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Record W4390742049 · doi:10.1016/j.jcis.2024.01.052

Yolk-shell construction of Co0.7Fe0.3 modified with dual carbon for broadband microwave absorption

2024· article· en· W4390742049 on OpenAlexaff
Lizheng Meng, Jiahao Wang, Junyao Qi, Xiangling Liu, Ling Li, Jiangni Yun, Gang Wang, Junfeng Yan, Jintao Bai

Bibliographic record

VenueJournal of Colloid and Interface Science · 2024
Typearticle
Languageen
FieldMaterials Science
TopicElectromagnetic wave absorption materials
Canadian institutionsMcGill University
FundersNatural Science Foundation of Shaanxi ProvinceNational Natural Science Foundation of China
KeywordsReflection lossMaterials scienceMicrostructureMicrowaveAnnealing (glass)Composite materialVoid (composites)PorosityMesoporous materialComposite numberChemical engineeringChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The rational and effective combination of multicomponent materials and the design of subtle microstructure for efficient microwave absorption are still challenging. In this study, carbon-coated CoFe with heterogeneous interfaces was space-restricted in the void space of hollow mesoporous carbon spheres through a facile approach involving electrostatic adsorption and annealing, and a high-performance microwave absorber (MAs) (denoted as Co 0.7 Fe 0.3 @C@void@C) was successfully prepared. The heterostructure, three-dimensional lightweight porous morphology, and electromagnetic synergy strategy enabled the Co 0.7 Fe 0.3 @C@void@C material with yolk-shell structure to exhibit surprising microwave absorption properties. When the annealing temperature and filler loading were 550° C and 15 wt%, respectively, the composites exhibited an effective absorption bandwidth (EAB) of 7.16 GHz at 2.48 mm and a minimum reflection loss of −24.1 dB at 2.11 mm. A maximum EAB of 7.21 GHz at 2.37 mm could be achieved for the composite prepared with an annealing temperature of 650° C. In addition, radar cross-section experiments demonstrated, the potential practical applicability of Co 0.7 Fe 0.3 @C@void@C. This work expands a new avenue to develop high-performance and lightweight MAs with ingenious microstructure.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.010
GPT teacher head0.253
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), 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

Citations50
Published2024
Admission routes1
Has abstractyes

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