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Record W4236164411 · doi:10.26434/chemrxiv.12887297

Enhanced Reactivity in Surface-Confined Water of Hierarchically Structured Polymers

2020· preprint· en· W4236164411 on OpenAlexaff
Marco Beaumont, Paul Jusner, Notburga Gierlinger, Alistair W. T. King, Antje Potthast, Orlando J. Rojas, Thomas Rosenau

Bibliographic record

VenueChemRxiv · 2020
Typepreprint
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsUniversity of British Columbia
FundersHorizon 2020 Framework ProgrammeChina Scholarship CouncilAcademy of Finland
KeywordsNanoporousPolymerChemical reactionMoleculeCelluloseReactivity (psychology)RegioselectivityChemistryReaction rateReaction mechanismChemical engineeringSurface modificationNanotechnologyMaterials sciencePolymer scienceOrganic chemistryCatalysisPhysical chemistry

Abstract

fetched live from OpenAlex

The remarkable efficiency of biological chemical reactions is the result of evolution and many of these reactions are promoted by confined water. This has inspired significant research endeavors exploiting the potential of this special water in chemistry. However, these systems are so far limited to complex artificial solids or biphasic systems and small molecules as reactants. Here, we show that the intrinsically present surface-confined water in hierarchically structured biopolymers can be used as nanomedium to promote chemical reactions. We found in the example of cellulose fibers that confined water was actively involved in the reaction mechanism and facilitated the surface acetylation of the fiber, increasing reaction kinetics, efficiency and regioselectivity. Our findings can be regarded as proof-of-principle that the hydration layer in nanoporous polymers can be exploited as medium to promote chemical reactions at their surface. This concept can likely be extended to other polymers and various reaction systems. <br>

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 score0.083
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.235
Teacher spread0.221 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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