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Record W4416010140 · doi:10.1063/5.0288735

Abnormal length-dependent thermal transport in silicon nitride nanoribbons by surface phonon polaritons

2025· article· en· W4416010140 on OpenAlexfundno aff
Sun Da, Jun Zhou, Yunshan Zhao

Bibliographic record

VenueJournal of Applied Physics · 2025
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsnot available
FundersInstitute of Nutrition, Metabolism and DiabetesGovernment of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsThermal conductivityPhononPolaritonMicroelectronicsSilicon nitrideDielectricSiliconWide-bandgap semiconductor

Abstract

fetched live from OpenAlex

Surface phonon polaritons (SPhPs) are hybrid excitations arising from the coupling of mid-infrared photons with optical phonons, generating evanescent waves that propagate along the surfaces of polar dielectric materials. Owing to their long wavelengths and extended propagation lengths, SPhPs are particularly promising for enhancing the heat dissipation in micro- and nano-electronic devices. In this study, we fabricated silicon nitride (SiNx) nanoribbons and transferred them onto suspended micro-platforms to systematically investigate their thermal transport property. The thermal conductivity measurements were conducted in a temperature range of 300–480 K for SiNx nanoribbons with varying lengths. A distinct length-dependent enhancement in thermal conductivity is observed, indicative of the quasi-ballistic transport behavior. We attribute this enhancement to the contribution of propagating SPhPs along the surfaces of the polar SiNx nanoribbons. Further quantitative analysis reveals that the SPhP-mediated component constitutes a substantial fraction of the total in-plane thermal conductance for the 70 μm-long nanoribbon at 480 K. Our findings provide direct evidence that SPhPs act as effective heat carriers in low-dimensional polar dielectric systems, providing valuable insights for optimizing the heat dissipation in next-generation microelectronics and silicon photonics.

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 categoriesnone
Consensus categoriesnone
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 score0.014
Threshold uncertainty score0.900

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.221
Teacher spread0.213 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2025
Admission routes1
Has abstractyes

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