Templated N-Doped and O-Doped Carbons for Energy Storage and Conversion
Bibliographic record
Abstract
Global energy demand has significantly increased in the last decades, and consequently, the environmental problems associated with the use of fossil fuels. More sustainable energy sources are required, and it is imperative to develop more efficient energy storage and conversion devices. In this context, batteries and fuel cells are promising options for stationary, portable, and transport applications.[1] Unfortunately, both polymer electrolyte membrane fuel cells (PEMFCs) and metal-air batteries are limited by the need for high Pt loadings in the cathode due to kinetic limitations imposed by the sluggish oxygen reduction reaction (ORR).[2] The conditions in the cathode can also result in the degradation of the carbon support (carbon corrosion) and Pt agglomeration, dislodgement, and dissolution.[3] Efforts aimed to solve these problems have intensified, principally the search for new catalysts materials to reduce or eliminate the need for Pt.[4] In the framework of these initiatives, heteroatoms-doped carbons obtained by carbonization of organic polymer gels (OPGs) are seen as attractive candidates for achieving some of these goals.[5] Even though resorcinol-formaldehyde polymers have been the most extensively studied, other precursors have been recently investigated for applications in batteries and supercapacitors.[6] In this study, high surface area mesoporous carbon materials with variable N and O contents were produced by carbonization of melamine-formaldehyde (MF) and resorcinol-formaldehyde (RF) polymer gels in the presence of SiO2 nanoparticles (20 to 200 nm) as hard-templates. Carbon products with up to 8 N-atom% and surface areas up to 500 m2/g were obtained by carbonization of nitrogen-rich melamine polymers (MF-C) depending on the annealing conditions (950°C or 1500°C). By adopting a similar approach, carbons with variable oxygen and nitrogen content were prepared by the combustion of resorcinol-formaldehyde (RF-C) and resorcinol-melamine-formaldehyde (RMF-C) polymers. The materials structure and chemical composition have been extensively investigated using a plethora of different techniques. Pt/MF-C and Pt/RF-C samples were prepared by an impregnation method to evaluate the stability of these catalysts under oxidizing and acidic conditions, as well as their catalytic activity toward the ORR. It was found that an increase in annealing temperature from 950 to 1500 oC resulted in a significant improvement in stability upon cycling the potential in accelerated ageing tests in acid media (0 to 1.4 V vs NHE) comparable or even better than the benchmark material Pt/Vulcan, and a preferential 4-electron reduction pathway for the ORR, as required for fuel cells and metal-air batteries applications. Preliminary data for the reduction of oxygen on Fe/MF-C and Fe/RF-C samples are underway, and preliminary results will also be presented. References [1] Yang et al., Chem. Rev., 111, 3577-3613, 2011. [2] Gasteiger et al., Appl. Catal., B56,9-35, 2005/Li Y. and Lu J., ACS Energy Lett., 2(6), 1370-1377, 2017. [3] Macauley et al., J. Electrochem. Soc., 165(6), F3148-F3160, 2018. [4] Shao et al., Chem. Rev., 116, 3594-3657, 2016. [5] Tesfaye et al., Sci. Rep., 9, 479, 2019. [6] Li et al., Micropor. Mesopor. Mat., 279, 293-315, 2019.
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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".