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Record W3204755606 · doi:10.18280/rcma.310409

The Electromagnetic Wave Absorption Performance and Mechanical Properties of Cement-Based Composite Material Mixed with Functional Aggregates with High Fe2O3 and SiC

2021· article· en· W3204755606 on OpenAlexvenueno aff
Zuoqun Zhang, Chaoshan Yang, Hua Cheng, Xiaohan Huang, Yuhao Zhu

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2021
Typearticle
Languageen
FieldMaterials Science
TopicElectromagnetic wave absorption materials
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceElectromagnetic radiationReflection lossComposite materialCementComposite numberAbsorption (acoustics)DielectricDielectric lossSinteringOpticsOptoelectronics

Abstract

fetched live from OpenAlex

Now there’re many researches on the electromagnetic radiation protection function of the cement-based electromagnetic wave absorbing materials, such materials have been widely used in various types of buildings. This paper proposed an idea for preparing a cement-based composite material by mixing functional aggregates with high content of Fe2O3 and SiC, that is, adding Fe3O4 powder and nano-SiC of different contents in the clay, and then sintering at 1190℃; the prepared aggregates showed obvious magnetic loss and dielectric loss to electromagnetic waves, and the numerical tube pressure could reach 16.83MPa. The double-layer reflectivity test board made of functional aggregates showed excellent electromagnetic wave absorption performance, its reflection loss was less than -10dB in the frequency range of 8~18GHz (corresponding to energy absorption greater than 90% EM), and its maximum RL reached -12.13dB. In addition, the compressive strength of the cement-based composite material at the age of 28 days reached 50.1 MPa, which can meet the strength requirements of building materials.

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: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.862

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 studies0.0000.001
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 head0.030
GPT teacher head0.204
Teacher spread0.174 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueRevue des composites et des matériaux avancésSame topicElectromagnetic wave absorption materialsFrench-language works237,207