Formulation of silica‐based corn oil transformed polyester acryl amide‐phenol formaldehyde corrosion resistant coating material
Bibliographic record
Abstract
Abstract It is important to check the economic losses due to spontaneous metallic corrosion of major infrastructures, precious equipment's, and ensure the sustainable growth. Fabrication of environmentally benign functional polymeric material by transforming agricultural waste and products into value added polymeric products is of great importance in recent times. Here, we report the formulation of functional hybrid polymeric material using corn oil (CO) as our starting material. The functionality of CO was improved by green route, to produce corn diol fatty amide (HECA). Functionalization of CO was achieved in systematic manner by following green chemistry approach. First, diol fatty amide (HECA) was prepared followed by conversion of terminal OH groups of HECA into acrylic ester using condensation reaction with acrylic acid (AA). The base polymeric moiety was further modified to include phenyl acetylene functionality and SiO2 nanoparticle entities. The formulation of hydrophobic stable polymeric structure is important to sustain highly corrosive environment and long‐lasting performance of the material. The inclusion of different functionality and hydrophobic groups were confirmed by Fourier‐transform infrared and proton nuclear magnetic resonance spectroscopies. The baked coating was finalized by addition of phenol formaldehyde polymeric resin in different phr (part per hundred part of resin). The transmission electron microscopy, scanning electron microscope, energy dispersive X‐ray spectroscopy, and elemental mapping of the coating material confirms the formation of a hybrid material with uniform distribution of inorganic and organic components. The potentiodynamic polarization and electrochemical impedance spectroscopic analysis verified the better resistive performance of inorganic hybrid over the counter organic coatings. The enhanced thermal stability, hydrophobic character, and corrosion resistance performance of CO based coating material indicates the potential of the synthesized green route materials.
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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".