Surface Spectroscopy of Nanomaterials for Detection of Diseases
Bibliographic record
Abstract
This chapter talks about nanohole arrays that are especially interesting for the combination of plasmonic techniques alongside electrochemical measurements, as they are simultaneously electrically conductive and support localized and propagating plasmons necessary for surface-enhanced techniques. It presents a brief introduction to the mechanisms of surface-enhanced Raman scattering (SERS) enhancement resulting from plasmonic nanostructures. Electrochemical-SERS (EC-SERS) not only provides a platform for label-free detection, with the ability to easily prepare nanostructured electrodes and the miniaturization of SERS instruments, but also potentially provides a simple, rapid, and portable method of analysis ideally placed to be used in future instances of disease diagnostics and management. The most common biomolecules targeted in the field of disease diagnostics include DNA, which codes for genes specific to a certain disease state, as well as antigens indicative of the presence of a disease.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".