Template Directed Synthesis of Plasmonic Gold Nanotubes with Tunable IR Absorbance
Bibliographic record
Abstract
A nearly parallel array of pores can be produced by anodizing aluminum foils in acidic environments. Applications of anodic aluminum oxide (AAO) membranes have been under development since the 1990's and have become a common method to template the synthesis of high aspect ratio nanostructures, mostly by electrochemical growth or pore-wetting. Recently, these membranes have become commercially available in a wide range of pore sizes and densities, leading to an extensive library of functional nanostructures being synthesized from AAO membranes. These include composite nanorods, nanowires and nanotubes made of metals, inorganic materials or polymers. Nanoporous membranes have been used to synthesize nanoparticle and nanotube arrays that perform well as refractive index sensors, plasmonic biosensors, or surface enhanced Raman spectroscopy (SERS) substrates, as well as a wide range of other fields such as photo-thermal heating, permselective transport, catalysis, microfluidics, and electrochemical sensing. Here, we report a novel procedure to prepare gold nanotubes in AAO membranes. Hollow nanostructures have potential application in plasmonic and SERS sensing, and we anticipate these gold nanotubes will allow for high sensitivity and strong plasmon signals, arising from decreased material dampening.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".