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Record W4383736175 · doi:10.3390/batteries9070365

Overcoming Challenges in Development of Manganese Oxide Supercapacitor Cathodes by Alkali-Free Hydrothermal Synthesis

2023· article· en· W4383736175 on OpenAlexafffund
Mahmoud Awad, Mohamed Nawwar, Igor Zhitomirsky

Bibliographic record

VenueBatteries · 2023
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSupercapacitorCapacitanceMaterials scienceElectrolyteElectrodeManganeseCathodeChemical engineeringHydrothermal synthesisFabricationOxideHydrothermal circulationNanotechnologyInorganic chemistryChemistryMetallurgy

Abstract

fetched live from OpenAlex

This investigation is motivated by the need in the development of manganese oxide cathodes for supercapacitors with high capacitance at high charge–discharge rates and enhanced capacitance retention in a wide range of charge–discharge rates. It also addresses the challenge of eliminating the time-consuming activation procedure, which limits the applications of Mn3O4 cathodes. The new approach is based on the use of environmentally friendly and biocompatible pH modifiers–dispersants, such as polyethylenimine (PEI) and meglumine (MG) for hydrothermal synthesis. In this approach, the use of inorganic alkalis is avoided. We demonstrate the benefits of this approach for the fabrication of manganese oxide nanoparticles, such as Mn-PEI and Mn-MG. Electrodes with a high active mass of 40 mg cm−2 are fabricated and electrochemically tested in 0.5 M Na2SO4 electrolyte. The method of electrode material fabrication offers benefits for the accelerated electrode activation procedure, which is practically eliminated for Mn-MG electrodes. The Mn-MG electrodes showed a remarkably high capacitance of 3.68 F cm−2 (93.19 F g−1) at a sweep rate of 100 mV s−1 and a high capacitance retention of 90.6% in the CV sweep range of 1–100 mV s−1.

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 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 score0.031
Threshold uncertainty score0.675

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.043
GPT teacher head0.248
Teacher spread0.204 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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