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Updated: May 10, 2025
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Operation of the Collaborative Composite Manufacturing CCM System
Published on: October 1, 2019
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Lasso Model-Based Optimization of CNC/CNF/rGO Nanocomposites.
Ghazaleh Ramezani1, Ixchel Ocampo Silva2, Ion Stiharu1
1Department of Mechanical and Industrial Engineering, Concordia University, Montreal, QC H3G 1M8, Canada.
Micromachines
|April 26, 2025
Summary
L-ascorbic acid enhances electrical conductivity in nanocomposites, while citric acid improves mechanical stability. Machine learning optimizes these materials for electronics and packaging.
Area of Science:
- Materials Science
- Nanotechnology
- Polymer Chemistry
Background:
- Cellulose nanocrystals (CNC) and nanofibers (CNF) are sustainable materials with potential in composites.
- Reduced graphene oxide (rGO) offers excellent electrical and mechanical properties.
- Combining CNC/CNF with rGO can create advanced nanocomposites, but optimizing their properties requires careful selection of fabrication methods.
Purpose of the Study:
- To investigate the impact of citric acid and L-ascorbic acid as reducing agents on the properties of CNC/CNF/rGO nanocomposites.
- To analyze the effects of these reducing agents on electrical conductivity and mechanical stability.
- To develop an advanced optimization framework for precise material tailoring and understanding composition-property relationships.
Main Methods:
- Fabrication of CNC/CNF/rGO nanocomposites using citric acid and L-ascorbic acid as reducing agents.
- Comprehensive characterization of electrical conductivity and mechanical properties.
- Implementation of a machine learning optimization framework, including regression models, a 30-hidden-layer neural network, and a LASSO model.
Main Results:
- L-ascorbic acid demonstrated superior reduction efficiency, yielding rGO with electrical conductivity up to 2.5 S/m.
- Citric acid promoted better dispersion of CNC and CNF, resulting in enhanced mechanical stability.
- The neural network model achieved excellent predictive performance (R² > 0.998), and the LASSO model identified key variable impacts.
Conclusions:
- The choice of reducing agent significantly influences the properties of CNC/CNF/rGO nanocomposites, with L-ascorbic acid favoring electrical conductivity and citric acid favoring mechanical stability.
- Advanced machine learning models provide powerful tools for optimizing material composition and predicting properties.
- These findings support the development of sustainable, multifunctional nanocomposites for applications in flexible electronics, smart packaging, and biomedical devices.
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