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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".