Carbon nanosheets derived from reconstructed lignin for potassium and sodium storage with low voltage hysteresis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 it