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Record W4409780793 · doi:10.1021/acsomega.5c01560

Durable ZrB<sub>2</sub>–ZrC Composite Materials as Advanced Electrodes for High-Performance Supercapacitors

2025· article· en· W4409780793 on OpenAlexaff
Aybike Paksoy, Ahmet Güngör, İpek Deniz Yıldırım, Seyedehnegar Arabi, Emre Erdem, Özge Balcı

Bibliographic record

VenueACS Omega · 2025
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Regina
FundersTürkiye Bilimsel ve Teknolojik Araştırma Kurumu
KeywordsSupercapacitorMaterials scienceComposite numberComposite materialElectrodeCapacitanceChemistry

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Boride and carbide-based materials attract increasing attention as promising options for energy storage applications. This research focuses on synthesizing pure boride and carbide compounds of zirconium (ZrB 2 and ZrC) and their composite powders using mechanical activation-assisted route and subsequent heating processes. The chemical and microstructural characterization results indicate that the synthesized composite powders are of high purity, possess submicron-scale particle sizes (below 400 nm), and exhibit a high surface area of up to 9.41 m 2 /g. Supercapacitor devices, using the resulting powders as symmetrical electrodes, exhibit high energy density values ranging from 5.8 to 8.8 Wh/kg. The ZrB 2 –15 wt % ZrC composite sample achieves the highest power density at 155 W/kg, compared to 118 W/kg for the pure ZrB 2 sample. Cycling tests demonstrate exceptional capacitance retention (99.4–99.9%) and cyclic stability, even after 5000 cycles, highlighting the high durability of the composite samples. These findings show that ZrB 2 –ZrC composites exhibit high energy and power density values and excellent cycling performance, making them strong candidates for use in high–performance supercapacitor 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 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 categoriesMeta-epidemiology (narrow)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.227
Teacher spread0.220 · 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.

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

Citations5
Published2025
Admission routes1
Has abstractyes

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