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Record W2067095187 · doi:10.1021/ma011861a

Probing Porous Polymer Resins by High-Field Electron Spin Resonance Spectroscopy

2002· article· en· W2067095187 on OpenAlexaff
D. Leporini, X. X. Zhu, Michael Krause, Gunnar Jeschke, H. W. Spieß

Bibliographic record

VenueMacromolecules · 2002
Typearticle
Languageen
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMicellePolymerPolymerizationDivinylbenzeneSpin probeChemistryChemical engineeringPulmonary surfactantPolymer chemistryElectron paramagnetic resonanceCopolymerPorosityMaterials scienceStyreneOrganic chemistryNuclear magnetic resonance

Abstract

fetched live from OpenAlex

High-field W-band (95 GHz) electron spin resonance spectroscopy of various spin probes was used to study the structure of highly cross-linked porous polymer resins based on a styrene−divinylbenzene matrix. The pores of these resins were created by template imprinting with reverse micelles solubilized in the mixture of monomers and cross-linkers prior to the polymerization. Functional groups in the resins were introduced by the use of polymerizable cosurfactants in the reverse micelles. Sufficiently large unpolar spin probes exhibit a distribution of mobilities that can be attributed to regions with different degrees of cross-linking in the polymer. The dynamics of a surfactant spin probe is sensitive to the presence of pores and the functionalization of the pore surface with highly polar groups. This effect disappears when the headgroup of the surfactant spin probe is esterified. It can be considered as a structural memory effect related to the use of reverse micelles as templates for imprinting. The result indicates that the pores can be filled or washed selectively.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
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)0.0040.000

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 head0.258
Teacher spread0.247 · 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

Citations16
Published2002
Admission routes1
Has abstractyes

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