Imaging and Absolute Extinction Cross-Section Measurements of Nanorods and Nanowires through Polarization Modulation Microscopy
Bibliographic record
Abstract
This paper describes an experimental technique for measuring the extinction cross-section of anisotropic nanomaterials. The experiments are performed by focusing a laser beam to a diffraction-limited spot under an optical microscope, and using a photoelastic modulator (PEM) to rotate the polarization of the light beam. Monitoring the transmitted beam with a lock-in amplifier referenced to twice the resonant frequency of the PEM yields the difference in extinction for light polarized parallel and perpendicular to the optical axis of the nanostructure. These experiments are the single particle analog of polarization modulation microscopy (PMM). Experimental results for gold nanorods and CdSe nanowires are presented that demonstrate the sensitivity and properties of this technique. In particular, we show that by collecting images at two laser polarizations separated by 45° it is possible to construct an extinction cross-section image. The advantages and disadvantages of the PMM technique compared to existing ways of measuring the extinction of nanoparticles (photothermal heterodyne imaging and spatial modulation spectroscopy) are discussed. Results from experiments where we collected simultaneous absorption and emission images are also presented for different shaped CdSe nanowires. These measurements provide insight into how the structure of the nanowire affects its photophysics.
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 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 | 0.001 | 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) | 0.001 | 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".