Evidence map›Paper›PMID 36777594›Full record

ArticleACS omega2023

Molecular Dynamics Simulations of Asphaltene Aggregation: Machine-Learning Identification of Representative Molecules, Molecular Polydispersity, and Inhibitor Performance.

Rémi Pétuya, Abhishek Punase, Emanuele Bosoni, Antonio Pedro de Oliveira Filho, Juan Sarria, Nirupam Purkayastha, Jonathan J Wylde, Stephan Mohr

Abstract read
In one paragraph

Article in ACS omega, 2023. 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. Article
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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

8 authors.

Rémi PétuyaNextmol (Bytelab Solutions SL), Barcelona 08018, Spain.ORCID https://orcid.org/0000-0002-3118-6966
Abhishek PunaseClariant Oil Services,Clariant Corporation, Houston, Texas 77258, United States.
Emanuele BosoniNextmol (Bytelab Solutions SL), Barcelona 08018, Spain.ORCID https://orcid.org/0000-0003-4585-5478
Antonio Pedro de Oliveira FilhoClariant Oil Services,Clariant Corporation, Houston, Texas 77258, United States.
Juan SarriaClariant Produkte (Deutschland) GmbH, Frankfurt 65929, Germany.
Nirupam PurkayasthaClariant Produkte (Deutschland) GmbH, Frankfurt 65929, Germany.
Jonathan J WyldeClariant Oil Services,Clariant Corporation, Houston, Texas 77258, United States.
Stephan MohrNextmol (Bytelab Solutions SL), Barcelona 08018, Spain.ORCID https://orcid.org/0000-0003-2510-5805

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Molecular dynamics simulations have been employed to investigate the effect of molecular polydispersity on the aggregation of asphaltene. To make the large combinatorial space of possible asphaltene blends accessible to a systematic study via simulation, an upfront unsupervised machine-learning approach (clustering) was employed to identify a reduced set of model molecules representative of the diversity of asphaltene. For these molecules, single asphaltene model simulations have shown a broad range of aggregation behaviors, driven by their structural features: size of the aromatic core, length of the aliphatic chains, and presence of heteroatoms. Then, the combination of these model molecules in a series of mixtures have highlighted the complex and diverse effects of molecular polydispersity on the aggregation process of asphaltene. Simulations yielded both antagonistic and synergistic effects mediated by the trigger or facilitator action of specific asphaltene model molecules. These findings illustrate the necessity of accounting for molecular polydispersity when studying the asphaltene aggregation process and have permitted establishing a robust protocol for the

Identifiers

PMID36777594
PMCPMC9909787

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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.