Mitigation of corrosion of zinc with amylopectin: Electrochemical, adsorption, and surface studies
Bibliographic record
Abstract
Abstract Carbohydrate polymers are proven to be potential green corrosion inhibitors because of their outstanding structural features and eco‐friendliness. Work undertaken reflects the effectiveness of biopolymer amylopectin (AMP) as an eco‐friendly green inhibitor to mitigate the deterioration of zinc in 0.1 M sulphamic acid (NH 2 SO 3 H). Electrochemical studies like potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS) were applied for corrosion rate measurement in the absence and presence of AMP. Thermodynamic and kinetic parameters were calculated and described in detail. The results were fitted into a suitable adsorption isotherm model, and an appropriate mechanism was proposed for the corrosion inhibition process. A detailed surface morphology study was done using scanning electron microscopy (SEM), electron‐dispersive X‐ray (EDX), and atomic force microscopy (AFM) techniques. The inhibition efficacy of AMP increased with an increase in its concentration and temperature. Upon the addition of the inhibitor, corrosion potential shifted more towards the positive side, indicating more control of the anodic process. The adsorption of AMP over zinc conformed to the Freundlich adsorption isotherm. For the concentration of 0.1 gL −1 , maximum corrosion inhibition efficiency of 74% could be accomplished. Surface studies reaffirmed the adsorption of AMP on the surface of the metal. Thus, AMP turned out to be an effective green inhibitor with economic benefits.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".