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Record W3049434730 · doi:10.17344/acsi.2020.5963

GO/PAMAM as a High Capacity Adsorbent for Removal of Alizarin Red S: Selective Separation of Dyes

2020· article· en· W3049434730 on OpenAlexaff
Mohammad Rafi, Babak Samiey, Chil-Hung Chen

Bibliographic record

VenueActa chimica slovenica · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAdsorptionThionineChemistryMethylene blueAmidoamineALIZARIN REDMethyl blueMethyl orangeDendrimerEthylenediamineAmine gas treatingNuclear chemistryAlizarinBenzidineMicroporous materialMoleculeInorganic chemistryOrganic chemistryPhotocatalysisCatalysis

Abstract

fetched live from OpenAlex

Adsorption of Alizarin Red S (ARS) on graphene oxide/poly(amidoamine) (GO/PAMAM) was studied at different ARS initial concentrations, temperatures, pHs, shaking rates and contact times. Adsorption sites of GO/PAMAM were phenolic -OHgroup of GO and amine groups (-NH2, -NH3+ and -NHR2+) of PAMAM dendrimer moieties of GO/PAMAM. At pH = 2 and 318 K, maximum adsorption capacity of the adsorbent was 1275.2 mg g-1 which is one of the highest capacity in the literature. Thus, GO/PAMAM in this work acted as a superadsorbent for ARS. At the incipient of adsorption, molecules were adsorbed on sites that was reaction-controlled step, (Ea = 114.5 kJ mol-1). Adsorption of on the remaining sites was diffusion-controlled. In alkaline media, two other types of ARS molecules were identified during that were adsorbed on and sites. Further increasing the pH of the solution, decreased the number these two sites and yielded a reduced adsorption capacity . Methylene blue (MB), thionine (Th), pyronin Y (PY), acridine orange (AO), methyl blue (MEB) and janus green (JG) dyes were selectively separated from their mixtures with ARS molecules using GO/PAMAM at pH of 2. The used adsorbent was recycled efficiently by using ethylenediamine very fast.

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 score0.056
Threshold uncertainty score0.501

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 studies0.0000.000
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 head0.020
GPT teacher head0.262
Teacher spread0.242 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

Explore more

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