Valorization of oat hulls and spent coffee grounds into pyridinium-modified granular adsorbents for water purification
Bibliographic record
Abstract
Agro- and food-waste biomass such as oat hull (Oh) or spent coffee ground (SCG) biomass can yield sustainable adsorbents for water treatment. However, adsorbents in powdered form often face constraints in practical applications (column applications due to backpressure, ease of handling and recovery), which can be alleviated with granular adsorbents that may have lower surface area and adsorption capacity. Granular adsorbents were prepared from 50% Oh or SCG, 10% Kaolinite (K), and 40% chitosan (Chi) as binder that offer active site for surface modification. Surface modification via crosslinking and furfuryl-pyridinium (Py) led to adsorbents SCG50-Py and Oh50-Py. These adsorbents were assessed to remove methyl orange (MO; anionic dye) or methylene blue (MB; cationic dye) from water. Materials characterization employed ^13 C solids NMR and FT-IR spectroscopy, thermogravimetry and solvent swelling (water & cyclohexane). Dye adsorption isotherms employed the Sips isotherm model to characterize the adsorption parameters. The water uptake of SCG50-Py and Oh50-Py was 100%, while the weight increased for Oh50-Py by 16% and SCG50-Py by 8% in cyclohexane. Prior to furfuryl-pyridinium modification, electrostatic forces dominated the adsorption process where either MO or MB dye adsorption occurred. Upon modification, SCG50-Py showed 74 mg g ^−1 MO and 24 mg g ^−1 MB dye adsorption capacity, whereas Oh50-Py observed 121 mg g ^−1 MO and 17 mg g ^−1 MB dye adsorption capacity. Surface modification via chemical crosslinking favored stable Oh50-based adsorbents, while the Py modification resulted in dual adsorption of anionic and cationic dyes. SCG50-based adsorbents observe higher stability, as compared to Oh50-based adsorbents without crosslinking. Valorization of under-utilized biomass for concerted MO and MB removal was achieved through a facile granulation and surface modification strategy. Future planned kinetic adsorption studies are anticipated to provide mechanistic insight, along with adsorbent reusability studies in laboratory and environmental water sources to further establish the utility of these systems for practical applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".