<scp>3D</scp> Grid of Carbon Tubes with <scp>Mn<sub>3</sub>O<sub>4</sub>‐NPs</scp>/<scp>CNTs</scp> Filled in their Inner Cavity as Ultrahigh‐Rate and Stable Lithium Anode
Bibliographic record
Abstract
Transition metal oxides are regarded as promising candidates of anode for next‐generation lithium‐ion batteries (LIBs) due to their ultrahigh theoretical capacity and low cost, but are restricted by their low conductivity and large volume expansion during Li + intercalation. Herein, we designed and constructed a structurally integrated 3D carbon tube (3D‐CT) grid film with Mn 3 O 4 nanoparticles (Mn 3 O 4 ‐NPs) and carbon nanotubes (CNTs) filled in the inner cavity of CTs (denoted as Mn 3 O 4 ‐NPs/CNTs@3D‐CT) as high‐performance free‐standing anode for LIBs. The Mn 3 O 4 ‐NPs/CNTs@3D‐CT grid with Mn 3 O 4 ‐NPs filled in the inner cavity of 3D‐CT not only afford sufficient space to overcome the damage caused by the volume expansion of Mn 3 O 4 ‐NPs during charge and discharge processes, but also achieves highly efficient channels for the fast transport of both electrons and Li + during cycling, thus offering outstanding electrochemical performance (865 mAh g −1 at 1 A g −1 after 300 cycles) and excellent rate capability (418 mAh g −1 at 4 A g −1 ) based on the total mass of electrode. The unique 3D‐CT framework structure would open up a new route to the highly stable, high‐capacity, and excellent cycle and high‐rate performance free‐standing electrodes for high‐performance Li‐ion storage.
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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 | 0.001 | 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".