A Fixed-Bed Column with an Agro-Waste Biomass Composite for Controlled Separation of Sulfate from Aqueous Media
Bibliographic record
Abstract
An agro-waste composite with a pelletized form was prepared and characterized via IR and 13C solids NMR spectroscopy. Thermal gravimetry analysis (TGA) was used to study the weight loss profiles, while SEM images provided insight on the biocomposite morphology, along with characterization of the sulfate adsorption properties under equilibrium and dynamic conditions. The sulfate monolayer adsorption capacity (qe = 23 mg/g) of the prepared agro-waste pellets was estimated from the adsorption isotherm results by employing the Langmuir model, and comparable fitting results were obtained by the Freundlich model. The dynamic adsorption properties were investigated via adsorption studies with a fixed bed column at pH 5.2. The effects of various parameters, including flow rate, bed height and initial concentrations of sulfate, were evaluated to estimate the optimal conditions for the separation of sulfate. The experimental data of the breakthrough curves were analyzed using the Thomas and Yoon–Nelson models, which provided satisfactory best-fits for the fixed bed kinetic adsorption results. The predicted adsorption capacities for all samples according to the Thomas model concur with the experimental values. The optimum conditions reported herein afford the highest dynamic adsorption capacity (30 mg/g) as follows: 1100 mg/L initial sulfate concentration, 30 cm bed height and 5 mL/min flow rate. The breakthrough time was measured to be 550 min. This study contributes to a strategy for controlled separation of sulfate using a sustainable biocomposite material that is suitable for fixed-bed column point-of-use water treatment systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".