Design and Characterization of Lysine Cross-Linked Mercapto-Acid Biocompatible Quantum Dots
Bibliographic record
Abstract
Semiconductor quantum dots (QDs) are a new generation of inorganic probes with advantageous properties over traditional organic-only probes for biological applications. A major hurdle in the use of QDs for biology is the inability of the hydrophobically synthesized QDs to interface with aqueous environments. There have been tremendous advances in the surface modification of hydrophobic QDs. However, none of the current techniques fits all of the criteria for an ideal QD coating for biological applications (e.g., maintain the small size and optical properties of QDs, have low nonspecific binding) while providing cost-effective, easy preparation on a large scale. We developed a highly stable biocompatible coating for the surface of ZnS-capped CdSe QDs that maintains all of the hydrophobic-coated QD optical properties. These QDs are prepared by first coating them with mercaptoundecanoic acid and are further cross-linked with the amino acid lysine in the presence of dicyclohexylcarbodiimide to form a stable hydrophilic shell. The surface contains carboxylic acid and amino functional groups for conjugation to biomolecules. Using a dynamic light scattering method, we found that the hydrodynamic diameter of these surface-modified QDs is approximately 20 nm. We demonstrated the feasibility of preparing >400 mg of the biocompatible QDs and the successful conjugation of proteins onto their surface. Finally, we characterized the QD stability and optical properties in various biologically relevant environments.
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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) | 0.000 | 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 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".