Interface interaction induced ultra-dense nanoparticles assemblies
Bibliographic record
Abstract
We demonstrate a simple and clean physical methodology for fabricating such nanoparticle assemblies (dense arrays and/or dendrites) related to the interfacial interaction between the constructed materials and the anodized aluminum oxide (AAO) porous templates. The interfacial interaction can be regulated by the surface tension of the constructed materials and the AAO membrane, and the AAO-template structure, such as pore size, membrane thickness and surface morphologies. Depending on the interfacial interaction between the constructed materials and the AAO templates, NP arrays with mean particle diameters from 3.8 ± 1.0 nm to 12.5 ± 2.9 nm, mean inter-edge spacings from 3.5 ± 1.4 nm to 7.9 ± 3.4 nm and areal densities from 5.6 × 10(11) NPs per cm(2) to 1.5 × 10(12) NPs per cm(2) are fabricated over large areas (currently ~2 cm × 3 cm). The fabrication process includes firstly thermal evaporation of metal layers no more than 10 nm thick on the pre-coated Si wafer by AAO templates with a thickness of less than 150 nm and mean pore sizes no more than 12 nm, and then removal of the AAO templates. The NP arrays can be stable for hours at a temperature slightly below the melting point of the constructed materials (e.g., ~800 °C for Au NPs for 4 hours) with little change in size and inter-particle separation. Using one of them (e.g., 11.8 nm Au NPs) as growth-oriented catalysts, ultra-thin (12.1 ± 2.3 nm) dense nanowires can be conveniently obtained. Furthermore, dendrite superstructures can be generated easily from eutectic alloy NPs with diameters of ~10 nm pre-formed by thermal evaporation of metal layers more than 20 nm thick on surface-patterned thick AAO templates (e.g., 500 nm). The resulting dendrites, dense arrays and other superstructures (i.e., nanorods and nanowires) formed using NP arrays as catalysts, should have broad applications in catalysis, information technology, photovoltaics and biomedical engineering.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".