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Updated: Jun 22, 2026

An Inverse Analysis Approach to the Characterization of Chemical Transport in Paints

An Inverse Analysis Approach to the Characterization of Chemical Transport in Paints

Published on: August 29, 2014

Simulation-guided exploration of PAINT parameter space for accurate molecular quantification.

Wei Shan Tan^1,2, Arthur M de Jong^3,2, Menno W J Prins^1,3,2,4

¹Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands. m.w.j.prins@tue.nl.

|November 7, 2025

Summary

This summary is machine-generated.

Optimizing molecular quantification with Point Accumulation for Imaging in Nanoscale Topography (PAINT) is crucial. This study developed a simulation framework to identify optimal PAINT parameters for accurate density and spatial distribution measurements.

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Last Updated: Jun 22, 2026

An Inverse Analysis Approach to the Characterization of Chemical Transport in Paints

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Published on: August 29, 2014

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Published on: June 18, 2021

Area of Science:

Biophysics
Nanotechnology
Molecular Imaging

Background:

Molecular quantification using Point Accumulation for Imaging in Nanoscale Topography (PAINT) is vital but sensitive to experimental parameters.
Accurate PAINT analysis requires understanding probe kinetics, imaging conditions, and surface properties.

Purpose of the Study:

To develop a simulation-guided framework for optimizing PAINT parameter space.
To identify conditions ensuring high accuracy (≥90%) in molecular density and spatial distribution quantification.

Main Methods:

Utilized Monte Carlo simulations to train a neural network surrogate model.
Performed Sobol sensitivity analysis to determine key factors influencing PAINT outputs.
Defined detection thresholds for point spread function density, localization cloud density, and binding event density.

Main Results:

Probe kinetics and concentration were identified as dominant factors affecting PAINT output variability.
The framework rapidly mapped viable parameter regimes for accurate quantification.
Interpretable spatial quantification in dense, clustered systems necessitates prior knowledge or enhanced resolution.

Conclusions:

The simulation-guided framework provides quantitative insights for optimizing PAINT experiments.
This approach supports the rational design of PAINT-compatible probes for broader molecular system applications.