Hybrid Aerogel Nanocomposite of Dendritic Colloidal Silica and Hairy Nanocellulose: an Effective Dye Adsorbent
Bibliographic record
Abstract
In this study, a new type of silica-cellulose hybrid aerogel was synthesized through a green and facile chemical cross-linking process. In a first step, dendritic fibrous nanostructured (colloidal) silica particles (DFNS) were prepared by a simple hydrothermal technique. Then, the surface of DFNS particles was functionalized with amine groups using 3-aminopropyltriethoxysilane to produce DFNS-NH 2 . In a second step, bifunctional hairy nanocellulose (BHNC) particles were functionalized with both aldehyde and carboxylic groups. The aldehyde groups of BHNC and the amine groups of DFNS-NH 2 chemically reacted through a Schiff base reaction to form a hybrid hydrogel nanocomposite. Therefore, no external cross-linker is required in the synthesis. This hybrid aerogel is very lightweight and highly porous with a density of 0.107 g mL –1 and a porosity of 93.0 ± 0.4%. It has a large surface area of 350 m 2 g –1, a large pore volume of 0.23 cm 3 g –1, and a small pore size of 3.9 nm. The developed aerogel contains both positively and negatively charged functional groups and is a highly efficient substrate for dye adsorption from water, for both cationic and anionic organic dyes. These aerogels were found to have an outstanding adsorption capacity toward methylene blue (MB) as a cationic dye and methyl orange (MO) as an anionic dye. The results show that the aerogels can adsorb MB and MO with a capacity of 270 and 300 mg dye/g adsorbent, respectively.
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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".