Preparation and characterisation of chitosan/bacterial <i>Escherichia coli</i> biocomposite for malachite green dye removal: modeling and optimisation of the adsorption process
Bibliographic record
Abstract
Herein, a new biocomposite was obtained by loading a bacterial suspension of Escherichia coli onto a chitosan matrix to produce a unique biocomposite (chitosan – E. coli) with effective properties for biosorption of malachite green dye from water. The physicochemical characteristics of the chitosan – E. coli biocomposite were studied by employing XRD, FTIR, FESEM-EDX and pHpzc. The optimisation of biosorption conditions was achieved using a Box-Behnken Design (RSM-BBD) to assess the effect of variables on the dye removal: biocomposite dose (0.02–0.1 g/100 mL), pH (4–8) and contact time (10–300 min). The optimal conditions for dye removal (84.3%) were achieved with a chitosan – E. coli dose of 0.1 g/100 mL, and a contact time of 160 minutes with a pH of 8. The equilibrium and kinetic experimental findings show the biosorption of malachite green dye by chitosan – E. coli follows the Langmuir and pseudo-second-order models, respectively. The maximum dye adsorption capacity (qmax) of malachite green for the chitosan – E. coli biocomposite was 164.7 mg/g, where the dye adsorption mechanism was attributed to several effects such as hydrogen bonding, n-π interactions, and electrostatic attraction. Thermodynamic analysis indicated that the biosorption process was spontaneous and endothermic, with a positive enthalpy change (ΔH° = 40.9 kJ/mol) and a negative Gibbs free energy (ΔG°) at all tested temperatures. The biocomposite chitosan – E. coli adsorbent exhibits favourable cationic dye adsorption that is anticipated to have utility for remediation of dye effluent in industrial wastewater.
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 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 | 0.000 | 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) | 0.000 | 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".