Carbon Nanotube Dispersion Quality Data for 666 Formulations

666 experimental carbon nanotube dispersions systematically varying 36 organic dispersants, 22 solvents, and two dispersion methods. The dataset, created by Hirokuni Jintoku and last updated in April 2026, quantifies dispersion quality (D_score) and structural integrity (I_G/I_D) to support machine learning-based formulation screening.

Use Cases

• Training ML models to predict CNT dispersibility based on molecular descriptors and experimental variables.
• Screening formulation and processing conditions to reduce experimental trial-and-error.
• Analyzing the influence of solvent-dispersant compatibility on dispersion quality.
• Extending the prescreening framework to other nanomaterial dispersion systems.

Strengths

• Contains 666 systematically varied experimental data points.
• Quantifies two key metrics: D_score for dispersion quality and I_G/I_D for structural integrity.
• Model performance metrics are provided (R^2 = 0.57 for D_score, R^2 = 0.73 for I_G/I_D).

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Limited quantitative accuracy was reported for dispersants outside the training set.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

figshare

Collection Method

Experimental investigation of combinatorial design space for CNT dispersions.

Time Range

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Freshness

Last updated 2026-04-09 17:37:31; freshness should be verified.

Geography

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