Nanostructured carbon-based materials for electrochemical applications
Bibliographic record
Abstract
Mesoporous carbons have been widely studied due to the relevance of these materials for applications in energy storage and conversion devices (batteries, supercapacitors, and fuel cells), mainly due to their high surface area, tunable pore distribution, and good electrical conductivity. Though carbon is chemically stable, mesoporous carbons used in fuel cells are susceptible to corrosion when exposed to high positive overpotential in acid media that result in dissolution and agglomeration of valuable noble catalyst nanoparticles. In this thesis, high-surface-area mesoporous carbons (~ 600 to 1000 cm2/g) were obtained by carbonization of resorcinol formaldehyde (RF) polymer gels; using poly-diallyl methylammonium chloride and SiO2 (~200 nm diameter) as soft- and hard-template, respectively, to tailor the textural properties of the carbon products. Accelerated ageing tests on platinized samples prepared with mesoporous carbons exposed to different annealing treatments showed a significant improvement in stability after annealing for two hours at 1500oC, over performing an in-house prepared Pt/ Vulcan carbon reference sample. The deposition of TiO2 on carbon was also intended to improve the catalyst/substrate stability at lower annealing temperatures, some preliminary results are also presented in the thesis. Overall, the thesis has contributed to the implementation of a flexible methodology for the synthesis of organic polymer gels and carbon gels, that it is expected will contribute to the development of novel heteroatom-doped carbons and non-precious metal catalyst materials for renewable energy, photo- and electrocatalysis, sensors, environmental remediation, and waste treatment.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".