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Record W2119399059 · doi:10.1002/ep.10566

Retention of copper and nickel from aqueous solutions using manganese oxide‐coated burned brick

2011· article· en· W2119399059 on OpenAlexaff
Nesrine Boujelben, Mbarka Gouider, Z. Elouear, Jalel Bouzid, M. Feki, Américo Montiel

Bibliographic record

VenueEnvironmental Progress & Sustainable Energy · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsTellabs (Canada)
Fundersnot available
KeywordsSorptionFreundlich equationNickelCopperAdsorptionAqueous solutionManganeseLangmuirChemistryLangmuir adsorption modelDiffusionInorganic chemistryMetallurgyMaterials scienceThermodynamicsPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract Experiments were conducted to evaluate copper and nickel sorption on artificially manganese oxide‐coated burned brick (MCBb), a waste by‐product of brick industry. The effect of metal concentration, contact time, solution pH, and temperature on the amount of Ni(II) and Cu(II) sorbed was studied and discussed. Langmuir and Freundlich isotherm constants and correlation coefficients for the present systems at different temperatures were calculated and compared. The equilibrium process was well described by the Langmuir isotherm model: the maximum sorption capacities (at 293 K) were 2.4 mg Ni/g and 3.7 mg Cu/g for MCBb. Isotherms were also used to evaluate the thermodynamic parameters (Δ G °, Δ H °, and Δ S °) of adsorption. The sorption kinetics was tested for the pseudo‐first order, pseudo‐second order, and intraparticle diffusion models. Good correlation coefficients were obtained for the pseudo‐second‐order kinetic model, showing that nickel and copper uptake process followed the pseudo‐second‐order rate expression. © 2011 American Institute of Chemical Engineers Environ Prog, 2011

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.549
Threshold uncertainty score0.998

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)0.0030.000

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.

Opus teacher head0.019
GPT teacher head0.203
Teacher spread0.183 · 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 teacher head, not a consensus.

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

Citations5
Published2011
Admission routes1
Has abstractyes

Explore more

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