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Record W7132911531

Interfacial Polymerization of Silica Nanoparticles to Create Modifiable, Ultra-Thin, Ultrafiltration Membranes

2023· dissertation· W7132911531 on OpenAlexaff
Dean Frank Stipanic

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsUltrafiltration (renal)NanoparticlePolymerizationPolymerMembraneInterfacial polymerizationNanomaterialsContact angle
DOInot available

Abstract

fetched live from OpenAlex

Interfacial polymerization is an industrially scalable technique, most notably used in the fabrication of reverse osmosis membranes, with the ability to create highly crosslinked thin films. This work successfully expands the interfacial polymerization chemistry library by utilizing 1D nanomaterials to create highly crosslinked, modifiable, defect-free nanoparticle membranes. Silica nanoparticles were used as a proof-of-concept due to their straightforward synthesis and chemical modification, exemplifying the feasibility of integrating nanoparticles into this established technique. Careful selection of the crosslinking agent in the interfacial polymerization scheme allowed for film thicknesses between 100 nm and 1.6 μm to be achieved. The nanoparticle layer exhibited poor mechanical stability under pressure and required a low pore size, high strength support membrane for stable operation. Chemical modification of the nanoparticle film with a hydrophobic polymer altered the water contact angle from 26° to 95°, showing ease of control over surface chemistry. This approach could be readily expanded to incorporate other types of nanoparticles and be applied to a wide range of applications beyond ultrafiltration.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.028
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.033
GPT teacher head0.351
Teacher spread0.317 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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