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Record W3111507543 · doi:10.1021/acs.inorgchem.0c02501

High Entropy and Low Symmetry: Triclinic High-Entropy Molybdates

2020· article· en· W3111507543 on OpenAlexaff
David Stenzel, I. Issac, Kai Wang, Raheleh Azmi, Ruby Singh, Jaehoon Jeong, Saleem Najib, Subramshu S. Bhattacharya, Horst Hahn, Torsten Brezesinski, Simon Schweidler, Ben Breitung

Bibliographic record

VenueInorganic Chemistry · 2020
Typearticle
Languageen
FieldEngineering
TopicHigh Entropy Alloys Studies
Canadian institutionsUniversity of Waterloo
FundersHorizon 2020 Framework ProgrammeHelmholtz AssociationDeutsche Forschungsgemeinschaft
KeywordsChemistryTriclinic crystal systemRaman spectroscopyEntropy (arrow of time)X-ray photoelectron spectroscopyMolybdateCrystal structureCrystallographyThermodynamicsInorganic chemistryPhysicsNuclear magnetic resonanceOptics

Abstract

fetched live from OpenAlex

Metal molybdates constitute a promising class of materials with a wide application range. Here, we report, to our knowledge for the first time, on the preparation and characterization of medium-entropy and high-entropy metal molybdates, synthesized by an oxalate-based coprecipitation approach. The high-entropy molybdate crystallizes in a triclinic structure, thus rendering it as high-entropy material with the lowest symmetry reported so far. This is noteworthy because high-entropy materials usually tend to crystallize into highly symmetrical structures. It is expected that application of the high-entropy concept to metal molybdates alters the material's characteristics and adds the features of high-entropy systems, that is, tailorable composition and properties. The phase purity and solid solution nature of the molybdates were confirmed by XRD, Raman spectroscopy, TEM, XPS, and ICP-OES.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.006
GPT teacher head0.187
Teacher spread0.181 · 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 source (direct Gemma or distilled Codex), 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

Citations18
Published2020
Admission routes1
Has abstractyes

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