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Record W2974628243 · doi:10.2166/wst.2019.317

Reactive adsorption of Safranin O: surface – pore diffusion modeling and degradation study

2019· article· en· W2974628243 on OpenAlexaff
Komal Sharma, Raj K. Vyas, Kailash Singh, Ajay K. Dalai

Bibliographic record

VenueWater Science & Technology · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAdsorptionSafraninChemistrySorptionMass transfer coefficientReaction rate constantActivated carbonHydrogen peroxideDiffusionMass transferAnalytical Chemistry (journal)DesorptionLangmuir adsorption modelActivation energyKineticsChromatographyPhysical chemistryThermodynamicsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Granular activated carbon was doped with iron (Fe-AC) and was used to study the removal of Safranin O (SO) using the Fe-AC/H2O2 system for reactive adsorption and Fe-AC for adsorption. Fe-AC and H2O2 doses were optimized to obtain maximum removal of SO. Maximum removal was found to be 96.1% after 5 h using 1.0 g/L Fe-AC and 5.0 mM hydrogen peroxide doses for 10 mg/L initial SO concentration. Kinetic study suggested the suitability of the pseudo-first-order model for reactive adsorption. The Langmuir isotherm explained well the sorption of SO onto Fe-AC. Parallel-pore-reactive adsorption model was applied and validated. By fitting the experimental data to the model, it is observed that the surface reaction rate coefficient, kr, was found to be five times that of the apparent rate constant, kapp. Parameters such as the external liquid film mass transfer coefficient, macro-pore and micro-pore diffusivities were estimated by regression analysis. Pore diffusion and surface reaction were found to be rate controlling for adsorption and reactive adsorption, respectively. An oxidative degradation of SO took place via hydroxylation and ring cleavage processes.

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 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 score0.247
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.222
Teacher spread0.213 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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