Evidence map›Paper›PMID 39118645›Full record

ArticleACS sustainable chemistry & engineering2024

Mapping the Flammability Space of Sustainable Refrigerant Mixtures through an Artificial Neural Network Based on Molecular Descriptors.

Carlos G Albà, Ismail I I Alkhatib, Lourdes F Vega, Fèlix Llovell

Abstract read
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In one paragraph

Article in ACS sustainable chemistry & engineering, 2024. 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.

Carlos G AlbàDepartment of Chemical Engineering, ETSEQ, Universitat Rovira i Virgili (URV), Campus Sescelades, Av. Països Catalans 26, 43007 Tarragona, Spain.
Ismail I I AlkhatibResearch and Innovation Center on CO2 and Hydrogen (RICH Center) and Department of Chemical and Petroleum Engineering, Khalifa University, PO Box 127788 Abu Dhabi, United Arab Emirates.ORCID https://orcid.org/0000-0002-6769-5383
Lourdes F VegaResearch and Innovation Center on CO2 and Hydrogen (RICH Center) and Department of Chemical and Petroleum Engineering, Khalifa University, PO Box 127788 Abu Dhabi, United Arab Emirates.ORCID https://orcid.org/0000-0002-7609-4184
Fèlix LlovellDepartment of Chemical Engineering, ETSEQ, Universitat Rovira i Virgili (URV), Campus Sescelades, Av. Països Catalans 26, 43007 Tarragona, Spain.ORCID https://orcid.org/0000-0001-7109-6810

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As the EU's mandates to phase out high-GWP refrigerants come into effect, the refrigeration industry is facing a new, unexpected reality: the introduction of more flammable yet environmentally compliant alternatives. This paradigm shift amplifies the need for a rapid, reliable screening methodology to assess the propensity for flammability of emerging fourth generation blends, offering a pragmatic alternative to laborious and time-intensive traditional experimental assessments. In this study, an artificial neural network (ANN) is meticulously constructed, evaluated, and validated to address this emerging challenge by predicting the normalized flammability index (NFI) for an extensive array of pure, binary, and ternary mixtures, reflecting a substantial diversity of compounds like CO

Identifiers

PMID39118645
PMCPMC11304399

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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.