Bibliographic record
Abstract
The paper presents the results of laboratory tests concerning the possibility of utilizing activated carbon produced in Poland and nonconventional adsorbents, such as modified Clarion clay and clinoptylolite, for removing organic phenol from water. The effect of contact time and pH of the model solution on the adsorption process was assessed during tests conducted under static conditions. A contact time of 30 min was considered as optimum, and the highest efficiency of the adsorption process was achieved using a pH of 7.0. Adsorbent doses affected the course and effectiveness of the adsorption process. The adsorption processes were best described by the Freundlich isotherm. On the basis of the isotherms, the adsorptive capacity of tested adsorbents was calculated. Activated carbon had an adsorptive capacity of 13.22 g/kg, modified Clarion clay 1.24 g/kg, and clinoptylolite 0.23 g/kg. The through-flow conditions were achieved by column filtration. On the basis of breakthrough curves, the adsorptive capacities were higher than those determined through static conditions, namely 61.23, 23.56, and 2.99 g/kg for activated carbon, modified Clarion clay, and clinoptylolite, respectively. Despite their inferior adsorptive characteristics, the modified Clarion clay and clinoptylolite may be applied in filtration systems prior to filters with activated carbon.Key words: phenol, adsorption process, activated carbon, modified clay, clinoptylolite.
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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".