Evidence map›Paper›PMID 41848954›Full record

ArticleDiscover nano2026

Predictive modeling for the mean diameter of carbon nanotubes produced by methane decomposition.

Shahad Almansour, Lulwah M Alkwai, Kusum Yadav, Basem Abu Zneid, Fatimah Pashtun

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Discover nano, 2026. 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
–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

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

5 authors.

Shahad AlmansourApplied College, University of Ha'il, Ha'il, Kingdom of Saudi Arabia.
Lulwah M AlkwaiCollege of Computer Science and Engineering, University of Ha'il, Ha'il, Kingdom of Saudi Arabia.
Kusum YadavCollege of Computer Science and Engineering, University of Ha'il, Ha'il, Kingdom of Saudi Arabia.
Basem Abu ZneidHourani Center for Applied Scientific Research, Faculty of Engineering, Al-Ahliyya Amman University, Amman, Jordan.
Fatimah PashtunFaculty of Engineering, Parwan University, Parwan, Afghanistan. fatimahpashtun@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The catalytic decomposition of methane represents an efficient route for carbon nanotube (CNT) synthesis, yet the quantitative prediction of CNT mean diameter under multivariate processing conditions remains challenging. This study aims to develop and compare a suite of data‑driven models capable of capturing the nonlinear correlations among compositional and operational parameters governing CNT growth. A dataset of 66 experimental entries from peer‑reviewed sources was compiled, including 16 catalysts and reaction‑related variables numerically encoded and normalized before modeling. Neural networks (ANN and CNN), kernel, linear, and nine ensemble regression algorithms were optimized via systematic hyperparameter tuning and evaluated using R

Indexed as

Carbon nanotube diameterEnsemble modelsMachine learningMethane decompositionSHAP interpretation

Identifiers

PMID41848954
PMCPMC13000054

What OpenQuestion holds

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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.