Cadmium Biosorption by S. fluitans: Treatment, Resilience and Uptake Relative to Other Sargassum spp. and Brown Algae
Bibliographic record
Abstract
Abstract Species of the brown algae Sargassum have been targeted for use in the implementation of strategies to remediate toxic heavy metal contamination in effluents and drinking waters. This work focusses on some of the intrinsic physico-chemical properties of the algal material and aspects of the sorption mechanism, in particular: their maximal metal uptake, the influence of particle size and their resilience to leaching during equilibrium batch experiments. In addition to S. fluitans, the database on cadmium uptake capacities by Sargassum is extended to include S. thunbergii and S. oligocystum, and these are compared to those of two common brown algae. Results of our experiments demonstrate that cadmium sorption is independent of the range of particle sizes investigated (<2 and 3–6 mm), thereby indicating that sorption is not a function of the specific surface area of the biomass exposed to the solution. Dissolved organic carbon (DOC) analyses reveal that leaching to the cadmium solutions during the metal sorption reaction is independent of the biomass preparations used to obtain the two size fractions but decreases with increasing final cadmium concentration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".