Thermodynamic insights into Cr( <scp>VI</scp> ) removal: Comparative analysis of anion exchange resin performance
Bibliographic record
Abstract
Abstract This study reports the thermodynamic experimental data of an ion exchange process applied for Cr(VI) removal from solutions prepared to simulate electroplating wastewater. Two commercial strong base anion resins, Purolite PFA300 and SSTPFA63, were used to generate experimental data sets. Total liquid‐phase concentrations of 1.0 and 3.0 meq/L were used in the binary equilibrium experiments, and Cr(VI) concentrations ranging from 50 to 350 mg/L were used in the adsorption tests. The experimental data were fitted to the thermodynamic models of Langmuir, Freundlich, ideal law of mass action (LAM), and non‐ideal LAM to evaluate their capacities to calculate the equilibrium composition. The obtained equilibrium data showed a high affinity of the Cr(VI) species for the resin phase. An Hmx isotherm behaviour was observed for SSTPFA63 resin, with a decrease in Cr(VI) concentration after an ionic fraction of 0.12 for the liquid phase. The Langmuir and Freundlich models fitted the results reasonably well, with linear correlation coefficients greater than 0.90. In contrast, the LAM models poorly fitted the experimental results, and the inclusion of non‐ideal coefficients did not improve the data representation. The binary Cr(VI)‐Cl − data is indispensable for process engineers who wish to apply ion exchange in the industry due to the importance of understanding how the target contaminant behaves in different concentrations of counter‐ion. From this perspective, our binary data could assist in the implementation of technology for Cr(VI) recovery and the reduction of environmental impact.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".