Simulation and Experimental Analysis of Microalgae and Membrane Surface Interaction
Bibliographic record
Abstract
The microalgae-induced membrane system applied in wastewater treatment has attracted attention due to microalgae’s outstanding nutrient fixation capacity and biomass harvesting. However, the fundamental understanding of the interaction of microalgae and membrane surfaces is still limited. This study presents experimental and numerical methods to analyze the attachment of microalgae to the membrane. An atomic force microscope (AFM) analysis confirmed that a polydimethylsiloxane (PDMS) sensor, as a simulated membrane surface, exhibited a rougher surface morphology than a polyurethane (PU) sensor did. The contact angle and adsorption analysis using a quartz crystal microbalance confirmed that the PDMS surface, representing the membrane surface, provided a better attachment affinity than the PU surface for microalgae because of the lower surface tension and stronger hydrophobicity of PDMS. The simulation studies of this work involved the construction of roughly circular-shaped particles to represent microalgae, rough flat surfaces to represent membrane surfaces, and the interaction energy between particles and surfaces based on XDLVO theory. The modeling results of the microalgae adsorption trend are consistent and verified with the experimental results. It was observed that the interfacial energy increased with increasing the size of particles and asperity width of the membrane surface. Contrarily, the predicted interaction energy dropped with elevating the number of asperities and asperity height of the microalgae and membrane. The most influential parameter for controlling interfacial interaction between the simulated microalgae and membrane surface was the asperity height of the membrane; changing the height from 50 nm to 250 nm led to alteration in the primary minimum from −18 kT to −3 kT. Overall, this study predicted that the microalgae attachment depends on the size of the asperities to a great extent and on the number of asperities to a lesser extent. These results provide an insight into the interaction of microalgae and membrane surface, which would provide information on how the performance of microalgae-based membrane systems can be improved.
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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".