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Термоэлектрические свойства твердых растворов Ag-=SUB=-8-=/SUB=-Ge-=SUB=-1-x-=/SUB=-Mn-=SUB=-x-=/SUB=-Te-=SUB=-6-=/SUB=-

2022· article· en· W4293710150 on OpenAlexaff
Р.Н. Рагимов, А.С. Кахраманова, Д.Г. Араслы, А.А. Халилова, И.Х. Мамедов, A. R. Khalilzade

Bibliographic record

VenueФизика и техника полупроводников · 2022
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceManganeseSolid solutionThermoelectric effectElectrical resistivity and conductivityAnalytical Chemistry (journal)DiffractionFigure of meritLattice constantSemiconductorSeebeck coefficientX-ray crystallographyCompressibilityCondensed matter physicsCrystallographyThermal conductivityMetallurgyChemistryThermodynamicsPhysicsOpticsComposite material

Abstract

fetched live from OpenAlex

Abstract. Ag8Ge1-xMnxTe6 solid solutions with different manganese content (x = 0; 0.05; 0.1; 0.2) were prepared by alloying and further pressing the powders under a pressure of 0.6 GPa. By the X-ray diffraction studies have shown that the introduction of manganese atoms leads to the compressibility of the Ag8Ge1-xMnxTe6 lattice. All p-type samples had high resistance below the transition at temperatures of 180 - 220 K. An increase in electrical conductivity in the range of 220 - 300 K was analyzed using the Mott ratio; at temperatures T > 320 K, semiconductor behavior is observed in all compositions. The highest thermoelectric figure of merit ZT = 0.7 at 550 K was obtained for a solid solution of the composition Ag8Ge1-xMnxTe6 (х = 0.05).

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient 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 score0.044
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0050.005
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0020.005
Science and technology studies0.0050.002
Scholarly communication0.0020.003
Open science0.0060.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.008

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 head0.011
GPT teacher head0.234
Teacher spread0.223 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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