A self-driving fluidic lab for data-driven synthesis of lead-free perovskite nanocrystals

2025/01/01 by Sina Sadeghi, Karin Mattsson, Joshua Glasheen +13 · 1 voice · 2 citations
Computer Science · Engineering · Materials Science · #Electronic and Structural Properties of Oxides #Machine Learning and ELM #Perovskite Materials and Applications

paper · pdf · doi:10.1039/d5dd00062a

openalex publication_date 2025/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

We present a self-driving fluidic lab with a modular hardware and software for data-driven synthesis optimization of eco-friendly colloidal semiconductor nanocrystals.

Citations

• Flow chemistry—Microreaction technology comes of age
• Surface Chemistry Engineering of Perovskite Quantum Dots: Strategies, Applications, and Perspectives
• Low dimensional metal halide perovskites and hybrids

Cited by

• Programmable Light-Driven Color Tuning of Perovskite Quantum Dots
• A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery

Discussions

• We're pleased to feature an article from Associate Editor Milad Abolhasani and colleagues. The paper shares a self-driving fluidic lab (SDFL) for autonomous optimisation of metal halid perovskite nano [bsky, 2 points, 0 comments]

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