Silicon Valley meets the ivory tower: Searchable data repositories for experimental nanomaterials research
Source abstract
Abstract
Abstract Centralized data repositories have proven useful in promoting reproducibility and transparency in many fields of research, particularly the life sciences. Nanomaterials research is frequently difficult to reproduce because of the complexity of the systems under study and a lack of standardization due to the exploratory nature of the field. One system that exemplifies this problem is that of the poly(3-hexylthiophene)-based organic field effect transistor, a platform used to study the process-structure-property relationships in semicrystalline polymeric semiconductors. Performance in this system is judged principally by charge carrier mobility, a fitted model parameter that describes how quickly charge can move through the active layer of a given transistor. Reported values of this electrical property vary by over six orders of magnitude for one material and are impacted by intrinsic material properties, processing conditions, device architectures and calculation methods. A database containing over 200 of these devices from 19 studies was compiled to demonstrate the impact of these parameters on reported performance as well as the utility of having such a database to search and explore. We confirm well-known trends such as the effect of polymer molecular weight, transistor channel length and thin film deposition method before going on to identify a standard device that can be compared across multiple studies. We find that an organized database of process-property information can be used to identify unpublished heuristic knowledge, to help authors standardize their reporting of methods, and to guide hypothesis generation and experimental design. A repository of the data used in this study is available at [ http://www.github.com/Imperssonator/OFET-Database] .
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Organic Electronics and Photovoltaics · Advanced Memory and Neural Computing · Machine Learning in Materials Science
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (44)
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