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Record W2010603615 · doi:10.1002/masy.200550823

Macroporous Silica Monoliths Derived from Glyceroxysilanes: Controlling Gel Formation and Pore Structure

2005· article· en· W2010603615 on OpenAlexaff
Zheng Zhang, Yang Chen, Richard Hodgson, Michael A. Brook, John D. Brennan

Bibliographic record

VenueMacromolecular Symposia · 2005
Typearticle
Languageen
FieldChemistry
TopicAnalytical Chemistry and Chromatography
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMonolithEthylene oxideChemical engineeringSorptionMonolithic HPLC columnChromatographyPhase (matter)PorosityChemistryDecompositionMaterials scienceIonic strengthDenaturation (fissile materials)OxideHigh-performance liquid chromatographyAdsorptionOrganic chemistryPolymerCopolymerNuclear chemistryCatalysis

Abstract

fetched live from OpenAlex

Abstract Diglycerylsilane (DGS), a member of the family of sugar‐based silanes, is converted into monolithic silica at low temperatures and at mild pH. These materials are suitable for the entrapment of proteins under conditions that generally offer protection against denaturation, particularly when compared to analogous silicas prepared from tetraethoxysilane (TEOS). However, the resulting monoliths did not have sufficient porosity to permit flow and, thus, could not be utilized as monolithic chromatographic supports for frontal affinity chromatography (FAC). It was demonstrated that poly(ethylene oxide) can be used to induce spinodal decomposition of the DGS‐derived sol, prior to gelation, leading to a meso‐ and macroporous silica monolith after cure, as demonstrated by nitrogen sorption analysis. High molecular weight PEO is required for effective phase separation to take place: below 10000 MW, no such phase separation occurs under the conditions employed. The amount and molecular weight of PEO is critical to the timing of gelation. If too much PEO is present, or ionic strength is increased, gelation occurs before it is possible to fill the chromatography column with the sol, while too little results in a lack of macropores. Proteins entrapped in this material are shown to be of comparable stability to those prepared in the absence of PEO, and can be used to chromatographically screen, with MS detection, potential drug candidates by changes in retention resulting from ligand binding.

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)
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 score0.017
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)0.0010.000

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 head0.003
GPT teacher head0.193
Teacher spread0.190 · 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 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

Citations5
Published2005
Admission routes1
Has abstractyes

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