Carbon Nanosheet Frameworks Derived from Peat Moss as High Performance Sodium Ion Battery Anodes
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
We demonstrate that peat moss, a wild plant that covers 3% of the earth's surface, serves as an ideal precursor to create sodium ion battery (NIB) anodes with some of the most attractive electrochemical properties ever reported for carbonaceous materials. By inheriting the unique cellular structure of peat moss leaves, the resultant materials are composed of three-dimensional macroporous interconnected networks of carbon nanosheets (as thin as 60 nm). The peat moss tissue is highly cross-linked, being rich in lignin and hemicellulose, suppressing the nucleation of equilibrium graphite even at 1100 °C. Rather, the carbons form highly ordered pseudographitic arrays with substantially larger intergraphene spacing (0.388 nm) than graphite (c/2 = 0.3354 nm). XRD analysis demonstrates that this allows for significant Na intercalation to occur even below 0.2 V vs Na/Na(+). By also incorporating a mild (300 °C) air activation step, we introduce hierarchical micro- and mesoporosity that tremendously improves the high rate performance through facile electrolyte access and further reduced Na ion diffusion distances. The optimized structures (carbonization at 1100 °C + activation) result in a stable cycling capacity of 298 mAh g(-1) (after 10 cycles, 50 mA g(-1)), with ∼150 mAh g(-1) of charge accumulating between 0.1 and 0.001 V with negligible voltage hysteresis in that region, nearly 100% cycling Coulombic efficiency, and superb cycling retention and high rate capacity (255 mAh g(-1) at the 210th cycle, stable capacity of 203 mAh g(-1) at 500 mA g(-1)).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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