Evidence map›Paper›PMID 39770101›Full record

ArticleMolecules (Basel, Switzerland)2024

Selection of Representative Asphaltene Molecules in an Asphalt Molecular Model Based on Quantum Chemistry and Statistical Analysis.

Jie Zhu, Ganyu Xia, Dejian Shen, Yangtao Li, Baosheng Jin, Shengxing Wu

Abstract read
In one paragraph

Article in Molecules (Basel, Switzerland), 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

6 authors.

Jie ZhuSchool of Architectural Engineering, Jinling Institute of Technology, No. 99, Hongjing Road, Nanjing 211169, China.
Ganyu XiaCollege of Civil and Transportation Engineering, Hohai University, No. 1, Xikang Road, Nanjing 210098, China.
Dejian ShenCollege of Civil and Transportation Engineering, Hohai University, No. 1, Xikang Road, Nanjing 210098, China.
Yangtao LiCollege of Civil Engineering, Nanjing Forestry University, No. 159, Longpan Road, Nanjing 210037, China.
Baosheng JinSchool of Energy and Environment, Southeast University, No. 2, Sipailou, Nanjing 210096, China.
Shengxing WuCollege of Civil and Transportation Engineering, Hohai University, No. 1, Xikang Road, Nanjing 210098, China.

Funding

Fundamental Research Funds for the Central Universities B230201060Jiangsu Civil Engineering & Architectural Society Research Project JSTJXH24117Jinling Institute of Technology jit-b-202359JSTI Group 22880050-D01-001JSTI Group 823088816Natural Science Foundation of Jiangsu Province BK20230955Natural Science Foundation of the Jiangsu Higher Education Institutions of China 24KJB570003
6 · The paper itself

Abstract

Asphaltenes, as the most complex and strongly polar component among the four components of asphalt, have a significant impact on the macroscopic physicochemical properties of asphalt. Currently, the vast variety of molecular structures used to characterize asphaltenes increases the construction complexity of asphalt molecular models. To construct a more realistic asphalt molecular model and reduce the construction difficulty, this investigation obtains the molecular morphology, molecular polarity, and infrared spectrum indicators of 21 asphaltene molecules through quantum chemical calculations. Based on statistical analysis methods, the differences among asphaltene molecules are explored, and suggestions for selecting representative asphaltene molecules are proposed. The investigation shows that AS2, AS3, AS12, AS15, and AS17 are representative molecules that are significantly different from other asphaltene molecules. Among them, AS2, AS15, and AS17 are significantly different from the other molecules in terms of polarity and functional groups, while AS3 and AS12 are significantly different from the other molecules in terms of aromatic carbon percentage. This investigation is expected to provide valuable insights into the intrinsic relationship between the nanoscale characteristics and macroscopic properties of asphalt molecules.

Indexed as

asphalteneasphalt molecular modelquantum chemical calculationsrepresentative moleculesstatistical analysis

Identifiers

PMID39770101
PMCPMC11676824

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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.