Sensitization efficiencies in Er‐doped SiO<i>x</i> films containing amorphous or crystalline silicon nanoclusters
Bibliographic record
Abstract
Abstract A suite of SiOx films with oxygen concentrations ranging from x = 1 to 1.8 (25 at% to 3.6 at% excess silicon) were synthesized by physical vapour deposition. Erbium was incorporated by co‐evaporation of erbium metal to a concentration of about 0.2 at%. A subsequent annealing step was performed at temperatures ranging from 400 °C to 1100 °C to induce phase separation and cluster growth, and to optically activate erbium ions. This range of compositions and annealing temperatures was used to generate a fluorescence map for nanocluster‐sensitized Er3+. Photoluminescence (PL) spectra were measured in the visible and near‐infrared wavelength regions to explore the nature of the energy transfer between Si‐NCs and Er3+ ions. The highest PL intensities for undoped films occurred for samples annealed above 1000 °C, containing silicon nanocrystals. In contrast, in Er‐doped films the strongest Er3+ emission at 1.54 μm was observed from films annealed at temperatures below 1000 °C, demonstrating that amorphous Si nanoclusters support effective energy transfer to the Er3+ ions. The sensitization was most efficient when the NC peak emission wavelength was near 660 nm (1.88 eV) matching the energy of the 4F9/2 band of Er3+ ions in a silica host. (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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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".