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Record W4392013985 · doi:10.1021/acs.jpcc.3c06417

Unraveling the Enhanced N<sub>2</sub> Activity on CuNi Alloy Catalysts for Ammonia Production: Experiments, DFT, and Statistical Analysis

2024· article· en· W4392013985 on OpenAlexafffund
Parastoo Agharezaei, Noguchi Goroh Tomohiro, Hirokazu Kobayashi, Hartmut Schlenz, Miho Yamauchi, Kulbir Kaur Ghuman

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2024
Typearticle
Languageen
FieldChemical Engineering
TopicAmmonia Synthesis and Nitrogen Reduction
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaMitacsJapan Society for the Promotion of ScienceCanada Research ChairsMinistry of Education, Culture, Sports, Science and TechnologyCanada Foundation for InnovationCompute Canada
KeywordsAlloyAmmonia productionAmmoniaCatalysisProduction (economics)Materials scienceMetallurgyChemical engineeringChemistryOrganic chemistryEngineeringEconomics

Abstract

fetched live from OpenAlex

One of the main challenges in designing catalysts for ammonia synthesis is to create active sites on the surface of the catalyst that prefers to reduce the strong N 2 molecule despite its highly stable structure. Binary alloys have been demonstrated as potential ammonia synthesis catalysts in the literature. However, for binary alloys to be commercially viable, one needs to improve their catalytic activity for N 2 reduction by strategically manipulating the several unique active sites present on their surface. Herein, by using computational tools, we created five different compositions of Cu x Ni 1– x (0.5 ≤ x ≤ 0.9) alloys via special quasi-random structure (SQS) and genetic algorithm (GA). The alloy with about 50% of Cu and 50% of Ni is predicted to have the highest catalytic activity based on the shift of the d-band center toward the Fermi level. We then synthesized MgO-supported Cu 0.5 Ni 0.5 nanoparticles and compared their activity for ammonia synthesis with that of Ni/MgO and Cu/MgO. It was found that the MgO-supported Cu 0.5 Ni 0.5 alloy possesses 21 times higher activity than Cu/MgO and 3 times higher than Ni/MgO for ammonia synthesis, confirming the computational results. To demonstrate the impact of alloying on the catalytic activity, we further investigated all the possible unique sites on the surface of the Cu 0.5 Ni 0.5 alloy for nitrogen reduction reaction (NRR) via density functional theory (DFT). The investigation of the 96 unique active sites on the Cu 0.5 Ni 0.5 surface demonstrated that the position and concentration of Ni atoms near each investigated adsorption site have a linear correlation with the adsorption energy of the N 2 . Along with the structural and electronic properties of the active sites modified by Ni, orientation of the N 2 molecule also plays an important role in determining the activity of the CuNi catalyst. These findings not only explained the notable increase in the activity of CuNi catalysts compared to the pure metals for NH 3 synthesis but also offered critical insights required to tailor the specific surface environment of CuNi catalysts for NRR. This knowledge can serve as a foundation for further developments in designing binary alloy catalysts for sustainable ammonia synthesis.

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 score0.013
Threshold uncertainty score0.396

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 head0.012
GPT teacher head0.264
Teacher spread0.252 · 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

Citations9
Published2024
Admission routes2
Has abstractyes

Explore more

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