Reduction of Thermal Conductivity in Nanowires by Combined Engineering of Crystal Phase and Isotope Disorder
Bibliographic record
Abstract
Nanowires are a versatile platform to investigate and harness phonon and thermal transport phenomena in nanoscale systems. With this perspective, we demonstrate herein the use of crystal phase and mass disorder as effective degrees of freedom to manipulate the behavior of phonons and control the flow of local heat in silicon nanowires. The investigated nanowires consist of isotopically pure and isotopically mixed nanowires bearing either a pure diamond cubic or a cubic-rhombohedral polytypic crystal phase. The nanowires with tailor-made isotopic compositions were grown using isotopically enriched silane precursors 28SiH4, 29SiH4, and 30SiH4 with purities better than 99.9%. The analysis of polytypic nanowires revealed ordered and modulated inclusions of lamellar rhombohedral silicon phases toward the center in otherwise diamond-cubic lattice with negligible interphase biaxial strain. Raman nanothermometry was employed to investigate the rate at which the local temperature of single suspended nanowires evolves in response to locally generated heat. Our analysis shows that the lattice thermal conductivity in nanowires can be tuned over a broad range by combining the effects of isotope disorder and the nature and degree of polytypism on phonon scattering. We found that the thermal conductivity can be reduced by up to ∼40% relative to that of isotopically pure nanowires, with the lowest value being recorded for the rhombohedral phase in isotopically mixed 28Six30Si1–x nanowires with composition close to the highest mass disorder (x ∼ 0.5). These results shed new light on the fundamentals of nanoscale thermal transport and lay the groundwork to design innovative phononic devices.
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 | 0.000 | 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 teacher head, 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".