Dye Removal Using Hairy Nanocellulose: Experimental and Theoretical Investigations
Bibliographic record
Abstract
Adsorption is a common technique for the treatment of dye-contaminated wastewater. Achieving a high dye removal capacity is a common challenge with sustainable, low-cost adsorbents. Recently, a class of easily functionalized, biorenewable cellulose nanoparticles called hairy nanocellulose has been developed. Electrosterically stabilized nanocrystalline cellulose (ENCC), which can be synthesized from wood pulp through a two-step oxidation by periodate and chlorite, is a form of hairy nanocellulose with a high negative charge density, and thus has the potential for a high adsorption capacity. In this work, the adsorption of methylene blue, a cationic dye, by ENCC was shown to occur up to charge stoichiometry (1400 mg dye/g adsorbent), at which point aggregation of ENCC-dye complexes is observed. A model is developed to show that the adsorption can be described by an ion-exchange mechanism and is influenced by the presence of other ions. Equilibrium dye removal is reduced at both high ionic strengths and low pH. To facilitate handling, composite hydrogel beads of sodium alginate and ENCC (ALG-ENCC beads) are developed, and their methylene blue removal capacity is shown to maintain a high removal capacity (1250 mg/g). ALG-ENCC beads provide a facile way to employ these nanoparticles on a larger scale, providing a potential means for the removal of dyes and other contaminants at larger wastewater volumes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 teacher head, 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".