Evidence map›Paper›PMID 36120015›Full record

ArticleACS omega2022

Predicting the Surface Tension of Deep Eutectic Solvents Using Artificial Neural Networks.

Tarek Lemaoui, Abir Boublia, Ahmad S Darwish, Manawwer Alam, Sungmin Park, Byong-Hun Jeon, Fawzi Banat, Yacine Benguerba, Inas M AlNashef

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 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 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

9 authors.

Tarek LemaouiLaboratoire de Biopharmacie Et Pharmacotechnie (LPBT), Ferhat Abbas Setif 1 University, 19000 Setif, Algeria.ORCID https://orcid.org/0000-0002-9634-7134
Abir BoubliaLaboratoire de Physico-Chimie des Hauts Polymères (LPCHP), Département de Génie des Procédés, Faculté de Technologie, Université Ferhat ABBAS Sétif-1, 19000 Sétif, Algeria.
Ahmad S DarwishCenter for Membrane and Advanced Water Technology (CMAT), Khalifa University, P.O. Box 127788, 127788 Abu Dhabi, United Arab Emirates.
Manawwer AlamDepartment of Chemistry, College of Science, King Saud University, P.O. Box 2455, 11451 Riyadh, Saudi Arabia.ORCID https://orcid.org/0000-0001-9540-8532
Sungmin ParkDepartment of Civil and Environmental Engineering, Hanyang University, 222-Wangsimni-ro, Seongdong-gu, 04763 Seoul, Republic of Korea.
Byong-Hun JeonDepartment of Earth Resources and Environmental Engineering, Hanyang University, 04763 Seoul, Republic of Korea.
Fawzi BanatCenter for Membrane and Advanced Water Technology (CMAT), Khalifa University, P.O. Box 127788, 127788 Abu Dhabi, United Arab Emirates.ORCID https://orcid.org/0000-0002-7646-5918
Yacine BenguerbaLaboratoire de Biopharmacie Et Pharmacotechnie (LPBT), Ferhat Abbas Setif 1 University, 19000 Setif, Algeria.ORCID https://orcid.org/0000-0002-8251-9724
Inas M AlNashefCenter for Membrane and Advanced Water Technology (CMAT), Khalifa University, P.O. Box 127788, 127788 Abu Dhabi, United Arab Emirates.ORCID https://orcid.org/0000-0003-4654-2932

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Studies on deep eutectic solvents (DESs), a new class of "green" solvents, are attracting increasing attention from researchers, as evidenced by the rapidly growing number of publications in the literature. One of the main advantages of DESs is that they are tailor-made solvents, and therefore, the number of potential DESs is extremely large. It is essential to have computational methods capable of predicting the physicochemical properties of DESs, which are needed in many industrial applications and research. Surface tension is one of the most important properties required in many applications. In this work, we report a relatively generalized artificial neural network (ANN) for predicting the surface tension of DESs. The database used can be considered comprehensive because it contains 1571 data points from 133 different DES mixtures in 520 compositions prepared from 18 ions and 63 hydrogen bond donors in a temperature range of 277-425 K. The ANN model uses molecular parameter inputs derived from the conductor-like screening model for real solvents (

Identifiers

PMID36120015
PMCPMC9475633

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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.