Animal Biopolymer-Plant Biomass Composites: Synergism and Improved Sorption Efficiency
Bibliographic record
Abstract
Pelletized biomaterial composites that contain chitosan (C) and torrefied wheat straw (S) at variable weight composition (C:S) were prepared using a facile blending process. The fractional content of the wheat straw was studied to elucidate the role of biomass on the pelletized product and effects of S-content on the physicochemical properties relevant to adsorption phenomena. Chitosan pellets (with and without S) were characterized by spectroscopic (FT-IR and 13C NMR) and thermal (TGA and DSC) techniques to provide support for their respective C:S composition. Confocal microscopy using fluorescein (FL) as a dye probe revealed the presence and an increase in the accessibility of the active sites for the composite pellets according to the S-content (wt %). Equilibrium and kinetic sorption studies using FL and reactive black (RB) dyes revealed an incremental adsorption affinity of the pellets with anionic dyes in variable charge states (FL and RB). The trend for dye adsorption parallels the incremental S-content (wt %) in the composite pellets. This study reports a first-example of a low-cost, facile, and sustainable approach for the valorization of straw and chitosan suitable for sorption-based applications in aqueous media.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".