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Record W4413832748 · doi:10.3390/app15179474

Porous Biomass Carbon Composites Derived from Canadian Goldenrod and Their Excellent Microwave Absorption

2025· article· en· W4413832748 on OpenAlexaboutno aff
Zhidai Zhou, Yan Yan, Jiaming Liu, Zhen He, Yuxin Wang

Bibliographic record

VenueApplied Sciences · 2025
Typearticle
Languageen
FieldMaterials Science
TopicElectromagnetic wave absorption materials
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceComposite materialMicrowaveBiomass (ecology)Carbon fibersPorosityPulp and paper industryComposite numberAgronomyComputer scienceEngineering

Abstract

fetched live from OpenAlex

Electromagnetic wave pollution has become a growing concern in recent decades. Biomass-derived carbon materials have attracted significant attention as wave-absorbing materials due to their easy availability, low cost, and environmental friendliness. In this study, the invasive plant Solidago canadensis (Canada goldenrod) in China was used as the carbon source, and a two-step pyrolysis and hydrothermal process was applied to create a porous composite material with magnetic CoFe2O4 particles. This improved the impedance matching of the biomass carbon and introduced multiple loss mechanisms. The combination of magnetic loss, interfacial polarization, dipole polarization, and multiple reflections in the biomass carbon produced a material with excellent microwave absorption properties. At 16.76 GHz with a thickness of 2.5 mm, the material achieved a minimum reflection loss of −35.21 dB and an effective absorption bandwidth of 7.76 GHz. This study presents a promising method for developing biomass-based absorbers and offers an efficient, cost-effective, and environmentally friendly solution for managing invasive species.

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 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 score0.022
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.010
GPT teacher head0.214
Teacher spread0.205 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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