Kinetic analysis of sucrose activated carbon for nutrient removal in water
Bibliographic record
Abstract
Abstract A renewable, green activated carbon made from sucrose (sugar) was compared with traditional bituminous coal-based granular activated carbon (GAC). Single and multi-component competitive adsorption of nitrate and phosphate from water was investigated. Langmuir and Freundlich isotherm models were fitted to data obtained from the nitrate and phosphate adsorption experiments. Nitrate adsorption fits closely to either Freundlich or Langmuir model for sucrose activated carbon (SAC) and GAC with a Langmuir adsorption capacity of 7.98 and 6.38 mg/g, respectively. However, phosphate adsorption on SAC and GAC demonstrated a selective fit with the Langmuir model with an adsorption capacity of 1.71 and 2.07 mg/g, respectively. Kinetic analysis demonstrated that adsorption of nitrate and phosphate follow pseudo-second-order kinetics with rate constant values of 0.061 and 0.063 g/(mg h), respectively. Competitive studies between nitrate and phosphate were demonstrated in preferential nitrate removal with GAC and preferential phosphate removal with SAC. Furthermore, nitrate and phosphate removals decreased from 75% removal to 35% removal when subject to multi-component solutions, which highlights the need for adsorption analysis in complex systems. Overall, SAC proved to be competitive with GAC in the removal of inorganic contaminants and may represent a green alternative to coal-based activated carbon.
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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".