Integrating Nanoreactor with O–Nb–C Heterointerface Design and Defects Engineering Toward High‐Efficiency and Longevous Sodium Ion Battery
Bibliographic record
Abstract
Abstract Sodium‐ion batteries (SIBs) have attracted much attention for their advantages of high operating voltage, environmental friendliness and cost‐effectiveness. However, the intrinsic defects of anode materials (such as poor electrical conductivity, sluggish kinetics, and large volume changes) hinder them from meeting the requirements for practical applications. Herein, a Nb2O5@carbon nanoreactor containing both a O–Nb–C heterointerface and oxygen vacancies (Nb2O5‐x@MEC) as an anode material is designed to drive SIBs toward extraordinary capacity and ultra‐long cycle life. The heterostructured nanoreactor both effectively immobilizes defective Nb2O5 by forming O‐Nb‐C heterointerface and offers homogeneous dispersion of Nb2O5 with desirable content to prevent their agglomeration. In addition, vast active interfaces, favored electrolyte infiltration, and a well‐structured ion–electron transportation channel are enabled by the framework, improving sodium ion storage and enhancing redox reaction kinetics. The enhancement brought by spatial confinement, defect implantation and heterointerface design give the composites a highly reversible sodiation–desodiation process and remarkable structural stability. By virtue of these superiorities, Nb2O5‐x@MEC delivers excellent performance, i.e., high areal capacity over 1.1 mAh cm‐2, admirable rate capability up to 20 A g‐1, and ultra‐long cycling performance over 5000 cycles, holding great promise for utilization in practically viable SIBs.
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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".