workflow characterizing nanoparticle monolayers biosensors

workflow characterizing nanoparticle monolayers biosensors analyzes SEM nanoparticle images to quantify nanoparticle distributions and classify particle subtypes for characterization of biosensor interfaces.

Key Features:

• Comprehensive image-analysis workflow: Implements acquisition, preprocessing, segmentation, labeling, and object classification steps for nanoparticle monolayers.
• Advanced image-processing evaluation: Utilizes novel artificial SEM images to objectively evaluate and compare image-processing methods.
• Machine learning integration: Incorporates semi-supervised machine learning through Ilastik to decompose complex nanoparticle images into particle subtypes such as singles, dimers, flat aggregates, and piles.

Scientific Applications:

• Biosensor film characterization: Quantifies the distribution and arrangement of nanoparticles within sensor films to relate nanoscale structure to biosensor performance.
• Materials science of heterogeneous nanostructures: Enables analysis of large ensembles of SEM images to characterize heterogeneous nanoparticle monolayers and ensembles.

Methodology:

Image acquisition of SEM nanoparticle images; preprocessing to reduce noise and correct artifacts; segmentation and labeling to delineate individual nanoparticles; object classification using semi-supervised machine learning via Ilastik into singles, dimers, flat aggregates, and piles; and evaluation of image-processing methods using novel artificial SEM images.