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Record W2910206228 · doi:10.1080/09593330.2018.1564072

How microwaves can help to study membrane ageing

2019· article· en· W2910206228 on OpenAlexfundno aff
Murielle Rabiller‐Baudry, Ludovic Paquin, Cindy Leperoux, Habi Diallo, H. Al Jawad, C. Sepré

Bibliographic record

VenueEnvironmental Technology · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsnot available
FundersInstitute of Circulatory and Respiratory HealthUniversité LibanaiseCHIST-ERAUniversité de Rennes 1Agence Nationale de la Recherche
KeywordsAgeingMembraneFoulingService lifeMaterials scienceChemistryChemical engineeringProcess engineeringComposite materialEngineeringBiochemistryBiology

Abstract

fetched live from OpenAlex

This paper studies original protocols of rapid PES/PVP membrane NaOCl degradation allowing at reaching ageing states that are representative of industrial ageing. The long term objective is to propose basis for further fundamental studies aiming at the improvement of the impact of membrane ageing on behaviour in UF (fouling and cleaning mastering). The key of several protocols is the use of ageing acceleration thanks to microwave irradiation, either continuous or pulsed ones, that can be further associated (or not) with short ageing time in UF conditions. To evaluate the representativeness of obtained aged membranes, comparisons are achieved between pristine, voluntary laboratory aged membranes and an industrial membrane at the end of its service-life. Several physico-chemical analyses were used (ATR-FTIR, SEM-EDX, contact angle, SEC-HPLC). Evaluation of UF performances were made in UF of a model protein (Lysozyme, 14,300 g.mol−1). Finally, the proof of concept is done that conditions using MW exist to reach ageing state representative of industrial ageing.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.007
GPT teacher head0.210
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2019
Admission routes1
Has abstractyes

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