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Record W2752847235 · doi:10.1002/cjce.23010

Ethanol:water blends separation using ultrafiltration membranes of poly(acrylamide‐co‐acrylic acid) partial sodium salt and polyacrylamide

2017· article· en· W2752847235 on OpenAlexvenueno aff
Heriberto Espinoza‐Gómez, Eduardo Saucedo‐Castillo, Lucı́a Z. Flores-López, Eduardo Rogel-Hernández, M. Brito Martínez, Fernando T. Wakida

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsMembraneEthanolUltrafiltration (renal)PolyacrylamideAcrylamideChemistryAcrylic acidSelectivityChromatographySodiumNuclear chemistryChemical engineeringPolymer chemistryPolymerOrganic chemistryBiochemistryCatalysisCopolymer

Abstract

fetched live from OpenAlex

Abstract In this work, poly(acrylamide‐co‐acrylic acid) partial sodium salt (P(AAM‐co‐AA)Na), and polyacrylamide (PAAM) membranes were synthesized for separation of ethanol:water blends. The membrane characteristics were evaluated to determine their ability to separate mixtures of ethanol:water and get dehydrate ethanol (95 %). Synthesized membranes showed higher selectivity to ethanol compared to existing polymeric membranes. The ethanol:water blends had an ethanol concentration of 0.10 to 0.70 L/L. The results showed that a mixture of ethanol:water (75:25 L/L) can be concentrated up to 95 % ethanol. The membranes’ stability were studied in absolute ethanol and ethanol:water blend (30:70 L/L) for a period of 30 days of permanent exposure. The membrane deterioration is due to the chemical modification of the membrane active surface layer. It was found that the membranes have a high selectivity (350 times higher) compared to existing membranes, and the substantial and flow of water was 12.15 kg · m −2 · h −1 .

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 score0.005
Threshold uncertainty score0.436

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 head0.016
GPT teacher head0.243
Teacher spread0.227 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

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