Plasmonic Film with Diluted Nanostructures for Light Energy Harvesting and Sensing
Bibliographic record
Abstract
Harvesting the electromagnetic light field in spatially dispersed nanostructures and focusing the sensing events at those locations enables a gain of several orders of magnitude in sensitivity. It significantly reduces the amount of target material needed to produce a measurable signal. This paper establishes that, contrary to common belief, at very low target concentrations, increasing the periodicity of a nanoparticle array instead of diminishing it significantly lowers the limit of detection (LOD) of such structured plasmonic sensors. We demonstrate this numerically on a thin gold film covered by an array of 50 nm diameter nanocylinders with periodicity ranging from 400 to 3000 nm. We found a minimal capture target volume per surface unit of a few tens of nm 3 /μm 2, which is an improvement of orders of magnitude from about 10 4 nm 3 /μm 2 necessary for obtaining the same minimal signal using a conventional propagative-based plasmon sensor, or about 10 3 nm 3 /μm 2 for a similar plasmonic biochip structure but with a typical 200–400 nm periodicity array range. Two key mechanisms are involved in achieving such a significant breakthrough. First, energy harvesting is enhanced by incorporating the underlying thin film, which enables the coupling of electromagnetic field energy into the localized plasmon mode of the nanoparticles. This harvesting coupling effect is shown to be limited by the film’s plasmon propagation attenuation length and, consequently, its appropriate thickness. Second, target binding events must be precisely positioned in areas of enhanced optical field intensity.
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.
How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".