Supported Transition Metal Phosphides: Activity Survey for HER, ORR, OER, and Corrosion Resistance in Acid and Alkaline Electrolytes
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Bibliographic record
Abstract
One of the main bottle necks for the introduction of fuel cell technology in the market, is their dependency on precious metals as catalyst. Focusing on the electrochemical reactions involved, the most sluggish is the Oxygen Reduction Reaction (ORR). Significant research has been performed to optimize for the amount of the precious metal used, whilst keeping the same activity. On the other hand, many investigations have been carried out to find non-precious metal catalysts with the same performance for ORR, Oxygen Evolution Reaction (OER), Hydrogen Evolution Reaction (HER) and Hydrogen Oxidation Reaction (HOR) [1]. One of those candidates are the metal phosphides [2], however, these catalysts nowadays only are active for ORR, HER, OER and they have very poor activity towards the HOR [3]. Here we present the work done related to the simple synthesis of different MP catalysts (M = Ni, Co, W, Cr and Mo) [4]; their catalytic activity towards H 2 and O 2 reactions; and their corrosion resistance in acidic and alkaline electrolytes. Co 2 P was found to have a very interesting ORR catalytic behaviour and peroxide generation under alkaline and acidic conditions respectively. To understand the activity of Co 2 P, an HRTEM analysis was done to understand the changes in the morphology before and after the ORR reaction. References [1] M. S. Faber and S. Jin, “Earth-abundant inorganic electrocatalysts and their nanostructures for energy conversion applications,” Energy Environ. Sci. , vol. 7, no. 11, pp. 3519–3542, Oct. 2014, doi: 10.1039/C4EE01760A. [2] A.-M. Alexander et al. , “Alternative catalytic materials: carbides, nitrides, phosphides and amorphous boron alloys,” Chem. Soc. Rev. , vol. 39, no. 11, pp. 4388–4401, Oct. 2010, doi: 10.1039/b916787k. [3] R. Prins and M. E. Bussell, “Metal Phosphides: Preparation, Characterization and Catalytic Reactivity,” Catal. Letters , vol. 142, no. 12, pp. 1413–1436, Dec. 2012, doi: 10.1007/s10562-012-0929-7. [4] A. Parra-Puerto, K. L. Ng, K. Fahy, A. E. Goode, M. P. Ryan, and A. Kucernak, “Supported Transition Metal Phosphides: Activity Survey for HER, ORR, OER, and Corrosion Resistance in Acid and Alkaline Electrolytes,” ACS Catal. , vol. 9, pp. 11515–11529, Nov. 2019, doi: 10.1021/acscatal.9b03359. Figure 1
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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 | 0.001 |
| 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) | 0.000 | 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 it