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Record W3004349295 · doi:10.1039/c9dt04789a

Templated synthesis of nickel nanoparticles embedded in a carbon layer within silica capsules

2020· article· en· W3004349295 on OpenAlexaff
Dandan Song, Jing Zheng, Libin Liu, Hamed Alsulami, Marwan Amin Kutbi, Jingli Xu, Min Zhang

Bibliographic record

VenueDalton Transactions · 2020
Typearticle
Languageen
FieldChemistry
TopicNanomaterials for catalytic reactions
Canadian institutionsToronto Metropolitan University
FundersNational Natural Science Foundation of ChinaNatural Science Foundation of Shanghai
KeywordsNickelNanoparticleLayer (electronics)Carbon fibersMaterials scienceChemical engineeringNanotechnologyMetallurgyComposite materialComposite number

Abstract

fetched live from OpenAlex

The encapsulation of small non-noble metal nanoparticles (NPs) within an inorganic layer has received considerable attention owing to their enhanced stability and high catalytic activity. Using a combination of emulsion-free polymerization, inner RF-Ni2+ and outer SiO2 coating, and subsequent carbonization treatment, herein, we have fabricated worm-like structured Ni-based composites in which a high density of nickel NPs are embedded in a carbon layer and also entrapped by SiO2 nanocages. We find that the carbonization temperature plays a vital role in adjusting the size of the Ni NPs. A detailed examination of the encapsulated nickel particles synthesized at 700 °C exhibited the best performance on the catalysis of the reduction of 4-NPs. Moreover, owing to the good alloying ability of the Ni NPs with noble metal NPs, the Ni-Pd alloy NPs are also entrapped in the SiO2 nanocages, which exhibit better performance on the catalysis than the Ni-based composites. The encapsulation of Ni-Pd alloys within SiO2 nanocages also improves stability against agglomeration and metal separation during catalytic operation.

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 score0.002
Threshold uncertainty score0.783

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)0.0010.000

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 head0.023
GPT teacher head0.241
Teacher spread0.218 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueDalton TransactionsSame topicNanomaterials for catalytic reactionsFrench-language works237,207