Ln3+-doped nanoparticles for upconversion and magnetic resonance imaging: some critical notes on recent progress and some aspects to be considered
Bibliographic record
Abstract
In this feature article we will critically discuss the synthesis and characterisation aspects of Ln(3+)-doped nanoparticles (NPs) that show upconversion, upon 980 nm excitation. Upconversion is a non-linear process that converts two or more low-energy photons, often near-infrared photons, into one of higher energy, e.g. blue and 800 nm from Tm(3+) and green and red from Er(3+) or Ho(3+). Nearly all researchers use the absorption of 980 nm light by Yb(3+) as the sensitiser for the co-doped emissive Ln(3+) ions. The focus will be on LnF(3) and MLnF(4) (M = alkali metal) as the host matrix, because most progress has been made with these. In particular we will argue that a detailed understanding of how the dopant ions and the host Ln(3+) ions are distributed (in the core) and how (doped) shell growth occurs is not well understood. Moreover, their use as optical and magnetic resonance imaging contrast agents will be discussed. We will argue that deep-tissue imaging beyond 600 μm with retention of optical resolution, i.e. to see fine structure such as blood capillaries in brain tissues, has not yet been achieved. Three key parameters have been identified as impediments: (i) the low absorption efficiency of the Yb(3+) sensitiser, (ii) the low quantum yield of upconversion, and (iii) the long-lived excited states. On the other hand, there are very encouraging results that suggest that these nanoparticles could be developed into very potent magnetic resonance imaging (MRI) contrast agents.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".