Synthesis and electrocatalytic properties of La<sub>0.8</sub>Sr<sub>0.2</sub>FeO<sub>3−δ</sub> perovskite oxide for oxygen reactions
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Bibliographic record
Abstract
Perovskites are important alternatives for precious metals as catalysts for bifunctional oxygen electrodes, involving oxygen evolution (OER) and reduction (ORR) reactions as is the case of regenerative fuel cells. In this work, strontium doped lanthanum ferrite La<sub>1−<em>x</em></sub>Sr<em><sub>x</sub></em>FeO<sub>3−<em>δ</em></sub> (<em>x</em> = 0; 0.1; 0.2; 0.3; 0.4; 0.6 and 1.0) powders were prepared by a self-combustion route. The oxides, in the form of carbon paste electrodes, were characterised by cyclic voltammetry in alkaline solutions. Data analyses lead to the selection of La<sub>0.8</sub>Sr<sub>0.2</sub>FeO<sub>3−<em>δ</em></sub> to prepare gas diffusion electrodes (GDEs). Cyclic voltammetry and steady state polarization curves were used, respectively, to assess the electrochemical behaviour of GDEs and to obtain kinetic data for both OER and ORR. It is concluded that the oxide preparation conditions/electrode configuration determine the electrode performance. The bifunctionality of the electrodes was assessed, under galvanostatic control, using a cycling protocol within the potential domains for OER and ORR. The potential window, i.e., the total combined overpotential between OER and ORR was found to be of ≈770 mV, value which compares well with that obtained under potentiostatic control. Even though the potential window keeps constant during 140 cycles, the increase in cycling time and/or current density (≥2.5 mA·cm<sup>−2</sup>) led to a gradual metallization of the GDE surface, as confirmed by<sup> </sup>Scanning Electron Microscopy and X-ray diffraction analysis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it