Controlled Growth/Patterning of Ni Nanohoneycombs on Various Desired Substrates
Bibliographic record
Abstract
We report a two-step process for the growth/patterning of Ni honeycomb nanostructures on various substrates, such as carbon paper, carbon nanotubes (CNTs), silicon wafers, and copper grids, via the combination of a sputter-coating/patterning technique and a replacement reaction solution method. The morphology, crystallinity, and chemical composition of the honeycombs were analyzed by SEM, TEM, high-resolution TEM, and EDX. These honeycombs are composed of numerous nanocells, several tens of nanometers in diameter and with cell wall thickness of approximately 10 nm, randomly connecting to each other. The growth process of honeycomb nanostructures has been systematically studied. Interestingly, the diameter and wall thickness of the cells could be easily tuned by simply adjusting the experimental parameters, such as the concentrations and cations of metal salts. Additionally, this simple method has been successfully extended to synthesize Co nanostructures with well-controlled morphologies, which indicates the great potential of this strategy in the synthesis of other metal nanostructures on various desired substrates. These metal-substrate composites, especially with desired patterns, are expected to be ideal candidates for wide application in modern electronic and optoelectronic devices, sensors, fuel cells, and energy storage systems.
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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".