Advancing Ionic Liquid Research with pSCNN: A Novel Approach for Accurate Normal Melting Temperature Predictions.

Tao Liang, Wei Liu, Kai Tan, Anan Wu, Xin Lu

Abstract read

In one paragraph

Article in ACS omega, 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

–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

0 citing papers in PubMed.

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

5 authors.

Tao LiangState Key Laboratory of Physical Chemistry of Solid Surface, Fujian Provincial Key Laboratory for Theoretical and Computational Chemistry, Departmental of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China.

Wei LiuState Key Laboratory of Physical Chemistry of Solid Surface, Fujian Provincial Key Laboratory for Theoretical and Computational Chemistry, Departmental of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China.

Kai TanState Key Laboratory of Physical Chemistry of Solid Surface, Fujian Provincial Key Laboratory for Theoretical and Computational Chemistry, Departmental of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China.ORCID https://orcid.org/0000-0001-8372-2778

Anan WuState Key Laboratory of Physical Chemistry of Solid Surface, Fujian Provincial Key Laboratory for Theoretical and Computational Chemistry, Departmental of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China.ORCID https://orcid.org/0000-0001-5243-9291

Xin LuState Key Laboratory of Physical Chemistry of Solid Surface, Fujian Provincial Key Laboratory for Theoretical and Computational Chemistry, Departmental of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China.ORCID https://orcid.org/0000-0003-4968-9462

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ionic liquids (ILs), known for their distinct and tunable properties, offer a broad spectrum of potential applications across various fields, including chemistry, materials science, and energy storage. However, practical applications of ILs are often limited by their unfavorable physicochemical properties. Experimental screening becomes impractical due to the vast number of potential IL combinations. Therefore, the development of a robust and efficient model for predicting the IL properties is imperative. As the defining feature, it is of practice significance to establish an accurate yet efficient model to predict the normal melting point of IL (

Identifiers

PMID39072063

DOI10.1021/acsomega.4c02393

PMCPMC11270577

What OpenQuestion holds

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LicenceCC BY-NC-ND

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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.