N‐Doping Fe‐C@Nb<sub>2</sub>CT<sub>x</sub> MXenes with High Stability and Strong Activity for Sodium‐Ion Storage and Overall Water Splitting
Bibliographic record
Abstract
Abstract The development of highly stable and strongly active electrode materials for sodium‐ion batteries (SIBs) and overall water splitting (OWS) is critical in sustainable energy storage and conversion systems. Here, a new electrode material N‐Fe‐C@Nb2CTx is introduced, with a layered sandwich structure consisting of N‐doping Fe‐MOF derived‐nanorods (Fe‐C) and Nb2CTx MXenes. Specifically, Nb2CTx obtained by etching Nb2AlC with HF acid is used as the main body to construct the layered sandwich structure with Fe‐C as the filler. Benefiting from this structure, Fe‐MOF grows in situ within Nb2CTx, which restrains MXenes aggregation and stacking and also alleviates the bulk effect of sodium‐ion embedding/de‐embedding, thus improving its stability. Again, the more exposed active sites from the layered sandwich structure and N‐doping introduction ensure high reactivity as electrode materials. In addition, Fe‐C nanorods strengthen the linkage between the Nb2CTx layers and N‐doping enhances the ion/electron transport rate, thereby boosting the effective mass transfer and electrical conductivity. Density functional theory (DFT) calculations show that Fe‐C and N‐doping help increase the density of states (DOS) and material electrical conductivity. Meanwhile, the generated oxygen species (*OH and *O) in OER are captured by in situ FT‐IR test. As a result, the N‐Fe‐C@Nb2CTx electrochemical test displays good electrochemical performance in SIBs and OWS.
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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".