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Record W3213844830 · doi:10.29169/1927-5129.2021.17.15

ZIF-Derived CuPt@Ag as Catalyst for Hydrogen Evolution Reaction

2021· article· en· W3213844830 on OpenAlexvenueno aff
Gurbet Yerlikaya, Mehmet Koca, Birgül Yazıcı, Murat Farsak, Gülfeza Kardaş

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2021
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsnot available
Fundersnot available
KeywordsCatalysisScanning electron microscopeDielectric spectroscopyMaterials scienceElectrochemistryChemical engineeringElectrodePolarization (electrochemistry)HydrogenInorganic chemistryAnalytical Chemistry (journal)ChemistryPhysical chemistryComposite material

Abstract

fetched live from OpenAlex

In this study, ZIF-Cu and ZIF-Pt were synthesized from 2-methyl imidazole with Cu and Pt salts in the methanol medium. The synthesized ZIFs were annealed to produce a CuPt nanocatalyst in the tube furnace. The Cu: Pt (3:1) nanocatalyst slurry was deposited on silver deposited carbon rod electrode (CE). The hydrogen evolution reaction (HER) activities for the catalyst were measured in a 1 M KOH solution by using potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) techniques. The morphological structure and composition of CuPt@Ag have been studied by scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDX). It was observed that the prepared electrode surface had not only a homogeneous and porous structure but also nano-sized particles distributed on the surface. It has been observed that the current is increased from 5.22 mA cm-2 to 25.80 mA cm-2 under -1.55 V potential at CuPt@Ag electrode. The high current density shows that HER efficiency increases on the prepared catalyst.

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)0.003
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 score0.019
Threshold uncertainty score0.641

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.020
GPT teacher head0.278
Teacher spread0.258 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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