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Record W4404176258 · doi:10.1021/acs.nanolett.4c03720

Thermal Conductivity in Biphasic Silicon Nanowires

2024· article· en· W4404176258 on OpenAlexafffund
Samik Mukherjee, Zhongwei Zhang, Marcin Wajs, María Chiara Spadaro, M. Gonzalez-Catala, Uri Givan, Stephan Senz, Jordi Arbiol, S. Francoeur, Sebastian Volz, Oussama Moutanabbir

Bibliographic record

VenueNano Letters · 2024
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsPolytechnique Montréal
FundersArmy Research OfficeAir Force Office of Scientific ResearchHorizon 2020 Framework ProgrammeCanada Foundation for InnovationNational Natural Science Foundation of ChinaAgencia Estatal de InvestigaciónMinistère de la Défense NationaleCanada Research ChairsGeneralitat de CatalunyaNatural Sciences and Engineering Research Council of CanadaEuropean Commission
KeywordsNanowirePhononThermal conductivityMaterials scienceCondensed matter physicsSiliconNanoscopic scaleSemiconductorPhonon scatteringDiamondScatteringCrystal (programming language)Chemical physicsPhase (matter)NanotechnologyOptoelectronicsChemistryPhysicsOpticsComposite material

Abstract

fetched live from OpenAlex

The work unravels the previously unexplored atomic-scale mechanism involving the interaction of phonons with crystal homointerfaces. Silicon nanowires with engineered isotopic content and crystal phases were chosen for this investigation. Crystal polytypism, manifested by the presence of both diamond cubic and rhombohedral phases within the same nanowire, provided a testbed to study the impact of phase homointerfaces on phonon transport. The lattice thermal conductivity and its temperature response were found to be markedly different in the presence of polytypism. Its origin, however, was not traced to any acoustic mismatch as the polytypic nanowires presented a similar phonon spectrum as their counterparts. Rather, phenomenological modeling and atomistic simulations identified and quantified the role of atomically rough homointerfaces and the subsequent phonon scattering from such homointerfaces in shaping the phonon behavior. This framework provides the inputs necessary to advance the design and modeling of phonon transport in nanoscale semiconductors.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.003

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.

Opus teacher head0.020
GPT teacher head0.245
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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