Cover Image

Hybrid CNC–MXene nanolubricant for tribological application: Characterization, prediction, and optimization of thermophysical properties evaluation

In the present work, hybrid Cellulose Nanocrystal–MXene (CNC–MXene) nanolubricants were prepared via a two-step method and investigated as potential heat-transfer hybrid nanofluids for the first time. CNC–MXene nanolubricants were synthesized via a two-step method by varying the weight percentage of...

Full description

Saved in:
Bibliographic Details
Main Authors: Sakinah, Muhamad Hisham, Norazlianie, Sazali, Kadirgama, Kumaran, Ramasamy, Devarajan, Mohd Kamal, Kamarulzaman, Samylingam, Lingenthiran, Aslfattahi, Navid, Kok, Chee Kuang
Format: Article
Language:English
Published: MDPI 2024
Subjects:
TJ Mechanical engineering and machinery
TS Manufactures
Online Access:http://umpir.ump.edu.my/id/eprint/42732/1/Hybrid%20CNC%E2%80%93MXene%20Nanolubricant%20for%20Tribological%20Application%20Characterization%2C%20Prediction%2C%20and%20Optimization%20of%20Thermophysical%20Properties%20Evaluation.pdf
http://umpir.ump.edu.my/id/eprint/42732/
https://doi.org/10.3390/pr12102146
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.ump.umpir.42732
record_format eprints
spelling my.ump.umpir.427322024-10-03T05:06:46Z http://umpir.ump.edu.my/id/eprint/42732/ Hybrid CNC–MXene nanolubricant for tribological application: Characterization, prediction, and optimization of thermophysical properties evaluation Sakinah, Muhamad Hisham Norazlianie, Sazali Kadirgama, Kumaran Ramasamy, Devarajan Mohd Kamal, Kamarulzaman Samylingam, Lingenthiran Aslfattahi, Navid Kok, Chee Kuang TJ Mechanical engineering and machinery TS Manufactures In the present work, hybrid Cellulose Nanocrystal–MXene (CNC–MXene) nanolubricants were prepared via a two-step method and investigated as potential heat-transfer hybrid nanofluids for the first time. CNC–MXene nanolubricants were synthesized via a two-step method by varying the weight percentage of CNC–MXene nanoparticles (ranging from 0.01 to 0.05 wt%) and characterized using Fourier-Transform Infrared Spectroscopy and TGA (Thermogravimetric Analysis). Response surface methodology (RSM) was used in conjunction with the miscellaneous design model to identify prediction models for the thermophysical properties of the hybrid CNC–MXene nanolubricant. Minitab 18 statistical analysis software and Response Surface Methodology (RSM) based on Central Composite Design (CCD) were utilized to generate an empirical mathematical model investigating the effect of concentration and temperature. The analysis of variance (ANOVA) results indicated significant contributions from the type of nanolubricant (p < 0.001) and the quadratic effect of temperature (p < 0.001), highlighting non-linear interactions that affect viscosity and thermal conductivity. The findings showed that the predicted values closely matched the experimental results, with a percentage of absolute error below 9%, confirming the reliability of the optimization models. Additionally, the models could predict more than 85% of the nanolubricant output variations, indicating high model accuracy. The optimization analysis identified optimal conditions for maximizing both dynamic viscosity and thermal conductivity. The predicted optimal values (17.0685 for dynamic viscosity and 0.3317 for thermal conductivity) were achieved at 30 °C and a 0.01% concentration, with a composite desirability of 1. The findings of the percentage of absolute error (POAE) reveal that the model can precisely predict the optimum experimental parameters. This study contributes to the growing field of advanced nanolubricants by providing insights into the synergistic effects of CNC and MXene in enhancing thermophysical properties. The developed models and optimization techniques offer valuable tools for tailoring nanolubricant formulations to specific tribological applications, potentially leading to improved efficiency and durability in various industrial settings. MDPI 2024 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/42732/1/Hybrid%20CNC%E2%80%93MXene%20Nanolubricant%20for%20Tribological%20Application%20Characterization%2C%20Prediction%2C%20and%20Optimization%20of%20Thermophysical%20Properties%20Evaluation.pdf Sakinah, Muhamad Hisham and Norazlianie, Sazali and Kadirgama, Kumaran and Ramasamy, Devarajan and Mohd Kamal, Kamarulzaman and Samylingam, Lingenthiran and Aslfattahi, Navid and Kok, Chee Kuang (2024) Hybrid CNC–MXene nanolubricant for tribological application: Characterization, prediction, and optimization of thermophysical properties evaluation. Processes, 12 (10). pp. 1-25. ISSN 2227-9717. (Published) https://doi.org/10.3390/pr12102146 10.3390/pr12102146
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TJ Mechanical engineering and machinery
TS Manufactures
spellingShingle TJ Mechanical engineering and machinery
TS Manufactures
Sakinah, Muhamad Hisham
Norazlianie, Sazali
Kadirgama, Kumaran
Ramasamy, Devarajan
Mohd Kamal, Kamarulzaman
Samylingam, Lingenthiran
Aslfattahi, Navid
Kok, Chee Kuang
Hybrid CNC–MXene nanolubricant for tribological application: Characterization, prediction, and optimization of thermophysical properties evaluation
description In the present work, hybrid Cellulose Nanocrystal–MXene (CNC–MXene) nanolubricants were prepared via a two-step method and investigated as potential heat-transfer hybrid nanofluids for the first time. CNC–MXene nanolubricants were synthesized via a two-step method by varying the weight percentage of CNC–MXene nanoparticles (ranging from 0.01 to 0.05 wt%) and characterized using Fourier-Transform Infrared Spectroscopy and TGA (Thermogravimetric Analysis). Response surface methodology (RSM) was used in conjunction with the miscellaneous design model to identify prediction models for the thermophysical properties of the hybrid CNC–MXene nanolubricant. Minitab 18 statistical analysis software and Response Surface Methodology (RSM) based on Central Composite Design (CCD) were utilized to generate an empirical mathematical model investigating the effect of concentration and temperature. The analysis of variance (ANOVA) results indicated significant contributions from the type of nanolubricant (p < 0.001) and the quadratic effect of temperature (p < 0.001), highlighting non-linear interactions that affect viscosity and thermal conductivity. The findings showed that the predicted values closely matched the experimental results, with a percentage of absolute error below 9%, confirming the reliability of the optimization models. Additionally, the models could predict more than 85% of the nanolubricant output variations, indicating high model accuracy. The optimization analysis identified optimal conditions for maximizing both dynamic viscosity and thermal conductivity. The predicted optimal values (17.0685 for dynamic viscosity and 0.3317 for thermal conductivity) were achieved at 30 °C and a 0.01% concentration, with a composite desirability of 1. The findings of the percentage of absolute error (POAE) reveal that the model can precisely predict the optimum experimental parameters. This study contributes to the growing field of advanced nanolubricants by providing insights into the synergistic effects of CNC and MXene in enhancing thermophysical properties. The developed models and optimization techniques offer valuable tools for tailoring nanolubricant formulations to specific tribological applications, potentially leading to improved efficiency and durability in various industrial settings.
format Article
author Sakinah, Muhamad Hisham
Norazlianie, Sazali
Kadirgama, Kumaran
Ramasamy, Devarajan
Mohd Kamal, Kamarulzaman
Samylingam, Lingenthiran
Aslfattahi, Navid
Kok, Chee Kuang
author_facet Sakinah, Muhamad Hisham
Norazlianie, Sazali
Kadirgama, Kumaran
Ramasamy, Devarajan
Mohd Kamal, Kamarulzaman
Samylingam, Lingenthiran
Aslfattahi, Navid
Kok, Chee Kuang
author_sort Sakinah, Muhamad Hisham
title Hybrid CNC–MXene nanolubricant for tribological application: Characterization, prediction, and optimization of thermophysical properties evaluation
title_short Hybrid CNC–MXene nanolubricant for tribological application: Characterization, prediction, and optimization of thermophysical properties evaluation
title_full Hybrid CNC–MXene nanolubricant for tribological application: Characterization, prediction, and optimization of thermophysical properties evaluation
title_fullStr Hybrid CNC–MXene nanolubricant for tribological application: Characterization, prediction, and optimization of thermophysical properties evaluation
title_full_unstemmed Hybrid CNC–MXene nanolubricant for tribological application: Characterization, prediction, and optimization of thermophysical properties evaluation
title_sort hybrid cnc–mxene nanolubricant for tribological application: characterization, prediction, and optimization of thermophysical properties evaluation
publisher MDPI
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/42732/1/Hybrid%20CNC%E2%80%93MXene%20Nanolubricant%20for%20Tribological%20Application%20Characterization%2C%20Prediction%2C%20and%20Optimization%20of%20Thermophysical%20Properties%20Evaluation.pdf
http://umpir.ump.edu.my/id/eprint/42732/
https://doi.org/10.3390/pr12102146
_version_ 1822924701477896192
score 13.244413

Similar Items