Machine Learning Assisted Single-Molecule Sensing Data for PFAS Quantification

283.8 KB of source data for the paper "Machine learning assisted single-molecule sensing towards standard-free quantification of per- and polyfluoroalkyl carboxylic acids". The dataset, authored by Jiaqi Zuo, was last updated on April 30, 2026 and is available under a CC-BY-4.0 license.

Use Cases

• Training ML models for standard-free chemical quantification based on single-molecule sensing data.
• Analyzing patterns in per- and polyfluoroalkyl carboxylic acid (PFAS) detection signals.
• Developing novel sensing methodologies for environmental contaminant monitoring.
• Benchmarking ML-assisted analytical techniques against traditional quantification methods.

Strengths

• Data is directly linked to a specific, peer-reviewed research paper.
• Available under a permissive CC-BY-4.0 license for reuse.
• File size of 283.8 KB indicates a focused, manageable dataset.

Limitations

• Row count and column definitions are unknown, requiring manual inspection after download.
• Description metadata is limited; actual data quality and structure require verification.

Provenance

Source

Jiaqi Zuo via figshare.

Collection Method

Source data for a research paper; likely generated from single-molecule sensing experiments.

Freshness

Last updated 2026-04-30 07:02:43; freshness should be verified.