Towards Understanding Aerogels' Efficiency for Oil Removal-A Principal Component Analysis Approach

Area of Science:

• Materials Science
• Environmental Science
• Data Science

Background:

• Aerogels derived from nanocellulose (NC), chitosan (CS), and graphene (G) oxide show promise for pollutant removal.
• Assessing adsorption potential requires advanced analytical methods to uncover complex relationships.

Purpose of the Study:

• To estimate the adsorption potential of NC, CS, and G-based aerogels for oil and organic contaminants.
• To utilize Principal Component Analysis (PCA) for uncovering hidden patterns in adsorption data.

Main Methods:

• Application of Principal Component Analysis (PCA) as a data mining tool.
• Comparative analysis of aerogel adsorption data using PCA.
• Data pre-treatment strategies, including outlier separation, were employed.

Main Results:

• PCA identified distinct adsorption behaviors between nanocellulose-based aerogels and chitosan/graphene-based aerogels.
• Excluding outliers significantly increased PCA's total variance from 64.02% to 79.82%, enhancing representativeness.
• A nearly 15% increase in total variance was observed compared to previous findings.

Conclusions:

• The study demonstrates the effectiveness of PCA in analyzing aerogel adsorption data.
• Outlier removal is crucial for accurate assessment and reveals significant bias in raw datasets.
• Aerogel material type significantly influences adsorption efficiency for oil and organic contaminants.