Evidence map›Paper›PMID 41271914›Full record

ArticleScientific reports2025

Intelligent prediction of heat capacity of polyethylene glycol polymer.

Jamal I Al-Nabulsi, Zaid Ajzan Alsalami, J Deepak, Johar Mgm, Anupama Routray, A Karthikeyan, Harjot Singh Gill, Yashwant Singh Bisht, Ahmad Abumalek

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Jamal I Al-NabulsiFaculty of Engineering, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan.
Zaid Ajzan AlsalamiDepartment of computers Techniques engineering, College of technical engineering, Islamic University of Najaf, Najaf, Iraq.
J DeepakDepartment of Mechanical Engineering, School of Engineering and Technology, JAIN (Deemed to be University), Bangalore, Karnataka, India.
Johar MgmManagement and Science University, Shah Alam, Selangor, Malaysia.
Anupama RoutrayDepartment of Mechanical Engineering, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, 751030, Odisha, India.
A KarthikeyanDepartment of Mechanical Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India.
Harjot Singh GillDepartment of Mechanical Engineering, Chandigarh University, Mohali, Punjab, India.
Yashwant Singh BishtDepartment of Mechanical Engineering, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, 248007, Uttarakhand, India.
Ahmad AbumalekFaculty of Engineering, Balkh University, Balkh, Afghanistan. abumalekahmad40@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Polyethylene glycol (PEG) exhibits tunable molar heat capacity, making it essential for thermal management and materials processing. Using an experimental databank of 528 observations, this study applies machine learning to develop predictive models based on temperature and molar mass. Data reliability was ensured through Monte Carlo-based outlier detection and correlation analysis, which confirmed temperature as the dominant predictor. Among the algorithms tested, Random Forest delivered the highest performance (R² = 0.9969, AARE = 8.29%), outperforming other methods that showed signs of overfitting. The results demonstrate that Random Forest provides a cost-effective, accurate, and scalable alternative to calorimetric experiments, enabling precise estimation of PEG heat capacity for applications in materials design, energy storage, and biomedical engineering.

Indexed as

Heat capacity predictionMachine learningMonte carlo outlier detectionPolyethylene glycol polymerSHAP analysis

Identifiers

PMID41271914
PMCPMC12638783

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LicenceCC BY-NC-ND
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