Water Evaporation Triggered Self‐Assembly of MXene on Non‐Carbonized Wood with Well‐Aligned Channels as Size‐Customizable Free‐Standing Electrode for Supercapacitors
Bibliographic record
Abstract
Herein, non‐carbonized wood‐based electrodes and separators with well‐aligned channels and excellent mechanical properties are developed for supercapacitors. To enhance the conductivity and boost the capacitance, Ti 3 C 2 (MXene) nanosheets with high electrical conductivity and excellent electrochemical activity are loaded into the wood cells via self‐assembly triggered by fast evaporating water in Ti 3 C 2 suspension. By the assistance of positive charged polydopamine microspheres with large surface area, the self‐restacking of Ti 3 C 2 nanosheets can be avoided and the high mass loading (50 wt%) can be achieved due to the extra driving force for Ti 3 C 2 absorption. Benefiting from the conductive Ti 3 C 2 nanosheets with massive active sites and the multiple well‐aligned channels in wood with efficient transportation pathways for charge carriers, the as‐designed free‐standing electrode shows a large areal capacitance of 1060 mF cm −2 at 0.5 mA cm −2 and high capacitance retention of 67% at 10 mA cm −2 . Also, this electrode is highly size‐customizable, showing a good ability to be industrially processed into various shapes and dimensions. Furthermore, an all‐wood based supercapacitor with Ti 3 C 2 /wood composites as two layers of electrodes and a wood slice as the separator is fabricated, presenting a high energy density of 10.5 μW h cm −2 at 389.9 μW cm −2 .
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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".