Transition Metal Doping of La<sub>0.3</sub>Ca<sub>0.7</sub>Fe<sub>0.7</sub>Cr<sub>0.3</sub>O<sub>3-δ</sub> for Nanoparticle-Enhanced Reversible CO<sub>2</sub>-CO Electrocatalysis
Bibliographic record
Abstract
Fluctuations in supply and demand result in wasted energy during off-peak electric grid hours, with Reversible Solid Oxide Cells (RSOCs) potentially providing an efficient means by which such energy can be stored to create a highly sustainable grid.1 A RSOC can catalyze reactions such as the CO2 Reduction Reaction (CO2RR) by operating in the Solid Oxide Electrolysis Cell (SOEC) mode when energy supply is high, or in the Solid Oxide Fuel Cell (SOFC) mode when demand is high while oxidizing CO.1 The estimated yearly energy storage requirement is about 2-3 months per year, with RSOCs capable of achieving this goal.1 Single phase perovskite materials with the composition La0.3M0.7Fe0.7Cr0.3O3-δ (M = Sr, Ca) (LMFCr) have emerged as promising electrocatalysts for both fuel and oxygen electrodes in symmetrical RSOCs due to their phase stability in air and fuels (pO2 ~ 0.21 – 10-21 atm) and their compatibility with doped ceria-buffered yttria- and scandia-stabilized zirconia (YSZ/SSZ) electrolytes.2,3 The Ca analogue of LMFCr (LCFCr) has generally shown better performance and compatibility with other cell components,3 with significant efforts directed towards further enhancing its CO2RR activity by employing various approaches, including nanoscale modifications.4 One such technique is nanoparticle (NP) exsolution, which can greatly increase the catalytic surface area.5 This method employs B-site doping with Ni or Co followed by exposure to reducing conditions to decorate the catalyst surface with B-site metal alloy NPs.4 These are strongly anchored to the bulk material, highly resistant to coking, and stable under the harsh conditions of RSOCs.4 In our initial work, the exsolution characteristics of Fe-Ni NPs from 5% Ni-doped LCFCr (LCFCrN) were studied in H2:N2 and CO2:CO atmospheres using ex situ XRD, SEM, and STEM-EDS.4 Exsolution kinetics were rapid in H2:N2 (pO2 ~ 10-23 atm) at 800 °C with NPs assuming an average size of 45 nm and an Fe-rich Fe0.64Ni0.36 composition within 1 h exposure to 5H2:95N2. On the other hand, 70CO:30CO2 atmospheres (pO2 ~ 10-20 atm) gave sluggish kinetics with the NPs achieving an average size of 40 nm and a Ni-rich FeNi3 composition even after more than 25 hours of treatment. This suggests stability to coarsening in highly reducing atmospheres.4 In more recent work, 5% Co-doped LCFCr (LCFCrCo) perovskites show visible Fe-Co NP formation at 800 °C upon exposure to 5H2:95N2 for at least 1 h or to 70CO:30CO2 for at least 5 h. Electrochemical characterization of Fe-Ni NP decorated LCFCrN (Fe-Ni@LCFCrN) was conducted on 2.5 cm diameter cells using ceria-buffered SSZ electrolyte. A LCFCrN ink was screen-printed onto a ~0.5 cm2 area on both sides of the cell followed by sintering at 1100 °C for 2 h. Au was painted onto each electrode, and the cell was sintered again at 825 °C for 1 h. Electrical connections were made via Au gauzes and wires. NP exsolution was induced by exposing the fuel electrode to 5H2:95N2 for 2 h at 800 °C. Various CO2:CO mixtures (100:0, 90:10, 70:30, 50:50) were supplied to the fuel electrode while air was supplied to the oxygen electrode with flow rates of 50 mL/min. Electrochemical performance was tested via cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and chronoamperometry. CVs showed that the NPs enhanced LCFCrN activity for CO2RR (~ 15%) and more notably for CO oxidation (~ 75%) at the same overpotential (~ 0.7 V), making the catalyst equally active for the two reactions. 15-minute potentiostatic tests indicated stable current densities of about –0.65, –0.634, and –0.618 A/cm2 in 100% CO2, 90CO2:10CO, and 70CO2:30CO respectively at a cell potential of 1.6 V. Medium-term (10 h) potentiostatic tests for CO2RR indicated excellent stability with a current density of –0.28 A/cm2 at a cell potential of 1.3 V in 70CO2:30CO (pO2 ~ 10-18 atm). The excellent electrochemical performance in both the SOFC and SOEC modes makes Fe-Ni@LCFCrN a very promising electrode material for RSOCs. Further work on LCFCrCo is underway, with comparisons being made between LCFCrN and LCFCrCo on CO2-CO electrocatalysis and NP characteristics. References Jensen, S. H.; Graves, C.; Mogensen, M.; Wendel, C.; Braun, R.; Hughes, G.; Gao, Z.; Barnett, S. A. Energy Environ. Sci. 8, 2471 (2015). Molero-Sánchez, B.; Addo, P.; Buyukaksoy, A.; Paulson, S.; Birss, V. I. Faraday Discuss. 182, 159 (2015). Molero-Sánchez, B.; Prado-Gonjal, J.; Avila-Brande, D.; Chen, M.; Moran, E.; Birss, V. I. J. Hydrog. Energy. 40, 1902 (2015). Ansari, H. M.; Bass, A. S.; Ahmad, N.; Birss, V. I. Mater. Chem. A (2022). Zhu, Y.; Dai, J.; Zhou, W.; Zhong, Y.; Wang, H.; Shao, Z. Mater. Chem. A. 6, 13582 (2018).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".