Surface chemistry of bovine serum albumin with hematite nanoparticles and its effect on arsenate adsorption
Bibliographic record
Abstract
Environmental context Hematite nanoparticles are efficient adsorbents for proteins and pollutants in environmental and biological systems. Hematite and the protein bovine serum albumin (BSA) were used as models to investigate the surface chemistry and competitive role of BSA in arsenate adsorption. Results show that surface BSA inhibits arsenate adsorption, potentially altering its mobility and bioavailability. Abstract The surface chemistry of metal oxide nanomaterials controls their health impacts and fate in environmental and biological systems. These systems contain proteins capable of binding to nanoparticles, which forms a protein corona that modifies the surface properties of the nanoparticles and reactivity towards pollutants. Using attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy, we investigate the adsorption of bovine serum albumin (BSA) and quantify the competitive effect of BSA on the adsorption kinetics of arsenate, AsV, to hematite nanoparticles. Experiments were conducted in the flow mode at pH 7. BSA was first adsorbed on hematite, then AsV was allowed to flow over the BSA/hematite thin film. Adsorption kinetic and thermodynamic parameters were calculated using a modified Langmuir adsorption model for both BSA and AsV. The adsorption thermodynamic model showed that BSA binds through two active sites with a binding energy of –41 kJ mol−1, which corresponds to the spontaneous formation of chemisorbed and physisorbed species. When AsV flowed over the BSA/hematite film, only 11 % of surface BSA was desorbed by AsV. This result highlights the inhibitory effect of BSA for AsV adsorption. Structural analysis of BSA revealed changes to the local conformational geometry upon adsorption to and desorption from hematite nanoparticles. Molecular docking simulations showed that the binding free energy of a modelled hematite nanoparticle towards the BSA surface is –6.8 kcal mol−1 (−28.5 kJ mol−1) owing to the formation of various bonds, which agrees with the adsorption kinetics modelling. Overall, surface BSA inhibits arsenate adsorption and therefore increases its mobility and bioavailability.
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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) | 0.001 | 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 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".