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Record W3173069003 · doi:10.1021/acs.chemmater.1c01272

Chiral Nematic Cellulose Nanocrystal/Germania and Carbon/Germania Composite Aerogels as Supercapacitor Materials

2021· article· en· W3173069003 on OpenAlexafffund
Christopher M. Walters, Gunwant K. Matharu, Wadood Y. Hamad, Erlantz Lizundia, Mark J. MacLachlan

Bibliographic record

VenueChemistry of Materials · 2021
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsFPInnovationsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceAerogelSupercapacitorLiquid crystalNanocrystalNanoparticleCelluloseComposite numberChemical engineeringNanotechnologyCapacitanceCarbon fibersCarbonizationComposite materialElectrodeOptoelectronics

Abstract

fetched live from OpenAlex

The development of novel aerogel materials with chiral nematic ordered structures offers exciting pathways for the fabrication of multifunctional hybrid materials with enhanced functionality. Aerogels prepared from cellulose nanocrystals are especially interesting due to their unique structural properties. To promote applicability in energy storage materials, it is often necessary to incorporate metals and metal oxides into three-dimensional porous nanostructures. In this study, germania was incorporated into a chiral nematic cellulose nanocrystal aerogel using a sol–gel method. Interestingly, our approach does not disturb the order of the original chiral nematic CNC aerogels, providing hybrid aerogels with a large concentration of randomly distributed GeO2 nanoparticles and specific surface areas up to 705 m2 g–1. Carbonization of the composite material afforded a highly ordered material with no collapse during compression and good shape recovery after release. The combination of the electrochemical double layer capacitance provided by the carbonaceous skeleton and the pseudocapacitive contribution from the GeO2 nanoparticles resulted in materials with a maximum Cp of 113 F g–1 that exhibited good capacitance retention. To push the boundaries of safer energy storage devices based on renewable resources, we demonstrate the preparation of an all-cellulose solid-state symmetric supercapacitor.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.009
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0090.000

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 head0.009
GPT teacher head0.217
Teacher spread0.208 · 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

Citations57
Published2021
Admission routes2
Has abstractyes

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