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Record W2998817952 · doi:10.1002/anie.201913030

Host–Guest Chemistry Within Cellulose Nanocrystal Gel Receptors

2020· article· en· W2998817952 on OpenAlexafffund
Dongjie Zhang, Miguel A. Soto, Lev Lewis, Wadood Y. Hamad, Mark J. MacLachlan

Bibliographic record

VenueAngewandte Chemie International Edition · 2020
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsFPInnovationsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaConsejo Nacional de Ciencia y Tecnología
KeywordsChemistryCelluloseMolecular recognitionMoleculeNanocrystalSupramolecular chemistryHost–guest chemistryNanotechnologyIon exchangeChemical engineeringCombinatorial chemistryOrganic chemistryIonMaterials science

Abstract

fetched live from OpenAlex

Abstract Cellulose nanocrystals (CNCs) spontaneously assemble into gels when mixed with a polyionic organic or inorganic salt. Here, we have used this ion‐induced gelation strategy to create functional CNC gels with a rigid tetracationic macrocycle, cyclobis(paraquat‐ p ‐phenylene) ( CBPQT 4+ ). Addition of [ CBPQT ]Cl 4 to CNCs causes gelation and embeds an active host inside the material. The fabricated CNC gels can reversibly absorb guest molecules from solution then undergo molecular recognition processes that create colorful host–guest complexes. These materials have been implemented in gel chromatography (for guest exchange and separation), and as elements to encode 2‐ and 3‐dimensional patterns. We anticipate that this concept might be extended to design a set of responsive and selective gel‐like materials functioning as, for instance, water‐pollutant scavengers, substrates for chiral separations, or molecular flasks.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.085
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0030.000

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 head0.022
GPT teacher head0.271
Teacher spread0.249 · 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

Citations24
Published2020
Admission routes2
Has abstractyes

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