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Record W4200257528 · doi:10.26434/chemrxiv-2021-6tl3z

Exploring Structural Nuances in Germanium Halide Perovskites using Solid-state 73Ge and 133Cs NMR Spectroscopy

2021· preprint· en· W4200257528 on OpenAlexafffund
Riley W. Hooper, Chuyi Ni, Dylan G. Tkachuk, Yingjie He, Victor V. Terskikh, Jonathan G. C. Veinot, Vladimir K. Michaelis

Bibliographic record

VenueChemRxiv · 2021
Typepreprint
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsNational Research Council CanadaUniversity of Alberta
FundersAlberta InnovatesCanada Research ChairsNational Research Council CanadaCanada Foundation for InnovationUniversity of AlbertaNatural Sciences and Engineering Research Council of CanadaGovernment of Alberta
KeywordsGermaniumHalideNuclear magnetic resonance spectroscopySolid-state nuclear magnetic resonanceChemistrySolid-stateMaterials scienceChemical physicsNanotechnologyPhysicsPhysical chemistryNuclear magnetic resonanceInorganic chemistryOptoelectronicsStereochemistrySilicon

Abstract

fetched live from OpenAlex

Metal-halide perovskites remain top candidates for better-performing photovoltaic devices but concerns with leading lead-based materials continue. Ge perovskites remain understudied for use in solar cells compared to their Sn-based counterparts. In this work, we undertake a combined 133Cs and 73Ge solid-state NMR and DFT study of the bulk CsGeX3 (X = Cl, Br, I) series. We show how seemingly small structural variations within germanium halide perovskites have major consequences on their 73Ge and 133Cs NMR signatures and reveal a near cubic-like phase at room temperature for CsGeCl3 with severe local Ge polyhedral distortion. Quantum chemical computations are effective at predicting the structural impact on NMR parameters for 73Ge and 133Cs. This study demonstrates the value of a combined experimental and theoretical approach for investigating attractive materials for energy applications – providing information that is out of reach with conventional characterization methods – and adds the challenging 73Ge nucleus to the toolkit.

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 score0.004

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 head0.080
GPT teacher head0.341
Teacher spread0.261 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

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