PFAS Rejection Data for Nanofiltration and Reverse Osmosis Membranes

2353 data points compiled from literature detail PFAS removal across various membranes and water conditions. Yukai Tomsovic used machine learning to model the influence of solute, membrane, and solution properties on rejection. The analysis identifies membrane water permeance and PFAS molecular volume as key factors and clarifies condition-dependent effects of background ions and organic matter.

Use Cases

• Training predictive models for PFAS rejection based on membrane properties and feedwater composition.
• Identifying critical data gaps in existing experimental studies on PFAS membrane transport.
• Guiding targeted membrane design by analyzing the main effects of permeance and molecular volume.
• Interpreting nonmonotonic effects of background ions and dissolved organic matter on removal efficiency.

Strengths

• 2353 data points provide a substantial foundation for analysis.
• Data spans a broad range of PFAS compounds, membranes, feedwater compositions, and operating conditions.
• Analysis evaluates 13 distinct experimental system descriptors.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data may reflect publication bias inherent to literature compilation.

Provenance

Source

figshare

Collection Method

Compiled from literature.

Freshness

Last updated 2026-04-30 09:30:58; freshness should be verified.