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Record W4234720901 · doi:10.1002/ange.201913022

Hybrid Plasmonic–Aerogel Materials as Optical Superheaters with Engineered Resonances

2019· article· en· W4234720901 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

VenueAngewandte Chemie · 2019
Typearticle
Languageen
FieldEngineering
TopicThermal Radiation and Cooling Technologies
Canadian institutionsInstitut National de la Recherche Scientifique
FundersH2020 European Research CouncilNational Science and Technology Program during the Twelfth Five-year Plan PeriodPostdoctoral Research Foundation of ChinaNational Natural Science Foundation of ChinaVolkswagen Foundation
KeywordsAerogelPlasmonMaterials scienceThermal insulationOptoelectronicsWork (physics)NanotechnologyMechanical engineeringLayer (electronics)

Abstract

fetched live from OpenAlex

Abstract Solar radiation is a versatile source of energy, convertible to different forms of power. A direct path to exploit it is the generation of heat, for applications including passive building heating, but it can also drive secondary energy‐conversion steps. We present a novel concept for a hybrid material which is both strongly photo‐absorbing and with superior characteristics for the insulation of heat. The combination of that two properties is rather unique, and make this material an optical superheater. To realize such a material, we are combining plasmonic nanoheaters with alumina aerogel. The aerogel has the double function of providing structural support for plasmonic nanocrystals, which serve as nanoheaters, and reducing the diffusion rate of the heat generated by them, resulting in large local temperature increases under a relatively low radiation intensity. This work includes theoretical discussion on the physical mechanisms impacting the system's balanced thermal equilibrium.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.553

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 head0.006
GPT teacher head0.185
Teacher spread0.179 · 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