Quantum confinement in Si and Ge nanostructures
Bibliographic record
Abstract
We apply perturbative effective mass theory as a broadly applicable theoretical model for quantum confinement (QC) in all Si and Ge nanostructures including quantum wells (QWs), wires (Q-wires), and dots (QDs). Within the limits of strong, medium, and weak QC, valence and conduction band edge energy levels (VBM and CBM) were calculated as a function of QD diameters, QW thicknesses, and Q-wire diameters. Crystalline and amorphous quantum systems were considered separately. Calculated band edge levels with strong, medium, and weak QC models were compared with experimental VBM and CBM reported from X-ray photoemission spectroscopy (XPS), X-ray absorption spectroscopy (XAS), or photoluminescence (PL). Experimentally, the dimensions of the nanostructures were determined directly, by transmission electron microscopy (TEM), or indirectly, by x-ray diffraction (XRD) or by XPS. We found that crystalline materials are best described by a medium confinement model, while amorphous materials exhibit strong confinement regardless of the dimensionality of the system. Our results indicate that spatial delocalization of the hole in amorphous versus crystalline nanostructures is the important parameter determining the magnitude of the band gap expansion, or the strength of the quantum confinement. In addition, the effective masses of the electron and hole are discussed as a function of crystallinity and spatial confinement.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".