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Record W4318253747 · doi:10.1021/acsami.2c18317

Tuning the Functionalization of Graphite for Hydrovoltaic Power Generation

2023· article· en· W4318253747 on OpenAlexafffund
Rahul Kumar, George Kay, Graham Beaton, Guojun Liu, Kevin G. Stamplecoskie

Bibliographic record

VenueACS Applied Materials & Interfaces · 2023
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsQueen's University
FundersCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsSurface modificationMaterials scienceGrapheneGraphiteNanotechnologyConductivityFabricationGraphite oxideOxideRenewable energySupercapacitorChemical engineeringElectrochemistryElectrodeComposite materialElectrical engineeringChemistry

Abstract

fetched live from OpenAlex

The recent discovery of hydrovoltaic devices for power generation has led to a rapid growth into new materials for harvesting energy specifically for this research field. Of the materials investigated, carbon materials have dominated, and graphene oxide (GO) has emerged as the leader. While graphite is conductive, it does not have functional groups to strongly interact with water, and highly functionalized GO forms strong interaction with water to generate necessary surface charges but does not typically have high conductivity. Herein, we report the fabrication and functionalization of a graphite-based structure, controlling the extent of oxidation to balance the effects of conductivity and functionalization to achieve high power outputs in hydrovoltaics. Devices prepared using the functionalized graphite achieve a power output of 53.3 μW/g. High power output and good film stability are key advances toward the practical application of hydrovoltaic devices for renewable energy.

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 categoriesnone
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 score0.027
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.036
GPT teacher head0.286
Teacher spread0.250 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations26
Published2023
Admission routes2
Has abstractyes

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