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Record W4282929452 · doi:10.1007/s12274-022-4478-0

Direct assembly between closed-shell coinage metal superatoms

2022· article· en· W4282929452 on OpenAlexaff
Famin Yu, Yu Zhu, Yang Gao, Rui Wang, Wanrong Huang, Yi Gao, Zhigang Wang

Bibliographic record

VenueNano Research · 2022
Typearticle
Languageen
FieldMaterials Science
TopicNanocluster Synthesis and Applications
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsDelocalized electronSuperatomLocalized molecular orbitalsChemistryChemical physicsSupermoleculeAtomic orbitalMetalChemical bondNanotechnologyElectronic structureMaterials scienceComputational chemistryBasis setDensity functional theoryLinear combination of atomic orbitalsMoleculePhysics

Abstract

fetched live from OpenAlex

Bottom-up constructing all-metal functional materials is challenging, because the metal clusters are prone to lose their original structures during coalensence. In this work, we report that closed-shell coinage metal superatoms can achieve direct chemical bonding without losing their electronic properties. The reason is that the supermolecule formed by two superatoms has the same number of bonding and anti-bonding supermolecular orbitals, in which the bonding orbitals contribute to bonding and the anti-bonding orbitals with anti-phase orbitals delocalized over each monomer to maintain the individual geometric and electronic structural properties. Further analysis indicates the interactions between two superatoms are too weak to break the structure of monomers, which is confirmed by the first-principles molecular dynamics simulations. With these superatoms as the basic units, a series of robust one-dimensional and two-dimensional nanostructures are fabricated. Our findings provide a general strategy to take advantage of superatoms in regulating bonding compared to natural atoms, which paves the way for the bottom-up design of materials with collective properties.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.216
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.092
GPT teacher head0.369
Teacher spread0.277 · 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; both teacher heads agree on what is shown here.

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

Citations20
Published2022
Admission routes1
Has abstractyes

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