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Record W2951988115 · doi:10.1021/acsomega.9b00981

Removal of Heavy Metal Water Pollutants (Co<sup>2+</sup> and Ni<sup>2+</sup>) Using Polyacrylamide/Sodium Montmorillonite (PAM/Na-MMT) Nanocomposites

2019· article· en· W2951988115 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Omega · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsUniversity of Alberta
FundersUniversidad de CartagenaUniversity of Alberta
KeywordsPolyacrylamideMontmorilloniteAdsorptionNanocompositeFreundlich equationMetal ions in aqueous solutionLangmuirNuclear chemistryCoprecipitationFourier transform infrared spectroscopyLangmuir adsorption modelChemistryMetalPolymerizationMaterials scienceChemical engineeringPolymerInorganic chemistryPolymer chemistryOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

solution using the 4:1 (w/w) nanocomposite. These results were higher than those obtained by polyacrylamide and nanoclay under the same conditions (removal yield between 87.40 and 94.50%), indicating that PAM/Na-MMT nanocomposites remove heavy metal water pollutants more efficiently and can be used as a novel adsorbent for further industrial applications.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.195
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.013
GPT teacher head0.231
Teacher spread0.218 · 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