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Record W2086380289 · doi:10.1080/09593332508618378

Biosorption of Heavy Metals by Red Algae (<i>Palmaria palmata</i>)

2004· article· en· W2086380289 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnvironmental Technology · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsMcGill University
Fundersnot available
KeywordsBiosorptionSorptionLangmuirFreundlich equationCadmiumCopperChemistryLangmuir adsorption modelAqueous solutionNuclear chemistryZincDry weightEnvironmental chemistryMetalAdsorptionBotanyBiology

Abstract

fetched live from OpenAlex

The biosorption of heavy metals from aqueous solutions was investigated, using a cheap and abundant dry biomass of red algae P. palmata. The Freundlich, Langmuir and Brunauer Emmer and Teller (BET) models were used to describe the uptake of lead (pb2+), copper (Cu2+), nickel (Ni2+), cadmium (Cd 2+) and zinc (Zn2+) on P. palmata. The good fits of the Langmuir and BET models to the experimental data reflected that the sorption on P. palmata was a multi-layer sorption, in which a Langmuir equation could be applied to each layer. The highest maximum sorption capacity q(max), derived from the Langmuir model was 15.17 mg g(-1) for lead and 6.65 mg g(-1) for copper (dry weight metal/dry weight biosorbent) at a pH of 5.5-6. The affinity of metals for P. palmata was found to decrease in the order: Pb2+ > Cd2+ > Cu2+ > Ni2+. The factors influencing copper and lead uptake were found to be contact time, pH, initial concentration and temperature. Biosorption of copper and lead was a rapid process, with 70% and 100% of the respective uptakes occurring within the first 10 minutes.

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.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.002

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 head0.004
GPT teacher head0.192
Teacher spread0.187 · 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