A comparative study of Raman enhancement in capillaries
Bibliographic record
Abstract
This work reports on the comparative studies of Raman enhancement in liquid core waveguides (LCWs). The theoretical considerations that describe Raman enhancement in LCWs is adapted to analyze and compare the performance of hollow core photonic crystal fibers (HCPCFs) to conventional Teflon capillary tubes. The optical losses in both platforms are measured and used to predict their performance for different lengths. The results show that for an optimal waveguide length, two orders of magnitude enhancement in the Raman signal can be achieved for aqueous solutions using HCPCFs. This length, however, cannot be achieved using normal capillary effects. By integrating the interface of the fluidic pump and the HCPCF into a microfluidic chip, we are able to control fluid transport and fill longer lengths of HCPCFs regardless of the viscosity of the sample. The long-term stability and reproducibility of Raman spectra attained through this platform are demonstrated for naphthalenethiol, which is a well-studied organic compound. Using the HCPCF platform, the detection limit of normal Raman scattering in the range of micro-molars has been achieved. In addition to the higher signal-to-noise ratio of the Raman signal from the HCPCF-platform, more Raman modes of naphthalenethiol are revealed using this platform.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 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".