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Record W2975900454 · doi:10.1039/c9ra05736f

Mussel-inspired immobilization of Au on bare and graphene-wrapped Ni nanoparticles toward highly efficient and easily recyclable catalysts

2019· article· en· W2975900454 on OpenAlexfundno aff
Fatima Mahnaz, Mohammad Mostafa-Al-Momin, Md. Saiful Islam Rubel, Md Nahid Ferdous, Md. Shafiul Azam

Bibliographic record

VenueRSC Advances · 2019
Typearticle
Languageen
FieldChemistry
TopicNanomaterials for catalytic reactions
Canadian institutionsnot available
FundersBangladesh University of Engineering and TechnologyUniversity of Alberta
KeywordsBimetallic stripCatalysisNanomaterial-based catalystNanocompositeNanoparticleMaterials scienceGrapheneChemical engineeringNickelThermal stabilityNanotechnologyChemistryMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

, respectively, outperforming the catalytic efficiency of its monometallic counterparts and other similar reported catalysts by large margins. The superior catalytic efficiency of the Ni-PD-Au was attributed to the well-known synergistic effect, which was experimentally investigated and compared with prior reports. Similar bio-inspired immobilization of AuNPs was also applied on graphene-wrapped NiNPs (Ni-G) instead of bare NiNPs to synthesize another composite catalyst (Ni-G-PD-Au), which yet again exhibited synergistic catalytic activity. A comparative study between the two nanocomposites suggested that Ni-PD-Au excelled in catalytic activity but Ni-G-PD-Au provided noteworthy stability showing ∼100% efficiency over 17 repeated cycles. However, along with excellent synergistic performance, both nanocomposites demonstrated high magnetization and thermal stability up to 350 °C ascertaining their easy separation and sustainability for high-temperature applications, respectively.

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 score0.010
Threshold uncertainty score0.616

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 head0.010
GPT teacher head0.233
Teacher spread0.224 · 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

Citations10
Published2019
Admission routes1
Has abstractyes

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