Plant extracts as sustainable and green corrosion inhibitors for protection of ferrous metals in corrosive media: A mini review
Bibliographic record
Abstract
Application of green corrosion inhibitors, which reduce corrosion rates to the appropriate level with low environmental impact, is one of the emerging key approaches of controlling corrosion in modern society. From the standpoint of environmental compatibility, this research field is undergoing significant developments. Nowadays, due to increasing ecological awareness, corrosion inhibitors are subject to stringent restrictions and regulations enforced by environmental agencies in a number of nations. According to these requirements, these chemicals must be environmentally acceptable and safe. In light of this, intensive research has been undertaken in recent years aimed at development of green corrosion inhibitors from plant extracts. Being readily available, inexpensive, biodegradable, and safe make these substances promising alternatives to the hazardous conventional corrosion inhibitors. The purpose of this review article is to summarize, in a brief manner, a compilation of recent prominent papers on utilizing plant extracts as sustainable and green corrosion inhibitors. In addition, some discussions were made on the benefits and drawbacks of employing these substances for protection of metals.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".