ArticleScientific reports2023
Machine learning approach for predicting electrical features of Schottky structures with graphene and ZnTiO
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
2 citing papers in PubMed.
- Predicting and analyzing the dielectric constant of polyvinyl alcohol reinforced with different nanofillers using machine learning modeling.Discover nano · 2026Article
- Predictive Modeling and Data-Driven Optimization of CdFeACS omega · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this research, for some different Schottky type structures with and without a nanocomposite interfacial layer, the current-voltage (I-V) characteristics have been investigated by using different Machine Learning (ML) algorithms to predict and analyze the structures' principal electric parameters such as leakage current (I
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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.