Electromagnetic modeling and experimental evaluation of plasmon-based molecular sensors
Bibliographic record
Abstract
The advances in nanoscience and nanotechnology in recent decades have renewed the interests in the optical properties of metals. Today, the field known as Plasmonics studies the control and manipulation of the electromagnetic near-fields of metallic nanostructures in order to realize novel subwavelength optical applications. In particular, this thesis explores the phenomenon of plasmon resonance for molecular sensing. Surface plasmon resonance (SPR) on flat metal surfaces is used for index-of-refraction sensing and localized surface plasmon resonance (LSPR) on metal nanospheres can produce surface-enhanced Raman scattering (SERS). The operation principles and the experimental evaluation of two SPR sensing devices are presented. An integrated sensor and a 2D wavelength-angle modulated version were estimated to have an angular sensitivity of 126°/RIU and 91°/RIU, respectively. Furthermore, through an implementation of a full vector multiple-multipole light scattering method, useful for the calculations of the field focusing efficiency between assemblies of metal nanospheres, we showed that optical frequency electric fields can be enhanced in gold nanoparticle assemblies by an order of 450 in nanometer volumes. Keywords: surface plasmons, plasmonics, optical biosensors, surface-enhanced Raman scattering, optics of metals, nanophotonics, nanomaterials, classical electrodynamics
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 | 0.001 | 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".