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Record W2740656430 · doi:10.1021/acs.chemmater.7b00841

All-Organic Sodium Hybrid Capacitor: A New, High-Energy, High-Power Energy Storage System Bridging Batteries and Capacitors

2017· article· en· W2740656430 on OpenAlexaff
Ranjith Thangavel, K. Karthikeyan, Dae-Ung Kim, Yun‐Sung Lee

Bibliographic record

VenueChemistry of Materials · 2017
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsWestern University
FundersNational Research Foundation of Korea
KeywordsCapacitorMaterials scienceSupercapacitorEnergy storageEnvironmentally friendlyPower densityNanotechnologyElectrodeElectrical engineeringPower (physics)ChemistryCapacitanceVoltageEngineering

Abstract

fetched live from OpenAlex

The development of hybrid capacitors (HCs) has become essential for meeting the rising demand for devices that simultaneously deliver high energy with high power. Although the challenge to develop high-performance HCs remains great, it is also simultaneously essential to develop an eco-friendly and cleaner energy storage system for sustainable future use. To date, hybrid capacitors utilize heavily toxic inorganic insertion electrodes and hazardous coke-derived porous carbon adsorption electrodes to host ions. Herein, we present a conceptually novel all-organic sodium hybrid capacitor (OHC), rationally designed by replacing the conventional electrodes with clean, green, and metal free organic molecules, to host ions. A high energy density of ∼95 Wh kg –1 and an ultrahigh power density of 7 kW kg –1 (based on active mass in both electrodes) are achieved with a low energy loss of ∼0.22% per 100 cycles (∼89% retention after 5000 cycles), outperforming conventional HCs. The outstanding energy–power behavior of OHC bridges the performance gap between batteries and capacitors. This research holds great promise for the development of next-generation eco-friendly, clean, green, and safer high-energy, high-power devices.

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 score0.006
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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.010
GPT teacher head0.204
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

Citations64
Published2017
Admission routes1
Has abstractyes

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