Evidence map›Paper›PMID 42205268›Full record

ArticleRSC advances2026

Machine learning-based prediction of biomass pyrolysis kinetics: integrating mechanistic modeling and compositional features.

Muhammad Asif, Luqman Hakeem, Chengxi Yao, Hira, Rimsha Bibi, Muhammad Bilal, Hassan Zeb

Abstract read
In one paragraph

Article in RSC advances, 2026. 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

7 authors.

Muhammad AsifInstitute of Energy and Environmental Engineering, University of the Punjab Lahore-54000 Punjab Pakistan hassanzeb.ieee@pu.edu.pk.ORCID https://orcid.org/0000-0001-5366-9996
Luqman HakeemLaboratoire de Chimie Physique - Matière et Rayonnement (LCPMR), UMR 7614, CNRS, Sorbonne Université 4 Place Jussieu F-75005 Paris France.
Chengxi YaoSchool of Mechanical Engineering, Sungkyunkwan University Jangan-Gu Suwon Gyeonggi-Do South Korea.
HiraDepartment of Chemistry, The Government Sadiq College Women University Bahawalpur 63000 Pakistan.
Rimsha BibiNational Center for Bioinformatics, Quaid-i-Azam University Islamabad Pakistan.
Muhammad BilalDepartment of Environmental Sciences, COMSATS University Islamabad-Abbottabad Campus Pakistan.ORCID https://orcid.org/0009-0001-5853-0835
Hassan ZebInstitute of Energy and Environmental Engineering, University of the Punjab Lahore-54000 Punjab Pakistan hassanzeb.ieee@pu.edu.pk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate determination of kinetic and thermodynamic parameters is vital for understanding biomass pyrolysis and optimizing renewable thermochemical conversion. In this study, sapodilla leaves were analyzed as representative lignocellulosic feedstock using both experimental and machine learning (ML) approaches. Thermogravimetric experiments at multiple heating rates, interpreted

Identifiers

PMID42205268
PMCPMC13202439

What OpenQuestion holds

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LicenceCC BY-NC
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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.