Can Aqueous Na2SO4-Based Neutral Electrolyte Increase Energy Density of Monolithic Wood Biochar Electrode Supercapacitor?
Bibliographic record
Abstract
The performance of supercapacitors is significantly influenced by both the nature and the concentration of the electrolyte employed. This study investigates the impact of a neutral electrolyte on the electrochemical properties of the maple-derived monolithic wood biochar (MWB)–sodium sulfate (Na2SO4) supercapacitor. The goal is to determine if a neutral electrolyte, in this case Na2SO4, can increase the supercapacitor energy density compared to a previous study employing a KOH electrolyte. Starting from examining the ion sizes and conductivities of salt species in KOH and Na2SO4 electrolytes, the difference in voltage window, measured specific capacitance, and resistance are discussed. By switching the electrolyte from 4 M KOH to 0.5/1 M Na2SO4, the voltage stability window was extended from 0.8 V to 1.4 V. For 1 M Na2SO4, the supercapacitor attains a specific capacitance of 46 F/g at 5 mA/g, accompanied by an energy density of 12.5 Wh/kg and a maximum power density of 300 W/kg. The MWB electrode, derived from naturally abundant wood, when combined with the non-toxic Na2SO4 electrolyte, offers an environmentally friendly and cost-effective energy storage solution. With a prolonged lifetime and minimal maintenance requirements, MWB-Na2SO4 supercapacitors emerge as a promising choice for diverse applications.
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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 | 0.001 |
| 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".