Electrochemical Properties of Ruthenium-Based Nanocrystalline Materials as Electrodes for Supercapacitors
Bibliographic record
Abstract
Nanocrystalline Ti x Fe y Ru z O n materials were prepared by mechanical alloying using high-energy ball milling. The electrochemical properties of the materials were investigated in 1 M NaOH and 1 M H 2 SO 4 aqueous solutions, using a composite electrode technology. The tested materials fall in two categories. On one hand, when the O/Ti ratio is larger than 1, Ru atoms are found in an hexagonal phase. Upon cycling in H 2 SO 4 or NaOH, these materials exhibit a significant increase of their capacitance from ∼5 to ∼50 F/g. This is due to the progressive growth of a ruthenium oxide layer at the surface. On the other hand, when the O/Ti ratio is smaller than 1, Ru atoms are found in a cubic phase (CsCl), along with Ti and Fe atoms. In that case, the growth of a stable oxide phase at the surface of the material occurs only when it is cycled in basic electrolyte. The maximum attainable capacitance is also close to 50−60 F/g. The individual crystallites of as-milled nanocrystalline materials suffer from a strong tendency to agglomerate together. For example, it is shown that the electrochemically active surface area of nanocrystalline RuO 2 is only increased by a factor of 2 when the crystallite size is decreased from 600 to 15 nm, which amounts to a 40-fold increase of the specific surface area. Thus, higher surface area materials were obtained by performing an additional milling step with Al, which is followed by a subsequent leaching of Al with a NaOH solution. With that procedure, the best performances were obtained with leached Ti 2 FeRuO 2, with a maximum capacitance of 110 F/g.
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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 | 0.001 | 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 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".