Evidence map›Paper›PMID 39328751›Full record

ArticleJACS Au2024

Predicting Colloidal Interaction Parameters from Small-Angle X-ray Scattering Curves Using Artificial Neural Networks and Markov Chain Monte Carlo Sampling.

Kelvin Wong, Runzhang Qi, Ye Yang, Zhi Luo, Stefan Guldin, Keith T Butler

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JACS Au, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Kelvin WongDepartment of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, U.K.ORCID https://orcid.org/0009-0003-4036-2727
Runzhang QiYusuf Hamied Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
Ye YangDepartment of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, U.K.ORCID https://orcid.org/0000-0001-9344-9349
Zhi LuoGuangdong Provincial Key Laboratory of Advanced Biomaterials, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China.ORCID https://orcid.org/0000-0001-5590-4849
Stefan GuldinDepartment of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, U.K.ORCID https://orcid.org/0000-0002-4413-5527
Keith T ButlerDepartment of Chemistry, University College London, Kathleen Lonsdale Building, Gower Place, London, WC1E 6BS, U.K.ORCID https://orcid.org/0000-0001-5432-5597

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Small-angle X-ray scattering (SAXS) is a characterization technique that allows for the study of colloidal interactions by fitting the structure factor of the SAXS profile with a selected model and closure relation. However, the applicability of this approach is constrained by the limited number of existing models that can be fitted analytically, as well as the narrow operating range for which the models are valid. In this work, we demonstrate a proof of concept for using an artificial neural network (ANN) trained on SAXS curves obtained from Monte Carlo (MC) simulations to predict values of the effective macroion valency (

Identifiers

PMID39328751
PMCPMC11423300

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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.