Atomic force microscopy study of ultrafiltration membranes: solute interactions and fouling in pulp and paper processing
Bibliographic record
Abstract
Abstract Atomic force microscopy (AFM) has been used to quantify directly the interaction (adhesion) of cellubiose and cellulose with two polymeric ultrafiltration membranes of similar molecular weight cut‐off (MWCO) but different materials (ES404 and EM006, PCI Membranes, UK). Membrane ES404 is made from polyethersulphone alone and EM006 is made of a polyethersulphone–polyacrylate blend chosen specifically to increase the hydrophilic properties and decrease the fouling properties of the membrane. Cellubiose‐modified silica probes were used to quantify the interaction of cellubiose with the clean membranes. Pure cellulose probes were used to quantify the interaction of cellulose with both clean and cellubiose‐fouled membranes. All measurements were made in 10 −2 M NaCl solution. It was found that the cellubiose‐modified probes had three times greater adhesion with the ES404 than with the EM006 membrane. The pure cellulose probe also had greater adhesion with the ES404 membrane. Cellubiose fouling of both membranes gave close to an order of magnitude increase in the adhesion of the cellulose probe. The results show how AFM in conjunction with the colloid probe technique can elucidate surface interactions in solution, in particular how macromolecular adsorption can modify the subsequent adhesion of particulates. Copyright © 2002 John Wiley & Sons, Ltd.
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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) | 0.001 | 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".