Hierarchical Porous Carbons Synthesized By CO<sub>2</sub> Conversion with CaCO<sub>3</sub> Nano-Template
Bibliographic record
Abstract
The technology to convert CO2 into valuable compounds has attracted attention as a key technology to mitigate global warming. Among them, carbon derived from CO2 has been widely used as an electrode material due to its highly porous property. However, it is difficult to control the porosity of the carbon material, and the synthesis conditions for the CO2 conversion into the porous carbon require a high energy cost. In this study, CO2 was converted into porous carbons with hierarchical micro-, meso-, macro-pores using NaBH4 as a reducing agent and CaCO3 as a nano-template at mild atmospheric pressure and temperature. The prepared CaCO3-templated hierarchical porous carbons had high specific surface area (1262 m2/g) with large pore volume (3.21 cm3/g). In addition, it had unique interconnecting pore structure which leads to the improvement of supercapacitive performance. CPC1_700, which was synthesized through an equivalent mass of CaCO3 and NaBH4 at 700 °C, exhibited a high capacitance of 270 F/g at 1 A/g, and retained its capacitance up to 170 F/g at high current density of 20 A/g. Furthermore, even without a pseudocapacitive behavior, it showed superior normalized capacitance of 21.4 µF/cm2 and relaxation time constant of 0.27 s compared to previous reported carbon materials. During long-term cycling, the capacitance was stable and remained above 90% up to 10,000 cycles. Thus, the facile CO2 conversion method and resultant CPCs are promising to be practical applications for supercapacitor electrode materials.
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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".