Surface-enhanced Raman scattering (SERS) optrodes for multiplexed on-chip sensing of nile blue A and oxazine 720
Bibliographic record
Abstract
The fabrication and on-chip integration of surface-enhanced Raman scattering (SERS) optrodes are presented. In the optrode configuration, both the laser excitation and the back-scattered Raman signal are transmitted through the same optical fiber. The SERS-active component of the optrode was fabricated through the self-assembly of silver nanoparticles on the tip of optical fibers. The application of SERS optrodes to detect dyes in aqueous solution indicated a limit of quantification below 1 nM, using nile blue A as a molecular probe. Using the optrode-integrated microfluidic chip, it was possible to detect several different dyes from solutions sequentially injected into the same channel. This approach for sequential detection of different analytes is applicable to monitoring on-chip chemical processes. The narrow bandwidth of the vibrational information generated by SERS allowed solutions of different compositions of two chemically similar dyes to be distinguished using a dilution microfluidic chip. These results demonstrate the advantages of the SERS-optrode for microfluidics applications by illustrating the potential of this vibrational method to quantify components in a mixture.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".