Dextran-Encapsulated Nanoparticles and Super-Nanoparticle Assemblies: Preparation from Quantum Dots, Fluorescent Polymers, and Magnetic Nanoparticles for Application to Cellular Immunolabeling
Bibliographic record
Abstract
Nanoparticles (NPs) continue to be developed as labels for bioanalysis and imaging due to their small size and, in many cases, emergent properties such as photoluminescence (PL) and superparamagnetism. Some applications stand to benefit from amplification of the advantageous properties of a NP, but this amplification is not a simple matter of scaling for size-dependent properties. One promising approach to amplification is, therefore, to assemble many copies of a NP into a larger but still nanoscale and colloidal entity. Here, we use multiple types of hydrophobic nanocrystal to show that amphiphilic dextran is a versatile material for the preparation and surface functionalization of such super-NP assemblies: CdSe/CdS/ZnS quantum dots (QDs), InP/ZnS QDs, and Si QDs; iron oxide magnetic NPs (MNPs); composites of QDs and MNPs; and composites of QDs and MNPs with fluorene-based and phenylenevinylene-based conjugated polymers. The amphiphilic dextran was also useful for the preparation of conjugated polymer NPs (CPNs) without the inclusion of inorganic nanocrystals. The prepared super-NPs and CPNs were characterized, physically and photophysically, at both the ensemble and the single-particle levels. Per colloidal entity, the super-QDs were orders of magnitude brighter than the individual QDs. This enhancement enabled assemblies of nominally more benign InP/ZnS and Si QDs to be competitive alternative materials to CdSe/CdS/ZnS QDs, which are normally much brighter when compared as individual nanocrystals. The dextran functionalization imparted low nonspecific binding and enabled the use of tetrameric antibody complexes (TACs) for simple and selective immunolabeling of cells with all of the prepared super-NP, CPN, and composite materials. Labeling with the super-QDs provided significantly enhanced PL signals, the super-MNPs enabled magnetic pull-down of cells, and both capabilities were concurrently available with composite assemblies. Overall, this study demonstrates that the preparatory method and functional benefits of amphiphilic dextran extend to a range of hydrophobic materials and combinations thereof. There is strong potential for assembling a diverse set of property-amplified designer labels that are ready-made for in vitro applications in bioanalysis and imaging.
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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 | 0.000 |
| 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 | 0.000 | 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 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".