Controlling the Interfacial Environment in the Electrosynthesis of MnO<sub><i>x</i></sub> Nanostructures for High-Performance Oxygen Reduction/Evolution Electrocatalysis
Bibliographic record
Abstract
High-performance, nonprecious metal bifunctional electrocatalysts for the oxygen reduction and evolution reactions (ORR and OER, respectively) are of great importance for rechargeable metal–air batteries and regenerative fuel cells. A comprehensive study based on statistical design of experiments is presented to investigate and optimize the surfactant-assisted structure and the resultant bifunctional ORR/OER activity of anodically deposited manganese oxide (MnO x ) catalysts. Three classes of surfactants are studied: anionic (sodium dodecyl sulfate, SDS), non-ionic ( t -octylphenoxypolyethoxyethanol, Triton X-100), and cationic (cetyltrimethylammonium bromide, CTAB). The adsorption of surfactants has two main effects: increased deposition current density due to higher Mn 2+ and Mn 3+ concentrations at the outer Helmholtz plane (Frumkin effect on the electrodeposition kinetics) and templating of the MnO x nanostructure. CTAB produces MnO x with nanoneedle (1D) morphology, whereas nanospherical- and nanopetal-like morphologies are obtained with SDS and Triton, respectively. The bifunctional performance is assessed based on three criteria: OER/ORR onset potential window (defined at 2 and −2 mA cm –2 ) and separately the ORR and OER mass activities. The best compromise among these three criteria is obtained either with Triton X-100 deposited catalyst composed of MnOOH and Mn 3 O 4 or SDS deposited catalyst containing a combination of α- and β-MnO 2, MnOOH, and Mn 3 O 4 .The interaction effects among the deposition variables (surfactant type and concentration, anode potential, Mn 2+ concentration, and temperature) reveal the optimal strategy for high-activity bifunctional MnO x catalyst synthesis. Mass activities for OER and ORR up to 49 A g –1 (at 1556 mV RHE ) and −1.36 A g –1 (at 656 mV RHE ) are obtained, respectively.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".