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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Utilization of Machine Learning Methods in Modeling Specific Heat Capacity of Nanofluids
Mamdouh El Haj Assad, Ibrahim Mahariq, Raymond Ghandour, et al.
Computers, materials & continua/Computers, materials & continua (Print) (2021) Vol. 70, Iss. 1, pp. 361-374
Open Access | Times Cited: 18

Showing 18 citing articles:

The promise of nanofluids: A bibliometric journey through advanced heat transfer fluids in heat exchanger tubes
Hamza Babar, Hongwei Wu, Wenbin Zhang, et al.
Advances in Colloid and Interface Science (2024) Vol. 325, pp. 103112-103112
Open Access | Times Cited: 34
Deep Learning and Machine Learning for Early Detection of Stroke and Haemorrhage
Zeyad Ghaleb Al-Mekhlafi, Ebrahim Mohammed Senan, Taha H. Rassem, et al.
Computers, materials & continua/Computers, materials & continua (Print) (2022) Vol. 72, Iss. 1, pp. 775-796
Open Access | Times Cited: 39
Improving thermodynamic properties and desorption temperature in MgH2 by doping Be: DFT study
Hafiz Hamid Raza, G. Murtaza, Samia Razzaq, et al.
Molecular Simulation (2023) Vol. 49, Iss. 5, pp. 497-508
Closed Access | Times Cited: 18
Applications of machine learning methods for boiling modeling and prediction: A comprehensive review
Mohammad Mehdi Rashidi, Mohammad Alhuyi Nazari, C. Harley, et al.
Chemical Thermodynamics and Thermal Analysis (2022) Vol. 8, pp. 100081-100081
Open Access | Times Cited: 21
Review of pool boiling improvement with additives and nanofluids utilizing artificial intelligence
Zhongdong Wang, Xin Sun, Mohammadreza Baghoolizadeh, et al.
International Communications in Heat and Mass Transfer (2025) Vol. 162, pp. 108659-108659
Closed Access
Energy and Exergy Analysis of a Manure Heating Dryer: A Numerical and Experimental Investigation
Fatemeh Kamrani, Ahmad Banakar, Mohammad Hadi Khoshtaghaza, et al.
(2025)
Closed Access
A new approach to determining the specific heat using the Arduino® platform and Newton’s law of cooling
Thalyta de Oliveira Inocêncio Martins, Frederico Alan de Oliveira Cruz, M Hahn, et al.
Physics Education (2024) Vol. 59, Iss. 2, pp. 025032-025032
Closed Access | Times Cited: 2
Machine learning methods for modeling nanofluid flows: a comprehensive review with emphasis on compact heat transfer devices for electronic device cooling
M. S. Abhijith, K. P. Soman
Journal of Thermal Analysis and Calorimetry (2024) Vol. 149, Iss. 12, pp. 5843-5869
Closed Access | Times Cited: 2
Review of stability enhanced nanofluids prepared by one-step methods—heat transfer mechanism and thermo-physical properties
Annie Aureen Albert, Harris Samuel D. G., V. Parthasarathy
Chemical Engineering Communications (2022) Vol. 210, Iss. 10, pp. 1822-1852
Closed Access | Times Cited: 11
Study of Rheological Behavior, Economic Performance and Development of a Model for MWCNT-ZnO (30:70)/10W40 Hybrid Nanofluid Using Response Surface Methodology
Mohammad Hemmat Esfe, Sayyid Majid Motallebi, Saeed Esfandeh, et al.
Korean Journal of Chemical Engineering (2024) Vol. 41, Iss. 3, pp. 909-921
Closed Access | Times Cited: 1
Employing deep learning for predicting the thermal properties of water and nano-encapsulated phase change material
Saihua Xu, Ali Basem, Hasan A Al-Asadi, et al.
International Journal of Low-Carbon Technologies (2024) Vol. 19, pp. 1453-1459
Open Access | Times Cited: 1
A convolutional neural network analysis of a heat pipe with Hybrid Nanofluids
K. Kumararaja, C. Srinivasa Kumar, B. Sıvaraman
International Journal of Ambient Energy (2021) Vol. 43, Iss. 1, pp. 6284-6296
Closed Access | Times Cited: 7
What is the rheological behavior of MWCNT-MgO(30:70)/SAE50 hybrid nanolubricant? (Focusing on viscosity sensitivity investigation using numerical simulation)
Mohammad Hemmat Esfe, Mahmoud Kiannejad Amiri, Mohammad Reza Sarmasti Emami, et al.
Tribology International (2022) Vol. 178, pp. 108004-108004
Closed Access | Times Cited: 5
An artificial intelligence based-model for heat transfer modeling of 5G smart poles
Ali Khosravi, Timo Laukkanen, Kari Saari, et al.
Case Studies in Thermal Engineering (2021) Vol. 28, pp. 101613-101613
Open Access | Times Cited: 4
Advancing nanofluid analysis: A GBR-GSO predictive model for accurate prediction of specific heat capacity in nanofluids
Hari Mohan Singh, Durga Prasad Sharma
Modern Physics Letters B (2024) Vol. 38, Iss. 23
Closed Access
Specific Heat Capacity Extraction of Soybean Oil/MXene Nanofluids Using Optimized Long Short-Term Memory
Mohammad Reza Chalak Qazani, Navid Aslfattahi, Vladimír Kulish, et al.
IEEE Access (2024) Vol. 12, pp. 59049-59062
Open Access
Predicting Specific Heat Capacity of Nanofluids Using Artificial Neural Network for Al2O3 particle with EG/Water based solution
Amit Kumar Gupta‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Priya Mathur
(2023), pp. 295-299
Closed Access | Times Cited: 1
Extension of the Simulation Model of Bubble Growth during Phase Change Heat Transfer Using Volume of the Fluid Flow Model
Muhammad Kalim, Asif Iqbal Ali, Adnan Khan
Mathematical Problems in Engineering (2022) Vol. 2022, pp. 1-8
Open Access
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