Carboxymethyl derivatives of flaxseed (<i>Linum usitatissimum</i> L.) gum: characterisation and solution rheology
Bibliographic record
Abstract
Summary Carboxymethyl ether moieties were introduced to flaxseed ( Linum usitatissimum L.) gum ( FG ) by reacting gum with monochloroacetic acid ( MCA ). Effects of ether forming reaction conditions, temperature, Na OH concentration and molar ratio of FG (anhydroxylose equivalent) to MCA , on the degree of substitution ( DS ) were investigated. Treatment at 70 °C, with 7.0 m Na OH , and molar ratio of MCA to FG of 10:1 over 3 h produced a polysaccharide with a DS of 0.824 ± 0.012. Both surface morphology and thermal behaviour were modified. Solutions of FG (0.5–4.0%, w/v) exhibited shear‐thinning behaviour where viscosity decreased with decreased FG concentration. After carboxymethylation, modified polymer solutions also exhibited shear‐thinning behaviour, where dynamic viscosity was lower than that of native FG solutions at the same concentration. The dynamic viscosities were lower for solutions of modified FG with higher DS and also lower for solutions made at lower FG concentrations (0.5–4.0%, w/v). A more liquid‐like behaviour with increasing FG DS was indicated as storage modulus G′ and loss modulus G″ decreased and became more frequency dependent. The substitution of hydroxyl groups with carboxymethyl groups suppressed entanglement between FG polysaccharide chains by reducing intermolecular association.
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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".