Interstitial diffuse radiance spectroscopy of gold nanocages and nanorods in bulk muscle tissues
Bibliographic record
Abstract
Radiance spectroscopy was applied to the interstitial detection of localized inclusions containing Au nanocages or nanorods with various concentrations embedded in porcine muscle phantoms. The radiance was quantified using a perturbation approach, which enabled the separation of contributions from the porcine phantom and the localized inclusion, with the inclusion serving as a perturbation probe of photon distributions in the turbid medium. Positioning the inclusion at various places in the phantom allowed for tracking of photons that originated from a light source, passed through the inclusion's location, and reached a detector. The inclusions with high extinction coefficients were able to absorb nearly all photons in the range of 650-900 nm, leading to a spectrally flat radiance signal. This signal could be converted to the relative density of photons incident on the inclusion. Finally, the experimentally measured quantities were expressed via the relative perturbation and arranged into the classical Beer-Lambert law that allowed one to extract the extinction coefficients of various types of Au nanoparticles in both the transmission and back reflection geometries. It was shown that the spatial variation of perturbation could be described as 1/r dependence, where r is the distance between the inclusion and the detector. Due to a larger absorption cross section, Au nanocages produced greater perturbations than Au nanorods of equal particle concentration, indicating a better suitability of Au nanocages as contrast agents for optical measurements in turbid media. Individual measurements from different inclusions were combined into detectability maps.
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) | 0.001 | 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".