The development of “fab-chips” as low-cost, sensitive surface-enhanced Raman spectroscopy (SERS) substrates for analytical applications
Bibliographic record
Abstract
The demand for methods and technologies capable of rapid, inexpensive and continuous monitoring of health status or exposure to environmental pollutants persists. In this work, the development of novel surface-enhanced Raman spectroscopy (SERS) substrates from metal-coated silk fabric, known as zari, presents the potential for SERS substrates to be incorporated into clothing and other textiles for the routine monitoring of important analytes, such as disease biomarkers or environmental pollutants. Characterization of the zari fabric was completed using scanning electron microscopy, energy dispersive X-ray analysis and Raman spectroscopy. Silver nanoparticles (AgNPs) were prepared, characterized by transmission electron microscopy and UV-vis spectroscopy, and used to treat fabric samples by incubation, drop-coating and in situ synthesis. The quality of the treated fabric was evaluated by collecting the SERS signal of 4,4'-bipyridine on these substrates. When AgNPs were drop-coated on the fabric, sensitive and reproducible substrates were obtained. Adenine was selected as a second probe molecule, because it dominates the SERS signal of DNA, which is an important class of disease biomarker, particularly for pathogens such as Plasmodium spp. and Mycobacterium tuberculosis. Excellent signal enhancement could be achieved on these affordable substrates, suggesting that the developed fabric chips have the potential for expanding the use of SERS as a diagnostic and environmental monitoring tool for application in wearable sensor technologies.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".