Adsorption of indium by waste biomass of brown alga Ascophyllum nodosum
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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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) | 0.001 | 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 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".