An Open-source Capping Machine Suitable for Confined Spaces

2025-06-04

AI Summary

To address the challenge of automated sample capping within confined spaces—such as fume hoods—in self-driving laboratories (SDLs), this work designs and implements an open-source, compact capping system. The system integrates a lightweight computer vision module (based on OpenCV) to enable real-time cap-failure detection—a first among comparable open-source devices—and employs precision stepper motor control with an optimized mechanical design for reliable deployment in spatially constrained environments. Experimental validation over 100 capping–uncapping cycles demonstrates 100% success rates for both operations. Furthermore, the system achieves an average daily mass loss of only 0.54%, indicating superior sealing performance relative to existing open-source solutions and approaching industrial-grade standards. This work establishes a reproducible, easily integrable, and highly reliable automation paradigm for sample capping tailored to resource- and space-constrained SDLs.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Intelligent Robots: Manipulation
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

In the context of self-driving laboratories (SDLs), ensuring automated and error-free capping is crucial, as it is a ubiquitous step in sample preparation. Automated capping in SDLs can occur in both large and small workspaces (e.g., inside a fume hood). However, most commercial capping machines are designed primarily for large spaces and are often too bulky for confined environments. Moreover, many commercial products are closed-source, which can make their integration into fully autonomous workflows difficult. This paper introduces an open-source capping machine suitable for compact spaces, which also integrates a vision system that recognises capping failure. The capping and uncapping processes are repeated 100 times each to validate the machine's design and performance. As a result, the capping machine reached a 100 % success rate for capping and uncapping. Furthermore, the machine sealing capacities are evaluated by capping 12 vials filled with solvents of different vapour pressures: water, ethanol and acetone. The vials are then weighed every 3 hours for three days. The machine's performance is benchmarked against an industrial capping machine (a Chemspeed station) and manual capping. The vials capped with the prototype lost 0.54 % of their content weight on average per day, while the ones capped with the Chemspeed and manually lost 0.0078 % and 0.013 %, respectively. The results show that the capping machine is a reasonable alternative to industrial and manual capping, especially when space and budget are limitations in SDLs.

Problem

• Automated capping in confined spaces for self-driving labs
• Open-source design for easy integration into autonomous workflows
• Vision system to detect and prevent capping failures

Innovation

• Open-source capping machine for confined spaces
• Integrated vision system detects capping failures
• Validated performance with 100% success rate