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Record W2904649178 · doi:10.1038/s41598-018-36270-x

Aluminum-ceramic composites for thermal management in energy-conversion systems

2018· article· en· W2904649178 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldEngineering
TopicThermal Radiation and Cooling Technologies
Canadian institutionsNexen (Canada)
Fundersnot available
KeywordsMaterials scienceCeramicHeat fluxComposite materialHeat sinkTinEnergy transformationLaserHeat transferMetallurgyMechanical engineeringOpticsThermodynamics

Abstract

fetched live from OpenAlex

Abstract The most important property of energy-conversion ceramics in high-power lighting devices based on laser diodes (LDs) is thermal durability because high-energy LDs act as excitation and heat sources for ceramics. Herein, aluminum-ceramic composites (ACCs) are introduced for the manipulation of heat generated during high-power lighting. The cerium-doped aluminum garnet (YAG:Ce) phosphor is selected as the energy-conversion ceramic material. The ACCs have an all-in-one structure bridged by a low-melting glass material. In ACCs, the heat flow from the ceramic to Al is manipulated by a heat-flux throttling layer (TL) comprised of Al and glass. During high-power lighting operation, the input-output temperature differences (T in − T out ) between the ceramic layer (input heat) and end face of the Al layer (output heat) are 13 and 3.9 °C in the absence and presence of the TL, respectively. A lower T in − T out means less heat is loss during heat flow from the ceramic to the metal due to the temperature gradient created by inserting the TL. The results provide a potential application for multi-energy-conversion systems, i.e., optical to heat and heat to electric energy, in terms of heat flow manipulation.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.316

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 head0.009
GPT teacher head0.208
Teacher spread0.199 · 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