Carbon Nitride Nanofibres with Exceptional Lithium Storage Capacity: From Theoretical Prediction to Experimental Implementation
Bibliographic record
Abstract
Abstract Graphitic carbon nitride nanosheet (i.e., g‐C3N4) is identified as a suitable graphene analogue due to its high theoretical capacity, wider and vacant structure, and easy synthesis method. Currently, g‐C3N4 nanosheet has limited application in lithium‐ion batteries (LIBs) which is mainly due to the lack of effective intercalation/deintercalation reaction sites, the high binding energy of the Li to the nanosheet, and insufficient conductivity and stability. Density functional theory calculation predicts that the edges of g‐C3N4 fibre have a suitable adsorption energy and bestow a balanced adsorption force and desorption freedom to Li. In order to verify this prediction, g‐C3N4 nanofibre is synthesized with the edges and pores, as well as higher pyridinic nitrogen content, using a simple polymerization/polycondensation method. The as‐prepared g‐C3N4 fibre delivers a remarkable specific capacity of 181.7 mAh g−1, as well as extraordinary stability and power density. At a high rate of 10C, the g‐C3N4 fibre still has a specific capacity of 138.6 mAh g−1 even after 5000 cycles, being the best‐performing g‐C3N4 electrode so far in literature. This work is exemplary in combining theoretical computing and experimental techniques in designing the next generation of electroactive materials for LIBs.
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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) | 0.002 | 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".