Organic Green Corrosion Inhibitors Derived from Natural and/or Biological Sources for Conservation of Metals Cultural Heritage
Bibliographic record
Abstract
Abstract In the last decade, there has been an increase in research related to green corrosion inhibitors for conservation of metals cultural heritage to help promote sustainable practices in the field that are safe, environmentally friendly, and ecologically acceptable. The most common are organic substances derived either from natural and/or biological sources: plant extracts and oils, amino acids, microorganisms, and biopolymers. The chapter will provide a review of these substances as corrosion inhibitors for metals conservation, by discussing the state-of-the-art research to date, with a special focus on cysteine. Most of the research has focused on the examination of such inhibitors on metal coupons with or without corrosion products using electrochemical techniques or weight-loss measurements to determine their effectiveness. Some of these studies have also considered the conservation principles for practice, i.e., reversibility of the treatment and the visual aspect of the modification of the treated metal surface. However, before such green inhibitors can be routinely applied by conservators, more research is required on their application to real artefacts/monuments using in situ corrosion measurements. Furthermore, given that the composition of a green inhibitor is highly dependent on its extraction process, research must also involve identifying the specific adsorption models and involved mechanisms to ensure reproducibility of results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.012 |
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".