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Record W4317620977 · doi:10.3390/nano13030426

Catalyst Design: Counter Anion Effect on Ni Nanocatalysts Anchored on Hollow Carbon Spheres

2023· article· en· W4317620977 on OpenAlexaff
Ryan O’Connor, Boitumelo J. Matsoso, Victor Mashindi, Pumza Mente, Lebohang Macheli, Beatriz D. Moreno, Bryan P. Doyle, Neil J. Coville, Dean H. Barrett

Bibliographic record

VenueNanomaterials · 2023
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsCanadian Light Source (Canada)
FundersUniversity of Cape TownNational Research Foundation
KeywordsNanomaterial-based catalystNickelCrystallinitySalt (chemistry)Carbon fibersMaterials scienceNanoparticleChlorideCatalysisIonDouble saltChemical engineeringInorganic chemistryChemistryNanotechnologyOrganic chemistryMetallurgyComposite materialComposite number

Abstract

fetched live from OpenAlex

Herein, the influence of the counter anion on the structural properties of hollow carbon spheres (HCS) support was investigated by varying the nickel metal precursor salts applied. TEM and SEM micrographs revealed the dimensional dependence of the HCS shell on the Ni precursor salt, as evidenced by thick (~42 nm) and thin (~23 nm) shells for the acetate and chloride-based salts, respectively. Importantly, the effect of the precursor salt on the textural properties of the HCS nanosupports (~565 m2/gNi(acet)) and ~607 m2/gNiCl), influenced the growth of the Ni nanoparticles, viz for the acetate-(ca 6.4 nm)- and chloride (ca 12 nm)-based salts, respectively. Further, XRD and PDF analysis showed the dependence of the reduction mechanism relating to nickel and the interaction of the nickel–carbon support on the type of counter anion used. Despite the well-known significance of the counter anion on the size and crystallinity of Ni nanoparticles, little is known about the influence of such counter anions on the physicochemical properties of the carbon support. Through this study, we highlight the importance of the choice of the Ni-salt on the size of Ni in Ni–carbon-based nanocatalysts.

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)0.001
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 score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.004

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 head0.028
GPT teacher head0.295
Teacher spread0.267 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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