2023
DOI: 10.1007/s13204-023-02959-3
|View full text |Cite
|
Sign up to set email alerts
|

Optimizing silver nanowire synthesis: machine learning improves and predicts yield for a polyol, millifluidic flow reactor

Destiny F. Williams,
Nick Rahimi,
James E. Smay
et al.
Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(1 citation statement)
references
References 45 publications
0
1
0
Order By: Relevance
“…19(d)). 147 RF uses sample and property bagging, reducing properties and creating diverse DTs. Analyzing DT outcomes yields a final prediction, leveraging anticipated outcomes to refine property selection.…”
Section: Helical Nanosprings Morphology Controlmentioning
confidence: 99%
“…19(d)). 147 RF uses sample and property bagging, reducing properties and creating diverse DTs. Analyzing DT outcomes yields a final prediction, leveraging anticipated outcomes to refine property selection.…”
Section: Helical Nanosprings Morphology Controlmentioning
confidence: 99%