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Record W2073669088 · doi:10.1063/1.3310319

An open-photoacoustic-cell method for thermal characterization of a two-layer system

2010· article· en· W2073669088 on OpenAlexaff
Nelson G. C. Astrath, F. B. G. Astrath, Jun Shen, Chao Lei, Jianqin Zhou, Zhong Sheng Liu, Titichai Navessin, Mauro Luciano Baesso, A. C. Bento

Bibliographic record

VenueJournal of Applied Physics · 2010
Typearticle
Languageen
FieldEngineering
TopicThermography and Photoacoustic Techniques
Canadian institutionsBC Innovation Council
FundersFundação AraucáriaConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsThermal diffusivityThermal conductivityMaterials scienceThermal resistanceThermal conductivity measurementPhotoacoustic effectLaser flash analysisThermalFOIL methodComposite materialLayer (electronics)Thermal effusivityPhotoacoustic imaging in biomedicineCharacterization (materials science)Analytical Chemistry (journal)OpticsThermal contact conductanceNanotechnologyChemistryThermodynamics

Abstract

fetched live from OpenAlex

In the present work, we use an open-photoacoustic-cell (OPC) operating at high frequency to measure thermal properties of two-layer system samples. Photothermal deflection technique is also employed to measure the samples. The effective thermal diffusivity measured using the OPC method is interpreted using the concept of effective thermal resistance for a series two-layer system. The results show the reliability of the photoacoustic method for a complete thermal characterization of the samples. In addition, by varying the sample effective thickness, the thermal diffusivity and conductivity of each layer are precisely determined. The effective thermal diffusivity, thermal conductivity, and specific heat of a porous catalyst layer (thickness varying from 13 to 53 μm) deposited on an aluminum foil (53 μm in thickness) were thus measured and found to be (3.7±0.3)×10−3 cm2/s, (7.5±0.7)×10−3 W/cm K, and (1.6±0.2) J/gK, respectively.

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)0.000
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: none
Teacher disagreement score0.442
Threshold uncertainty score0.515

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.011
GPT teacher head0.262
Teacher spread0.251 · 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

Citations44
Published2010
Admission routes1
Has abstractyes

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