Small and Bright Lithium-Based Upconverting Nanoparticles
Bibliographic record
Abstract
In the context of light-mediated tumor treatment, the application of ultraviolet (UV) radiation can initiate drug release and photodynamic therapy. However, its limited penetration depth in tissues impedes the subcutaneous applicability of such radiation. On the contrary, near-infrared (NIR) light is not energetic enough to initiate secondary photochemical processes, but can pierce tissues at a significantly greater depth. Upconverting nanoparticles (UCNPs) unify the advantages of both extremes of the optical spectrum, they can be excited by NIR irradiation and emit UV light through the process of upconversion, effective NIR-to-UV generation being attained with UCNPs as large as 100 nm. However, in anticipation of biomedical applications, the size of UCNPs must be greatly minimized to favor their cellular internalization; yet straightforward size reduction negatively affects the NIR-to-UV upconversion efficiency. Herein, we propose a two-step strategy to obtain small yet bright lithium-based UCNPs. First, we synthesized UCNPs as small as 5 nm by controlling the relative amount of coordinating ligands, namely oleylamine (OM) and oleic acid (OA). Although these UCNPs were chemically unstable, particle coarsening via an annealing process in the presence of fresh OA yielded structurally stable and highly monodisperse sub-10 nm crystals. Second, we grew a shell with controlled thickness on these stabilized cores of UCNPs, improving the NIR-to-UV upconversion by orders of magnitude. Particularly in the case of LiYbF 4:Tm 3+ /LiYF 4 UCNPs, their NIR-to-UV upconversion surpassed the gold standard 90 nm-sized LiYF 4:Tm 3+, Yb 3+ UCNPs. All in all, these UCNPs show great potential within the biomedical framework as they successfully combine the requirements of small size, deep tissue NIR penetration and bright UV emission.
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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 | 0.000 |
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".