Coupling multidimensional chromatography with plasmonic sensing: an exploration of electrochemical SERS as a detection modality for 2D-LC
Bibliographic record
Abstract
Complex mixtures that may contain hundreds, if not thousands, of unique compounds pose an analytical challenge for complete and detailed analyses. For example, green tea is a well-known complex substance requiring advanced separation techniques and ultrasensitive detection methods for full characterization. Separation and identification of components in green tea help gauge tea quality, as green tea is one of the most consumed beverages in the world. In this work, the coupling of two-dimensional liquid chromatography (2D-LC) and electrochemical surface-enhanced Raman spectroscopy (EC-SERS) was explored for the first time to analyze compounds in green tea. 2D-LC offers enhanced separation compared to conventional HPLC systems due to the second dimension of separation. EC-SERS was used as an offline detection modality and qualitative tool to help identify the compounds in green tea fractions collected from the second dimension separation. 2D-LC and EC-SERS are coupled in this work for the first time and offer a pathway forward for sensitive and selective separation and identification of components in complex mixtures.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".