Optimization of an adsorption process for tetrafluoroborate removal by zirconium (IV)-loaded orange waste gel from aqueous solution
Bibliographic record
Abstract
This investigation provides new insights into the effective removal of tetrafluoroborate (BF4-) by means of bio-sorption on waste generated in the orange juice industry. It was undertaken to evaluate the feasibility of zirconium (IV)-loaded saponified orange waste gel for BF4- removal from an aqueous solution. Batch adsorption experiments were carried out to study the influence of various factors such as pH, presence of competing anions, contact time, initial BF4- concentration and temperature on the adsorption of BF4-. The optimum BF4- removal was observed in the equilibrium pH region 2-3. The presence of coexisting anions showed no adverse effect on BF4- removal except SO4(2-). The equilibrium data at different temperatures were reasonably interpreted by the Langmuir adsorption isotherm and the maximum adsorption capacities were evaluated as 2.65, 3.28, 3.87 and 4.77 mmol g(-1) at 293, 298, 303 and 313 K, respectively. Thermodynamic parameters such as deltaGo, deltaHo and deltaSo indicated that the nature of BF4- adsorption is spontaneous and endothermic. The results obtained from this study demonstrate the potential usability of orange waste after juicing as a good BF4- selective adsorbent.
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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) | 0.001 | 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".