Sustainable Corrosion Prevention: A Theoretical Synthesis of Renewable Materials and Bio-Inhibitors
Bibliographic record
Abstract
Corrosion is a pervasive issue with significant environmental and economic impacts, necessitating the development of sustainable and effective prevention methods. This review synthesizes the literature on renewable materials and bio-inhibitors, both of which offer environmentally friendly alternatives to traditional chemical inhibitors. Renewable materials such as biopolymers and natural fibers provide durable, biodegradable barriers against corrosion, while bio-inhibitors, derived from plants and microbes, offer chemical protection by neutralizing corrosive agents. This paper proposes a new theoretical framework that integrates these two approaches, leveraging the strengths of renewable materials as carriers for bio-inhibitors to enhance corrosion resistance. The potential for synergistic effects between the two components is explored, highlighting their combined capability to offer long-term, eco-friendly corrosion protection across diverse industrial applications. Future research directions focus on improving these materials' scalability, durability, and customization for specific environmental conditions. By advancing the development of sustainable corrosion prevention systems, this integrated approach contributes to global efforts to reduce environmental pollution and improve material longevity.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".