Isolation of Monovalent Quantum Dot–Nucleic Acid Conjugates Using Magnetic Beads
Bibliographic record
Abstract
Control of the valency that is achieved in the decoration of quantum dots (QDs) remains a challenge due to the high surface area of nanoparticles. A population distribution of conjugates is formed even when reactions involve use of one-to-one molar equivalents of the ligand and QD. Monovalent conjugates are of particular interest to enable the preparation of multinanoparticle constructs that afford improved analytical functionality. Herein, a facile method for the formation and purification of QD-DNA monoconjugates (i.e., 1 DNA per QD) is described. Using diethylaminoethyl (DEAE) functionalized magnetic beads, a protocol was developed and optimized to selectively isolate QD-DNA monoconjugates from a mixture. Monoconjugates prepared with oligonucleotides as short as 19 bases and as long as 36 bases were successfully isolated. The monoconjugates were isolated in less than 5 min with isolation efficiencies between 68% and 93%, depending on the length of oligonucleotide that was used. The versatility of the method was demonstrated by purifying monoconjugates prepared from commercially available, water-soluble QDs. The isolation of monoconjugates was confirmed using agarose gel electrophoresis and single molecule fluorescence spectroscopy. Examples are provided comparing the analytical performance of monoconjugates to collections of nanoparticles of mixed valencies, indicating the significance of this separation method to prepare nanomaterials for bioassay design.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".