Adsorptive removal of anionic and non‐ionic surfactants from aqueous phase using <i>Posidonia oceanica</i> (L.) marine biomass
Bibliographic record
Abstract
Abstract BACKGROUND: In this study, the capability of low‐cost, renewable and abundant marine biomass Posidonia oceanica (L.) for adsorptive removal of anionic and non‐ionic surfactants from aqueous solutions have been carried out in batch mode. Several experimental key parameters were investigated including exposure time, pH, temperature and initial surfactant concentration. RESULTS: It was found that the highest surfactant adsorption capacities reached at 30 °C were determined as 2.77 mg g−1 for anionic NaDBS and as 1.81 mg g−1 for non‐ionic TX‐100, both at pH 2. The biosorption process was revealed as a thermo‐dependent phenomenon. Equilibrium data were well described by the Langmuir isotherm model, suggesting therefore a homogeneous sorption surface with active sites of similar affinities. The thermodynamic constants of the adsorption process (i.e. ΔG°, ΔH° and ΔS°) were respectively evaluated as − 8.28 kJ mol−1, 48.07 kJ mol−1 and − 42.38 J mol−1 K−1 for NaDBS and − 9.67 kJ mol−1, 95.13 kJ mol−1 and − 174.09 J mol−1 K−1 for TX‐100. CONCLUSION: Based on this research, valorization of highly available Posidonia oceanica biomass, as biological adsorbent to remove anionic and non‐ionic surfactants, seems to be a promising technique, since the sorption systems studied were found to be favourable, endothermic and spontaneous. Copyright © 2007 Society of Chemical Industry
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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".