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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...

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Main Authors: Sakinah Muhamad Hisham, Norazlianie Sazali, Kumaran Kadirgama, Devarajan Ramasamy, Mohd Kamal Kamarulzaman, Lingenthiran Samylingam
Format: Article
Language:English
English
Published: MDPI AG 2024
Subjects:
T1-995 Technology (General)
TA1-2040 Engineering (General). Civil engineering (General)
Online Access:https://eprints.ums.edu.my/id/eprint/42196/1/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/42196/2/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/42196/
https://doi.org/10.3390/pr12102146
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spelling my.ums.eprints.421962024-12-10T06:54:28Z https://eprints.ums.edu.my/id/eprint/42196/ Hybrid CNC–MXene nanolubricant for tribological application: characterization, prediction, and optimization of thermophysical properties evaluation Sakinah Muhamad Hisham Norazlianie Sazali Kumaran Kadirgama Devarajan Ramasamy Mohd Kamal Kamarulzaman Lingenthiran Samylingam T1-995 Technology (General) TA1-2040 Engineering (General). Civil engineering (General) 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 AG 2024 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/42196/1/ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/42196/2/FULL%20TEXT.pdf Sakinah Muhamad Hisham and Norazlianie Sazali and Kumaran Kadirgama and Devarajan Ramasamy and Mohd Kamal Kamarulzaman and Lingenthiran Samylingam (2024) Hybrid CNC–MXene nanolubricant for tribological application: characterization, prediction, and optimization of thermophysical properties evaluation. Processes, 12. pp. 1-24. https://doi.org/10.3390/pr12102146
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic T1-995 Technology (General)
TA1-2040 Engineering (General). Civil engineering (General)
spellingShingle T1-995 Technology (General)
TA1-2040 Engineering (General). Civil engineering (General)
Sakinah Muhamad Hisham
Norazlianie Sazali
Kumaran Kadirgama
Devarajan Ramasamy
Mohd Kamal Kamarulzaman
Lingenthiran Samylingam
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
Kumaran Kadirgama
Devarajan Ramasamy
Mohd Kamal Kamarulzaman
Lingenthiran Samylingam
author_facet Sakinah Muhamad Hisham
Norazlianie Sazali
Kumaran Kadirgama
Devarajan Ramasamy
Mohd Kamal Kamarulzaman
Lingenthiran Samylingam
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 AG
publishDate 2024
url https://eprints.ums.edu.my/id/eprint/42196/1/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/42196/2/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/42196/
https://doi.org/10.3390/pr12102146
_version_ 1818835188003635200
score 13.232414

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