Hyperspectral Raman imaging using Bragg tunable filters of graphene and other low‐dimensional materials
Bibliographic record
Abstract
Hyperspectral Raman imaging is presented as a powerful method to acquire quantitative as well as qualitative information on low‐dimensional materials. The method is, however, not widely used due to limitations of the Raman scanning instruments. Here we present a hyperspectral Raman system based on Bragg tunable filtering that is capable of global imaging with significantly reduced acquisition time and improved sensitivity compared to scanning confocal Raman microscopes. The operation principles of the instrument are presented, and the performance is benchmarked using a calibrated carbon nanotube sample. Examples of various applications are shown to illustrate the abilities of the technique to characterize samples deposited on oxidized silicon substrates, including graphene stacks prepared by chemical‐vapor deposition, exfoliated MoS2, and carbon nanotubes filled with dye molecules. The wealth of information available through this hyperspectral Raman imaging technique opens many new ways to probe the properties of complex low‐dimensional materials. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".