Cysteine as an Alternative Eco-Friendly Corrosion Inhibitor for Absorption-Based Carbon Capture Plants
Bibliographic record
Abstract
Inorganic corrosion inhibitors are commonly applied to mitigate severe corrosion in absorption-based carbon capture plants. They are, however, not environmentally friendly, posing a health risk, harming the environment, and making chemical handling and disposal costly. Therefore, this study evaluated the corrosion inhibition performance of an amino acid, namely cysteine, with the aim of providing an eco-friendly alternative to the commercial inorganic corrosion inhibitors. Electrochemical and weight loss corrosion measurements showed that cysteine was effective in protecting carbon steel at all process operating conditions. At 80 °C, a 500 ppm cysteine could provide up to 83% and 99% inhibition efficiency under static and dynamic flow conditions, respectively. Its inhibition efficiency could be improved when the cysteine concentration, solution temperature, and flow condition were altered. Cysteine was an anodic corrosion inhibitor and underwent spontaneous, endothermic, and combined physical and chemical adsorption that followed the Langmuir adsorption isotherm model. The quantum chemical analysis indicated that cysteine had a high reactivity with metal surfaces due to its low energy gap and high dipole moment. The EDX analysis revealed a significant sulphur content on the metal substrate, indicating that cysteine's mercapto group played an integral role in forming an effective adsorption layer on the metal interface.
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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 | 0.001 | 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 | 0.001 |
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 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".