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Record W4389474762 · doi:10.1016/j.jwpe.2023.104649

Lindane removal by membrane nanofiltration

2023· article· en· W4389474762 on OpenAlexaboutno aff
Adrián Ruiz-Gutiérrez, Javier Lasobras, Joaquı́n Coronas, M. Menéndez, José Miguel Luque‐Alled

Bibliographic record

VenueJournal of Water Process Engineering · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsnot available
FundersMinisterio de UniversidadesUniversidad de ZaragozaGobierno de AragónEuropean Commission
KeywordsPermeanceLindaneNanofiltrationMembraneChemistryHexachlorocyclohexaneMembrane technologyChromatographyPesticidePermeation

Abstract

fetched live from OpenAlex

Lindane is the γ-isomer of 1,2,3,4,5,6-hexachlorocyclohexane (HCH) and it has been widely used in the pesticide industry due to its insecticide properties. In the production of lindane, significant quantities of other HCH isomers are generated as byproducts. All these compounds are typically stored in landfills, often with poor management practices. Polyamide membranes, two commercial membranes from Alfa Laval (NF99HF and NF99) and homemade thin film composite membranes prepared by interfacial polymerization on a porous polyimide support, were evaluated for water nanofiltration (NF) of HCH isomers, including lindane. Homemade membranes, with a lindane rejection of 91.3 % and a permeance of 2.7 L·m−2·h−1·bar−1, offered the highest performance in terms of both permeance and rejection. NF99 and homemade membranes were proven to be equally effective in removing α-HCH and β-HCH, showing similar results. Homemade membranes achieved 99.5 and 99.8 % rejections for α-HCH and β-HCH, respectively. Long-term experiments were carried out with NF99 and homemade membranes to assess their stability during filtrations with lindane solutions for up to 214 h. Homemade TFC membrane maintained its initial rejection (ca. 93.5 %) after 214 h, although its permeance decreased over time, ending in 2.5 L·m−2·h−1·bar−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 score0.107
Threshold uncertainty score0.269

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 head0.008
GPT teacher head0.220
Teacher spread0.212 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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