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Record W4285737143 · doi:10.18280/rcma.320304

Glycerol /Phthalic Anhydride Novel Nano Composite for Microwave Absorbing Applications

2022· article· en· W4285737143 on OpenAlexvenueno aff
Batool Abdul Rasool Zaki, Farhan Lafta Rashid, Mohammad N. Al-Baiati

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2022
Typearticle
Languageen
FieldMaterials Science
TopicElectromagnetic wave absorption materials
Canadian institutionsnot available
Fundersnot available
KeywordsPhthalic anhydrideMaterials scienceMicrowaveAttenuationNanocompositeReflection lossNano-ReflectivityComposite numberBandwidth (computing)RadarComposite materialNanoparticleOpticsNanotechnologyChemistryOrganic chemistryComputer science

Abstract

fetched live from OpenAlex

High-efficiency radar wave absorbing material was prepared utilizing nanoparticles poly (glycerol /phthalic anhydride), and the effect of using these nanocomposites with thermal paint on the properties of electromagnetic absorbing was analyzed and investigated. Results show that when the percentage of nano polymers is increased from 1% to 3%, with the presence of 2.5% of iron oxide, the reflectivity loss value rises dramatically. For the reflectivity up to 27.2dB at a frequency of 8.3 GH and a bandwidth of (8.1-8.2GH), there is a clear attenuation of the radar waves, which is confined within a bandwidth of GH (9-9.3GH). This material can be utilized as a high- efficiency radar wave absorbing applications.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
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
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.290
Teacher spread0.232 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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