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A Preliminary Study on the Removal of Methylene Blue from Aqueous Solution using Moringa Pods as Bioadsorbent under Column Operation

2017· article· en· W2626195385 on OpenAlexvenueno aff
Mirla Rodríguez, Saúl Flores, Alexandra Argotte

Bibliographic record

VenueJournal of Applied Solution Chemistry and Modeling · 2017
Typearticle
Languageen
FieldMedicine
TopicHealthcare and Environmental Waste Management
Canadian institutionsnot available
Fundersnot available
KeywordsMethylene blueChemistryAdsorptionPoint of zero chargeAqueous solutionFourier transform infrared spectroscopyAnalytical Chemistry (journal)Nuclear chemistryScanning electron microscopeInfrared spectroscopyChromatographyMaterials scienceChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Moringa pods (MP) was used as bioadsorbent to remove methylene blue (MB) from aqueous solutions under fixed-bed column operation. The bioadsorbent was analyzed by Fourier transform infrared spectroscopy (FT-IR) and scanning electron microscopy (SEM). Surface area (Brunauer-Emmet-Teller) and point of zero charge (pHPZC) were determined. In this study, pH influence (2.0 to 10.0) was evaluated, keeping constant conditions of bed height (11.0 cm), bioadsorbent dose (2.0 g), dye concentration (20 mg L-1), volume (25 mL), contact time (24 h) and room temperature. The MB concentration was determined using UV-spectrophotometry at 662 nm. The results showed that the adsorption was pH dependent. The highest dye removal occurs at pH 7.0 (˜100%). Infrared spectrum and morphological changes observed by SEM indicate the existence of bioadsorption phenomenon. An adsorption mechanism possible by intermolecular interactions was proposed. The study revealed the applicability of MP for removal of MB at low cost, efficient, eco-friendly and relatively neutral pH.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.670
Threshold uncertainty score0.377

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 studies0.0000.000
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 head0.093
GPT teacher head0.342
Teacher spread0.248 · 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 designSimulation or modeling
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

Citations0
Published2017
Admission routes1
Has abstractyes

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