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Record W4406761142 · doi:10.3390/nano15030177

Improving the Oxygen Evolution Reaction Performance of Ternary Layered Double Hydroxides by Tuning All Three Cations’ Electronic Structures

2025· article· en· W4406761142 on OpenAlexaff
Gayi Nyongombe, Mohamed Siaj, M.S. Dhlamini

Bibliographic record

VenueNanomaterials · 2025
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversité du Québec à Montréal
FundersNational Research Foundation
KeywordsOxygen evolutionIntercalation (chemistry)Layered double hydroxidesBimetallic stripTernary operationMaterials scienceX-ray photoelectron spectroscopyWater splittingElectrolysisInorganic chemistryChemical engineeringConductivitySulfurCatalysisChemistryElectrolyteElectrochemistryElectrodeMetalMetallurgyPhysical chemistryComputer science

Abstract

fetched live from OpenAlex

The pursuit of efficient and sustainable hydrogen production is essential in the fight against climate change. One important method for achieving this is the electrolysis of water, particularly through the oxygen evolution reaction (OER). Recent studies indicate that trimetallic layered double hydroxides (LDHs) can enhance OER performance compared to bimetallic LDHs. This improvement occurs because the third cation alters the electronic structures of the other two cations, thereby increasing the intermediates' binding energies and enhancing electrical conductivity. This study proposes an approach enabling the modulation of the electronic structures of all three cations involved in the synthesis of the trimetallic LDHs. It suggested intercalating sodium dodecyl sulfate (SDS) into the interlayer of the trimetallic NiFe-La-LDH. A successful intercalation of SDS has been confirmed through the XRD, FT-IR, EDS, and XPS. This has expanded the interlayer distance which was beneficial for the electrical conductivity. Furthermore, SDS generated sulphur, which modulated the electronic structures of all three cations enriching the active sites and improving electrical conductivity and OER performance compared to its counterparts. This approach is beneficial: 1. The interlayer can be further enlarged by using different doping ratios of SDS. 2. Sulphur can enrich the active sites and improve the OER performance.

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 score0.011
Threshold uncertainty score0.998

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 head0.218
Teacher spread0.211 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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