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Record W2359944951

Studies on the Removal of Parafuchsin from Aqueous Solution by Calcined-β-CD-Fe-LDHs and its Kinetics

2014· article· en· W2359944951 on OpenAlexaff
Zhang Ku

Bibliographic record

VenueFain kemikaru · 2014
Typearticle
Languageen
FieldMaterials Science
TopicLayered Double Hydroxides Synthesis and Applications
Canadian institutionsScience North
Fundersnot available
KeywordsCalcinationCoprecipitationLayered double hydroxidesKineticsAqueous solutionPhotocatalysisChemistryCrystallizationChemical engineeringNuclear chemistryMaterials scienceInorganic chemistryCatalysisPhysical chemistryHydroxideOrganic chemistry
DOInot available

Abstract

fetched live from OpenAlex

In this article,β-cyclodextrin(β-CD) intercalated ferrum-containing hydrotalcite was fleetly prepared by the technology of microwave-crystallization and low saturated state of coprecipitation.The purposed sample of β-CD-Fe-LDHs and calcined-β-CD-Fe-LDHs were characterized by means of XRD,IR and SEM.The results show that the synthesized β-CD-Fe-LDHs with the flexible slice have the structure of LDHs.The calcined product is in a uniform particle state.Calcined-β-CD-Fe-LDHs were then applied to the removal of parafuchsin.The effects of photocatalyst types,reaction temperature,parafuchsin mass concentration and cycle life on the parafuchsin removal were investigated.It is shown that the percentage of removal for parafuchsin solution of 50 mg / L can reach 95.70% under the optimum conditions,and the removal rate can still reach 79.99% even after 5 cycles.The kinetics curves of removal show that the removal is in accordance with the first-order kinetics model within the first 40 minutes under different photocatalyst mass concentrations and parafuchsin mass concentrations.The results suggest that calcined-β-CD-Fe-LDHs can be used as a novel and efficient photocatalyst for the removal of parafuchsin.

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 score0.010
Threshold uncertainty score0.372

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 head0.035
GPT teacher head0.269
Teacher spread0.235 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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