Innovative in situ investigations using synchrotron‐based micro tomography and molecular dynamics simulation for fouling assessment in ceramic membranes for dairy and food industry
Bibliographic record
Abstract
Abstract Proteins in dairy streams result in organic fouling and function loss for ceramic membrane's porous structure. Synchrotron‐based X‐ray microtomography (SR‐µCT) is a new method with a high signal to noise ratio and accordingly significant level of accuracy. The goal of this study was to perform an in situ assessment of ceramic membrane fouling in the dairy stream filtration process, using SR‐µCT. This study attempted to assess porosity variation and membrane fouling through different layers from the top, middle, and bottom layers of the ceramic microfiltration membrane before and after skimming milk filtration. Molecular dynamics simulation (MDS) was used for depth understanding of milk protein interactions with the ceramic membrane. Fouling was found to be more intense on the top of the ceramic membrane even though the top layer was slightly more porous, which indicates that top layers were more prone to fouling. A regression model was derived to correlate the porosity loss due to membrane fouling at different membrane thicknesses. The MDS results showed more affinity of the milk proteins to the ceramic membrane compared to water molecules. The MDS studies showed how the presence of the milk's protein macromolecules could change the hydrophilicity trend on the membrane's surface.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 source (direct Gemma or distilled Codex), 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".