A Hybrid Stainless-Steel SPME Microneedle Electrode Sensor for Dual Electrochemical and GC-MS Analysis
Bibliographic record
Abstract
A mechanically robust in-tube stainless steel microneedle solid phase microextraction (SPME) platform for dual electrochemical and chromatographic detection has been demonstrated. The SPME microneedle was fabricated by layer-by-layer (LbL) in-tube coating, consisting of carbon nanotube (CNT)/cellulose nanocrystal (CNC) film layered with an electrically conductive polyaniline (PANI) hydrogel layer (PANI@CNT/CNC SPME microneedle (MN)). The PANI@CNT/CNC SPME MN showed effective analysis of caffeine by GC-MS with an LOD of 26 mg/L and excellent precision across the dynamic range. Additionally, the PANI@CNT/CNC SPME MN demonstrated a 67% increase in sensitivity compared to a commercial SPME fiber, while being highly robust for repeated use without loss in performance. For electrochemical detection, the PANI@CNT/CNC SPME MN showed excellent performance for the detection of 3-caffeoylquinic acid (3-CQA). The dynamic range and limits of detection (LOD) for 3-CQA analysis were 75-448 mg/L and 11 mg/L, respectively. The PANI@CNT/CNC SPME MN was demonstrated to accurately determine the caffeine content and 3-CQA in tea samples and dark roast coffee, respectively. The PANI@CNT/CNC SPME MN was used for semiquantitative antioxidant determination and composition analysis in kiwi fruit using electrochemistry and SPME-coupled GC-MS, respectively.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".