Bottom-up synthesis of nitrogen-doped porous carbon scaffolds for lithium and sodium storage
Bibliographic record
Abstract
Here we report an effective bottom-up solution-phase process for the preparation of nitrogen-doped porous carbon scaffolds (NPCSs), which can be employed as high-performance anode materials for both lithium-ion batteries (LIBs) and sodium-ion batteries (SIBs). The as-obtained NPCSs show favorable features for electrochemical energy storage such as high specific surface area, appropriate pore size distribution (3.9 nm in average), large pore volume (1.36 cm3 g−1), nanosheet-like morphology, a certain degree of graphitization, enlarged interlayer distance (0.38 nm), high content of nitrogen (∼5.6 at%) and abundant electrochemically-active sites. Such a unique architecture provides efficient Li+/Na+ reservoirs, and also possesses smooth electron transport pathways and electrolyte access. For LIBs, the anodes based on NPCSs deliver a high reversible capacity of 1275 mA h g−1 after 250 cycles at 0.5 C (1 C = 372 mA g−1), and outstanding cycling stabilities with a capacity of 518 mA h g−1 after 500 cycles at 5 C and 310 mA h g−1 after 1500 cycles even at 10 C. For SIBs, the anodes based on NPCSs display a reversible capacity of 257 mA h g−1 at 50 mA g−1, and superior long-term cycling performance with a capacity of 191 mA h g−1 after 1000 cycles at 200 mA g−1.
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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".