Exploiting Anisotropy of Plasmonic Nanostructures with Polarization Modulation Infrared Linear Dichroism Microscopy (µPM‐IRLD)
Bibliographic record
Abstract
Abstract Metallic nanostructures that exhibit plasmon resonances in the mid‐infrared range are of particular interest for a variety of optical processes where the infrared excitation and/or emission can be enhanced. This plasmon‐mediated enhancement can potentially be used toward highly sensitive detection of an analyte(s) by techniques such as surface‐enhanced infrared absorption (SEIRA). To maximize the SEIRA enhancement, it is necessary to prepare highly tuned plasmonic resonances over a defined spectral range that can span over several microns. Noteworthy, nanostructures with anisotropic shapes exhibit multiple resonances that can be exploited by controlling the polarization of the input light. This study demonstrates the role of polarization‐modulation infrared linear dichroism coupled to microscopy measurements (µPM‐IRLD) as a powerful means to explore the optical properties of anisotropic nanostructures. Quantitative µPM‐IRLD measurements are conducted on a series of dendritic fractals as model structures to explore the role of structural anisotropy on the resulting surface‐enhanced infrared absorption and sensing application. Once functionalized with an analyte, the µPM‐IRLD SEIRA results highlight that it is possible to selectively enhance further vibrational modes of analytes making use of the structural anisotropy of the metallic nanostructure.
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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 | 0.000 | 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 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".