Low‐Curvature Wood‐Derived Thick Electrodes with High Capacity via In Situ Grown Nanoflower‐Like Ni/Co Bimetallic MOFs for Aqueous Nickel–Zinc Battery
Bibliographic record
Abstract
Abstract Nickel–Zinc Battery (NZB) faces a dual bottleneck: limited mass loading capacity stemming from its electrode structure and capacity degradation caused by Ni lattice rotation during charging and discharging. Herein, this study develops a porous carbon current collector with low curvature based on natural wood to optimize the electrode structure, thereby increasing the loading of active substances and significantly accelerating the ion transport kinetics in thick electrodes. Meanwhile, Nickel/Cobal bimetallic metal‐organic framework (Ni/Co‐MOF), serving as active materials, is in situ grown on carbonized wood (CW), which modulates the electronic structure and coordination environment, thereby stabilizing the M‐H2‐H3 phase transition and mitigating stress and lattice degradation. The constructed self‐supported Ni/Co‐MOF@CW (N 4 C 1 M@CW‐N2) electrode exhibits a high areal capacity of 1.71 mAh cm −2 at a current density of 5 mA cm −2 , with a high active substance loading of 12.3 mg cm −2 . In addition, the assembled Ni/Co‐MOF@CW//Zn (N 4 C 1 M@CW‐N2//Zn) battery demonstrates excellent electrochemical performance with an energy density of 3.54 mWh cm −2 at a power density of 70.34 mW cm −2 . And after 8000 cycles, the capacity retention rate is as high as 88.9%. The synthetic strategy combining a self‐supported wood‐derived electrode with Ni/Co‐MOF provides new directions for the next generation of high‐performance electrochemical energy storage 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".