Luminescent Erbium-Based Nanoparticles Synthesized by Pulsed Laser Ablation in Liquid
Bibliographic record
Abstract
Erbium and erbium oxide nanoparticles (Er-NPs) have been synthesized in deionized (DI) water using the green and environmentally friendly technique of pulsed laser ablation in liquid (PLAL), with laser fluence ranging from 2.5 to 20.9 J/cm 2 . Owing to the careful examination of the nanoparticle morphology, crystal structure, and chemical composition, the occurrence of various growth regimes is evidenced. The size of the Er-NPs is found to increase with the laser fluence, and the formed nanoparticles are surrounded by a thin hydroxide layer of a few nanometers thickness, originating from water and chemical residues. The activation of 4f–4f optical transitions associated with trivalent Er 3+ ions is promoted by the formation of erbium oxide. The Er-NPs having diameters lower than 100 nm are made of Er 2 O 3, whereas Er-NPs of larger dimensions are made of an oxidized erbium oxide matrix containing a large amount of excess Er, the concentration of which increases gradually inside the Er-NPs. The formation of this graded Er/Er 2 O 3 core–shell structure gives rise to a decrease in the Er visible photoluminescence emission. These findings shed light on the influence of the PLAL laser fluence on both the geometry and the composition of Er-NPs, as well as its consequence on their photoemission capacity, making them relevant for easy implementation with tunable properties in advanced photonics devices.
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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".