Biosorption of Ni (II) from aqueous solution using Acer saccharum leaves (ASL) as a potential sorbent
Bibliographic record
Abstract
In this research work Powder of Acer Saccharum Leaves (PASL) were used as a non-conventional, cost-effective sorbent for removal of Ni (II) from aqueous solutions in batch systems. The sorbent was characterised with respect to surface area, pore volume, etc. It was found that PASL has high specific surface area (11.99 m2/g) and contains Ni (II) binding functional groups (e.g. Hydroxyl and Carboxyl).The biosorbent was characterized by FTIR, SEM and X-ray mapping, and the powder surface area was determined by BET. The optimum biosorption conditions were determined as a function of pH, biomass dosage, contact time, and initial metal ion concentration. The maximum Ni (II) biosorption was obtained at pH 4.0. The equilibrium data were better fit by the Langmuir isotherm and Freundlich isotherm models. The kinetic studies indicate that the biosorption process of the metal ion Ni (II) has followed well the pseudo-second order, avrami and intraparticle diffusion models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".