Surface Enhanced Raman Spectroscopy (SERS) Detection of β-Estradiol in Milk by Molecularly Imprinted Polymers on Biogenic Silica and Silver Nanoparticles
Bibliographic record
Abstract
The highly sensitive surfaced-enhanced Raman spectroscopy (SERS) and the directive selection of molecularly imprinted polymers (MIP) provided a simple and rapid method to detect the content of β-estradiol in milk sample which exists in low concentration at natural level. The MIPs were synthesized by surface polymerization of β-estradiol (template), methacrylic acid (the monomer), and ethylene glycol dimethacrylate (cross-linking agent), with 4,4’ azobis (4-cyanopentanoyl) chloride initiator covalently grafted on the biogenic silica surface. The NIPs were also synthesized in a similar version to MIPs, but polymerization done without β-estradiol template. The surface morphology of MIPs and NIPs by scanning electron microscopy (SEM) showed a clear difference on their structures. Silver and silica nanoparticles were served as SERS active substrates for signal enhancement. The limit of detection (LOD) for MIPs-silver nanoparticles was 1.0 ppm, whereas for MIPs-silica, the LOD was 0.78 ppm. As the result, biogenic silica nanoparticles gave a more enhanced Raman signal compared to the conventional silver nanoparticles. Discipline: Chemistry Faculty Mentor: Dr. Samuel Mugo
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".