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Record W2784783748 · doi:10.5539/ijc.v10n1p81

Green Preparation of Sulfonated Polystyrene/Polyaniline/Silver Composites with Enhanced Anticorrosive Properties

2018· article· en· W2784783748 on OpenAlexvenueno aff
Guangfu Liao

Bibliographic record

VenueInternational Journal of Chemistry · 2018
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsnot available
Fundersnot available
KeywordsPolyanilineFourier transform infrared spectroscopyPolyvinylpyrrolidonePolystyreneComposite materialMaterials scienceChemical engineeringNanocompositeChemistryPolymer chemistryPolymerPolymerization

Abstract

fetched live from OpenAlex

Sulfonated polystyrene/polyaniline/silver (SPS/PANI/Ag) composites with enhanced anticorrosive properties have been successfully prepared by using SPS microspheres as substrates and utilizing polyvinylpyrrolidone (PVP) as reducing agent and stabilizing agent. Our method is an environmentally friendly method because of the absence of any toxic reagents in the whole process. Fourier transform infrared Spectrum (FTIR), field emission scanning electron microscopy (FESEM), and energy disperse spectroscopy (EDX) results confirmed the formation of pure PANI, SPS/PANI composites, and SPS/PANI/Ag composites. Powder X-ray diffraction (XRD) patterns indicate that the obtained Ag nanoparticles are crystalline. The anticorrosive studies indicate that both SPS/PANI composites and SPS/PANI/Ag composites have enhanced anticorrosive properties in comparison to pure PANI in various corrosive environments. Please find the scheme in PDF format.

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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.013
GPT teacher head0.275
Teacher spread0.262 · 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

Citations24
Published2018
Admission routes1
Has abstractyes

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