High-Density Sodium and Lithium Ion Battery Anodes from Banana Peels
Bibliographic record
Abstract
Banana peel pseudographite (BPPG) offers superb dual functionality for sodium ion battery (NIB) and lithium ion battery (LIB) anodes. The materials possess low surface areas (19-217 m(2) g(-1)) and a relatively high electrode packing density (0.75 g cm(-3) vs ∼1 g cm(-3) for graphite). Tested against Na, BPPG delivers a gravimetric (and volumetric) capacity of 355 mAh g(-1) (by active material ∼700 mAh cm(-3), by electrode volume ∼270 mAh cm(-3)) after 10 cycles at 50 mA g(-1). A nearly flat ∼200 mAh g(-1) plateau that is below 0.1 V and a minimal charge/discharge voltage hysteresis make BPPG a direct electrochemical analogue to graphite but with Na. A charge capacity of 221 mAh g(-1) at 500 mA g(-1) is degraded by 7% after 600 cycles, while a capacity of 336 mAh g(-1) at 100 mAg(-1) is degraded by 11% after 300 cycles, in both cases with ∼100% cycling Coulombic efficiency. For LIB applications BPPG offers a gravimetric (volumetric) capacity of 1090 mAh g(-1) (by material ∼2200 mAh cm(-3), by electrode ∼900 mAh cm(-3)) at 50 mA g(-1). The reason that BPPG works so well for both NIBs and LIBs is that it uniquely contains three essential features: (a) dilated intergraphene spacing for Na intercalation at low voltages; (b) highly accessible near-surface nanopores for Li metal filling at low voltages; and (c) substantial defect content in the graphene planes for Li adsorption at higher voltages. The <0.1 V charge storage mechanism is fundamentally different for Na versus for Li. A combination of XRD and XPS demonstrates highly reversible Na intercalation rather than metal underpotential deposition. By contrast, the same analysis proves the presence of metallic Li in the pores, with intercalation being much less pronounced.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".