MétaCan
Menu
Back to cohort
Record W2806051086 · doi:10.4236/jeas.2016.82003

Adsorption Kinetics and Thermodynamics Study of Butylparaben on Activated Carbon Coconut Based

2018· article· en· W2806051086 on OpenAlexaff
Patrick Athéba, N’guadi Blaise Allou, Patrick Drogui, Albert Trokourey

Bibliographic record

VenueJournal of Encapsulation and Adsorption Sciences · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsAdsorptionFreundlich equationChemistryActivated carbonEnthalpyEndothermic processLangmuirBiocharStandard molar entropyThermodynamicsGibbs free energyAqueous solutionLangmuir adsorption modelStandard enthalpy of formationOrganic chemistryPyrolysis

Abstract

fetched live from OpenAlex

In this work, low cost coconut biochar based activated carbon (CBAC) was used for adsorption of Butylparaben (BPB) from aqueous medium. The prepared CBAC was characterized using BET, Boehm analysis and the adsorption equilibrium, kinetics and thermodynamics studies of BPB adsorption were carried out. During batch adsorption runs, the effects of factors, such as contact time (0 - 300 min), CBAC dose (200 - 800 mg), pH (3 - 11) and solution temperatures (303 - 348 K) were investigated on BPB removal. Experimental results reveal that the BPB removal efficiency on CBAC is higher than 97% under acidic and neutral conditions. Equilibrium data were fitted by Langmuir, Freundlich and Temkin isotherm models with correlation coefficient more than 0.9. The pseudo-second order kinetic model was observed to fit well the adsorption data. Thermodynamic analysis shows positive values of standard Gibb’s free energy, suggesting the non-spontaneity of the process. The changes in enthalpy (0.2 J.mol-1) and entropy (19 J.mol-1) were found to be endothermic with an increase of randomness. The high adsorption efficiency of the synthesized coconut biochar materials with low cost indicates that it may be a promising adsorbent for removing organic compounds.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.909
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.023
GPT teacher head0.274
Teacher spread0.251 · 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.

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

Citations20
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Encapsulation and Adsorption SciencesSame topicAdsorption and biosorption for pollutant removalFrench-language works237,207