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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:

A novel integrated model to improve the dynamic viscosity of MWCNT-Al2O3 (40:60)/Oil 5W50 hybrid nano-lubricant using artificial neural networks (ANNs)
Mohammad Hemmat Esfe, Reza Esmaily, Mohamad Khaje Khabaz, et al.
Tribology International (2022) Vol. 178, pp. 108086-108086
Closed Access | Times Cited: 40

Showing 1-25 of 40 citing articles:

Using of artificial neural networks and different evolutionary algorithms to predict the viscosity and thermal conductivity of silica-alumina-MWCN/water nanofluid
Mohammadreza Baghoolizadeh, Dheyaa J. Jasim, S. Mohammad Sajadi, et al.
Heliyon (2024) Vol. 10, Iss. 4, pp. e26279-e26279
Open Access | Times Cited: 17
Novel design of intelligent Bayesian networks to study the impact of magnetic field and Joule heating in hybrid nanomaterial flow with applications in medications for blood circulation
Saeed Ehsan Awan, Muhammad Awais, Robicca Shamim, et al.
Tribology International (2023) Vol. 189, pp. 108914-108914
Closed Access | Times Cited: 24
Novel Bayesian distributed adaptive neural structure for titanium and aluminium alloy nanofluidic model with gyrotactic microorganisms
Zahoor Shah, Muhammad Asif Zahoor Raja, Muhammad Shoaib, et al.
Tribology International (2024) Vol. 193, pp. 109457-109457
Closed Access | Times Cited: 13
Prediction and extensive analysis of MWCNT-MgO/oil SAE 50 hybrid nano-lubricant rheology utilizing machine learning and genetic algorithms to find ideal attributes
Mohammadreza Baghoolizadeh, Mostafa Pirmoradian, S. Mohammad Sajadi, et al.
Tribology International (2024) Vol. 195, pp. 109582-109582
Closed Access | Times Cited: 10
Investigation of gold adsorption by ironbark biochar using response surface methodology and artificial neural network modelling
Mahmuda Akter Mele, Ravinder Kumar, Tewodros Kassa Dada, et al.
Journal of Cleaner Production (2024) Vol. 456, pp. 142317-142317
Open Access | Times Cited: 10
Artificial neural network modeling of thermal characteristics of WO3-CuO (50:50)/water hybrid nanofluid with a back-propagation algorithm
Yiran Qu, Dheyaa J. Jasim, S. Mohammad Sajadi, et al.
Materials Today Communications (2024) Vol. 38, pp. 108169-108169
Closed Access | Times Cited: 8
Convective flow dynamics with suspended carbon nanotubes in the presence of magnetic dipole: Intelligent solution predicted Bayesian regularization networks
Saeed Ehsan Awan, Robicca Shamim, Muhammad Awais, et al.
Tribology International (2023) Vol. 187, pp. 108685-108685
Closed Access | Times Cited: 18
Review of tribological properties of nanoparticle-based lubricants and their hybrids and composites
Ye Zar Ni Htwe, Aws S. Al-Janabi, Yasmin Wadzer, et al.
Friction (2023) Vol. 12, Iss. 4, pp. 569-590
Open Access | Times Cited: 15
Prediction of the thermal behavior of multi-walled carbon nanotubes-CuO-CeO2 (20-40-40)/water hybrid nanofluid using different types of regressors and evolutionary algorithms for designing the best artificial neural network modeling
Reza Rostamzadeh‐Renani, Mohammadreza Baghoolizadeh, S. Mohammad Sajadi, et al.
Alexandria Engineering Journal (2023) Vol. 84, pp. 184-203
Open Access | Times Cited: 14
Machine learning-based model for the intelligent estimation of critical heat flux in nanofluids
Shahin Alipour Bonab, Mohammad Yazdani-Asrami
Nano Express (2024) Vol. 5, Iss. 2, pp. 025012-025012
Open Access | Times Cited: 4
Accurate prediction of the rheological behavior of MWCNT-Al2O3/water-ethylene glycol nanofluid with metaheuristic-optimized machine learning models
Yi Ru, Ali B.M. Ali, Karwan Hussein Qader, et al.
International Journal of Thermal Sciences (2025) Vol. 211, pp. 109691-109691
Closed Access
Effect of surface modification on the stability and thermophysical properties of copper oxide nanolubricants
Zhihui Jia, Shan Qing, Zhu Tao, et al.
Chemical Physics (2025), pp. 112639-112639
Closed Access
Prediction of the viscosity of iron-CuO/water-ethylene glycol non-Newtonian hybrid nanofluids using different machine learning algorithms
Mohammed Shorbaz Graish, Ali B.M. Ali, Murtadha M. Al-Zahiwat, et al.
Case Studies in Chemical and Environmental Engineering (2025), pp. 101180-101180
Open Access
Exploring the impact of particle stability, size, and morphology on nanofluid thermal conductivity: A comprehensive review for energy applications
Sajid Farooq, Muhammad Habib, Olavo Cardozo, et al.
Advances in Colloid and Interface Science (2025), pp. 103495-103495
Closed Access
Obtaining an accurate prediction model for viscosity of a new nano-lubricant containing multi-walled carbon nanotube-titanium dioxide nanoparticles with oil SAE50
Yuelei Zhang, Karrar A. Hammoodi, S. Mohammad Sajadi, et al.
Tribology International (2023) Vol. 191, pp. 109185-109185
Closed Access | Times Cited: 11
Investigation of best artificial neural network topology to model the dynamic viscosity of MWCNT-ZnO/SAE 5W30 nano-lubricant
Mohammad Hemmat Esfe, S. Ali Eftekhari, As’ad Alizadeh, et al.
Materials Today Communications (2023) Vol. 35, pp. 106074-106074
Closed Access | Times Cited: 9
Obtaining the optimal lubrication conditions by investigating the viscosity of MWCNT (25%)-TiO2(75%)/ oil SAE40 hybrid nanofluid by response surface methodology
Davood Toghraie, Seyed Naser Hosseini Tamrabad, Soheyl Alidoust, et al.
Tribology International (2023) Vol. 186, pp. 108585-108585
Closed Access | Times Cited: 9
Dynamic viscosity prediction of nanofluids using artificial neural network (ANN) and genetic algorithm (GA)
Halil İbrahim Topal, Beytullah Erdoğan, Oğuz Koçar, et al.
Journal of the Brazilian Society of Mechanical Sciences and Engineering (2024) Vol. 46, Iss. 7
Closed Access | Times Cited: 3
Artificial Neural Network (ANN) approach in predicting the thermo-solutal transport rate from multiple heated chips within an enclosure filled with hybrid nanocoolant
Tawsif Mahmud, Jiaul Haque Saboj, Preetom Nag, et al.
International Journal of Thermofluids (2024), pp. 100923-100923
Open Access | Times Cited: 3
Significance of the inclined magnetic field on the water-based hybrid nanofluid flow over a nonlinear stretching sheet
Ebrahem A. Algehyne, Afrah Al‐Bossly, Fuad S. Alduais, et al.
Nanotechnology (2023) Vol. 34, Iss. 21, pp. 215401-215401
Closed Access | Times Cited: 8
Thermal conductivity prediction of WO3-CuO-Ag (35:40:25)/water hybrid ternary nanofluid with Artificial Neural Network and back-propagation algorithm
Chunlei Lin, Junhui Zhou, Qianqian Lu, et al.
Materials Today Communications (2023) Vol. 36, pp. 106807-106807
Closed Access | Times Cited: 8
Numerical investigation of rigidity and flexibility parameters effect on superstructure foundation behavior using three-dimensional finite element method
Seyed Amir Mousavian Hejazi, Atra Feyzpour, Mohamad Khaje Khabaz, et al.
Case Studies in Construction Materials (2023) Vol. 18, pp. e01867-e01867
Open Access | Times Cited: 7
Dynamic viscosity prediction using artificial intelligence for an antifreeze containing MWCNT–alumina hybrid nanopowders
Suqin Hua, Dheyaa J. Jasim, As’ad Alizadeh, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 126, pp. 107046-107046
Closed Access | Times Cited: 7
Dynamic Viscosity Prediction for MWCNT-MgO (10%-90%) -SAE40 Oil Hybrid Nano-lubricant Using Artificial Neural Network and Multi-Dimensional Nonlinear Least Square Curve Fitting
Xiaojian Lin, Ali Basem, Mortatha Al‐Yasiri, et al.
Chinese Journal of Chemical Engineering (2024)
Closed Access | Times Cited: 2
Development of knowledge management for viscosity of nanolubricant in hot and cold lubrication conditions
Mohammad Hemmat Esfe, Rouhollah Tavallaee, Morteza Sarbaz Karajabad, et al.
Tribology International (2023) Vol. 188, pp. 108873-108873
Closed Access | Times Cited: 5
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