Synthesis and Characterization of Zinc Carboxylates as Aqueous Corrosion Inhibitors for Mild Steel and 2024, 6061, and 7075 Aluminum Alloys
Bibliographic record
Abstract
Synthesis and characterization of environmentally friendly metallo-organic corrosion inhibitors for protection of mild steel and certain aluminum alloys are being sought to replace carcinogenic hexavalent chromium based corrosion inhibitors. Given the toxicity and carcinogenicity of chromates, the purpose of this study is not just synthesizing any efficient corrosion inhibitors for certain alloys of certain metals to be applied in different environments, but also to find environmentally friendly corrosion inhibitors for successful chromate replacements. In this regard, the standard for an environmentally friendly inhibitor is considered as having acceptable or no toxicity compared to chromate inhibitors. Studying the reasons underlying the success of chromate inhibitors seems to be the first reasonable approach one might take before formulating chromate replacements. Therefore, synergistic combinations of carboxylic acids and zinc cation, which is well-known for its cationic inhibiting efficiency producing carboxylate salts of zinc with a general formula of (Zn) (hydroxyacid)2 are aimed to be synthesized and characterized. Practical results have proven the hypothesized advantages of zinc carboxylates correct and yielded that certain zinc carboxylates can be suitable corrosion inhibitors for certain metals and their alloys under specific circumstances.
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".