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Metal biosorption equilibria in a ternary system

2000· article· en· W2128117966 on OpenAlexaff
K. H. Chong, Bohumil Volesky

Bibliographic record

VenueBiotechnology and Bioengineering · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsMcGill University
Fundersnot available
KeywordsTernary operationBiosorptionTernary numeral systemChemistryCopperThermodynamicsMetalZincAscophyllumMetal ions in aqueous solutionAqueous solutionBinary numberDissociation (chemistry)SorptionBinary systemInorganic chemistryAdsorptionPhysical chemistryAlgaePhysicsOrganic chemistryMathematicsBotany

Abstract

fetched live from OpenAlex

Equilibrium metal uptake performance of a biosorbent prepared from Ascophyllum nodosum seaweed biomass was studied using aqueous solutions containing copper, cadmium, and zinc ions in binary and ternary mixtures. Triangular equilibrium diagrams can graphically represent all the ternary equilibrium sorption data. Application of the multicomponent Langmuir model to describe the three-metal system revealed its nonideal characteristics, whereby the value of apparent dissociation constants for the respective metals differed for each system. This restricted the prediction of the ternary equilibria from the binary systems. However, some predictions of the ternary system behavior from the model were consistent with experimental data and with conclusions postulated from the three possible binary subsystems. (c) 1996 John Wiley & Sons, Inc.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.003
GPT teacher head0.163
Teacher spread0.160 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations120
Published2000
Admission routes1
Has abstractyes

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