Solar fuel production provides a sustainable route towards simultaneous energy harvesting and storage. However, this technology is hampered by the complexity and slow manual screening of the chemical design space to find suitable catalytic and light-harvesting materials. One solution is offered by automation, which has begun changing the landscape of material discovery and energy research.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout

References
Burger, B. et al. A mobile robotic chemist. Nature 583, 237–241 (2020).
Buglioni, L., Raymenants, F., Slattery, A., Zondag, S. D. A. & Noël, T. Technological innovations in photochemistry for organic synthesis: flow chemistry, high-throughput experimentation, scale-up, and photoelectrochemistry. Chem. Rev. 122, 2752–2906 (2021).
Bai, Y. et al. Accelerated discovery of organic polymer photocatalysts for hydrogen evolution from water through the integration of experiment and theory. J. Am. Chem. Soc. 141, 9063–9071 (2019).
Lignos, I. et al. Synthesis of cesium lead halide perovskite nanocrystals in a droplet-based microfluidic platform: fast parametric space mapping. Nano Lett. 16, 1869–1877 (2016).
Sliozberg, K. et al. Fe–Cr–Al containing oxide semiconductors as potential solar water-splitting materials. ACS Appl. Mater. Interfaces 7, 4883–4889 (2015).
Batchelor, T. A. A. et al. Complex-solid-solution electrocatalyst discovery by computational prediction and high-throughput experimentation. Angew. Chem. Int. Ed. 60, 6932–6937 (2021).
Li, Z. et al. Scalable fabrication of perovskite solar cells. Nat. Rev. Mater. 3, 18017 (2018).
Granda, J. M., Donina, L., Dragone, V., Long, D.-L. & Cronin, L. Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature 559, 377–381 (2018).
Bédard, A.-C. et al. Reconfigurable system for automated optimization of diverse chemical reactions. Science 361, 1220–1225 (2018).
Zhong, M. et al. Accelerated discovery of CO2 electrocatalysts using active machine learning. Nature 581, 178–183 (2020).
Acknowledgements
V.A. is grateful for financial support from St John’s College Cambridge (Title A Research Fellowship) and the Winton Programme for the Physics of Sustainability.
Author information
Authors and Affiliations
Contributions
K.P.S. and V.A. conceived and wrote the manuscript.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Rights and permissions
About this article
Cite this article
Sokol, K.P., Andrei, V. Automated synthesis and characterization techniques for solar fuel production. Nat Rev Mater 7, 251–253 (2022). https://doi.org/10.1038/s41578-022-00432-1
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41578-022-00432-1