Synthesis of Uniform NaLnF<sub>4</sub> (Ln: Sm to Ho) Nanoparticles for Mass Cytometry
Bibliographic record
Abstract
Over the past decade, there have been extensive developments in the field of lanthanide-based nanoparticles (NPs). Most studies have focused on the application of upconverting NaYF 4 -based NPs for deep tissue imaging and paramagnetic NaGdF 4 NPs for MRI. Current applications for the remaining members of the lanthanide series are rather limited. Recently, a novel bioanalytical technique known as mass cytometry (MC) has been developed which can benefit from the entire lanthanide series of NPs. MC is a high-throughput multiparametric cell-by-cell analysis technique based on atomic mass spectrometry that uses antibodies labeled with metal isotopes for biomarker detection. NaLnF 4 NPs offer the promise of high sensitivity coupled with multiparameter detection, provided that NPs can be synthesized with a narrow size distribution. Here we describe the synthesis of six members of this NP family (NaSmF 4, NaEuF 4, NaGdF 4, NaTbF 4, NaDyF 4, NaHoF 4 ) with the appropriate size (5–30 nm) and size distribution (CV < 5%) for MC. We employed the coprecipitation method developed by Li and Zhang [ Nanotechnology 2008, 19, 345606], and for each member of this series, we examined the heating rate, final reaction temperature, and composition of the reaction mixture in an attempt to optimize the synthesis. For each of the six NaLnF 4, in the range of the target sizes, we were able to identify “sweet spots” in the reaction conditions to obtain NPs with a narrow size distribution. In addition, we investigated the oleate surface coverage of the NPs and the effect of long-term storage (2 years) on the colloidal stability of the NPs. Finally, NaTbF 4 NPs were rendered hydrophilic via lipid encapsulation and tested for nonspecific binding with KG1a and Ramos cells by mass cytometry.
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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 | 0.001 |
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".