Water Oxidation Catalysis by Nanoparticulate Manganese Oxide Thin Films: Probing the Effect of the Manganese Precursors
Bibliographic record
Abstract
Nanoparticulate manganese oxides, formed in Nafion polymer from a series of molecular manganese complexes of varying nuclearity and metal oxidation state, are shown to effectively catalyze water oxidation under neutral pH conditions with the onset of electrocatalysis occurring at an overpotential of only 150 mV. Although XAS experiments indicate that each complex generates the same material in Nafion, the catalytic activity varied substantially with the manganese precursor and did not correlate with the amount of MnO x present in the films. The XAS and EPR studies indicated that the formation of the nanoparticulate oxide involves the dissociation of the complex into Mn(II) species followed by oxidation on application of an external bias. TEM studies of the most active films, derived from [Mn(Me 3 TACN)(OMe) 3 ] + and [(Me 3 TACN) 2 Mn III 2 (μ-O)(μ-CH 3 COO) 2 ] 2+ (Me 3 TACN = N,N′,N″-trimethyl-1,4,7-triazacyclononane), revealed that highly dispersed MnO x nanoparticles (10–20 nm and 6–10 nm, respectively) were generated in the Nafion film. In contrast, the use of [Mn(OH 2 ) 6 ] 2+ resulted in both a higher manganese oxide loading and aggregated nanoparticles with 30–100 nm approximate size, which were less effective water oxidation catalysts. Much higher turnover frequencies (TOFs) were observed for films derived from the two complexes, viz., ∼20 molecules of O 2 per Mn per hour in dark and 40 molecules of O 2 per Mn per hour under illumination at an overpotential of 350 mV, when compared with MnO x films made with [Mn(OH 2 ) 6 ] 2+ . This corresponds to a TOF > 100 molecules of O 2 per Mn per second for a 10 nm MnO x nanoparticle. Thus, the catalytic activity is dependent on the ability to generate well-defined, dispersed nanoparticles. Electrochemical and spectroscopic methods have been used to follow the conversion of the molecular precursors into MnO x and to further evaluate the origin of differences in catalytic activity.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".