Evidence map›Paper›PMID 41586253›Full record

ArticleJournal of radioanalytical and nuclear chemistry2025

One-pot neutron imaging of liquid-gas system: a parametric method study.

Jonatan Šercl, Jongmin Lee, Eric Ricardo Carreón Ruiz, Martin Melčák, Jan Heyda, Ondřej Vopička, Pavel Trtik

Abstract read
In one paragraph

Article in Journal of radioanalytical and nuclear chemistry, 2025. 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
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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

7 authors.

Jonatan Šercl *Department of Physical Chemistry, University of Chemistry and Technology, Technická 5, Prague 6, 166 28 Prague, Czech Republic.
Jongmin Lee *PSI Center for Neutron and Muon Sciences, Paul Scherrer Institute, 5232 Villigen, Switzerland.
Eric Ricardo Carreón RuizPSI Center for Neutron and Muon Sciences, Paul Scherrer Institute, 5232 Villigen, Switzerland.
Martin MelčákDepartment of Physical Chemistry, University of Chemistry and Technology, Technická 5, Prague 6, 166 28 Prague, Czech Republic.
Jan HeydaDepartment of Physical Chemistry, University of Chemistry and Technology, Technická 5, Prague 6, 166 28 Prague, Czech Republic.
Ondřej VopičkaDepartment of Physical Chemistry, University of Chemistry and Technology, Technická 5, Prague 6, 166 28 Prague, Czech Republic.
Pavel TrtikPSI Center for Neutron and Muon Sciences, Paul Scherrer Institute, 5232 Villigen, Switzerland.ORCID 0000-0002-6481-5191

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present advancements in the one-pot neutron imaging method for investigating hydrocarbon-based liquid-gas interactions under varying pressure and temperature. Improvements in hardware enable the optimization of cell geometry and material, overall leading to reduced uncertainty in surface tension measurements. The integration of snubbers minimizes initial pressure surge, improving experimental stability. A Python-based pipeline is developed for image batch-processing, incorporating rigorous image reduction algorithms specific to the one-pot approach. This results in efficient data management and enhanced meniscus shape detection. Moreover, we provide a parametric study on chosen variables to further demonstrate the one-pot neutron imaging as a powerful tool for studying interfacial phenomena and fluid for a wide variety of applications. Supplementary Information: The online version contains supplementary material available at 10.1007/s10967-025-10561-w.

Indexed as

DiffusionLiquid–gas interfaceNatural gasNeutron radiographyPetroleumSurface tension

Identifiers

PMID41586253
PMCPMC12827288

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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.