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Preparation and Properties of Fe3O4 Biomimetic Micro-nano Structure Coatings

2010· article· en· W1860994067 on OpenAlexvenueno aff
Xiaozhou Tian, Jianfang Wang, Siwen Qian, Wenjian Wu, Gang Xu

Bibliographic record

VenueAdvances in natural science/Advances in natural sciences · 2010
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceNano-NanoparticleLotus effectPolymerizationPolymerMicrostructureContact angleComposite numberIn situ polymerizationNanotechnologySiliconComposite materialMetallurgyRaw materialChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Nanoparticles filling is one of the most effective methods to build the micro-nano structure. In this paper, the composite coatings containing Fe3O4 nanoparticles were prepared from fluorinated silicon polymer by in-situ polymerization. FT-IR was used to characterize the structure of the composite material. SEM and AFM were performed to observe the microstructure of the coatings. The contact angle of water and coatings was tested. The results showed that the biomimetic micro-nano structure of the coatings, which formed on the glass plate, was exactly familiar with that of the surface of lotus leaves. Keyword: micro-nano structure; Fe3O4 nanoparticles; in-situ polymerization; biomimetic

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.028
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.007
Scholarly communication0.0000.007
Open science0.0010.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.293
Teacher spread0.285 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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