Experimental study and modelling of an azo colourant dynamic adsorption onto functional cross‐linked chitosan/ceramic particles in a fixed bed column
Bibliographic record
Abstract
Abstract The use of azo dyes in industrial activities generates a large volume of contaminated wastewater; these pollutants in water bodies affect aquatic biota and human health. A functional biocomposite sorbent material was synthesized using cross‐linked chitosan with oxalic acid that forms a coating on alumina ceramic particles (AOCh). The removal of Reactive Red 195, a reactive azo dye, using a fixed‐bed adsorption column filled with this material was tested. AOCh was physico‐chemically characterized by Fourier transform infrared spectroscopy–total attenuated reflection (FTIR‐ATR), scanning electron microscopy–energy dispersion spectrometry X‐ray (SEM‐EDS), X‐ray diffraction (XDR), thermo‐gravimetric analysis (TGA), and Z ‐potential. The dynamic adsorption performance was analyzed from experimental breakthrough curves obtained in fixed‐bed columns by modifying different operating conditions (bed depth, volumetric flow rate, and dye inlet concentration). Equilibrium adsorption isotherms were determined under dynamic conditions and compared with batch results. The maximum adsorption capacity of the dynamic equilibrium isotherm obtained from the continuous assays was 331 mg/g; this value was the highest in comparison to other tested materials reported in the literature. Different dynamic adsorption models were applied to fit experimental data, including Thomas, Bohart–Admas, Yoon–Nelson, logistic general model, bed depth surface time (BDST), and modified dose response (Yan) models. A critical analysis of these equations was presented, showing the equivalences and the relationship among the coefficients. The Yan model achieved the highest level of agreement between the experimental and predicted values of the breakthrough curves. The use of this model enables scaling‐up the industrial process for dye removal. The present work proposed a novel biosorbent material and contributes to the analysis of industrial dye removal under dynamic conditions.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".