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Record W4410251069 · doi:10.1016/j.nxmate.2025.100699

High-entropy oxide for enhanced supercapacitors and precise electrochemical detection of dopamine at nanomolar levels

2025· article· en· W4410251069 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNext Materials · 2025
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Waterloo
FundersKarpagam Academy of Higher Education
KeywordsSupercapacitorDopamineOxideElectrochemistryNanotechnologyMaterials scienceChemistryInternal medicineMedicineElectrodePhysical chemistryMetallurgy

Abstract

fetched live from OpenAlex

The ability to synthesize multi-metal elements into a single-component material at the nanoscale, known as high entropy oxide (HEO) is earning great attention, especially in the field of electrocatalysis. However, the present methods for the synthesis of HEO often involve non-noble, noble, or refractory elements, which require complicated synthesis methods, making the control of shape and size highly challenging. In this regard, a class of six dissimilar elements (Co, Ni, Mn, Mo, V and Zn) with combination of non-noble and refractory elements has been formed a new type of (Co 0.5 Ni 0.5 Mn 0.5 Mo 0.5 V 0.5 Zn 0.5 )O based HEO. The multi-element interaction and carbonization network enhance ion conductivity, boosting specific capacitance to 698.4 F.g⁻¹ , far surpassing conventional metal oxides. In addition, the HEO on screen printed electrode exhibited a notable increase in the oxidation peak current for the oxidation of dopamine, which can detect dopamine at nanomolar levels.

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.

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 score0.007
Threshold uncertainty score0.869

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 communication0.0000.000
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 head0.013
GPT teacher head0.236
Teacher spread0.223 · 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