An exploration of 2D-LC-SERS : a novel offline detection modality for multidimensional chromatography
Bibliographic record
Abstract
Multidimensional liquid chromatography (2D-LC) provides better resolving and separation power than conventional high-performance liquid chromatography (HPLC), and over the past decade has increasingly been applied in many different fields. 1 This thesis seeks to explore the extent to which surface-enhanced Raman spectroscopy (SERS) can be used as an offline detection modality for 2D-LC.This thesis hypothesizes that careful selection and modification of a three dimensional (3D) SERS substrate will be useful for characterization of fractions collected using 2D-LC.In particular, a mixture of four polyphenolic molecules was chosen for this proof-of-concept study.An optimised 2D-LC method was developed as part of this thesis.Various materials were evaluated as potential 3D-SERS substrates, with the most promising option being cellulose-based filter paper.Various modification strategies were explored to enhance the interaction between the polyphenolic molecules and the filter paper substrate.In the end, SERS-based detection of 2D-LC fractions proved challenging, even after optimization.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".