Electrochemical Performance of Pre-Modified Birch Biochar Monolith Supercapacitors by Ferric Chloride and Ferric Citrate
Bibliographic record
Abstract
This study investigated the electrochemical properties of supercapacitors by pre-modifying thick birch biochar monoliths with FeCl3 or C6H5FeO7 solutions prior to wood pyrolysis. The pre-modification introduced iron species to the surface, promoting the specific surface area, charge-stored species, and surface functionalities, which enhanced the gravimetric capacitance. X-ray diffraction confirmed the successful loading of Fe3O4 and Fe. SEM implied the wider distribution of iron-rich particulates and porous carbon via self-pyrolysis on the biochar surface modified with 1.0 M C6H5FeO7. Contact angle measurements demonstrated the enhanced wettability of the biochar surfaces following pre-modification, with the C6H5FeO7-modified samples exhibiting superior wettability compared to the other groups. The gravimetric capacitance of the supercapacitor was dramatically promoted and reached 210 F/g and 219 F/g, respectively, when modified with 1.0M C6H5FeO7 and 1.0 M FeCl3 at a 5 mA/g current density. Compared to the birch biochar modified with 1.0 M FeCl3, the 1.0 M C6H5FeO7 had a higher current response peak and capacitive behavior in the CV analysis, demonstrated better ion diffusion capacity, and had lower charge-transfer resistance in the EIS results. But, a slight irreversible process on the electrode of the 1.0 M C6H5FeO7 group led to a lower level of the supercapacitor capacitance retention. The results using ferric solution pre-impregnation show how iron species doping can improve capacitance behavior, providing a feasible scheme for the modification of thick biochar monolith.
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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".