(279f) Data Intensification in Flow for Accelerated Synthesis Space Mapping of Inorganic Nanomaterials

The ability to accelerate the exploration of synthesis–property relationships of functional materials could significantly boost the rate at which new materials are discovered, optimized, and manufactured. Self-driving labs (SDLs) have risen as a sustainable tool to reduce the time-to-solution in chemical and materials sciences by integrating lab automation, robotics, and artificial intelligence (AI) to enable closed-loop, self-guided experiments based on the human-defined scientific problem [1]. The SDLs' developments over the past few years have been driven by process intensification (PI) principles, to achieve faster, safer, and more efficient processes toward Self-Driving Fluidic Labs (SDLs) that leverage flow reactors with in-situ characterization techniques.

Despite the success of SDFLs in reducing the time-to-solution in chemical and materials sciences, they suffer from prolonged waiting times, focusing only on steady-state measurements and ignoring the transient measurements. However, if harnessed properly, the transient information of flow chemistry platforms can provide valuable insights into the underlying process mechanisms and could be used to expedite experimentation, provide actionable feedback to process controllers and enrich the AI agent of the SDFL [2,3]. In this work, we present dynamic flow experiments as a data-rich strategy for rapid quantum dot (QD) synthesis-parameter—material-property mappings at least 100× faster than steady-state flow chemistry strategies. Dynamic flow experiments take advantage of in-situ characterization techniques capable of generating time-series data from sudden or controlled changes in one or more process parameters. Through mapping the instantaneous to steady-state equivalent residence time, we demonstrate the unique data intensification capability of continuous flow reactors (>100× experimental data throughput) while reducing the total chemical consumption (~3) in a shorter time when compared to traditional steady-state flow experiments in the same system.

We use Cadmium Selenide (CdSe) QDs as a proof-of-concept case study for accelerated material parameter space mapping. This data-intensified approach leads to a considerable improvement in terms sustainability of SDFLs. Furthermore, the resulting Big experimental data generated from this data intensification strategy (>10,000 experimental data/day) highlights a sustainable pathway to generate a digital-twin representation of the reactive system for subsequent autonomous closed-loop experimentation.

References:

[1] M. Abolhasani, E. Kumacheva, The rise of self-driving labs in chemical and materials sciences, Nat. Synth. 2 (2023) 483–492. https://doi.org/10.1038/s44160-022-00231-0.

[2] F. Florit, A.M.K. Nambiar, C.P. Breen, T.F. Jamison, K.F. Jensen, Design of dynamic trajectories for efficient and data-rich exploration of flow reaction design spaces, React. Chem. Eng. 6 (2021) 2306–2314. https://doi.org/10.1039/D1RE00350J.

[3] K.C. Aroh, K.F. Jensen, Efficient kinetic experiments in continuous flow microreactors, React. Chem. Eng. 3 (2018) 94–101. https://doi.org/10.1039/C7RE00163K.