Study of equilibrium isotherms of biosorption of lead ions onto <i>Posidonica oceanica</i> biomass: estimation of steric and energetic parameters using a statistical mechanics approach
Bibliographic record
Abstract
Experimental adsorption isotherms of metal ions, such as Pb2+, from an aqueous solution onto raw and modified Posidonia materials with various contents of succinyl groups (from 29.8% to 39.2%) have been analyzed using a multilayer adsorption model. For such a purpose, a double layer model was selected to describe the adsorption process. The establishment of the model expression is based on a statistical physics treatment, and especially on the grand canonical formalism. We mainly introduce four parameters affecting the adsorption process, namely, the fraction or the number of adsorbed ions per site, the receptor site density, and the energetic parameters related to each adsorbed layer. The study of the anchorage number allows us to follow the evolution of the lead adsorption when varying the experimental conditions. The evolution of the effectively occupied receptor site density shows that the van der Waals and hydrogen bonds are involved during the adsorption process. The magnitudes of the adsorption energies indicate that lead ions are physisorbed onto Posidonia. Furthermore, the disorder during the adsorption process was followed via the investigation of the entropy. The adsorption process was spontaneous as it is indicated by the free adsorption enthalpy.
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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".