Metal Selectivity of <i>Sargassum</i> spp. and Their Alginates in Relation to Their α-<scp>l</scp>-Guluronic Acid Content and Conformation
Bibliographic record
Abstract
The discovery of a consistent and unusual enrichment in homopolymeric alpha-L-guluronic acid G-blocks in alginates extracted from a suite of Sargassum brown algae is described in this study. 1H NMR spectroscopy was used to characterize these alginates which display homopolymeric guluronic acid block (G-block) frequency values (F(GG)) between 0.37 and 0.81. The presence of these G-blocks results in an enhanced selectivity for cadmium or calcium relative to monovalent ions such as sodium and the proton as well as smaller divalent ions such as magnesium. Results of competitive exchange experiments for the Cd-Ca-alginate system yield selectivity coefficient, K*(Cd)Ca, values between 0.43 +/- 0.10 and 1.32 +/- 0.02 for a range in F(GG) of 0.23 to 0.81. In contrast to the Cd-Ca-alginate system, the Mg-Ca-alginate and Mg-Cd-alginate systems yielded maximum values of K*(Mg)Ca (18.0 +/- 1.4) and K*(Mg)Cd (16.0 +/- 0.9) for the alginates extracted from Sargassum fluitans (F(GG) = 0.81; Cuba) and Sargassum thunbergii (F(GG) = 0.75; Korea), respectively. Selectivity studies with mixed-metal pair alginate systems highlight the importance of the specific macromolecular conformation of the alginate polymer in determining metal binding behavior in multiple-metal systems. Furthermore, they demonstrate the importance of the conformation of the alginate as it occurs within the tissue of Sargassum in determining the metal binding behavior of this algal biosorbent. The unique composition of the alginates present in species of Sargassum may represent a distinct advantage over other brown algal species when considering their implementation for the strategic removal of toxic heavy metals from contaminated and industrial wastewaters.
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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) | 0.000 | 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".