Aqueous synthesis and growth of morphologically controllable, hierarchical Ni(OH)<sub>2</sub> nanostructures
Bibliographic record
Abstract
We report a simple route for the synthesis of several morphologies of self-assembling hierarchical Ni(OH) 2 nanostructures, by the reaction of NiSO 4 and NH 4 OH in aqueous solution, at a constant temperature, using neither surfactant nor template. Both morphology and microstructure depend on the concentrations of the reactants, the reaction temperature and the anions (Cl − , .., N O 3 − and S O 4 2 − ) present. The nanostructures have been characterized by scanning electron microscopy (SEM) and x-ray diffraction (XRD). When S O 4 2 − is used, irrespective of the presence of other anions, only microspheres of hierarchical Ni(OH) 2 nanosheets are present, suggesting that this anion plays a critical role in microsphere formation. Electrochemical characterizations of Ni(OH) 2 nanosheets show good supercapacitor performance, with relatively high capacity and excellent rate capability, indicating that these hierarchical Ni(OH) 2 nanosheets are serious candidates for energy storage applications. The growth mechanism for nanosheet formation is discussed, based on SEM observations under different preparation conditions, detailing the transition from nanoparticles to nanowires to nanosheets. The specific surface area and the thickness of our Ni(OH) 2 nanosheets have been determined to be 149.6 m 2 g −1 and 20–30 nm, respectively.
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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".