Strategies for automating analytical and bioanalytical laboratories

Area of Science:

• Analytical Chemistry
• Laboratory Automation
• Biotechnology

Background:

• Analytical measurement methods are crucial across production, quality control, diagnostics, environmental monitoring, and research.
• Manual laboratory processing of offline samples is common when direct inline or online methods are not feasible.
• Despite increasing automation, (bio)analytical laboratories exhibit lower automation degrees due to process complexity, stringent conditions, and intricate sample matrices.

Purpose of the Study:

• To explore automation strategies for (bio)analytical laboratory processes.
• To address the challenges hindering higher automation levels in these labs.
• To discuss the evolution of automation concepts, including robotic systems and future distributed models.

Main Methods:

• Review of existing automation strategies in (bio)analytical laboratories.
• Analysis of factors influencing the selection of automation concepts.
• Discussion of classical liquid handler systems, central robot systems, and emerging collaborative robot-based distributed automation.

Main Results:

• Automation significantly enhances throughput and improves the quality of results in analytical measurements.
• The complexity of processes, required conditions, and sample matrices are key barriers to automation.
• Advancements in collaborative robots offer potential for more flexible, distributed automation systems.

Conclusions:

• Automation is essential for improving efficiency and reliability in (bio)analytical measurements.
• Overcoming challenges related to process complexity and sample matrices is key to increasing automation.
• Future automation will likely involve more flexible, adaptable systems, including distributed models enabled by collaborative robots.