Crystallization Solvent Screening Data for Yield and Purity

2.4 MB of data from a screening approach for pharmaceutical crystallization processes. The dataset, authored by Phoebe Eccles and last updated in June 2026, describes a workflow using high-throughput experimentation and modeling to estimate impurity rejection and crystallization yield across solvents and antisolvents for three industrially relevant compounds.

Use Cases

• Training predictive models for crystallization yield based on solvent properties.
• Screening for optimal solvent-antisolvent combinations to maximize purity upgrade.
• Informing crystallization process design space using high-throughput solubility data.

Strengths

• Models described achieved an R² of 0.88 for predicting crystallization yield.
• Models described achieved an R² of 0.73 for predicting purity.
• Workflow was validated on three industrially relevant compounds.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Phoebe Eccles via figshare

Collection Method

High-throughput experimentation and modeling informed by experimentally determined impurity retention mechanisms.

Freshness

Last updated 2026-06-04 16:06:15; freshness should be verified.