Facile Synthesis of Perovskite-Type Sm1-xSrxMnO3 (0 ≤ x ≤ 0.8), a Non-Precious Metal Oxides and its Electrocatalytic Analysis Towards the Oxygen Evolution Reaction (OER)
Bibliographic record
Abstract
LaNiO3, La1-xSrxCoO3, La1-xSrxMnO3 are a few examples of the perovskite-type oxides that hold great potential to be used as catalysts in numerous technologically significant processes such as the electrocatalysis of the oxygen evolution reaction [1][2][3][4] , CO and hydrocarbons oxidation and the nitrogen oxides reduction 5 .Keeping this in mind, the present study brings up the synthesis of Strontium based Samarium Perovskite manganites (Sm1-xSrxMnO3 (0 x 0.8)) by a sol-gel low-temperature technique using malic acid.The physicochemical characterization of the synthesized electrocatalyst is done by using Scanning Electron Microscopy (SEM) and X-ray diffraction (XRD) technique.Furthermore, the electrocatalytic analysis towards water electrolysis were done by performing cyclic voltammetry between 0 and 0.7 V & Tafel experiments.Apart from this the perovskites have been also analysed for their kinetic and thermodynamic parameters.Among the prepared oxide catalysts, Sm0.6Sr0.4MnO3was found to be most electrocatalytically active with a current density of 126.6mA/cm 2 at 800mV and a Tafel slope of 112 mV decade -1 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
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".