A Monte Carlo simulation based on first-passage distributions for spatio-temporal detection on potentiometric sensor...

Area of Science:

• Computational chemistry and physics
• Sensor technology and instrumentation
• Materials science

Background:

• Particle diffusion modeling is crucial for understanding chemical processes in sensing arrays.
• Existing simulation techniques face limitations in speed and efficiency.
• Accurate spatio-temporal modeling is required for advanced potentiometric sensor applications.

Purpose of the Study:

• To present a novel, high-speed simulation approach for particle diffusion using Monte Carlo first-passage (MCFP) sampling.
• To apply and validate the MCFP technique for spatio-temporal modeling of potentiometric sensing arrays, specifically ion-sensitive field-effect transistors (ISFETs).
• To demonstrate the MCFP algorithm's ability to overcome the speed limitations of current simulation methods.

Main Methods:

• Developed a Monte Carlo first-passage (MCFP) simulation algorithm for particle diffusion.
• Applied MCFP to model proton detection by ion-sensitive field-effect transistor (ISFET) arrays.
• Validated MCFP simulations against a benchmark random walk algorithm for temporal accuracy.
• Validated MCFP simulations against experimental data from a microchip-based ISFET array observing localized pH changes.

Main Results:

• MCFP temporal simulations closely matched random walk results (average r^2 = 0.901).
• MCFP simulations accurately estimated mean array output over time in experimental validation (average r^2 = 0.749 ± 0.170, max 0.986).
• MCFP simulations achieved over a tenfold increase in speed compared to random walk methods.
• MCFP successfully recreated spatial signal pattern trends and showed consistency with experimental variance within two standard deviations.

Conclusions:

• The MCFP simulation approach offers a significant speed improvement for spatio-temporal modeling in potentiometric sensing.
• MCFP provides accurate estimations of sensor array output and spatial patterns, validated by both computational benchmarks and experimental data.
• This novel method enhances the efficiency of simulating particle diffusion for applications like ISFET-based sensing arrays.