MétaCan
Menu
Back to cohort
Record W2155585268 · doi:10.6000/1927-5129.2014.10.11

Biosortion of Cr(VI) from Aqueous Solutions Using Chemically Modified Okra Powder

2014· article· en· W2155585268 on OpenAlexvenueno aff
Joelington do C. Conceição, Antônio Santos Silva, Antônio Wilson M. de C. Costa, Edilson de Jesús, Victor Hugo S. Ramos

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsnot available
FundersUniversidade Federal de SergipeFundação de Apoio à Pesquisa e à Inovação Tecnológica do Estado de Sergipe
KeywordsAqueous solutionFreundlich equationAdsorptionLangmuirCitric acidChemistryAbsorbanceNuclear chemistryFourier transform infrared spectroscopyChromatographyChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

The main objective of this work was to use the okra powder modified with citric acid as biosorbent for the removal of Cr(VI) from aqueous solutions. Samples of Cr(VI) solution were prepared by diluting the 100 ppm solution diluted from the 1000 ppm stock solution prepared with K2Cr2O7. The determination of Cr(VI) was performed spectrophotometrically by the method of complexation with 1,5-diphenylcarbazide, measuring the absorbance at a 540 nm wavelength. The experiments were conducted at 25 ± 1 °C; 2, 5, 7 and 8 initial pH; 10-240 minutes contact time and 0.05 g of adsorbent. The Freundlich and Langmuir models were used to fit the experimental data. The Freundlich model provided R2=0.9599, indicating adsorption on heterogeneous conditions. Analysis of fluorescence of X-rays, SEM and FTIR were used to prove the okra powder efficiency in the removal of Cr(VI) from aqueous solutions.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.936

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)0.0010.000

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 head0.030
GPT teacher head0.244
Teacher spread0.214 · 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

Citations7
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Basic & Applied SciencesSame topicAdsorption and biosorption for pollutant removalFrench-language works237,207