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Record W2012544676 · doi:10.1021/jp804044s

Phase-Controlled Synthesis of MnO<sub>2</sub> Nanocrystals by Anodic Electrodeposition: Implications for High-Rate Capability Electrochemical Supercapacitors

2008· article· en· W2012544676 on OpenAlexaff
Weifeng Wei, Xinwei Cui, Weixing Chen, Douglas G. Ivey

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2008
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNanocrystalMaterials scienceElectrochemistryHorizontal scan ratePhase (matter)Crystal (programming language)SupercapacitorCrystal structureCapacitanceCapacitive sensingSalt (chemistry)Chemical engineeringEthylenediaminetetraacetic acidNanotechnologyCrystallographyElectrodeCyclic voltammetryChemistryChelationMetallurgyPhysical chemistry

Abstract

fetched live from OpenAlex

The crystal structure of anodically electrodeposited MnO 2 nanocrystals can be manipulated by introducing complexing agents in the electrodeposition solutions. MnO 2 nanocrystals with three types of crystal structures were observed: hexagonal ε-MnO 2 (complex-free), defective rock salt MnO 2 (ethylenediaminetetraacetic acid), and defective antifluorite MnO 2 (citrate). The capacitive performance of the MnO 2 nanocrystals depends strongly on their crystal structures. MnO 2 with defective rock salt and antifluorite structures exhibit better capacitive properties than ε-MnO 2 . The electrochemical capacitance differences can be explained in terms of the crystal chemistry. In both the defective rock salt and antifluorite MnO 2, an anomalous trend was observed. The specific capacitance does not decrease with increasing scanning rate. A possible reason is that certain physicochemical changes, such as phase transformations or morphology changes, occur preferentially at high cycling rates.

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)0.001
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 score0.001
Threshold uncertainty score0.646

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.009
GPT teacher head0.231
Teacher spread0.222 · 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

Citations132
Published2008
Admission routes1
Has abstractyes

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