Nuclear Quadrupolar Resonance Structural Characterization of Halide Perovskites and Perovskitoids: A Roadmap from Electronic Structure Calculations for Lead–Iodide-Based Compounds
Bibliographic record
Abstract
Metal halide perovskites, including some of their related perovskitoid structures, form a semiconductor class of their own, which is arousing ever-growing interest from the scientific community. With halides being involved in the various structural arrangements, namely, pure corner-sharing MX 6 (M is metal and X is halide) octahedra, for perovskite networks, or alternatively a combination of corner-, edge-, and/or face-sharing for related perovskitoids, they represent the ideal probe for characterizing the way octahedra are linked together. Well known for their inherently large quadrupolar constants, which is detrimental to the resolution of nuclear magnetic resonance spectroscopy, most abundant halide isotopes ( 35/37 Cl, 79/81 Br, 127 I) are in turn attractive for magnetic field-free nuclear quadrupolar resonance (NQR) spectroscopy. Here, we investigate the possibility of exploiting NQR spectroscopy of halides to distinctively characterize the various metal halide structural arrangements, using density functional theory simulations. Our calculations nicely match the available experimental results. Furthermore, they demonstrate that compounds with different connectivities of their MX 6 building blocks, including lower dimensionalities such as 2D networks, show distinct NQR signals in a broad spectral window. They finally provide a roadmap of the characteristic NQR frequency ranges for each octahedral connectivity, which may be a useful guide to experimentalists, considering the long acquisition procedures typical of NQR. We hope this work will encourage the incorporation of NQR spectroscopy to further our knowledge of the structural diversity of metal halides.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".