Partners at 6TMIC have recently released the first outcomes of modelling efforts for two electrochemical technologies proposed by FIREFLY: Gas Diffusion Electrocrystallisation (GDEx) and Electrochemical Transformation in Molten Salts (ETMS). By simulating these processes under numerous conditions, partners are building up knowledge that will later feed the design of the reactor, the operational strategies as well as the digital optimisation tools foreseen within the predictive tool.
As the modelling work continues, future updates will incorporate additional technologies such as mechanochemical processing (MCP) and electrochemical recovery from molten salts (ERMS).
GDEx and recovery of precious metals
GDEx is a cutting-edge electrochemical technique designed to recover metals from complex liquid streams, such as leachates and organic solvents, while simultaneously synthesising platinum group metal (PGM) nanoparticles. These nanoparticles, including palladium (Pd), platinum (Pt), and rhodium (Rh), are directly usable as high-performance catalysts.
The process operates through simultaneous (electro)chemical reactions. At the cathode, hydrogen evolution and carbon dioxide reduction produce hydrogen and carbon monoxide. The hydrogen acts as a reducing agent for dissolved metal ions, while the carbon monoxide serves as a capping agent, controlling nanoparticle size. Meanwhile, oxygen evolution and other anodic reactions occur on the opposite side of the cell.
To optimise this complex system, researchers conducted more than 2600 simulations, exploring combinations of single, binary, and ternary metal solutions under varying initial concentrations and operation conditions. A representative case involved a trimetallic mixture of Pd²⁺, Pt⁴⁺, and Rh³⁺, tested under specific carbon dioxide flow rates and current densities, demonstrated a strong alignment between simulations and experimental results across diverse operating conditions. These findings revealed consistent system behaviour and showed that the simulations could be reliably extrapolated to diverse operating scenarios, as presented in the figure below. Variations in species concentration and flow dynamics provided valuable insights into how the process can be fine-tuned for maximum efficiency.
(a) Dissolved concentration of hydrogen (cH2-(ℓ)) at current densities of 30 (J30) and 10 (J10) mA cm-2, and carbon dioxide flow rates of 5 (Q5), 10 (Q10), and 15 (Q15) sccm. Dots represent the experimental values, while lines indicate the simulation results. (b) Normalised concentration (cMn+(t) / cMn+(t=0)) of palladium ions in solution over time at constant liquid flow rate of 100 ml min-1. The current density and gas flow rates correspond to the indicated in the labels and defined in (a). Filled dots represent the experimental results, and empty dots correspond to the simulation results. (c) Normalised concentration of platinum ions, and (d) Normalised concentration of rhodium ions
ETMS and new metal extraction methods
The ETMS process, based on the Fray-Farthing-Chen (FFC) Cambridge method, offers a more sustainable alternative to traditional high-temperature smelting. It enables the extraction of metals such as titanium, tantalum and zirconium from their oxides using molten salt electrolysis at lower temperatures.
The process uses a molten salt electrolyte that facilitates the dissolution of calcium oxide and supports electrochemical reactions. During operation, metal cations from a solid oxide pellet are reduced at the cathode, releasing oxide ions into the electrolyte. At the anode, gas (such as CO₂ or O₂) evolves depending on the electrode material and setup. Although the process can yield relatively pure metals, residual oxygen in the final product is a common issue that limits its performance.
In FIREFLY, KUL developed a C++ model to simulate the ETMS process, focusing on the reduction of titanium dioxide (TiO₂). The model captures key electrochemical mechanisms and ion transport phenomena and was validated against experimental data. Simulations tracked current-time behaviour, polarisation curves, and the growth of titanium layers over time.
Despite its potential, the ETMS process currently suffers from low current efficiency and high energy consumption, challenges attributed to secondary reactions. To address this, the modelling team is refining the simulation framework to include these reactions and improve predictive accuracy. A total of 735 simulations were performed to generate datasets for training AI models, which will be integrated into FIREFLY’s digital prediction tool.
