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Record W4367173367 · doi:10.1021/acs.langmuir.3c00587

Interaction Mechanisms and Predictions of the Biofouling of Polymer Films: A Combined Atomic Force Microscopy and Quartz Crystal Microbalance with Dissipation Monitoring Study

2023· article· en· W4367173367 on OpenAlexfundno aff
Asma Eskhan, Neveen AlQasas, Daniel Johnson

Bibliographic record

VenueLangmuir · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsnot available
FundersTamkeenYork UniversityNew York University Abu Dhabi
KeywordsDLVO theoryQuartz crystal microbalanceBiofoulingMicavan der Waals forceAdsorptionMembranePolymerAdhesionChemical engineeringHamaker constantChemistryColloidSurface forces apparatusFoulingPolymer adsorptionNanotechnologyMaterials scienceComposite materialOrganic chemistryMolecule

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Biofouling of polymeric membranes is a severe problem in water desalination and treatment applications. A fundamental understanding of biofouling mechanisms is necessary to control biofouling and develop more efficient mitigation strategies. To shed light on the type of forces that govern the interactions between biofoulants and membranes, biofoulant-coated colloidal AFM probes were employed to investigate the biofouling mechanisms of two model biofoulants, BSA and HA, toward an array of polymer films commonly used in membrane synthesis, which included CA, PVC, PVDF, and PS. These experiments were combined with quartz crystal microbalance with dissipation monitoring (QCM-D) measurements. The Derjaguin, Landau, Verwey, and Overbeek (DLVO) and the extended-DLVO (XDLVO) theoretical models were applied to decouple the overall adhesion interactions between the biofoulants and the polymer films into their component interactions, i.e., electrostatic (El), Lifshitz–van der Waals (LW), and Lewis acid–base (AB) interactions. The XDLVO model was found to predict better the AFM colloidal probe adhesion data and the QCM-D adsorption behavior of BSA onto the polymer films than the DLVO model. The ranking of the polymer films’ adhesion strengths and adsorption quantities was inversely proportional to their γ – values. Higher normalized adhesion forces were quantified for the BSA-coated colloidal probes with the polymer films than the HA-coated colloidal probes. Similarly, in QCM-D measurements, BSA was found to cause larger adsorption mass shifts, faster adsorption rates, and more condensed fouling layers than HA. A linear correlation ( R 2 = 0.96) was obtained between the adsorption standard free energy changes (Δ G ads ° ) estimated for BSA from the equilibrium QCM-D adsorption experiments and the AFM normalized adhesion energies ( W AFM / R ) estimated for BSA from the AFM colloidal probe measurements. Eventually, an indirect approach was presented to calculate the surface energy components of biofoulants characterized by high porosities from Hansen dissolution tests to perform the DLVO/XDLVO analyses.

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 categoriesnone
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 score0.048
Threshold uncertainty score0.214

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)0.0000.000

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 head0.007
GPT teacher head0.272
Teacher spread0.265 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations27
Published2023
Admission routes1
Has abstractyes

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