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Record W2910800626 · doi:10.1002/anie.201813686

Heteroatom‐Doped Porous Carbon Materials with Unprecedented High Volumetric Capacitive Performance

2019· article· en· W2910800626 on OpenAlexaff
Huile Jin, Xin Feng, Jun Li, Matthew Li, Yuanzhi Xia, Yifei Yuan, Chao Yang, Bin Dai, Zhiqun Lin, Jichang Wang, Jun Lü, Shun Wang

Bibliographic record

VenueAngewandte Chemie International Edition · 2019
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of WindsorUniversity of Waterloo
FundersNational Natural Science Foundation of China
KeywordsHeteroatomMaterials scienceDopingPorosityCapacitive sensingCarbon fibersNanotechnologyComposite materialOptoelectronicsEngineeringElectrical engineeringChemistryOrganic chemistryComposite number

Abstract

fetched live from OpenAlex

Abstract The design of carbon‐based materials with a high mass density and large porosity has always been a challenging goal, since they fulfill the demands of next‐generation supercapacitors and other electrochemical devices. We report a new class of high‐density heteroatom‐doped porous carbon that can be used as an aqueous‐based supercapacitor material. The material was synthesized by an in situ dehalogenation reaction between a halogenated conjugated diene and nitrogen‐containing nucleophiles. Under the given conditions, pyridinium salts can only continue to perform the dehalogenation if there is residue water remaining from the starting materials. The obtained carbon materials are highly doped by various heteroatoms, leading to high densities, abundant multimodal pores, and an excellent volumetric capacitive performance. Porous carbon tri‐doped with nitrogen, phosphorous, and oxygen exhibits a high packing density (2.13 g cm−3) and an exceptional volumetric energy density (36.8 Wh L−1) in alkaline electrolytes, making it competitive to even some Ni‐MH 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.009
GPT teacher head0.212
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), 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

Citations232
Published2019
Admission routes1
Has abstractyes

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