Exploring Structural Nuances in Germanium Halide Perovskites Using Solid-State <sup>73</sup> Ge and <sup>133</sup> Cs NMR Spectroscopy
Bibliographic record
Abstract
Metal halide perovskites remain top candidates for higher-performance photovoltaic devices, but concerns about leading lead-based materials remain. Ge perovskites remain understudied for use in solar cells compared to their Sn-based counterparts. In this work, we undertake a combined 73 Ge and 133 Cs solid-state Nuclear Magnetic Resonance (NMR) spectroscopy and density functional theory (DFT) study of the bulk CsGeX 3 (X = Cl, Br, or I) series. We show how seemingly small structural variations within germanium halide perovskites have major effects on their 73 Ge and 133 Cs NMR signatures and reveal a near-cubic phase at room temperature for CsGeCl 3 with severe local Ge polyhedral distortion. Quantum chemical computations are effective at predicting the structural impact on NMR parameters for 73 Ge and 133 Cs. This study demonstrates the value of a combined solid-state NMR and DFT approach for investigating promising materials for energy applications, providing information that is out of reach with conventional characterization methods, and adds the challenging 73 Ge nucleus to the NMR toolkit.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".