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 Nd3+-doped LnNPs dispersed in an aqueous environment. Three relevant quantitative findings affecting the PLQY of Nd3+-doped LnNPs are identified: (i) the PLQY of core LnNPs is improved 3-fold upon inert shell coating; (ii) PLQY decreases with increasing Nd3+ 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 Nd3+ 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 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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".