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Record W7115819286

Environment-Responsive Membranes for Bioseparations

2008· dissertation· en· W7115819286 on OpenAlexaff

Bibliographic record

VenueMacSphere (McMaster University) · 2008
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein purification and stability
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMembranePolyvinylidene fluorideTernary operationUltrafiltration (renal)Synthetic membraneProtein purification
DOInot available

Abstract

fetched live from OpenAlex

Environment-responsive membranes were created by modification of a commerical polyvinylidene fluoride (PVDF) membrane support with a thermo-responsive hydrogel composed of poly N-vinyllactams cross-linked with bisacrylamide. The modified membranes were then characterized by their percentage mass gains as well as by their valve effect in response to changes in salt concentration. One set of membranes, with a large valve effect, was selected for highest retention of intermediately sized proteins was examined for ultrafiltration-based protein separation applications. A batch separation protocol featuring pulsed sample injection technique (PSIT) was then used to sieve single proteins and to fractionate a synthetic binary protein mixture and a synthetic ternary protein mixture with some success, demonstrating the potential of these environment-responsive membranes for use in multi-component separations. A second set of membranes, with a small valve effect, was selected for its ability to alter between hydrophobic and hydrophilic states under different environmental conditions and its potential in hydrophobic interaction membrane chromatography (HIMC) applications was successfully demonstrated by comparing against a benchmark membrane that is used successfully for HIMC applications in prior literature.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.931
Threshold uncertainty score1.000

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)0.0070.000

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 head0.012
GPT teacher head0.223
Teacher spread0.211 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2008
Admission routes1
Has abstractyes

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