Use of Molecular Dynamics Tools for Simulating the Adsorption of Peptides on Metal Surfaces to Determine the Stability of Biocomposite Hybrid Material in a Recovery of Metal Particles Context
Bibliographic record
Abstract
The growing interest in sustainable development and circular economy has contributed with developing environmentally friendly technologies for recovering critical and strategic minerals (CSM). In this study, molecular modeling was employed to simulate the formation of peptide-metal biocomposites as an eco-friendly approach to recover CSM by adsorption. The molecular interactions involved in the adsorption of glutathione (GSH), which is a three-amino acid peptide (γ- l -glutamyl- l -cysteinylglycine), onto the surfaces of particles of palladium (Pd), platinum (Pt) and gold (Au) were simulated. The modeling process was performed in several steps, comprising the molecular structure construction (with the software Avogadro), the molecular volume (with spartan’20), the volume of control (with Packmol.exe), the molecular interaction in the volume of control (with Tinker9), and the visualization of adsorbed molecules (with VMD). The adsorption conditions for simulations were temperature of 298 K, pressure of 1 atm, and pH 7 for the neutralized form of GSH. The number of peptides adsorbed, counted with VMD, was determined with the criterion that peptides located at 3.5 Å or less away from the surface of metals were considered adsorbed. The Langmuir isotherm fitted better the simulation data for three metals than Freundlich isotherm, and the calculated maximum adsorption capacity of Pd, Pt and Au was 72, 42, and 46 mg of GSH/g of metal, respectively. The adsorption energy of GSH on Pd, Pt and Au surfaces was calculated simulating the interactions among the chemical species present in the control volume and doing an energy balance. This adsorption energy ranged from − 27 to − 4 kcal/mol was in accordance close proximity to data reported in the literature for the adsorption energy of peptides with the three metals tested, confirming that the modeling procedure developed in this research is appropriate for calculating main adsorption parameters of peptide adsorption on metal surfaces.
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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 | 0.001 |
| 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".