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Record W4390043147 · doi:10.1021/acsomega.3c09331

Comprehensive Characterization of Commercial Reverse Osmosis Membranes through High-Temperature Cross-Flow Filtration

2023· article· en· W4390043147 on OpenAlexafffund
Pooria Karami, Sadegh Aghapour Aktij, Kazem Moradi, Masoud Rastgar, Behnam Khorshidi, Farshad Mohammadtabar, John T. Peichel, Michael McGregor, Ahmad Rahimpour, João B. P. Soares, Mohtada Sadrzadeh

Bibliographic record

VenueACS Omega · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsSuncor Energy (Canada)University of TorontoUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Natural Resources Limited
KeywordsMembraneReverse osmosisFiltration (mathematics)Forward osmosisMaterials scienceOsmosisChemical engineeringCross-flow filtrationChemistryEngineeringMathematics

Abstract

fetched live from OpenAlex

Developing thermally stable reverse osmosis membranes is a potential game-changer in high-temperature water treatment. In this work, the performance of three commercial reverse osmosis membranes was evaluated with a series of high-temperature filtrations. The membranes were tested with different filtration methodologies: long-term operation, cyclic tests, controlled stepwise temperature increment, and permeability tests. The morphological and physiochemical characterizations were performed to study the impact of high-temperature filtration on the membranes' chemical composition and morphological characteristics. An increase in the temperature deteriorated the membrane performance in terms of water flux and salt rejection. Flux decline at high temperatures was recognized as the primary concern for high-temperature filtrations, restricting the applications of commercial membranes for long-term operations. This research provides valuable insights for researchers aiming to thoroughly characterize reverse osmosis membranes at high temperatures.

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 score0.249
Threshold uncertainty score0.805

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.022
GPT teacher head0.270
Teacher spread0.247 · 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

Citations22
Published2023
Admission routes2
Has abstractyes

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