An Open-source Capping Machine Suitable for Confined Spaces
Francisco Munguia-Galeano, Louis Longley, Satheeshkumar Veeramani, Zhengxue Zhou, Rob Clowes, Hatem Fakhruldeen, and Andrew I. Cooper

TL;DR
This paper presents an open-source, compact capping machine with vision recognition, validated through repeated tests and benchmarking, offering a space- and cost-effective alternative for automated sample capping in confined laboratory environments.
Contribution
The paper introduces a novel open-source capping machine designed specifically for confined spaces, integrating vision for failure detection, and validated through extensive testing and benchmarking.
Findings
Achieved 100% success rate in capping and uncapping tests.
Capped vials lost only 0.54% of content over three days.
Performance comparable to industrial and manual capping methods.
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…
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Taxonomy
TopicsInnovative Microfluidic and Catalytic Techniques Innovation · Microfluidic and Capillary Electrophoresis Applications · Biosensors and Analytical Detection
