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Ti/C and Ti/B Nanocomposites: Comparison of Sorption-Desorption Properties

2009· article· en· W2047445575 on OpenAlexaff
T. I. Khomenko, А. В. Леонов, C. Borchers, E.Z. Kurmaev, Alexandr Moewes

Bibliographic record

VenueDiffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena · 2009
Typearticle
Languageen
FieldEngineering
TopicIntermetallics and Advanced Alloy Properties
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMaterials scienceDesorptionSorptionNanocompositeBoronHydrogenAmorphous solidChemical engineeringEconomies of agglomerationAnalytical Chemistry (journal)AdsorptionComposite materialCrystallographyPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Hydrogen sorption-desorption properties of Ti/B and Ti/C nanocomposites prepared under ball-milling of the corresponding powders in H2 flow were studied using kinetic, microscopic and spectroscopic techniques. Amorphous boron was found to be more effective in spurring Ti – H2 interaction than carbon because of the following properties: (1) significant fragmentation of Ti powder by preventing agglomeration of the particles; (2) unhindered hydrogen access to the surface of Ti nanoparticles through the boron matrix; (3) appearance of new occupation sites available for H atoms, which are characterized by low H2 desorption temperature. The dynamics of the formation of these sites and the H2 distribution between different occupation sites in dependence on phase composition and morphology were studied for the Ti/B system.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.292
Teacher spread0.246 · 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

Citations2
Published2009
Admission routes1
Has abstractyes

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Same venueDiffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomenaSame topicIntermetallics and Advanced Alloy PropertiesFrench-language works237,207