A Tale of Seemingly “Identical” Silicon Quantum Dot Families: Structural Insight into Silicon Quantum Dot Photoluminescence
Bibliographic record
Abstract
Two quantum dots, both alike in composition, but differing in structure, where we lay our scene. From broader classes, to bring deeper understanding, to the crystalline core that drives the quantum dot's sheen. In this contribution we examine two families of silicon quantum dots (SiQDs) that bring to mind the Capulets and the Montagues in Shakespeare’s Romeo and Juliet because of their stark similarities and differences. SiQDs are highly luminescent, heavy-metal-free and based upon earth-abundant elements. As such, they have attracted attention for far reaching applications ranging from biological imaging to luminescent solar concentrators to light-emitting diodes that rely on their size-dependent optical response. Unfortunately, correlating SiQD “size” to their photoluminescence maximum is often challenging. Herein, we provide essential structural insight into the correlation of SiQD dimension and PL maximum through a direct comparison of samples that exhibit statistically identical physical dimensions (d TEM ) and chemical compositions, but different crystallite size (d XRD ) and PL maxima. We then expand the scope of this investigation and systematically compare groupings of SiQDs: one in which the d XRD and d TEM agree and one where d XRD < d TEM . This latter comparison clearly shows d XRD better predicts SiQD optical response when using the well-established effective mass approximation.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".