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High Performance Supercapacitors Electrode Derived from the Pine Needles

2014· article· en· W2029341215 on OpenAlexfundno aff
Pei Yu Wang, Guo Heng Zhang, Hai Yan Jiao, Xiao Ping Zheng

Bibliographic record

VenueAdvanced materials research · 2014
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsnot available
FundersNatural Science Foundation of Gansu ProvinceRyerson UniversityNorthwest University for NationalitiesNational Science Foundation
KeywordsSupercapacitorCarbonizationMaterials scienceCapacitanceElectrodeActivated carbonElectrochemistryElectrolyteCapacitive sensingCurrent densitySpecific surface areaChemical engineeringEnergy storageNanotechnologyOptoelectronicsComposite materialElectrical engineeringPower (physics)ChemistryAdsorptionOrganic chemistryCatalysisEngineering

Abstract

fetched live from OpenAlex

In this paper, Pine needles (PNs) were used to synthesize porous activated carbon by the carbonization and activation processes. The material for PNs were carbonized at 600 °C and activated at 800 °C(denoted as AC-800) show typical amorphous character, and display porous structures with high specific surface areas 2729 m2/g via XRD and BET measurements. As the electro-active material, AC-800 electrode exhibit ideal capacitive behaviors in aqueous electrolytes, and the maximal specific capacitance is as high as 286 F/g at the current density of 1 A/g. Furthermore, AC-800 electrode shows excellent electrochemical cycle stability with ~94 % initial capacitance being retained after 2000 cycles. The desirable capacitive performances enable the PNs to act as a new biomass source of carbonaceous materials for high performance supercapacitors and low-cost electrical energy storage 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 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 score0.003

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 head0.031
GPT teacher head0.294
Teacher spread0.263 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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