The Relation Between Bubble-Fibre Interaction and Material Properties in Foam Forming
Bibliographic record
Abstract
Foam forming of cellulose fibre materials is based on an interaction between fibres and bubbles, which can take several material properties to new levels. To control the formed structure, the mechanisms of this interaction have been systematically investigated. This started with captive bubble studies where we analysed the interaction of a single bubble with various smooth cellulose and silica model surfaces. The bubbles adhered only to hydrophobic surfaces, and this attraction was sensitive to the surface tension. From this simplest case, the studied system gradually became more complex. We found that a bubble adheres weakly also to a submerged cellulose nanofibre (CNF) film, which could be explained by nanoscale surface roughness capturing nanobubbles. The interaction with real fibres was studied by pressing a single bubble against a fibre bed in water and sodium dodecyl sulphate (SDS) solution. Fibre type and surface tension had all apparent effects on the attachment. In the case of natural fibres, the presence of hydrophobic lignin clearly increased the fibre attachment on a bubble, while added SDS decreased the attachment with all fibre types. These findings agreed with the mechanisms found earlier using the model surfaces. Finally, when forming thick nonwoven materials using hydrophilic and hydrophobic viscose fibres, differences in fibre network structure and strength properties depended on the fibre hydrophobicity and surfactant type, as suggested by the results obtained in simpler systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".