Fabrication of SERS‐Active Substrates using Silver Nanofilm‐Coated Porous Anodic Aluminum Oxide for Detection of Antibiotics
Bibliographic record
Abstract
We have developed a silver nanofilm-coated porous anodic aluminum oxide (AAO) as a surface-enhanced Raman scattering (SERS)-active substrate for the detection of trace level of chloramphenicol, a representative antibiotic in food systems. The ordered aluminum template generated during the synthesis of AAO serves as a patterned matrix on which a coated silver film replicates the patterned AAO matrix to form a 2-dimensional ordered nanostructure. We used atomic force microscopy and scanning electron microscopy images to determine the morphology of this nanosubstrate, and characterized its localized surface plasmon resonance by ultraviolet-visible reflection. We gauged the SERS effect of this nanosubstrate by confocal micro-Raman spectroscopy (782-nm laser), finding a satisfactory and consistent performance with enhancement factors of approximately 2 × 10(4) and a limit of detection for chloramphenicol of 7.5 ppb. We applied principal component analysis to determine the limit of quantification for chloramphenicol of 10 ppb. Using electromagnetic field theory, we developed a detailed mathematical model to explain the mechanism of Raman signal enhancement of this nanosubstrate. With simple sample pretreatment and separation steps, this silver nanofilm-coated AAO substrate could detect 50 ppb chloramphenicol in milk, indicating good potential as a reliable SERS-active substrate for rapid detection of chemical contaminants in agricultural and food products.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".