Electrochemical Activation‐Induced Structural Transformation in Ni(<scp>OH</scp>)<sub>2</sub>/<scp>Ti<sub>3</sub>C<sub>2</sub>T</scp><i><sub>x</sub></i>/<scp>NF</scp> Systems with Enhanced Electrochemical Performance for Hybrid Supercapacitors
Bibliographic record
Abstract
Exploring a novel strategy for large‐scale production of battery‐type Ni(OH)2‐based composites, with excellent capacitive performance, is still greatly challenging. Herein, we developed a facile and cost‐effective strategy to in situ grow a layer of Ni(OH)2/Ti3C2Tx composite on the nickel foam (NF) collector, where Ti3C2Tx is not only a conductive component, but also a catalyst that accelerates the oxidation of NF to Ni(OH)2. Detailed analysis reveals that the crystallinity, morphology, and electronic structure of the integrated electrode can be tuned via the electrochemical activation, which is beneficial for improving electrical conductivity and redox activity. As expected, the integrated electrode shows a specific capacity of 1.09 C cm−2 at 1 mA cm−2 after three custom activation cycles and maintains 92.4% of the initial capacity after 1500 cycles. Moreover, a hybrid supercapacitor composed of Ni(OH)2/Ti3C2Tx/NF cathode and activated carbon anode provides an energy density of 0.1 mWh cm−2 at a power density of 0.97 mW cm−2, and excellent cycling stability with about 110% capacity retention rate after 5000 cycles. This work would afford an economical and convenient method to steer commercial Ni foam into advanced Ni(OH)2‐based composite materials as binder‐free electrodes for hybrid supercapacitors.
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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".