Fe3O4/TiO2 Nanocomposite Photocatalytic Performance and ML Predictions

A 5.5 KB Excel dataset contains experimental results for Fe3O4/TiO2 nanocomposites used to degrade Reactive Yellow 145 dye. It was created by Safdar Abbas Kazmi and last updated in June 2026. The data supports a study where a Convolutional Neural Network model achieved a predictive R² of 0.91 for photodegradation efficiency.

Use Cases

• Training regression models to predict dye removal efficiency based on synthesis and reaction parameters.
• Benchmarking machine learning algorithms (like CNN) against traditional statistical methods for chemical process optimization.
• Analyzing relationships between nanocomposite properties (e.g., bandgap, surface area) and photocatalytic performance.

Strengths

• Dataset is openly licensed under CC-BY-4.0.
• The underlying study reports specific performance metrics, such as 84.51% dye removal and a model R² of 0.91.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small at 5.5 KB, indicating limited scope.

Provenance

Source

figshare

Collection Method

Data likely originates from laboratory experiments on synthesized nanocomposites, as described in the study.

Freshness

Last updated 2026-06-02 18:00:38; freshness should be verified.