MétaCan
Menu
Back to cohort

The study of multi-phase rare-earth phosphate-borosilicate glass composites synthesized by the ceramic method via a 1-step pathway

2024· article· en· W4405463989 on OpenAlexaff
Ebenezer Arthur, Andrew P. Grosvenor

Bibliographic record

VenueJournal of Non-Crystalline Solids · 2024
Typearticle
Languageen
FieldMaterials Science
TopicNuclear materials and radiation effects
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsBorosilicate glassComposite materialMaterials scienceCeramicPhase (matter)Glass-ceramicPhosphateRare earthPhosphate glassChemistryMetallurgyDopingOptoelectronics

Abstract

fetched live from OpenAlex

Multi-phase glass-ceramic composites (i.e., composite materials containing multiple ceramic crystallites dispersed in a glass matrix) that have applications as potential nuclear waste form materials have been synthesized and examined. Crystallites of xenotime-type (YPO 4 ) phosphates and monazite-type (LaPO 4 ) phosphates were dispersed in borosilicate glass . The composition of the glass matrix was varied to eliminate the formation of unwanted secondary phases. Powder X-ray diffraction (XRD), X-ray absorption near-edge spectroscopy (XANES), Scanning Electron Microscopy (SEM), and Energy-Dispersive X-ray spectroscopy (EDX) were employed to investigate the long-range order, local chemical environment, and morphology of the composite materials. The XRD patterns and XANES spectra revealed the exclusive presence of LaPO 4 and YPO 4 phases within the composite subsequent to the modification of the glass matrix. The SEM images and EDX maps collected from these composite materials also showed only the presence of LaPO 4 and YPO 4 after modification of the glass composition by removing Na 2 O and CaO.

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 score0.001

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)0.0000.000

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 head0.012
GPT teacher head0.304
Teacher spread0.292 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Non-Crystalline SolidsSame topicNuclear materials and radiation effectsFrench-language works237,207