Innovative nanostructured BANB <sub>0.2</sub> IR <sub>0.8</sub> O <sub>3</sub> catalyst for oxygen evolution reaction in severe sulphuric acid environment
Bibliographic record
Abstract
Abstract In this study, the deactivation of BaNb 0.2 Ir 0.8 O 3 nano catalyst in sulphuric acid environment was addressed. The BaNb 0.2 Ir 0.8 O 3 nano catalyst was synthesized using activated reactive synthesis (ARS) method. The synthesized material is intended to be used in the zinc electrowinning process to decrease the overpotential for the oxygen evolution reaction (OER), thus improving the energy efficiency of the process. ARS combines solid state reaction (SSR) with mechanosysnthesis techniques, that is, ball milling. The XRD analysis proved the formation of the crystalline structure after solid‐state reaction. High‐energy ball milling (HEBM) resulted in a decrease in crystallite size of the solid, composed of highly agglomerated nano particles. The particles were then deagglomerated using low‐energy milling, resulting in a 20‐fold increase in the specific surface area (SSA) of the powders compared to the initial SSR step. These catalysts could be deactivated by the formation of the BaSO 4 phase in a sulphuric acid environment. This challenge was tackled via partial leaching of Ba from the catalyst surface before the electrolysis process. XPS analysis was used to characterize the chemical composition of the catalyst. Electrochemical measurements showed that the new synthesis route, resulted in a twofold reduction in Tafel slope and improved catalytic activity and stability in sulphuric acid environment. The samples exhibited 230 and 280 mV overpotentials at two different current densities of 100 and 500 A/m 2 , respectively as well as a very good stability compared to the samples without Ba leaching.
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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) | 0.001 | 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".