PMMA Microspheres with Embedded Lanthanide Nanoparticles by Photoinitiated Dispersion Polymerization with a Carboxy-Functional Macro-RAFT Agent
Bibliographic record
Abstract
Functional poly(methyl methacrylate) (PMMA) microbeads with a very narrow size distribution were synthesized by photoinitiated RAFT dispersion polymerization in aqueous ethanol using an acrylic acid–oligo(ethylene glycol) copolymer as a macro-RAFT agent. These particles are a prototype for multiparameter bead-based assays employing mass cytometry, a technique in which metal-encoded beads are injected into the plasma torch of an inductively coupled plasma mass spectrometer (ICP-MS), and the metal ions generated are detected by time-of-flight mass spectrometry. To label the beads, the polymerization reaction was carried out in the presence of various types of small (ca. 5 nm) lanthanide fluoride (LnF 3 ) nanoparticles (e.g., LaF 3, CeF 3, and TbF 3 ) with polymerizable methacrylate groups on their surface. The type of metal ion and the metal content of the PMMA microbeads could be varied by changing the composition of the reaction medium. An important feature of these microbeads is that acrylic acid groups in the corona are available for covalent attachment of biomolecules. As a proof of concept, FITC–streptavidin (FITC-SAv) was covalently coupled to the surface of a Ln-encoded microbead sample. The number of FITC-SAv binding sites on the beads was determined through three parallel assays involving biotin derivatives. Interaction of the beads with a biotin–tetramethylrhodamine derivative was monitored by fluorescence, whereas interaction of the beads with a biotin-DOTA-Lu derivative was monitored both by ICP-MS and by mass cytometry. Each measurement detected an average of ca. 5 × 10 4 biotins per microsphere. Control experiments with beads covalently labeled with FITC–bovine serum albumin (FITC-BSA) showed only very low levels of nonspecific binding.
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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 | 0.000 | 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 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".