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Sodium Hexa-titanate nanocomposites modified with trace amounts of ruthenium displayed enhanced supercapacitance

2025· article· en· W4408701395 on OpenAlexafffund
Akansha Seervi, Jesse S. Dondapati, Akhtar Bayat, Seshasai Srinivasan, Amin Reza Rajabzadeh

Bibliographic record

VenueMaterials Chemistry and Physics · 2025
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHEXARutheniumNanocompositeMaterials scienceSodiumTitanateChemical engineeringNanotechnologyChemistryComposite materialCeramicOrganic chemistryMetallurgyCatalysisEngineeringBiochemistry

Abstract

fetched live from OpenAlex

Sodium hexa-titanate (Na 2 Ti 6 O 13 ) nanostructures exhibit high electrochemical surface area and unique structural properties. In this study, sodium hexa-titanate nanostructures were grown from titanium (Ti) substrate (Ti/Na 2 Ti 6 O 13 ) and were investigated for supercapacitance by doping oxygen vacancies and modifying with trace amounts of ruthenium (Ru) to form nanostructure composites. An electrochemical reduction method was used for inducing oxygen vacancies into the lattice structure of Ti/Na 2 Ti 6 O 13 nanocomposites to enhance electrochemical and supercapacitance properties. Several Characterization techniques like EDS, XRD and XPS were employed to identify and optimize structure property relationship to achieve supercapacitance. An enhanced specific capacitance of 3300 mF cm −2 g −1 was achieved for Ru–Ti/Na 2 Ti 6 O 13 for Ru ultra-low loading of 0.4 μg of Ru in contrast to a specific capacitance of 100 mF cm −2 g −1 observed for unmodified Ti/Na 2 Ti 6 O 13 . • Modified Sodium Hexa-titanate nanocomposites for enhanced supercapacitance • Ruthenium based doping strategies to enhance supercapacitance • Experimental evidence showing a 33 fold increase in supercapacitance • EDS, XRD and XPS employed to identify and optimize structure property relationship

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)0.000
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 score0.003
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.007
GPT teacher head0.215
Teacher spread0.208 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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