Wood Biochar Monolith-Based Approach to Increasing the Volumetric Energy Density of Supercapacitor
Bibliographic record
Abstract
The electrode material in commercial supercapacitors has high electrical resistivity, intrinsic to the activated carbon powder–organic binder mixture. Consequently, electrode materials are coated on current collectors as thin films to reduce the device resistance. However, the thin-film configuration limits the volume fraction of an active electrode material (activated carbon) and holds the volumetric energy density low. Wood biochar monoliths (WBMs) have a low-tortuosity porous structure and a conductive carbon matrix, a combination desirable for binder-free electrodes with high energy density. This article reports a novel approach for fabricating high-performance, WBM-based thick electrodes. The approach combines wood pulping, mechanical compression, and thermal carbonization, transforming a common softwood-white pine into an intensified wood biochar monolith (IWBM) with high bulk density (0.617 g/cm 3 ) and pore utilization efficiency (28 μF/cm 2 ) at the same time. Furthermore, the thick (1.2 mm) electrode made with the IWBM exhibited record-high areal capacitances (7.58–9.36 F/cm 2 ) and a high volumetric capacitance (78.0 F/cm 3 ) attributed to the densified, easily accessible pore network and the conductive carbon matrix in the IWBM. Moreover, the white pine WBM is readily augmented with pseudocapacitive materials (e.g., RuO 2 ). A supercapacitor cell with two symmetric pine WBM electrodes displayed no performance attenuation after 10,000 cycles. This study provides a practical approach to increasing the volumetric energy density, demonstrating the potential of WBMs as a low-cost, high-performance alternative to advanced nanoporous carbon materials such as graphene and carbon nanotubes.
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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".