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Record W7129419575 · doi:10.48505/nims.5885

Introduction to rare earth materials

2025· article· xx· W7129419575 on OpenAlexfundno aff
Ashlee J. Howarth, Toshiki Mori

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2025
Typearticle
Languagexx
FieldMaterials Science
TopicLayered Double Hydroxides Synthesis and Applications
Canadian institutionsnot available
FundersJST-Mirai ProgramNatural Sciences and Engineering Research Council of CanadaNorthwestern University
KeywordsRare earthDiversity (politics)LanthanideElectronic materials

Abstract

fetched live from OpenAlex

The rare-earth elements include Sc, Y and the 15 lanthanoids from La to Lu. Owing, in part, to their diverse coordination numbers and geometries and relatively localized orbitals, the rare-earth materials find application in lighting, displays, catalysis, hydrogen storage, photovoltaics, magnetism, magnetocalorics, thermoelectrics, biomedical science and sensing, amongst others. In addition to their various applications, there are diverse classes of materials that can be synthesized using rare-earth elements including coordination complexes, polymers, metal–organic frameworks (MOFs), solid-state inorganic materials, and nanoparticles, etc.1 Therefore, in addition to the structural diversity of rare-earth materials, the unique electronic properties of the rare-earth elements, particularly the lanthanoids, engenders the resulting materials with tunable luminescence, and magnetic behaviour, etc.1,2 This themed collection showcases both the structural diversity and versatile physical properties of rare-earth materials. Here we have chosen several papers from this themed collection to feature, while there are other papers in the collection that advance the materials chemistry of rare-earth elements, and we believe that all papers will be of high interest to the community.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient 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 score0.408
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.267
Teacher spread0.253 · 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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueInstitutional Repositories DataBase (IRDB)Same topicLayered Double Hydroxides Synthesis and ApplicationsFrench-language works237,207