Fanjin Wang provides raw SEM images for the paper 'Active transfer learning assisted sustainable electrohydrodynamic atomization with gamma-valerolactone'. The images depict nanoparticles prepared using the novel solvent gamma-valerolactone under various processing parameters. The data was collected to establish a machine learning model for predicting atomization product properties and was acquired with a Zeiss Gemini SEM under an SE2 detector.
Use Cases
- Training computer vision models for nanoparticle morphology analysis based on SEM images.
- Predicting nanoparticle properties based on atomization processing parameters mentioned in the description.
- Benchmarking sustainable solvent performance in electrohydrodynamic atomization based on experimental data.
Strengths
- Images were acquired with a Zeiss Gemini SEM under an SE2 detector, indicating a specific, high-quality instrument.
- Data is associated with a specific research paper, providing academic context.
- License is CC-BY-4.0, allowing for open sharing and reuse.
Limitations
- Description metadata is limited; actual data quality, organization, and file formats require manual inspection after download.
- Row count and dataset size are unknown, which may limit suitability assessment.
- Column-level documentation is absent; folder structure and image semantics must be inferred.
Provenance
- Source
- Fanjin Wang via figshare.
- Collection Method
- Images acquired with a Zeiss Gemini SEM under an SE2 detector.
- Time Range
- null
- Freshness
- Last updated 2026-04-14 11:34:12; freshness should be verified.
- Geography
- null