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Record W2255403248 · doi:10.1002/cphc.201500944

Cu‐Based Nanocomposites as Multifunctional Catalysts

2015· article· en· W2255403248 on OpenAlexaff
Bharat Kumar, Gyandshwar Kumar Rao, Soumen Saha, Ashok K. Ganguli

Bibliographic record

VenueChemPhysChem · 2015
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Ottawa
FundersInstitute of Nano Science and TechnologyIndian Institute of Technology Delhi
KeywordsNanorodNanocompositeCatalysisSonogashira couplingMaterials scienceNanoparticleYield (engineering)Chemical engineeringHydrothermal circulationAgglomerateHydrothermal synthesisNanotechnologyCoupling reactionChemistryComposite materialOrganic chemistryPalladium

Abstract

fetched live from OpenAlex

Herein, we report the synthesis of Cu/Cu2 O nanocomposites by a one-step hydrothermal process at 180 °C, for which the resulting morphology is dependent on the hydrothermal reaction time (24, 72, and 120 h). With a longer reaction time of 120 h, a rod-shape morphology is obtained, whereas at 72 and 24 h assemblies of nanoparticles are obtained. The rod-shaped (120 h) particles of the Cu/Cu2 O nanocomposites show a much higher efficiency (6.3 times) than the agglomerates and 2.5 times more than the assemblies of nanoparticles for the hydrogen-evolution reaction. During the oxygen-evolution reaction, the nanorods produce a current that is 5.2 and 3.7 times higher than that produced by the agglomerated and assembled nanoparticles, respectively. The electrocatalysts are shown to be highly stable for over 50 cycles. As catalysts for organic synthesis, a 100 % yield is achieved in the Sonogashira cross-coupling reaction with the nanorods, which is higher than with the other nanocomposite particles. This result demonstrates the significant enhancement of yield obtained with the nanorods for cross-coupling reactions.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.094
Threshold uncertainty score1.000

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)0.0000.002

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 head0.018
GPT teacher head0.238
Teacher spread0.221 · 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.

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

Citations14
Published2015
Admission routes1
Has abstractyes

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