From manual synthesis to automated processes

One of SMART´s most significant advances concerns the experimental infrastructure required to accelerate these searches.

The project has optimized CP-ino, a coprecipitation module consisting of 15 parallel reactors with automated pumping, pH control, mixing and heating. SMART has also developed FSS-ino for automated solid-state synthesis and implemented a filtration unit capable of processing six samples simultaneously.

The goal is not simply to run more experiments. It is to create a more reproducible and interconnected workflow in which synthesis, characterization and electrochemical evaluation can progressively become part of the same integrated platform.

Automation is also being extended to electrochemical cells themselves. SMART has worked on new two- and three-electrode cell designs intended to facilitate high-throughput testing, with successive design iterations aimed at improving sealing, assembly and overall performance.

Seeing what happens inside the battery

Faster experimentation must be accompanied by the ability to understand the resulting data. SMART is therefore developing dedicated tools for high-throughput characterization and operando studies.

These include airtight sample holders for XRD analysis of air- and moisture-sensitive materials, together with a multisample system designed to analyse plates containing up to 28 samples at the ALBA Synchrotron.

The project has also combined operando XRD and XAS at ALBA´s MSPD and NOTOS beamlines to monitor structural and electronic changes in different layered oxides during electrochemical operation. Operando SAXS measurements and ex situ measurements of Prussian White materials at different states of charge have also been performed to deepen the understanding of their electrochemical mechanisms.

These approaches make it possible to move beyond simply identifying which material performs best. They help explain why it performs as it does, what transformations occur during operation and which parameters can be modified to further optimize performance.

Turning data into a decision-making tool

The fourth pillar of SMART concerns the management of the growing amount of experimental information.

The project has created an internal database and common protocols for identifying samples and connecting information from synthesis, XRD, microscopy and electrochemical testing. In parallel, Python routines have been developed to automate data processing and visualization, including the extraction of relevant electrochemical parameters and automatic SAXS data reduction.

SMART is also moving towards an active-learning system supported by Bayesian optimization. The BOSS software has been incorporated into the workflow so that models and experiments can iteratively inform each other, progressively directing human and machine effort towards the most informative areas of the materials space.

These developments are already generating new scientific contributions. The interim report lists four publications directly arising from SMART, covering topics including Prussian White materials, structure–electrochemistry relationships in P2 oxides and ADEL, an automated drop-cast electrode setup for high-throughput screening of battery materials.

Overall, SMART demonstrates how materials development can evolve from a sequence of independent experiments towards an integrated ecosystem combining synthesis, characterization, electrochemistry and data.

For CIC energiGUNE, this evolution goes beyond accelerating sodium-ion battery research. It represents another step towards a new generation of platforms capable of making materials discovery and optimization increasingly faster, more reproducible and more knowledge-driven.