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Record W4205515774 · doi:10.3390/membranes12010096

Low-Temperature Hydrophilic Pervaporation of Lactic Acid Esterification Reaction Media

2022· article· en· W4205515774 on OpenAlexfundno aff
Elena González Díaz, Sonia Álvarez, Susana Luque, J. Raziel Álvarez

Bibliographic record

VenueMembranes · 2022
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsnot available
FundersMinisterio de Economía y CompetitividadMountain Equipment Co-operative
KeywordsPervaporationLactic acidEthyl lactatePermeationChemistryMembraneDehydrationEthanolSelectivityChromatographyOrganic chemistryChemical engineeringCatalysisBiochemistry

Abstract

fetched live from OpenAlex

Esterification reactions show a limited conversion due to the presence of water, which favors the opposite reaction. The removal of water from the reaction mixture increases the production of the ester. Pervaporation is an effective dehydration technique, usually applied to binary mixtures. The effect on pervaporation of a reactive multicomponent system involving water, ethanol, ethyl lactate and lactic acid with high acid concentration (13.5 wt. %) at relatively low temperatures (40-80 °C) was studied. Three hydrophilic membranes mainly fabricated for dehydration purposes from Sulzer Chemtech were used, i.e., PERVAP™ 3100, PERVAP™ 2216 and PERVAP™ 1131. The last one revealed as the most suitable for the application and it was further characterized with binary and ternary solutions. The membrane showed high affinity for the lactic acid. The acid permeation played a key role in the water/ethanol and water/ethyl lactate selectivity. Lactic acid permeates and crystalizes in the permeate side of the membrane at very low water concentration (below 2 wt. %), causing a drop in flux and membrane selectivity. Ethyl lactate is responsible of the loss of integrity of the membranes.

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 score0.010
Threshold uncertainty score0.524

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 head0.011
GPT teacher head0.205
Teacher spread0.194 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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