Unlocking Efficient Thermochemical Energy Processes With Computational Materials Design Through The Compound Energy Formalism

Cyclical chemical looping involves the thermal reduction of metal oxide to release O2 at high temperatures, followed by its oxidation using O-containing molecules like O2, H2O, or CO2. This process is a promising method for solar thermochemical water splitting (STCH), oxygen separation, and thermochemical energy storage (TCES). The efficiency and economic viability of this process hinge on the thermodynamics of metal oxide reduction. This dissertation presents innovative methods to enhance the performance of these processes, with a specific focus on STCH and TCES by advancing thermodynamic characterization and screening of potential metal oxides, thereby reducing H2 costs.A novel CALPHAD approach, the CrossFit Compound Energy Formalism (CEF), integrates theoretical (density functional theory) and experimental (thermogravimetric) data to develop thermodynamic models for desired materials. The CrossFit-CEF was applied to BaxSr1-xFeO3-δ identifying matching thermodynamics and off-stoichiometric values to literature (~100-180 kJ/mol O2 reduction enthalpies across the BaxSr1-xFeO3-δ compositional range). Comparisons with the traditional van ‘t Hoff thermodynamic extraction technique reveal that the CrossFit-CEF method significantly outperforms conventional methods. For instance, the CEF method was employed to extract thermodynamic data for CaFexMn1-xO3-δ and identify optimal TCES CaFexMn1-xO3-δ compositions. The CrossFit-CEF method found the same thermodynamic trends on less than half the data utilized in a van ‘t Hoff approach and determined that the optimal CaFexMn1-xO3-δ composition had the minimal Fe concentration synthesized (x=0.625), achieving ~60 kJ/mol TCES. Bayesian Inference was employed was employed to expedite data collection. When combined with the CrossFit-CEF method, Bayesian Inference assesses the likelihood that the current model accurately describes the data, providing confidence intervals for the model. This approach reduces the amount of data needed for accurate thermodynamic modeling by 50%. Finally, the CrossFit-CEF and Bayesian methods are integrated into a system-level STCH model to optimize and accelerate materials design for specific plant operating conditions. Overall, this dissertation introduces methods that yield more accurate thermodynamic models with reduced data requirements. The time saved in data collection enables screening of more materials, expediting material identification and optimization. The materials identified through these techniques are expected to enhance chemical looping cycles, leading to increased H2 production efficiency and reduced costs.