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Record W3199973963 · doi:10.26434/chemrxiv.11956599.v1

Hybrid Inorganic-Organic Perovskite Glasses

2020· preprint· en· W3199973963 on OpenAlexfundno aff
Bikash Kumar Shaw, Ashlea R. Hughes, Maxime Ducamp, David A. Keen, François‐Xavier Coudert, Frédéric Blanc, Thomas D. Bennett

Bibliographic record

VenueChemRxiv · 2020
Typepreprint
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsnot available
FundersScience and Engineering Research BoardRoyal SocietyMcMaster UniversityGrand Équipement National De Calcul IntensifAgence Nationale de la RechercheEngineering and Physical Sciences Research CouncilUniversity of Canterbury
KeywordsDicyanamidePerovskite (structure)Materials scienceQuenching (fluorescence)FerroelectricityIonic bondingPhase (matter)MultiferroicsIonic conductivityIonMineralogyChemical engineeringCrystallographyIonic liquidChemistryDielectricPhysical chemistryOptoelectronicsOrganic chemistryOptics

Abstract

fetched live from OpenAlex

Hybrid perovskites occupy a prominent position within solid-state materials chemistry due to their (e.g.) ionic transport, ferroelectric and multiferroic properties. Here we show that a series of [TPrA][M(Dca) 3 ] perovskites (TPrA = tetrapropylammonium cation; Dca = dicyanamide anion; M = Mn, Fe, Co) melt below 300 °C. A combined experimental-computational approach reveal the melting mechanism, and demonstrates that the hybrid perovskites form glasses upon melt quenching which largely retain the inorganic-organic bonding of the crystalline phase. The very low thermal conductivities of these glasses (~ 0.2 W m -1 K -1 ), moderate electrical conductivities (10 -2 – 10 -4 S m -1 ) and thermo-mechanical properties reminiscent of polymeric materials identify them as a new family of functional glass-formers.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.065
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.016
GPT teacher head0.215
Teacher spread0.199 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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