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Record W2476014247 · doi:10.1039/c6ob01353h

Electrostatically promoted dynamic hybridization of glucans with cationic polythiophene

2016· article· en· W2476014247 on OpenAlexafffund
Gaku Fukuhara, Mami Imai, Denis Fuentealba, Yuki Ishida, Hiroki Kurohara, Cheng Yang, Tadashi Mori, Hiroshi Uyama, Cornelia Bohne, Yoshihisa Inoue

Bibliographic record

VenueOrganic & Biomolecular Chemistry · 2016
Typearticle
Languageen
FieldMaterials Science
TopicSupramolecular Self-Assembly in Materials
Canadian institutionsUniversity of Victoria
FundersJapan Society for the Promotion of ScienceNatural Sciences and Engineering Research Council of CanadaMitsui and Company
KeywordsCationic polymerizationPolythiopheneChemistryGlucanDuplex (building)Combinatorial chemistryPolymer chemistryPolymerOrganic chemistryBiochemistryConductive polymerDNA

Abstract

fetched live from OpenAlex

Hybridizing natural macromolecules with synthetic polymers is an efficient general method for constructing sophisticated supramolecular architectures. To comprehensively elucidate the controversial hybridization mechanism of glucans with synthetic polymers, the hybridization behaviors of triple-stranded curdlan (Cur) and schizophyllan (SPG) with cationic polythiophene (PyPT) were investigated in aqueous DMSO solutions by using UV-vis, circular dichroism (CD), fluorescence, fluorescence excitation, and NMR spectroscopy methods, as well as theoretical calculations, dynamic light scattering, and zeta potential measurements. Upon mixing with glucan, a hetero-triplex formed, which was dynamic and greatly accelerated by heating and by adding a base or a salt. The hetero-triplex disassembled into a hetero-duplex in highly basic solutions. Thus, polycationic polymers, such as PyPT, are expected to serve as a versatile tool for unzipping glucan homo-triplexes and promoting subsequent hybridization in aqueous solution, while the detailed mechanism elucidated in the present study contributes to the rational design of hybridization partners.

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 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 score0.264
Threshold uncertainty score0.999

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)0.0020.000

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 head0.002
GPT teacher head0.196
Teacher spread0.194 · 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

Citations11
Published2016
Admission routes2
Has abstractyes

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