MétaCan
Menu
Back to cohort
Record W2898761675 · doi:10.1002/smtd.201800344

One‐Step Synthesis of NiMn‐Layered Double Hydroxide Nanosheets Efficient for Water Oxidation

2018· article· en· W2898761675 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

VenueSmall Methods · 2018
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsLakehead University
FundersGuangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation TechnologyNational Natural Science Foundation of China
KeywordsLayered double hydroxidesOverpotentialExfoliation jointHydroxideMaterials scienceChemical engineeringHydrothermal circulationAdsorptionNoble metalMetalInorganic chemistryNanotechnologyElectrochemistryChemistryGrapheneMetallurgyElectrodeOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract Layered double hydroxides (LDHs) are noble metal–free 2D materials promising for water oxidation. One‐step synthesis of ultrathin NiMn‐LDHs nanosheets is successfully achieved at room temperature avoiding the multiple tedious steps (e.g., hydrothermal treatment, exfoliation). The as‐prepared NiMn‐LDHs (1.3 nm thickness) exhibit the twofold enhancement of the activity and a reduction of overpotential by 80 mV at 10 mA cm −2 in comparison with the traditional NiMn‐LDHs in 0.1 m NaOH, which is superior to the previously reported LDH‐derived electrocatalysts. The combination of theoretical and experimental results manifest that the largely enhanced electrocatalytic water oxidation activity of NiMn‐LDHs nanosheets is associated with the highly exposed active sites with a nearly optimal intermediates (*OH and *O) adsorption energy.

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)0.002
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 score0.095
Threshold uncertainty score0.790

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.036
GPT teacher head0.307
Teacher spread0.271 · 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