AFION self-driving lab
Idea Proposed
The AFION self-driving lab is an automated system that integrates:
- Microfluidic reactors (for precise chemical reactions)
- In-flow spectroscopic nanoparticle characterization (to analyze results in real time)
- Machine learning algorithms (to optimize synthesis conditions)
This system enables on-demand synthesis of various plasmonic nanoparticles without human intervention.
How does it work?
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Automated Synthesis
- Uses microfluidic reactors to control reagent flow and mixing.
- Conducts photochemical reactions to synthesize nanoparticles.
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Real-time Analysis
- Uses spectroscopy to measure nanoparticle properties (size, shape, and optical behavior).
- Data is continuously collected and analyzed.
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Machine Learning Optimization
- An ML algorithm (Gryffin) predicts the best reaction conditions.
- The system runs experiments in a closed-loop cycle, learning from each result and improving synthesis conditions over time.
How can we benefit from this?
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Faster Nanoparticle Development
- Instead of manual trial-and-error, this system autonomously finds optimal reaction conditions, reducing time and effort.
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Higher Precision and Consistency
- Produces nanoparticles with highly controlled properties, which is crucial for applications in sensors, drug delivery, imaging, and photovoltaics.
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Cost-Effective and Scalable
- Uses fewer materials and automates synthesis, making nanoparticle production more efficient and scalable.
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Exploration of New Nanomaterials
- Can discover and optimize new types of nanoparticles, leading to advancements in nanotechnology and materials science.
Implementation
Implementation of this can be found here: Wu, T., Kheiri, S., Hickman, R.J. et al. Self-driving lab for the photochemical synthesis of plasmonic nanoparticles with targeted structural and optical properties. Nat Commun 16, 1473 (2025). https://doi.org/10.1038/s41467-025-56788-9
Sources & citation
Wu, T., Kheiri, S., Hickman, R.J. et al. Self-driving lab for the photochemical synthesis of plasmonic nanoparticles with targeted structural and optical properties. Nat Commun 16, 1473 (2025). https://doi.org/10.1038/s41467-025-56788-9