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Record W3148722180 · doi:10.1063/5.0045606

Potassium-activated anionic copper and covalent Cu–Cu bonding in compressed K–Cu compounds

2021· article· en· W3148722180 on OpenAlexafffund
Xuyan Cao, Biao Wan, Hanyu Liu, Lailei Wu, Yansun Yao, Huiyang Gou

Bibliographic record

VenueThe Journal of Chemical Physics · 2021
Typearticle
Languageen
FieldChemistry
TopicInorganic Chemistry and Materials
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaDepartment of Education of Hebei Province
KeywordsCopperCovalent bondZigzagAlkali metalAtomic orbitalChemistryCrystallographyMetalElectronic structurePotassiumMolecular orbitalPhase (matter)Chemical physicsInorganic chemistryComputational chemistryElectronMoleculeOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Elemental copper and potassium are immiscible under ambient conditions. It is known that pressure is a useful tool to promote the reaction between two different elements by modifying their electronic structure significantly. Here, we predict the formation of four K–Cu compounds (K3Cu2, K2Cu, K5Cu2, and K3Cu) under moderate pressure through unbiased structure search and first-principles calculations. Among all predicted structures, the simulated x-ray diffraction pattern of K3Cu2 perfectly matches a K–Cu compound synthesized in 2004. Further simulations indicate that the K–Cu compounds exhibit diverse structural features with novel forms of Cu aggregations, including Cu dimers, linear and zigzag Cu chains, and Cu-centered polyhedrons. Analysis of the electronic structure reveals that Cu atoms behave as anions to accept electrons from K atoms through fully filling 4s orbitals and partially extending 4p orbitals. Covalent Cu–Cu interaction is found in these compounds, which is associated with the sp hybridizations. These results provide insights into the understanding of the phase diversity of alkali/alkaline earth and metal systems.

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 score0.003
Threshold uncertainty score0.562

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 integrity0.0000.001
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 head0.017
GPT teacher head0.248
Teacher spread0.231 · 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

Citations7
Published2021
Admission routes2
Has abstractyes

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