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Record W2557744069 · doi:10.1039/c6cp07351d

Achieving enhanced ionic mobility in nanoporous silica by controlled surface interactions

2016· article· en· W2557744069 on OpenAlexaff
Mounesha N. Garaga, Luis Aguilera, Negin Yaghini, Aleksandar Matic, Michael Persson, Anna Martinelli

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2016
Typearticle
Languageen
FieldMaterials Science
TopicMesoporous Materials and Catalysis
Canadian institutionsAkzoNobel (Canada)
FundersVetenskapsrådet
KeywordsNanoporousIonic bondingChemical engineeringChemistrySurface (topology)NanotechnologyMaterials scienceIonOrganic chemistry

Abstract

fetched live from OpenAlex

ImTFSI), and one protic ammonium based (diethylmethylammonium methanesulfonate, DEMAOMs) ionic liquid. Both these ionic liquids display higher ionic mobility when confined in functionalized silica as compared to untreated silica nano-pores, an improvement that is more pronounced at low pore filling factors (i.e. in the nano-sized pore domains) and observed in the whole temperature window investigated (i.e. from -10 to 140 °C). Solid-state NMR, diffusion NMR and dielectric spectroscopy concomitantly demonstrate this effect. The origin of this enhancement is explained in terms of weaker intermolecular interactions and a consequent flipped-ion effect at the silica interface strongly supported by 2D solid-state NMR experiments. The possibility to significantly enhance the ionic mobility by controlling the nature of surface interactions is extremely important in the field of materials science and highlights these structurally tunable gels as promising solid-like electrolytes for use in energy relevant devices. These include, but are not limited to, Li-ion batteries and proton exchange membrane (PEM) fuel cells.

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 score0.002
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 integrity0.0000.000
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 head0.007
GPT teacher head0.242
Teacher spread0.235 · 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

Citations44
Published2016
Admission routes1
Has abstractyes

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