Mn(II)‐ and Zn(II)‐ Based Nanocomposites Metallopolymer for Corrosion Protective Coatings
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".