The Active‐Core/Active‐Shell Approach: A Strategy to Enhance the Upconversion Luminescence in Lanthanide‐Doped Nanoparticles
Bibliographic record
Abstract
Abstract Nanoparticles of NaGdF 4 doped with trivalent erbium (Er 3+ ) and ytterbium (Yb 3+ ) are prepared by a modified thermal decomposition synthesis from trifluoroacetate precursors in 1‐octadecene and oleic acid. The nanoparticles emit visible upconverted luminescence on excitation with near‐infrared light. To minimize quenching of this luminescence by surface defects and surface‐associated ligands, the nanoparticles are coated with a shell of NaGdF 4 . The intensity of the upconversion luminescence is compared for nanoparticles that were coated with an undoped shell (inert shell) and similar particles coated with a Yb 3+ ‐doped shell (active shell). Luminescence is also measured for nanoparticles lacking the shell (core only), and doped with Yb 3+ at levels corresponding to the doped and undoped core/shell materials respectively. Upconversion luminescence was more intense for the core/shell materials than for the uncoated nanoparticles, and is greatest for the materials having the “active” doped shell. Increasing the Yb 3+ concentration in the “core‐only” nanoparticles decreases the upconversion luminescence intensity. The processes responsible for the upconversion are presented and the potential advantages of “active‐core”/“active‐shell” nanoparticles are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".