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Record W4383913305 · doi:10.1002/slct.202300833

Mn(II)‐ and Zn(II)‐ Based Nanocomposites Metallopolymer for Corrosion Protective Coatings

2023· article· en· W4383913305 on OpenAlexaff
Manawwer Alam, Anujit Ghosal, Fahmina Zafar, Mukhtar Ahmed, Mohammad Altaf

Bibliographic record

VenueChemistrySelect · 2023
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of Manitoba
FundersKing Saud University
KeywordsMaterials scienceNanocompositeCorrosionFourier transform infrared spectroscopyChemical engineeringScanning electron microscopePolymerPolymer nanocompositeDielectric spectroscopyCoatingMetal ions in aqueous solutionComposite materialMetalElectrochemistryMetallurgyChemistryPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

Abstract The transition from conventional polymeric material to green and sustainable, environment‐friendly biomaterials has been actively explored. The objective of the work involves the formulation of an oleo‐polymer (corn oil, CO), hybridization with metallic ions (Zn 2+ and Mn 2+ ) through coordination bonding, study of the impact of fully/half‐filled d‐orbitals, and development of eco‐friendly polymeric coating material. The formation of the nanoclusters (10–20 nm) within the polymeric matrix was estimated by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Fourier transform infrared (FTIR) analysis supports the predicted chemical mechanism involved during poly‐urethanation of the metal‐containing CO fatty amides. Interestingly, opposite to our expectations the metal ions with fully filled d‐orbital (Zn 2+ ) showed relatively (Mn 2+ ) better thermal and anti‐corrosion properties. The good adhesive strength of metallopolymers and the impact of the d‐orbital electrons on the corrosion protective performance, which was by electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization (PDP). These nanocomposite coatings could be a suitable alternatives to petrochemical‐based polymers.

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 score0.001
Threshold uncertainty score0.699

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 studies0.0010.000
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 head0.018
GPT teacher head0.262
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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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