Debonding Performance of Various Cationic Surfactants on Networks Made of Bleached Kraft Fibers
Bibliographic record
Abstract
Debonding agents are applied in the paper industry for various purposes, for example, to increase the softness of tissue paper. In this work, the debonding capacities of three cationic aliphatic ammonium surfactants and one alkoxylated amine surfactant on kraft fibers were determined. The results showed that the adsorption of the alkoxylated amine surfactant (AAS) was higher than that of others on the fibers, but a cationic cetyltrimethyl ammonium surfactant (cetyltrimethyl ammonium bromide, CAB) was the most effective debonding agent, probably because of its relatively long hydrophobic chain. By applying CAB at levels of up to 20 mg/g, the tensile and burst indices were reduced by 37% and 41%, respectively. By applying AAS at levels of up to 20 mg/g, the tensile and burst indices of the networks were reduced by 18.6%, and 14.2%, respectively. The tear index of the fiber networks negligibly changed upon application of AAS, but increased by 19% upon application of CAB. The strain of the fiber networks prior to rupturing increased upon application of AAS, whereas it decreased upon application of CAB, which implies that the surfactants have different debonding mechanisms. The debonding efficiency of CAB was independent of both the refining revolutions and basis weights of the fiber networks.
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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 | 0.001 |
| 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".