ArticleChemistryOpen2026
Extracting Knowledge of 2D-Hybrid Halide Perovskite Materials: A Data Mining Approach.
Article in ChemistryOpen, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
2D-hybrid halide perovskites are semiconductor materials with excellent optical properties that have been widely studied due to their potential as materials applicable to green energy production. Data mining techniques are powerful tools to recognize and extract relevant patterns from databases. In this study, data mining techniques are used to extract relationships among geometric characteristics, composition, and the bandgap of 2D-hybrid halide perovskites. Our analysis reveals patterns that connect the chemical properties of the organic spacer cation with the bandgap, like the distance between the halogen in the perovskite and the terminal nitrogen of the interlayer organic cation, and the relation among the type of organic interlayer cation, the interlayer distance, and the perovskite layer phase. Furthermore, it is found that aromatic cations lead to bandgaps between 2.2 and 2.4 eV. These results are consistent with previous experimental reports and provide insight into the structure-property relationships, thus illustrating the utility of data mining techniques for extracting valuable knowledge to optimize and design new materials with improved properties.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.