Upconversion of Ln<sup>3+</sup>‐based Nanoparticles for Optical Bio‐imaging
Bibliographic record
Abstract
This chapter provides an overview of upconversion nanoparticles, including some of the characteristics of the trivalent lanthanide ions, with an emphasis on the optical and magnetic properties. A detailed discussion of the various non-linear processes is given. The synthesis strategies of upconversion systems are briefly discussed. Subsequently, some basic and advanced characterization techniques are briefly presented, followed by a study of bioimaging, which is the focus of this chapter. This section of the chapter is divided into two parts. The first part deals with only cell studies. These studies are a mere proof of principle, as upconversion for the imaging of cell cultures is not really necessary. The second part deals with those studies that also included small animal studies. The author argues that these are encouraging studies but much better nanoparticles are needed, better in the sense that they have to give us more photons per second per nanoparticles; in other words, the currently available systems are not yet efficient enough. Finally, those articles that include multimodal imaging modalities, especially those that combine with magnetic resonance imaging (MRI), are discussed. The author argues that it is not immediately evident why it is beneficial to combine optical and MR imaging in one and the same nanoparticle, for these two techniques have very different depth penetration. In addition, for any other combination, one has to show the advantages of that particular combination. This could, for instance, be fewer false positives or a better overall resolution.
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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) | 0.001 | 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) | 0.007 | 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 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".