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Record W2249178608 · doi:10.1021/acs.jpcc.5b07862

Crystal Structures and Chemical Bonding of Magnesium Carbide at High Pressure

2015· article· en· W2249178608 on OpenAlexaff
Hanyu Liu, Guoying Gao, Yinwei Li, Jian Hao, John S. Tse

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2015
Typearticle
Languageen
FieldMaterials Science
TopicMXene and MAX Phase Materials
Canadian institutionsUniversity of Saskatchewan
FundersPriority Academic Program Development of Jiangsu Higher Education InstitutionsNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsChemical bondMaterials scienceCarbideStoichiometryValence electronChemical physicsValence (chemistry)Crystal structureMetallic bondingElectronic structureCrystallographyMagnesiumMetalElectronChemistryComputational chemistryPhysical chemistryMetallurgy

Abstract

fetched live from OpenAlex

Recent studies of the magnesium carbide (Mg–C) system under pressure were motivated by the successful high-pressure and high-temperature synthesis of Mg 2 C and Mg 2 C 3 . Here, we systematically investigate the high-pressure structures and chemical bonding of the Mg 2 C, Mg 2 C 3, and MgC 2 system using the swarm optimization technique in combination with first-principles electronic structure methodology. The structural evolution with pressure of the Mg–C systems clearly shows a systematic trend with a progressive increase of electron donation from the Mg to C. To accommodate the electrons, the C valence sp orbitals rebybridized continually and adopted different modes of chemical bonding. We demonstrated that the evolution of the electronic and crystal structures can be explained from a Zintl–Klemen charge-transfer concept. Therefore, at sufficiently high pressure metallic MgC 2 and Mg 2 C transformed to semiconductors, while Mg 2 C 3 undergoes an insulator–metal transition. The present results established the richness of carbon bonding of different stoichiometries under high pressure.

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 score0.272

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 head0.013
GPT teacher head0.248
Teacher spread0.234 · 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

Citations21
Published2015
Admission routes1
Has abstractyes

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