Evidence map›Paper›PMID 39544717›Full record

ArticleFrontiers in chemistry2024

Predicting the solubility of CO

Hongling Qin, Ke Wang, Xifei Ma, Fangfang Li, Yanrong Liu, Xiaoyan Ji

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
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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

6 authors.

Hongling QinEnergy Engineering, Division of Energy Science, Luleå University of Technology, Luleå, Sweden.
Ke WangCAS Key Laboratory of Green Process and Engineering, State Key Laboratory of Mesoscience and Engineering, Beijing Key Laboratory of Ionic Liquids Clean Process, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, China.
Xifei MaCAS Key Laboratory of Green Process and Engineering, State Key Laboratory of Mesoscience and Engineering, Beijing Key Laboratory of Ionic Liquids Clean Process, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, China.
Fangfang LiEnergy Engineering, Division of Energy Science, Luleå University of Technology, Luleå, Sweden.
Yanrong LiuCAS Key Laboratory of Green Process and Engineering, State Key Laboratory of Mesoscience and Engineering, Beijing Key Laboratory of Ionic Liquids Clean Process, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, China.
Xiaoyan JiEnergy Engineering, Division of Energy Science, Luleå University of Technology, Luleå, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As ionic liquids (ILs) continue to be prepared, there is a growing need to develop theoretical methods for predicting the properties of ILs, such as gas solubility. In this work, different strategies were employed to obtain the solubility of CO

Indexed as

CO2 solubilityCOSMO-RSionic liquidmachine learningN2 solubility

Identifiers

PMID39544717
PMCPMC11560425

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.