Raman-based detection of bacteria using silver nanoparticles conjugated with antibodies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Surface enhanced Raman scattering (SERS) has been used to detect bacteria captured by polyclonal antibodies sorbed onto protein-A-modified silver nanoparticles. The selectivity and discrimination of the technique were assured by using a specific antibody to the model bacterium, Escherichia coli. As the SERS enhancement mechanism depends upon the metal surface proximity, 8 nm was considered as the optimum distance between the bacterium and the nanoparticle surface. Spectral reproducibility was verified using Principal Components Analysis to differentiate the clusters corresponding to the biomolecules and/or bacteria sorbed onto nanoparticles. Compared to the normal Raman spectrum, the SERS technique resulted in an intensity enhancement of over 20-fold.
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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 | 0.000 | 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) | 0.000 | 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 it