Adsorption of indium by waste biomass of brown alga Ascophyllum nodosum
Why this work is in the frame
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Bibliographic record
Abstract
The biosorption capacities of dried meal and a waste product from the processing for biostimulant extract of Ascophyllum nodosum were evaluated as candidates for low-cost, effective biomaterials for the recovery of indium(III). The use of indium has significantly grown in the last decade, because of its utilization in hi-tech. Two formats were evaluated as biosorbents: waste-biomass, a residue derived from the alkaline extraction of a commercial, biostimulant product, and natural-biomass which was harvested, dried and milled as a commercial, "kelp meal" product. Two systems have been evaluated: ideal system with indium only, and double metal-system with indium and iron, where two different levels of iron were investigated. For both systems, the indium biosorption by the brown algal biomass was found to be pH-dependent, with an optimum at pH3. In the ideal system, indium adsorption was higher (maximum adsorptions of 48 mg/g for the processed, waste biomass and 63 mg/g for the natural biomass), than in the double metal-system where the maximum adsorption was with iron at 0.07 g/L. Good values of indium adsorption were demonstrated in both the ideal and double systems: there was competition between the iron and indium ions for the binding sites available in the A. nodosum-derived materials. Data suggested that the processed, waste biomass of the algae, could be a good biosorbent for its indium absorption properties. This had the double advantages of both recovery of indium (high economic importance), and also definition of a virtuous circular economic innovative strategy, whereby a waste becomes a valuable resource.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it