MétaCan
Menu
Back to cohort
Record W2989589778 · doi:10.1002/bit.27240

Development of isoporous microslit silicon nitride membranes for sterile filtration applications

2019· article· en· W2989589778 on OpenAlexaff
Evan Wright, Joshua J. Miller, Matthew Csordas, Andrew R. Gosselin, Jared A. Carter, James L. McGrath, David R. Latulippe, James Roussie

Bibliographic record

VenueBiotechnology and Bioengineering · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsMcMaster University
FundersNational Institute of General Medical SciencesFoundation for the National Institutes of HealthNational Science Foundation
KeywordsMembraneFiltration (mathematics)PermeanceMaterials scienceSilicon nitridePolymerNanotechnologySiliconChemical engineeringChemistryPermeationOptoelectronicsComposite materialEngineeringBiochemistry

Abstract

fetched live from OpenAlex

The widely used 0.2/0.22 µm polymer sterile filters were developed for small molecule and protein sterile filtration but are not well-suited for the production of large nonprotein biological therapeutics, resulting in significant yield loss and production cost increases. Here, we report on the development of membranes with isoporous sub-0.2 μm rectangular prism pores using silicon micromachining to produce microslit silicon nitride (MSN) membranes. The very high porosity (~33%) and ultrathin (200 nm) nature of the 0.2 µm MSN membranes results in a dramatically different structure than the traditional 0.2/0.22 µm polymer sterile filter, which yielded comparable performance properties (including gas and hydraulic permeance, maximum differential pressure tolerance, nanoparticle sieving/fouling behavior). The results from bacteria retention tests, conducted according to the guidance of regulatory agencies, demonstrated that the 0.2 µm MSN membranes can be effectively used as sterile filters. It is anticipated that the results and technologies presented in this study will find future utility in the production of non-protein biological therapeutics and in other biological and biomedical applications.

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 score0.036
Threshold uncertainty score0.355

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 head0.010
GPT teacher head0.216
Teacher spread0.207 · 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

Citations15
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueBiotechnology and BioengineeringSame topicMembrane Separation TechnologiesFrench-language works237,207