Boron Doping of Silicon Quantum Dots via Hydrogen Silsesquioxane-Capped Diffusion for Photovoltaics and Medical Imaging
Bibliographic record
Abstract
Doped silicon quantum dots (SiQDs) with defined size, dopant distribution, and surface chemistry are highly sought after as a scientific curiosity because their unique properties offer a wide array of potential applications including multimodal medical imaging and photovoltaic devices. This report describes a diffusion-based postsynthesis doping method for incorporating high concentrations of B (2.5–5.0 at. %) into preformed SiQDs of predefined sizes while simultaneously maintaining their structure and morphology. The processing temperature, atmosphere, and QD size all strongly influence the resulting B-doped SiQDs. The as-synthesized doped SiQDs exhibit size-dependent photoluminescence spanning the visible to near-infrared spectral regions, are compatible with aqueous environments, and are readily rendered compatible with organic solvents upon functionalization with appropriate alkoxide surface groups. Being able to effectively tune the size and surface chemistry of the present B-doped SiQDs make them excellent candidates for use in targeted applications such as multimodal imaging and photovoltaic 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".