Ultrafine MoO<sub>2</sub>‐Carbon Microstructures Enable Ultralong‐Life Power‐Type Sodium Ion Storage by Enhanced Pseudocapacitance
Bibliographic record
Abstract
The achievement of the superior rate capability and cycling stability is always the pursuit of sodium‐ion batteries (SIBs). However, it is mainly restricted by the sluggish reaction kinetics and large volume change of SIBs during the discharge/charge process. This study reports a facile and scalable strategy to fabricate hierarchical architectures where TiO 2 nanotube clusters are coated with the composites of ultrafine MoO 2 nanoparticles embedded in carbon matrix (TiO 2 @MoO 2 ‐C), and demonstrates the superior electrochemical performance as the anode material for SIBs. The ultrafine MoO 2 nanoparticles and the unique nanorod structure of TiO 2 @MoO 2 ‐C help to decrease the Na + diffusion length and to accommodate the accompanying volume expansion. The good integration of MoO 2 nanoparticles into carbon matrix and the cable core role of TiO 2 nanotube clusters enable the rapid electron transfer during discharge/charge process. Benefiting from these structure merits, the as‐made TiO 2 @MoO 2 ‐C can deliver an excellent cycling stability up to 10 000 cycles even at a high current density of 10 A g −1 . Additionally, it exhibits superior rate capacities of 110 and 76 mA h g −1 at high current densities of 10 and 20 A g −1 , respectively, which is mainly attributed to the high capacitance contribution.
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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".