The main effects of climate change, like more frequent and severe clime, dirtier air, higher wildlife extinction rates, more acidic oceans, and higher sea levels, can be devastating for the Earth ecosystem, humans and global economy in the decades ahead. Therefore, it is very important to reduce greenhouse gas emissions. There are multiple approaches to solve this, but we focused on the following i) reduce dependency on fossil fuels by increasing the competitivity of renewable energies, ii) CO2 extraction from the atmosphere to convert it into useful products and iii) increase efficiency of industrial processes. During this thesis, I will work on CO2 electroreduction to convert it into products suitable for fuels cells using an automatic testing system to attack climate change using these three tools at once.
The previous work of Adrianna Nogalska and Ricard Garcia Valls consisted of a leaf-like system that can capture CO2 by making it pass through membrane pores to the next compartment to be finally converted to potassium bicarbonate, which is a reagent that can be used directly for electroreduction to hydrocarbons. One of the most promising hydrocarbons obtained is formic acid (FA), because it is relatively stable at atmospheric conditions and is also easy to transport and store, together with high energy density, that makes it a very attractive hydrogen carrier. With the previously commented method, CO2 can be absorbed from the atmosphere by transforming it into formic acid rather than the most conventional methods used nowadays to obtain FA, that relay on fossil fuels emitting large concentrations of CO2 instead of removing it. The disadvantage of obtaining FA via electroreduction in front the conventional ones is the lower efficiency and scalability of the reaction, that makes it way less competitive in the market yet. The electroreduction of CO2 is attractive because the electrical energy used to trigger the reaction is stored in form of formic acid which is a product with way higher energy density than common lithium batteries, therefore a good ally to be able to store large quantities of energy. That is necessary to overcome the main problem of renewable energies, which is its non-continuous generation and the difficulty to store large quantities of energy in conventional lithium batteries. Hence, during this thesis, the electroreduction of raw potassium bicarbonate acting as a CO2 source is performed to study its parameters to make the reaction more efficient. Along this research, we focused on bulk Tin (Sn) as electrocatalyst due to its low cost, easiness to use and its selectiveness toward FA among other novel ceria-based (CeO2) GDL catalysts prepared by Eva Chinarro Martín from Instituto de Cerámica y Vidrio, ICV-CSIC. Some reaction parameters are studied to evaluate their effect on the reaction efficiency: KHCO3 concentration, pure CO2 gas pre-saturation, applied potential.
Due to the high number of parameters to be studied and therefore, the high number of experiments to be performed, it is necessary to design a reactor as small as possible, easy to assembly-disassembly and manipu-late. For this purpose, novel additive manufacturing techniques have been used, such as Fused Deposition Modelling and Stereolithography 3D printing. Those manufacturing techniques present several advantages in front of conventional subtractive ones, like faster prototyping, possibility to print more complex objects therefore reducing the total number of parts, simplifying the assembly, and reducing its size and manufacturing price.
To conclude, we were able to obtain electroreduction efficiencies around 20% when only bicarbonate was using as CO2 source and efficiencies up to almost 50% when the bicarbonate solution was pre-saturated with pure CO2. An automatic testing system was also developed to improve repeatability and reduce human error, while increasing massively the total number of experiments due to the possibility to escalate the sys-tem. This system, together with a very small 3D printed reactor are also able to reduce the price of the experiments, therefore, we open the possibility to use machine learning for the optimization of the parameters.
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