Evidence map›Paper›PMID 38530820›Full record

ArticlePloS one2024

On characterization of entropy measure using logarithmic regression model for Copper(II) Fluoride.

Muhammad Kamran Siddiqui, Mazhar Hussain, Sana Javed, Sadia Khalid, Tayyaba Noor, Fikadu Tesgera Tolasa

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.7field-weighted citation impact, top 29% of its field
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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

6 authors at 2 institutions in 1 country.

Muhammad Kamran SiddiquiDepartment of Mathematics, COMSATS University Islamabad, Lahore Campus, Pakistan.ORCID 0000-0002-2607-4847
Mazhar HussainDepartment of Mathematics, COMSATS University Islamabad, Lahore Campus, Pakistan.
Sana JavedDepartment of Mathematics, COMSATS University Islamabad, Lahore Campus, Pakistan.
Sadia KhalidDepartment of Mathematics, COMSATS University Islamabad, Lahore Campus, Pakistan.
Tayyaba NoorDepartment of Mathematics, COMSATS University Islamabad, Lahore Campus, Pakistan.
Fikadu Tesgera TolasaDepartment of Mathematics, Dambi Dollo University, Oromia, Ethiopia.ORCID 0009-0000-8925-9745
COMSATS University Islamabad · PKDambi Dollo University

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The versatile uses of Copper(II) Fluoride (CuF2) are well known; these include its usage as a precursor in chemical synthesis as well as its contribution to the creation of sophisticated materials and electronics. There are interesting opportunities to study the interactions between these elements because of their unique crystal structure, which contains copper ions and fluoride anions. Its potential in optoelectronic devices and conductive qualities also make it a viable material for next-generation technologies. To better understand the structural properties of CuF2 and how they affect its entropy, we present new Zagreb indices in this study and use them to calculate entropy measures. We also build a regression model to clarify the relationship between the calculated indices and entropy levels. The findings of our investigation offer significant understanding regarding the ability of the suggested Zagreb indices to extract meaningful content and their correlation with entropy in the context of CuF2. This information is important for understanding CuF2 alloys and for exploring related complex materials.

Indexed as

CopperFluoridesEntropyCopperFluorides

Identifiers

PMID38530820
PMCPMC10965071
OpenAlexW4393188598

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.