(Invited) Optical Characterization of Nanomaterial By Means of Hyperspectral Global Imaging
Bibliographic record
Abstract
Fluorescence and Raman spectroscopy are powerful techniques to probe the intrinsic properties of low dimensional materials such as carbon nanotubes and graphene. In this study, we present how a hyperspectral imaging platform based on Bragg gratings designed for global imaging can help improve the fabrication methods of various nanomaterials. Spectrally and spatially resolved maps were acquired on different samples over a million points. The intrinsic specificity of Raman scattering combined with the analysis performed by the global imaging modality makes it a useful method to assess large maps (hundreds of microns) of the spatial distribution of defects, number of layers and stacking order. On the other hand, hyperspectral photoluminescence provides straightforward information on photostability and allows multiplexing. This presentation will highlight how hyperspectral microscopy can be used to study carbon nanotubes based fluorophores, CVD graphene and MoS2.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".