Furfuryl-pyridinium-functionalization of flaxseed gum for effective methylene blue removal from aqueous solution
Bibliographic record
Abstract
This study describes the modification of flaxseed gum (FSG) via furfural to obtain a furfuryl-pyridinium modified adsorbent (Flax-Py) with improved adsorption characteristics towards methylene blue (MB) versus results obtained for unmodified FSG. Materials characterization was achieved via complementary spectral methods (NMR, X-ray photoelectron spectroscopy, FT-IR, Raman, and XRD) as well as thermogravimetric analysis (TGA) and ζ-potential measurements. Synthetic modification of FSG to yield Flax-Py that led to enriched N-content (2.75 atom-% vs. 0.76 atom-% for FSG), and the conversion of furfuryl to pyridinium was incomplete (∼50%), where both moieties were identified. The adsorption isotherms at pH 7 showed that FSG possessed favorably high MB adsorption capacity (∼540 mg/g), while the adsorption capacity of Flax-Py was notably lower (167 mg/g). Flax-Py facilitated 100% MB removal at low dye concentrations (1–10 mg/L) versus 50% MB removal for the FSG bioadsorbent. Further, Flax-Py showed moderate loss of adsorption with MB (∼12%–16% dye removal) after five regeneration cycles. Flax-Py revealed improved settling characteristics (time, density, gelation, and solubility) that facilitate enhanced phase separation of FSG fractions by circumventing the need for centrifugation. The adsorption mechanism was attributed primarily to H-bonding effects with secondary contributions due to electrostatic interactions (negative ζ-potential above pH 4.2). This study successfully modified FSG to obtain an improved adsorbent (Flax-Py) attaining nearly quantitative MB dye removal at environmentally relevant concentrations (<15 mg/L).
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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".