In‐MOF‐Derived Hierarchically Hollow Carbon Nanostraws for Advanced Zinc‐Iodine Batteries
Bibliographic record
Abstract
Abstract Hollow carbon materials are regarded as crucial support materials in catalysis and electrochemical energy storage on account of their unique porous structure and electrical properties. Herein, an indium‐based organic framework of InOF‐1 can be thermally carbonized under inert argon to form indium particles through the redox reaction between nanosized indium oxide and carbon matrix. In particular, a type of porous hollow carbon nanostraw (HCNS) is in situ obtained by combining the fusion and removal of indium within the decarboxylation process. The as‐synthesized HCNS, which possesses more charge active sites, short and quick electron, and ion transport pathways, has become an excellent carrier for electrochemically active species such as iodine with its unique internal cavity and interconnected porous structure on the tube wall. Furthermore, the assembled zinc‐iodine batteries (ZIBs) provide a high capacity of 234.1 mAh g−1 at 1 A g−1, which ensures that the adsorption and dissolution of iodine species in the electrolyte reach a rapid equilibrium. The rate and cycle performance of the HCNS‐based ZIBs are greatly improved, thereby exhibiting an excellent capacity retention rate. It shows a better electrochemical exchange capacity than typical unidirectional carbon nanotubes, making HCNS an ideal cathode material for a new generation of high‐performance batteries.
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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".