MétaCan
Menu
Back to cohort
Record W2982814273 · doi:10.1139/cjc-2019-0368

Enhanced removal of Eriochrome Black T in wastewater by zirconium-based MOF/graphene oxide

2019· article· en· W2982814273 on OpenAlexvenueno aff
Jiaqi Bu, Yuan Lu, Yanling Ren, Yuexin Lv, Yong Meng, Xin Peng

Bibliographic record

VenueCanadian Journal of Chemistry · 2019
Typearticle
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsnot available
Fundersnot available
KeywordsAdsorptionEriochrome Black TChemistryLangmuir adsorption modelZirconiumGrapheneOxideInorganic chemistryZeta potentialFourier transform infrared spectroscopyNuclear chemistryX-ray photoelectron spectroscopyChemical engineeringOrganic chemistryNanoparticle

Abstract

fetched live from OpenAlex

The zirconium-based MOF/graphene oxide (UiO-66-NH 2 /GO) composites were prepared by ultrasonic dispersing different amounts of graphene oxide (GO) in a well-dissolved zirconium tetrachloride/H 2 BDC-NH 2 mixture, obtaining 2 wt% (UiO-66-NH 2 /GO-1), 5 wt% (UiO-66-NH 2 /GO-2), and 10 wt% (UiO-66-NH 2 /GO-3) GO composites. The products were characterized by XRD, FTIR, SEM, BET, Raman, UV, XPS, and Zeta potential. Adsorption experiments on simulated Eriochrome Black T (EBT) printing and dyeing wastewater were carried out using UiO-66-NH 2 /GO, and the optimal conditions for adsorption were obtained by exploring the effects of initial EBT concentration, time, pH, and salt ionic strength. Adsorption isotherms, kinetics, mechanism, and regeneration were also researched. The adsorption behavior was consistent with the Langmuir isotherm and fully compliant with pseudo secondary dynamics model. The adsorption capacity of UiO-66-NH 2 /GO-2 was found to be the highest of the three products, which was 263.158 mg/g. Therefore, the UiO-66-NH 2 /GO-2 composite was considered to be an excellent adsorbent for the adsorption of EBT from organic dye wastewater.

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 score0.002
Threshold uncertainty score0.466

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 head0.003
GPT teacher head0.166
Teacher spread0.163 · 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

Citations35
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of ChemistrySame topicGraphene and Nanomaterials ApplicationsFrench-language works237,207