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Multiphysics Design & Analysis of Silver-Based Low-Emissivity Coating Technology for Energy Saving Sustainable Windows Applications

2024· article· W7131122471 on OpenAlexaff
Laila Salman, D. Mateychuk, K. Ghaffari, Anthony Leger, A. Pati

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicNanomaterials and Printing Technologies
Canadian institutionsAnsys (Canada)
Fundersnot available
KeywordsCoatingMultiphysicsRay tracing (physics)AttenuationEnergy (signal processing)Window (computing)Leverage (statistics)

Abstract

fetched live from OpenAlex

Modern energy saving windows, using low-emissivity (Low-E) technology, are unique and cost-effective solution. This manufacturing technology has been utilized in buildings and has increasingly been recommended in the transportation sector with mobile communication enablement. The presented work will leverage the utilization of physics-based simulation to characterize both the high frequency electromagnetic and optical performance for silver-based low-E Coating designs. Validation with published measurements for double and triple silver coatings are presented. Wave optics and ray tracing analysis are combined to account for sub-micron features while modeling the larger window models. In addition, high frequency analysis is performed to characterize the signal attenuation through the windows in realistic scenarios.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.805
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.017
GPT teacher head0.256
Teacher spread0.239 · 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.

Study designSimulation or modeling
Domainnot available
GenreMethods

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 routes1
Has abstractyes

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