Inert Shell Effect on the Quantum Yield of Neodymium-Doped Near-Infrared Nanoparticles: The Necessary Shield in an Aqueous Dispersion
Bibliographic record
Abstract
Lanthanide-doped nanoparticles (LnNPs) are versatile near-infrared (NIR) emitting nanoprobes that have led to their growing interest for use in biomedicine-related imaging. Toward the brightest LnNPs, high photoluminescence quantum yield (PLQY) values are attained by implementing core/shell engineering, particularly with an optically inert shell. In this work, a thorough investigation is performed to quantify how an outer inert shell maintains the PLQY of Nd 3+ -doped LnNPs dispersed in an aqueous environment. Three relevant quantitative findings affecting the PLQY of Nd 3+ -doped LnNPs are identified: (i) the PLQY of core LnNPs is improved 3-fold upon inert shell coating; (ii) PLQY decreases with increasing Nd 3+ doping despite the inert shell; and (iii) solvent quenching has a major influence on the PLQY of the LnNPs, though it is relatively lessened for high Nd 3+ doping. Overall, we shed new light on the impact of the LnNP architecture on the NIR emission, as well as on the quenching effects caused by doping concentration and solvent molecules.
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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".