Luminescent Terbium Probe for Time‐Resolved FRET and NSET Binding Assays with Quantum Dots and Gold Nanoparticles
Bibliographic record
Abstract
Abstract Time‐gated or time‐resolved FRET (TR‐FRET) assays are important tools in biosensing, bioimaging, drug screening, and molecular diagnostics. Efficient TR‐FRET assays require stable lanthanide complexes with high absorption cross sections, high quantum yields, and long photoluminescence lifetimes. Owing to their challenging synthesis, such complexes are relatively rare and new components are of potential interest when developing TR‐FRET probes. Here, we evaluate the recently developed Tb complex CoraFluor‐1 concerning its analytical performance in terbium‐to‐quantum dot FRET and terbium‐to‐gold nanoparticle NSET assays using the prototypical biological recognition system of streptavidin and biotin. Biological binding was quantifiable at sub‐picomolar concentrations in small sample volumes, with broad applicability demonstrated across three commercial fluorescence plate readers used for time‐resolved, spectrally‐resolved, and clinical bioanalysis. Overall, CoraFluor‐1 provided excellent analytical performance as both FRET and NSET donor, validating its potential for developing new TR‐FRET probes for biosensing and bioimaging.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".