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Record W2441787701 · doi:10.2166/wqrj.2004.027

Cadmium Biosorption by S. fluitans: Treatment, Resilience and Uptake Relative to Other Sargassum spp. and Brown Algae

2004· article· en· W2441787701 on OpenAlexaff
Thomas A. Davis, Fadi El Cheikh Ali, Elisa Giannitti, Bohumil Volesky, Alfonso Mucci

Bibliographic record

VenueWater Quality Research Journal · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsMcGill University
Fundersnot available
KeywordsSorptionSargassumCadmiumBrown algaeBiosorptionChemistryEnvironmental chemistryLeaching (pedology)AlgaeMetalBotanyAdsorptionEcologyBiologyOrganic chemistrySoil water

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.623
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.064
GPT teacher head0.360
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2004
Admission routes1
Has abstractyes

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