Non Noble Metal Catalysts for the Oxygen Reduction Reaction from Mixed MOFs
Bibliographic record
Abstract
Aiming at reducing the cost of fuel cells, the research of non noble metal catalyst has been a hot topic. Recently the performance of non noble metal catalyst has been improved to a level that is similar with noble metal catalyst, leaving the stability a problem to be resolved. The development of non noble metal catalyst has three stages. The first stage is based on porphyrin or phthalocyanine, where nitrogen and iron are from the same precursor. The catalyst is usually pyrolyzed at 400-1000ºC in inert atmosphere [1]；The second stage is characterized by the division of nitrogen and iron precursor, inorganic salt is used as iron precursor and nitrogen containing organic molecular is nitrogen precursor, or inorganic salt is used as iron precursor and NH 3 is nitrogen precursor. Both is pyrolyzed at 400-1000ºC in inert atmosphere[2]；The performance of these two kinds of catalysts are poor before the breakthrough made by Pro. Jean-Pol Dodelet from INRS Canada, who improved the MEA performance to 0.26Acm -2 at 0.7V[3]。The performance is then greatly improved at the third stage of non noble metal catalyst, which can be characterized by loading iron salt and nitrogen precursor on MOFs, the pyrolysis is also necessary. The performance of this kind of catalyst is greatly improved. For example the H 2 /O 2 MEA performance of 0.7Acm -2 at 0.7V by Jean-Pol Dodelet[4]; H 2 /O 2 MEA performance of 0.6Acm -2 at 0.7V by Dijia Liu from Argonne National Lab[5]; H 2 /air MEA performance of 0.3Acm -2 at 0.7V by Sanjeev Mukerjee from Northeastern University6]. In this work we report some recent work of our group in the performance of non noble metal catalyst based on mixed MOFs materials. A preliminary result of H 2 /O 2 MEA performance of 0.2Acm -2 at 0.7V is reached, which is improving. Figure (a): The ORR performance of the as prepared non noble metal catalyst in O 2 saturated 0.1M HClO 4 solution at 1600rpm; (b): H 2 /O 2 MEA performance of the as prepared non noble metal catalyst References: Jasinski, R., A New Fuel Cell Cathode Catalyst. Nature, 1964. 201(4925): p. 1212-1213. Jaouen, F., et al., Non-noble electrocatalysts for O-2 reduction: How does heat treatment affect their activity and structure? Part II. Structural changes observed by electron microscopy, Raman, and mass spectroscopy. Journal of Physical Chemistry C, 2007. 111(16): p. 5971-5976. Lefèvre, M., et al., Iron-Based Catalysts with Improved Oxygen Reduction Activity in Polymer Electrolyte Fuel Cells. Science, 2009. 324(5923): p. 71-74. Proietti, E., et al., Iron-based cathode catalyst with enhanced power density in polymer electrolyte membrane fuel cells. Nat Commun, 2011. 2: p. 416. Barkholtz, H.M., et al., Highly Active Non-PGM Catalysts Prepared from Metal Organic Frameworks. Catalysts, 2015. 5(2): p. 955-965. Li, J., et al., Structural and mechanistic basis for the high activity of Fe-N-C catalysts toward oxygen reduction. Energy & Environmental Science, 2016. 9(7): p. 2418-2432.
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 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 | 0.001 | 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 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".