{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/smarttrap-automated-precision-experiments","title":"SmartTrap: Automated Precision Experiments with Optical Tweezers","arxiv_id":"2505.05290","date":"2025-05-08","proceeding":null,"authors":["Martin Selin","Antonio Ciarlo","Giuseppe Pesce","Lars Bengtsson","Joan Camunas-Soler","Vinoth Sundar Rajan","Fredrik Westerlund","L. Marcus Wilhelmsson","Isabel Pastor","Felix Ritort","Steven B. Smith","Carlos Bustamante","Giovanni Volpe"],"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.","url_abs":"https://arxiv.org/abs/2505.05290v1","url_pdf":"https://arxiv.org/pdf/2505.05290v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"smarttrap-automated-precision-experiments","repo_url":"https://github.com/softmatterlab/smarttrap","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}