Lanthanide-Based Heteroepitaxial Core–Shell Nanostructures: Compressive <i>versus</i> Tensile Strain Asymmetry
Bibliographic record
Abstract
Heteroepitaxial core-shell nanostructures have been proven advantageous in a wide variety of applications, ranging from luminescence enhancement, band gap engineering, multimodal theranostics, to catalysis. However, precisely tailoring the epitaxial growth is challenging, and a general understanding of the parameters that impact epitaxial growth remains unclear. Here we demonstrate the critical role of the sign of the lattice mismatch of the shell relative to the core (compressed/tensile) in generating lanthanide-based core-shell structures, a parameter conventionally not considered in heteroepitaxial design. We took advantage of the very gradual contraction of lanthanide ions along the series to control precisely both the magnitude and the sign of lattice mismatch and investigated multiple sodium lanthanide fluoride (NaLnF4) core-shell heterostructures of variable composition and size. We discovered that the tensile strained shells adapt to the core crystallite shape (i.e., conformal) and lattice structure (i.e., coherent), while under identical magnitude of mismatch, the compressively strained shells are neither conformal nor coherent to the core. This striking asymmetry between the tensile and compressively strained epitaxial growth arises from the fundamental anharmonicity of the interatomic interactions between the attractive and repulsive pairs. From a broader perspective, our findings redefine the a priori design consideration and provide a fundamental insight on the necessity to include the sign of lattice mismatch and not just its magnitude in designing heteroepitaxial core-shell nanostructures.
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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) | 0.000 | 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".