Principles of conjugating quantum dots to proteins via carbodiimide chemistry
Bibliographic record
Abstract
The covalent coupling of nanomaterials to bio-recognition molecules is a critical intermediate step in using nanomaterials for biology and medicine. Here we investigate the carbodiimide-mediated conjugation of fluorescent quantum dots to different proteins (e.g., immunoglobulin G, bovine serum albumin, and horseradish peroxidase). To enable these studies, we developed a simple method to isolate quantum dot bioconjugates from unconjugated quantum dots. The results show that the reactant concentrations and protein type will impact the overall number of proteins conjugated onto the surfaces of the quantum dots, homogeneity of the protein-quantum dot conjugate population, quantum efficiency, binding avidity, and enzymatic kinetics. We propose general principles that should be followed for the successful coupling of proteins to quantum dots.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".