Scrutinizing the Nanostructural and Nanomechanical Features of Regenerated Cellulose Ultrafiltration Membranes
Bibliographic record
Abstract
Ultrafiltration (UF) membranes have been widely used for many separation processes in which high performance is required. Commercial regenerated cellulose UF membranes with variable molecular weight cut-offs were characterized by high performance atomic force microscopy (AFM) using the novel quantitative nanomechanical mapping mode and the versatility of its signal channels towards nanoscale features elucidation of the materials surface. In addition, Raman spectroscopy was applied in order to investigate some possible chemical behavior changes associated with the UF membranes’ cut-offs. Overall, the results showed that the proposed AFM method was reliable to gain qualitative and quantitative data at unprecedented nanoscale resolution and such information can be used to distinguish UF membranes according to their specific molecular weight cut-offs and properties even on situations in which the molecular behavior were not influenced by the UF membrane’ cut-off. This approach can be useful on quality control procedures of researchers and manufacturers producing or modifying these polymeric materials.
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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".