Tailoring Sol–Gel-Derived Silica Materials for Optical Biosensing
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
The last two decades have seen a revolution in the area of sol–gel-derived materials as media for the immobilization of biomolecules for biosensor fabrication. Such materials are suitable for the entrapment of a range of biomolecules, from enzymes to antibodies and even functional nucleic acids (FNA) such as aptamers and DNA enzymes. Recent progress in the development of “protein friendly” sol–gel processing methods has allowed these materials to be utilized as components of numerous biosensors, using delicate biomolecules such as luciferease and kinases, or even membrane-bound receptors as biorecognition elements. In addition, such materials have proven to be particularly versatile in the fabrication of biosensors, being amenable to methods such as dipcasting, contact printing, or even noncontact inkjet printing to form a bioselective coating on a range of substrates. In this review, we provide an overview of advances in biofriendly sol–gel processing methods developed in our research group and others, and we highlight accomplishments in the immobilization of both proteins and FNA within silica based materials. We then describe methods for interfacing biomolecule-doped materials to optical biosensors, with emphasis on fiber optic sensors, microarray-based multianalyte sensors and bioactive paper-based test strips. In each case, the material processing requirements for fabrication of different devices is emphasized. Finally, a brief perspective on potential future areas of research in the field of sol–gel based biocomposites is provided.
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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