MétaCan
Menu
Back to cohort
Record W3109102779 · doi:10.1002/anie.202013339

Fast Li‐Ion Conductivity in Superadamantanoid Lithium Thioborate Halides

2020· article· en· W3109102779 on OpenAlexafffund
Kavish Kaup, Abdeljalil Assoud, Jue Liu, Linda F. Nazar

Bibliographic record

VenueAngewandte Chemie International Edition · 2020
Typearticle
Languageen
FieldMaterials Science
TopicCrystal Structures and Properties
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaBASF
KeywordsHalideLithium (medication)IonNeutron diffractionPolarizabilityThiophosphateIonic conductivityInorganic chemistryChemistryIonic bondingStoichiometryConductivityCrystal structureMaterials scienceCrystallographyPhysical chemistryMoleculeOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Lithium thioborates are promising fast Li‐ion conducting materials, with similar properties to their lithium thiophosphate counterparts that have enabled the development of solid‐state Li‐ion batteries. By comparison, thioborates have scarcely been developed, however, offering new space for materials discovery. Here we report a new class of lithium thioborate halides that adopt a so‐called supertetrahedral adamantanoid structure that houses mobile lithium ions and halide anions within interconnected 3D structural channels. Investigation of the structure using single‐crystal XRD, neutron powder diffraction, and neutron PDF reveals significant lithium and halide anion disorder. The phases are non‐stoichiometric, adopting slightly varying halide contents within the materials. These new superadamantanoid materials exhibit high ionic conductivities up to 1.4 mS cm−1, which can be effectively tuned by the polarizability of the halide anion within the channels.

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 score0.003

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)0.0010.000

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 head0.027
GPT teacher head0.249
Teacher spread0.221 · 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

Citations27
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueAngewandte Chemie International EditionSame topicCrystal Structures and PropertiesFrench-language works237,207