Evaluation of Nano-Hydroxyapatite/Nano-Kaolin Composite Additives on the Physical Properties of Hot Mix Asphalt Binder
- Authors
-
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Sani MAMOUDOU
Author
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Abdulmumin A. SHUAIBU
Author
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Aliyu USMAN
Author
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- Keywords:
- Bitumen, Hot mix asphalt, Nano-Hydroxyapatite, Nano-Kaolin, Response surface methodology.
- Abstract
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This study evaluates the impact of a nano-hydroxyapatite (NHAp) and nano-kaolin clay (NKc) blend on the physical properties of penetration-grade asphalt binder, to enhance binder performance and durability. NHAp was synthesised from discarded animal bones, while the NKc was processed from the Kankara kaolin deposit. A response surface methodology (RSM)-based central composite design (CCD) was employed to blend NHAp and NKc and to test the physical properties of the conventional hot mix asphalt binder, including penetration, softening point, ductility, flash and fire points, and viscosity. Furthermore, RSM was used for mathematical modelling and optimisation of the desired physical properties of HMA binder. Results revealed a progressive reduction in penetration (66.3 mm to 35.3 mm) and an increase in viscosity (81 s to 122 s), indicating enhanced stiffness and resistance to deformation. The softening point increased moderately (45 °C to 48 °C, reflecting improved high-temperature stability, while flash and fire points rose sharply (292 °C to 308 °C) and (305 °C to 320 °C), respectively, signifying improved thermal safety. However, ductility decreased significantly with higher NKc content (from 110 cm down to 74 cm), underscoring a trade-off between stiffness and flexibility. Statistical analysis (ANOVA) revealed the importance of both individual and interaction effects (p < 0.05) of the input variables, with NKc exerting a dominant influence on most characteristics, while NHAp performed a moderating role, particularly in maintaining ductility. Optimisation with RSM revealed an optimal blend of around 0.61% NHAp and 3.42% NKc, resulting in a balanced performance with 59% of desirability, while validation testing revealed prediction errors of less than 5%.
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- Published
- 17-08-2026
- Section
- Articles
- License
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Copyright (c) 2026 Sani MAMOUDOU, Abdulmumin A. SHUAIBU, Aliyu USMAN (Author)

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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