MétaCan
Menu
Back to cohort

Solid-State Synthesis of Mg<sub>2</sub>X (X=Si, Ge, Sn and Pb) via Bulk Mechanical Alloying

2007· article· en· W2052879423 on OpenAlexaff
Tatsuhiko Aizawa, Ren Bo Song

Bibliographic record

VenueMaterials science forum · 2007
Typearticle
Languageen
FieldMaterials Science
TopicMXene and MAX Phase Materials
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceThermoelectric effectTinThermoelectric materialsFabricationMelting pointAtomic diffusionSolid solutionGermaniumMagnesiumChemical engineeringMetallurgySiliconThermodynamicsComposite materialThermal conductivity

Abstract

fetched live from OpenAlex

Magnesium base alloys and compounds are attractive for various applications as a functional material. In particular, a series of binary system by Mg2X (X = Si, Ge, Sn and Pb) has fascinated many researchers and engineers by its thermoelectric properties and semi-conductive performance. Many barriers in its processing rejects precise investigation of these types of semiconductive compounds: high vaporizing pressure and mechanical adhesion of magnesium, reaction of germanium and tin with crucibles, and, difference of melting point among elements, X. Solidstate processing via the bulk mechanical alloying enables us to directly fabricate these Mg2X semiconductive materials in high density performs. The precise control of chemical composition leads to an investigation on the dilution and enrichment of X in Mg2X. Two types of solid-state reactivity are introduced: e.g. synthesis of Mg2Si from an elemental mixture Mg – Si, which is a nucleationcontrolled process, while synthesis of Mg2Sn from Mg – Sn is a diffusion-controlled process. The thermoelectricity of these Mg2X is evaluated for discussion on the validity and effectiveness of this new PM route as a reliable tool for fabrication of thermoelectric compounds.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.014
GPT teacher head0.275
Teacher spread0.261 · 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.

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

Citations5
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueMaterials science forumSame topicMXene and MAX Phase MaterialsFrench-language works237,207