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Record W3202282112 · doi:10.1071/en21091

Surface chemistry of bovine serum albumin with hematite nanoparticles and its effect on arsenate adsorption

2021· article· en· W3202282112 on OpenAlexaff
A.M. Eid, Shea Kraemer, Hind A. Al‐Abadleh

Bibliographic record

VenueEnvironmental Chemistry · 2021
Typearticle
Languageen
FieldEnergy
TopicIron oxide chemistry and applications
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsArsenateAdsorptionChemistryHematiteBovine serum albuminAttenuated total reflectionNanoparticleArseniteProtein adsorptionLangmuir adsorption modelInorganic chemistryArsenicChemical engineeringChromatographyInfrared spectroscopyOrganic chemistryMineralogy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.005
GPT teacher head0.188
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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