Probing atomic-scale processes at the ferrihydrite-water interface with reactive molecular dynamics
Bibliographic record
Abstract
Interfacial processes involving metal (oxyhydr)oxide phases are important for the mobility and bioavailability of nutrients and contaminants in soils, sediments, and water. Consequently, these processes influence ecosystem health and functioning, and have shaped the biological and environmental co-evolution of Earth over geologic time. Here we employ reactive molecular dynamics simulations, supported by synchrotron X-ray spectroscopy to study the molecular-scale interfacial processes that influence surface complexation in ferrihydrite-water systems containing aqueous $${\text {MoO}_4}^{2-}$$ MoO 4 2 - . We validate the utility of this approach by calculating surface complexation models directly from simulations. The reactive force-field captures the realistic dynamics of surface restructuring, surface charge equilibration, and the evolution of the interfacial water hydrogen bond network in response to adsorption and proton transfer. We find that upon hydration and adsorption, ferrihydrite restructures into a more disordered phase through surface charge equilibration, as revealed by simulations and high-resolution X-ray diffraction. We observed how this restructuring leads to a different interfacial hydrogen bond network compared to bulk water by monitoring water dynamics. Using umbrella sampling, we constructed the free energy landscape of aqueous $${\text {MoO}_4}^{2-}$$ MoO 4 2 - adsorption at various concentrations and the deprotonation of the ferrihydrite surface. The results demonstrate excellent agreement with the values reported by experimental surface complexation models. These findings are important as reactive molecular dynamics opens new avenues to study mineral-water interfaces, enriching and refining surface complexation models beyond their foundational assumptions. Graphic Abstract
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".