Hierarchical Ni(HCO<sub>3</sub>)<sub>2</sub> Nanosheets Anchored on Carbon Nanofibers as Binder‐Free Anodes for Lithium‐Ion Batteries
Bibliographic record
Abstract
Transition metal carbonates are promising anodes for energy storage and conversion due to their advantages of facile synthesis, large theoretical capacities, and high electrochemical activities. However, the poor electronic conductivity, slow ion transport, and high volume expansion lead to the capacity loss of transition metal carbonates. Herein, hierarchical Ni(HCO3)2 nanosheets are directly grown on carbon nanofibers (CNFs) via electrospinning and hydrothermal methods. The 3D CNFs can not only improve the electronic conductivity and shorten diffusion length but also fasten electron/ionic transport and release the volume change. Ni(HCO3)2 nanosheets anchored on CNFs exhibit excellent lithium storage performance with a high cycling stability and good rate capability. It delivers an initial discharge capacity of 3807.6 mAh g−1 and a reversible capacity of 1261.1 mAh g−1 after 100 cycles at 200 mA g−1. This work may shed light on the preparation of other binder‐free transition metal carbonates and make them as high‐performance electrodes in flexible electronic devices.
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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".