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Record W2113522223 · doi:10.1039/c3cs60276a

Controlling morphology and porosity to improve performance of molecularly imprinted sol–gel silica

2013· review· en· W2113522223 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChemical Society Reviews · 2013
Typereview
Languageen
FieldMaterials Science
TopicMesoporous Materials and Catalysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNanotechnologyMolecular imprintingMesoporous materialImprinting (psychology)Materials sciencePolymerPorosityMesoporous silicaSol-gelNanometreMolecularly imprinted polymerChemical engineeringChemistryOrganic chemistrySelectivityCatalysisComposite material

Abstract

fetched live from OpenAlex

The wealth of molecular precursors for organic and inorganic polymers has resulted in an incredible volume of molecular imprinting literature. The vast majority of reports deal with organic polymer systems, and molecular imprinting in silica can still be considered a small niche in the field. In this review, we present key concepts of molecular imprinting, sol-gel processing, and the synthesis of templated mesoporous silica. We take a small fraction of the literature and use it to understand the ways in which molecular imprinting in siliceous materials of controlled morphology has achieved success in the past fifteen years. Using selected case studies rather than a comprehensive review of the entire field, our goal is to illustrate the key aspects of imprinted silica-based materials as demonstrated by judiciously controlled systems, looking first at control on the micrometre scale in bulk phase materials, and then on the nanometre scale in templated mesoporous materials.

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.

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)0.000
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.725
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.027
GPT teacher head0.286
Teacher spread0.259 · 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