Correlating semiconductor nanoparticle architecture and applicability for the controlled encoding of luminescent polymer microparticles
Bibliographic record
Abstract
Luminophore stained micro- and nanobeads made from organic polymers like polystyrene (PS) are broadly used in the life and material sciences as luminescent reporters, for bead-based assays, sensor arrays, printable barcodes, security inks, and the calibration of fluorescence microscopes and flow cytometers. Initially mostly prepared with organic dyes, meanwhile luminescent core/shell nanoparticles (NPs) like spherical semiconductor quantum dots (QDs) are increasingly employed for bead encoding. This is related to their narrower emission spectra, tuneability of emission color, broad wavelength excitability, and better photostability. However, correlations between particle architecture, morphology, and photoluminescence (PL) of the luminescent nanocrystals used for encoding and the optical properties of the NP-stained beads have been rarely explored. This encouraged us to perform a screening study on the incorporation of different types of luminescent core/shell semiconductor nanocrystals into polymer microparticles (PMPs) by a radical-induced polymerization reaction. Nanocrystals explored include CdSe/CdS QDs of varying CdS shell thickness, a CdSe/ZnS core/shell QD, CdSe/CdS quantum rods (QRs), and CdSe/CdS nanoplatelets (NPLs). Thereby, we focused on the applicability of these NPs for the polymerization synthesis approach used and quantified the preservation of the initial NP luminescence. The spectroscopic characterization of the resulting PMPs revealed the successful staining of the PMPs with luminescent CdSe/CdS QDs and CdSe/CdS NPLs. In contrast, usage of CdSe/CdS QRs and CdSe QDs with a ZnS shell did not yield luminescent PMPs. The results of this study provide new insights into structure-property relationships between NP stained PMPs and the initial luminescent NPs applied for staining and underline the importance of such studies for the performance optimization of NP-stained beads.
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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) | 0.000 | 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".