Energy storage ultra porous carbon blacks by high temperature oxidation
Bibliographic record
Abstract
Ultra porous carbonaceous nanoparticles were prepared by judicious oxidation of various commercial carbon blacks (CBs) at high temperatures (1200 °C). X-ray diffraction, N 2 adsorption and microscopy analyses revealed that during such oxidation, O 2 diffuses through and reacts with CB, disordering its crystalline structure. The concurrent external and internal oxidation of CB results in tiny pores that greatly increase the specific surface area, SSA , from 240 up to 2185 ± 199 m 2 /g. This is about 150–200 % larger than the SSA of CB oxidized at low temperatures (450–550 °C), 50–100 % larger than the SSA of most porous CB commercially available and on par with that of commercial activated carbons (e.g. YP80). The potential of this ultra porous CB generated here for energy storage is showcased using electric double layer capacitors (EDLCs). The gravimetric capacitance of EDLCs using the above high SSA CB as active material is up to 60 % larger than those obtained from EDLCs based on YP80 or Ketjenblack at high scan rates (≥ 100 mV/s) and current densities of 0.02–5 A/g. The superior rate performance of these CBs is attributed to the high concentration of pores with a 2–8 nm radius formed largely by internal oxidation. Such pores cannot be produced at large concentrations by low temperature oxidation of CB that is used widely to enhance CB porosity. Hence, close control of the oxidation dynamics of CB can substantially increase supercapacitor performance. • Ultra porous carbon blacks (CBs) are produced by high temperature oxidation. • Oxygen diffuses & reacts with the bulk CB particle, disordering its nanostructure. • The oxidized CB has high specific surface area, 2185 m 2 /g, & pores of 2–8 nm radius. • This porous CB enables superior supercapacitance than any commercial carbon. • Judicious CB oxidation is essential to improve CB performance in energy storage.
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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 | 0.000 | 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) | 0.000 | 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".