Carbon Nitride Nanofibres with Exceptional Lithium Storage Capacity: From Theoretical Prediction to Experimental Implementation
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
Abstract Graphitic carbon nitride nanosheet (i.e., g‐C 3 N 4 ) is identified as a suitable graphene analogue due to its high theoretical capacity, wider and vacant structure, and easy synthesis method. Currently, g‐C 3 N 4 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‐C 3 N 4 fibre have a suitable adsorption energy and bestow a balanced adsorption force and desorption freedom to Li. In order to verify this prediction, g‐C 3 N 4 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‐C 3 N 4 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‐C 3 N 4 fibre still has a specific capacity of 138.6 mAh g −1 even after 5000 cycles, being the best‐performing g‐C 3 N 4 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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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) | 0.007 | 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