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Record W2964316382 · doi:10.14447/jnmes.v13i4.135

Preparation and Electrochemical Characterization of Ordered Mesoporous Carbon/PbO Host-guest Composite Electrode Materials for Supercapacitor

2010· article· en· W2964316382 on OpenAlexvenueno aff
Ji Cheng Feng, Jia Zhao, Ping Liu, Bohejin Tang, Jing Xu

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2010
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsnot available
Fundersnot available
KeywordsSupercapacitorMaterials scienceMesoporous materialHorizontal scan rateComposite numberCapacitanceElectrochemistryElectrodeCarbon fibersDesorptionIncipient wetness impregnationChemical engineeringComposite materialCharacterization (materials science)Cyclic voltammetryAdsorptionNanotechnologyCatalysisChemistryOrganic chemistrySelectivityPhysical chemistry

Abstract

fetched live from OpenAlex

Ordered mesoporous carbon/PbO host-guest composites were prepared by incipient wetness impregnation method. The XRD, TEM and N2 adsorption/desorption isotherms tests confirm the host-guest structure of the composites. The PbO loading amount affects the structure and electrochemical properties of the composites and the optimum amount of Pb (NO3)2 added is found to be 60 % of that of the saturated solution. Besides, the specific capacitance of the composite (83 F g-1) is more than twice of that of the pristine PbO (37 F g-1) at the scan rate of 5 mV s-1 and is more or less equal to that of the ordered mesoporous carbon at the scan rate of 200 mV s-1 (the specific capacitance of the composite is 58 F g-1), demonstrating excellent rate capability. Furthermore, the composite electrode material shows a stable cycle life in the potential range of 0-0.9 V after 500 cycles.

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 head0.010
GPT teacher head0.251
Teacher spread0.241 · 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

Citations4
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueJournal of New Materials for Electrochemical SystemsSame topicSupercapacitor Materials and FabricationFrench-language works237,207