Inorganic Nanoparticles as Donors in Resonance Energy Transfer for Solid-Phase Bioassays and Biosensors
Bibliographic record
Abstract
Bioassays for the rapid detection and quantification of specific nucleic acids, proteins, and peptides are fundamental tools in many clinical settings. Traditional optical emission methods have focused on the use of molecular dyes as labels to track selective binding interactions and as probes that are sensitive to environmental changes. Such dyes can offer good detection limits based on brightness but typically have broad emission bands and suffer from time-dependent photobleaching. Inorganic nanoparticles such as quantum dots and upconversion nanoparticles are photostable over prolonged exposure to excitation radiation and tend to offer narrow emission bands, providing a greater opportunity for multiwavelength multiplexing. Importantly, in contrast to molecular dyes, nanoparticles offer substantial surface area and can serve as platforms to carry a large number of conjugated molecules. The surface chemistry of inorganic nanoparticles offers both challenges and opportunities for the control of solubility and functionality for selective molecular interactions by the assembly of coatings through coordination chemistry. This report reviews advances in the compositional design and methods of conjugation of inorganic quantum dots and upconversion nanoparticles and the assembly of combinations of nanoparticles to achieve energy exchange. Our interest is the exploration of configurations where the modified nanoparticles can be immobilized to solid substrates for the development of bioassays and biosensors that operate by resonance energy transfer (RET).
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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 | 0.000 |
| 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".