Modification of clinoptilolite with dialkylphosphinic acid for the selective removal of cobalt ( <scp>II</scp> ) and nickel ( <scp>II</scp> ) from hydrometallurgical effluent
Bibliographic record
Abstract
Abstract This study describes the selective removal of cobalt (II) and nickel (II) from hydrometallurgical effluent using modified clinoptilolite. X‐ray fluorescence (XRF), x‐ray diffraction (XRD), scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR) analysis were used to characterize the natural and modified clinoptilolites with dialkylphosphinic acid. In a batch experiment, the influence of different variables such as initial concentration, pH, particle size, clinoptilolites dosage, temperature, and contact time was evaluated. Higher removal efficiency was obtained at initial concentration of 600 mg/L, pH 6, particles size of 1000‐1400 μm, dosage of 10 g/100 mL, a temperature of 25°C, and contact time of 300 minutes. The experimental data fit satisfactorily to the pseudo‐second order kinetic and to the Langmuir isotherm model. Thermodynamic parameters like Gibb's free energy (ΔG o ), enthalpy (ΔH o ), and entropy (ΔS o ) were also evaluated. The results revealed that modified clinoptilolite with dialkylphosphinic acid could be successfully employed for the selective removal of cobalt (II) and nickel (II) from hydrometallurgical effluent.
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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) | 0.000 | 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 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".