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Record W2995475639 · doi:10.1002/ceat.201900231

Polyvinyl Alcohol/Alginate/Zeolite Nanohybrid for Removal of Metals

2019· article· en· W2995475639 on OpenAlex
Amin Tabatabaeefar, Ali Reza Keshtkar, Marzieh Talebi, Hossain Abolghasemi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChemical Engineering & Technology · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
Fundersnot available
KeywordsAdsorptionZeolitePolyvinyl alcoholFourier transform infrared spectroscopyMetal ions in aqueous solutionLangmuir adsorption modelDesorptionNuclear chemistryScanning electron microscopeMaterials scienceChemistryChemical engineeringInorganic chemistryMetalCatalysisOrganic chemistryMetallurgyComposite material

Abstract

fetched live from OpenAlex

Abstract A novel polyvinyl alcohol/alginate/zeolite nanohybrid adsorbent for the adsorption of Ni(II) and Co(II) metal ions was prepared by the casting method. The prepared adsorbent was characterized by Fourier transform infrared spectroscopy, scanning electron microscopy as well as Barrett‐Joyner‐Halenda and Brunauer‐Emmett‐Teller analyses. The optimum adsorption conditions in terms of content of zeolite nanoparticles, adsorbent dosage, and initial pH were determined. The kinetic data for both ions were well described by the double‐exponential kinetic model. The obtained Langmuir maximum adsorption capacities of Ni(II) and Co(II) metal ions were 81.51 and 79.58 mg g −1 , respectively. The adsorption/desorption experiments showed a good performance after 5 cycles of adsorption.

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.

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 score0.059
Threshold uncertainty score0.641

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)0.0010.000

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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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