Highly Active Lanthanum Perovskite Electrocatalysts (LaMnxCo1-xO3 (0 ≤ x ≤ 1)) by Tuning the Mn:Co Ratio for ORR and MOR in Alkaline Medium
Bibliographic record
Abstract
Lanthanum-based perovskites (LaMnxCo1-xO3 (0 ≤ x ≤ 1)) were synthesized using a solution combustion synthesis technique with variable ratios of Co and Mn to investigate the surface property and electrocatalytic characteristics (stability and activity of catalyst) for methanol oxidation reaction (MOR), oxygen reduction reaction (ORR), and oxygen evolution reaction (OER) under alkaline medium (KOH). The structural, chemical, and morphological characterizations of the synthesized catalyst were performed by XRD, FTIR, SEM, TEM, and XPS techniques as a function of the Mn:Co elemental ratio. The time–temperature profile during the combustion process was also monitored to study the completion of the combustion reaction and to understand its impact on the structure of the perovskites. SEM/EDX and XPS analysis confirmed the formation of the targeted ratio of Mn and Co on the catalyst. Cyclic voltammetry (CV) and linear sweep voltammetry (LSV) results revealed that all perovskite samples with different Co:Mn ratios were active for ORR, OER, and MOR. The LaMnxCo1-xO3 perovskite with x = 0.4 showed the highest current density compared to the other samples toward all the electrocatalytic reactions under alkaline reaction conditions. Graphical AbstractOther Information Published in: Electrocatalysis License: https://creativecommons.org/licenses/by/4.0 See article on publisher's website: http://dx.doi.org/10.1007/s12678-022-00772-0
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".