Evidence map›Paper›PMID 40363028›Full record

ArticlePolymers2025

Thermal Degradation of Palm Fronds/Polypropylene Bio-Composites: Thermo-Kinetics and Convolutional-Deep Neural Networks Techniques.

Abdulrazak Jinadu Otaru, Zaid Abdulhamid Alhulaybi Albin Zaid

Abstract read
In one paragraph

Article in Polymers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

2 authors.

Abdulrazak Jinadu OtaruDepartment of Chemical Engineering, College of Engineering, King Faisal University, Al Ahsa 31982, Saudi Arabia.ORCID 0000-0002-3057-4991
Zaid Abdulhamid Alhulaybi Albin ZaidDepartment of Chemical Engineering, College of Engineering, King Faisal University, Al Ahsa 31982, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identifying sustainable and efficient methods for the degradation of plastic waste in landfills is critical for the implementation of the Saudi Green Initiative, the European Union's Strategic Plan, and the 2030 United Nations Action Plan, all of which are aimed at achieving a sustainable environment. This study assesses the influence of palm fronds (PFR) on the thermal degradation of polypropylene plastic (PP) using TGA/FTIR experimental measurements, thermo-kinetics, and machine learning convolutional deep learning neural networks (CDNN). Thermal degradation operations were conducted on pure materials (PFR and PP) as well as mixed (blended) materials containing 25% and 50% PFR, across degradation temperatures ranging from 25 to 600 °C and heating rates of 10, 20, and 40 °C·min

Indexed as

machine learningpalm frondspolypropylenethermal degradationthermo-kinetic

Identifiers

PMID40363028
PMCPMC12073120

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.