Enhancing Sensitivity and Selectivity of Long-Period Grating Sensors using Structure-Switching Aptamers Bound to Gold-Doped Macroporous Silica Coatings
Bibliographic record
Abstract
High surface area, sol-gel derived macroporous silica films doped with gold nanoparticles (AuNP) are used as a platform for high-density affinity-based immobilization of functional structure-switching DNA aptamer molecules onto Michelson interferometer long-period grating (LPG) fiber sensors, allowing for label-free detection of small molecular weight analytes such as adenosine triphosphate (ATP). The high surface area afforded by the sol-gel derived material allowed high loading of DNA aptamers, while the inclusion of gold nanoparticles within the silica film provided a high refractive index (RI) overlay, which is required to enhance the sensitivity of the LPG sensor according to our numerical simulations. By using a structure-switching aptamer construct that could release an oligonucleotide upon binding of ATP, the effective change in RI was both enhanced and inverted (i.e., binding of ATP caused a net reduction in molecular weight and refractive index), resulting in a system that prevented signals originating from nonspecific binding. This is the first report on the coupling of aptamers to LPG fiber sensors and the first use of high RI AuNP/silica films as supports to immobilize biomolecules onto the LPG sensor surface. The dual functionality of such films to both improve binding density and LPG sensor cladding refractive index results in a substantial enhancement in the sensitivity of such sensors for small molecule detection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 | 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".