Nondestructive analysis of nanomaterials using optofluidic assisted Raman spectroscopy
Bibliographic record
Abstract
This talk will review and compare the different optofluidic techniques for enhancing the retrieved Raman signal of nanomaterials in liquids and aerosols. Recent progress on this front utilizing optofluidics such as photonic crystal waveguides will be discussed. Techniques and applications to combine surface enhanced with optofluidic-assisted Raman spectroscopy will be also reviewed. Challenges and future opportunities to advance optofluidics-assisted Raman spectroscopy that are carried out using portable Raman spectrometers and controlled using handheld controllers such as mobile phones will be presented. As an example, a detailed, non-destructive characterization of CdTe nanoparticles using Raman spectroscopy using concentrations of 2 mg/mL, will be highlighted. Our platform allows clear vibrational modes corresponding to the structure and interactions of the QDs to be observed. These vibrational modes include those of the CdTe core, Te defects, CdSTe interface, thiol agent and carboxylate-metal complexes. These modes are correlated with the crystallinity of the QD core, interfacial structure formed upon stabilization, QD-thiol interaction mechanisms, water solubility of the QDs and their potential bio-conjugation abilities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".