic and Molecular Physics, Sichuan University, Chengdu, China.

Funding

Scientific Research Foundation for Doctoral of the Affiliated Cancer Hospital of Zhengzhou University 3101030102/1017
6 · The paper itself

Abstract

Correlations of the shear viscosity of quartz nanofluids with particle concentration, particle size, and temperature were investigated with molecular dynamics simulations and density functional theory (DFT) calculations. A new understanding to the experimentally concluded correlations was addressed in terms of microscopic particle-water interfacial interaction in three aspects. First, the viscosity of quartz nanofluids at different particle concentrations, particle sizes, and temperatures were simulated using the equilibrium molecular dynamics simulations method to reproduce the experimental observations. At the same particle size, the nanofluid viscosity decreases significantly with temperature and increases with nanoparticle volume concentration, and at the same volume concentration, the nanofluid viscosity increases with the decrease of particle size. Second, DFT calculations confirm a stronger particle-water interaction than that among water molecules. The important role of particle-water interaction in the viscosity determination of nanofluids was revealed. Finally, a correlation was proposed to fit the simulated results and compared with earlier two-parameter correlations. One parameter in the correlation is indeed a constant, while the other is a function of SiO

Indexed as

Density functional theory calculationMolecular dynamics simulationQuartz nanofluidsViscosity

Identifiers

PMID35708874
PMCPMC9203409

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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.