SmartTrap: Automated Precision Experiments with Optical Tweezers

TL;DR

SmartTrap is an autonomous optical tweezers platform that combines deep learning, precise feedback control, and microfluidics to perform complex experiments with high precision and reproducibility, reducing manual effort and operator bias.

Contribution

This work introduces SmartTrap, the first fully autonomous optical tweezers system integrating real-time deep learning-based tracking and custom electronics for continuous, high-precision experiments.

Findings

SmartTrap operates continuously with high data quality over extended periods.

It reduces manual labor and operator bias in optical tweezers experiments.

Demonstrated applications in biophysics, cell mechanics, and colloidal science.

Abstract

There is a trend in research towards more automation using smart systems powered by artificial intelligence. While experiments are often challenging to automate, they can greatly benefit from automation by reducing labor and increasing reproducibility. For example, optical tweezers are widely employed in single-molecule biophysics, cell biomechanics, and soft matter physics, but they still require a human operator, resulting in low throughput and limited repeatability. Here, we present a smart optical tweezers platform, which we name SmartTrap, capable of performing complex experiments completely autonomously. SmartTrap integrates real-time 3D particle tracking using deep learning, custom electronics for precise feedback control, and a microfluidic setup for particle handling. We demonstrate the ability of SmartTrap to operate continuously, acquiring high-precision data over extended periods of time, through a series of experiments. By bridging the gap between manual experimentation and autonomous operation, SmartTrap establishes a robust and open source framework for the next generation of optical tweezers research, capable of performing large-scale studies in single-molecule biophysics, cell mechanics, and colloidal science with reduced experimental overhead and operator bias.