Carbon nanosheets derived from reconstructed lignin for potassium and sodium storage with low voltage hysteresis
Bibliographic record
Abstract
Lignin is the second most abundant and the only nature polymer rich in aromatic units. Although aromatic-unit-rich precursors often yield soft carbon after carbonization, the side chains in lignin crosslink with the aromatic units and form a rigid three-dimensional (3D) structure which eventually leads to hard carbons. Through a graphene oxide-catalyzed decomposition and repolymerization process, we successfully reconstructed lignin by partially tailoring the side chains. Compared to directly carbonized lignin, the carbonized reconstructed lignin possesses significantly fewer defects, 86% fewer oxygen-functionalities, 82% fewer micropores, and narrower interlayer space. These parameters can be tuned by the amount of catalysts (graphene oxide). When tested as anode for K-ion and Na-ion batteries, the carbonized reconstructed lignin delivers notably higher capacity at low-potential range (especially for Na-storage), shows much-improved performance at high current density, and most importantly, reduces voltage hysteresis between discharge and charge process by more than 50%, which is critical to the energy efficiency of the energy storage system. Our study reveals that the voltage hysteresis in K-storage is much severer than that in Na-storage for all samples. For practical K-ion battery applications, the voltage hysteresis deserves more attention in future electrode materials design and the reconstruct ion strategy introduced in this work provides potential low-cost solution.
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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".