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Record W4389393854 · doi:10.1038/s41598-023-48355-3

Promotion of hydrogen evolution from seawater via poly(aniline-co-4-nitroaniline) combined with 3D nickel nanoparticles

2023· article· en· W4389393854 on OpenAlexaff
Saleh Moradi‐Alavian, Amir Kazempour, Meysam Mirzaei-Saatlo, Habib Ashassi‐Sorkhabi, Abbas Mehrdad, Elnaz Asghari, Jacob J. Lamb, Bruno G. Pollet

Bibliographic record

VenueScientific Reports · 2023
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersUniversity of Tabriz
KeywordsTafel equationElectrocatalystDielectric spectroscopyMaterials scienceNickelAnilineChemical engineeringElectrochemistryNanoparticleInorganic chemistryElectrodeChemistryNanotechnologyOrganic chemistryPhysical chemistryMetallurgy

Abstract

fetched live from OpenAlex

Abstract This work reports the synthesis of poly (aniline-co-4-nitroaniline) deposited on a three-dimensional nanostructured nickel (3D-Ni) film, where both layers were fabricated via potentiostatic electrodeposition. The obtained electrocatalyst exhibited excellent electrochemical activity for the Hydrogen Evolution Reaction (HER) with small overpotentials of − 195 and − 325 mV at − 10 and − 100 mAcm −2 , respectively, and a low Tafel slope of 53.3 mV dec −1 in seawater. Additionally, the electrocatalyst exhibited good stability after 72 h operation under a constant potential of − 1.9 V vs . RHE. The efficient HER performance of the as -prepared catalyst was found to originate from the synergy between the conducting polymer and three-dimensional nickel nanoparticles with a large electrochemical active surface area. Moreover, the results obtained from electrochemical impedance spectroscopy (EIS) measurements revealed that the presence of 3D-Ni layer improved the kinetics of HER by reducing the charge transfer resistance for the electrocatalyst.

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 score0.008
Threshold uncertainty score0.799

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.217
Teacher spread0.207 · 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

Citations48
Published2023
Admission routes1
Has abstractyes

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