Interaction Mechanisms and Predictions of the Biofouling of Polymer Films: A Combined Atomic Force Microscopy and Quartz Crystal Microbalance with Dissipation Monitoring Study
Bibliographic record
Abstract
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.
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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) | 0.000 | 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".