Nanohole arrays in chemical analysis: manufacturing methods and applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Since the last decade, nanohole arrays have emerged from an interesting optical phenomenon to the development of applications in photophysical studies, photovoltaics and as a sensing template for chemical and biological analyses. Numerous methodologies have been designed to manufacture nanohole arrays, including the use of focus ion beam milling, soft-imprint lithography, colloidal lithography and, more recently, modified nanosphere lithography (NSL). With NSL or colloidal lithography, the experimental conditions control the density of the nanosphere mask and, thus, the aspect of the nanohole arrays. Low surface coverage of the nanosphere mask produces disordered nanoholes. Ordered nanohole arrays are obtained with a densely packed nanosphere mask in combination with electrochemical deposition of the metal, glancing angle deposition (GLAD) or etching of the nanospheres prior to metal deposition. A review of these methodologies is presented here with an emphasis on the optical properties of nanoholes interesting in analytical chemistry. In particular, applications of these novel plasmonic materials will be demonstrated as substrates for a localized surface plasmon resonance (LSPR), Surface Plasmon Resonance (SPR), surface enhanced Raman spectroscopy (SERS), and in electrochemistry with nano-patterned electrodes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it