Waste recovered caffeine as a sustainable corrosion inhibitor and therapeutic lead: A combined DFT, MD & docking approach
Bibliographic record
Abstract
This study highlights the dual functionality of caffeine extracted from spent coffee grounds as both a green corrosion inhibitor and a potential broad-spectrum therapeutic agent. Using DFT (B3LYP/6‑311G(d,p)), caffeine exhibited favorable electronic properties (ΔEgap = 4.42 eV, dipole moment = 5.21 D), supporting its strong adsorption capabilities. Molecular Dynamics simulations under acidic conditions confirmed stable, near-flat adsorption on Fe(110), Cu(111), and Al(111) surfaces, with significant interaction energies, indicating robust protective behavior. Simultaneously, molecular docking revealed promising binding affinities of caffeine to SARS-CoV-2 Mpro and Plasmodium falciparum kinases, suggesting antiviral and anti-malarial potential. This theoretical work demonstrates caffeine’s value as a sustainable, multifunctional molecule with applications in both corrosion control and drug discovery.
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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 | 0.000 | 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) | 0.000 | 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 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".