
doi: 10.1039/d2lc00416j
pmid: 36106479
Improving surfactant-laden microdroplet size prediction using data-driven methods.
T-JUNCTION, Technology, Biochemistry & Molecular Biology, MICROFLUIDICS, Science & Technology, Multidisciplinary, BUBBLES, Bayes Theorem, Analytical, Microfluidic Analytical Techniques, Biochemical Research Methods, Chemistry, Surface-Active Agents, ARTIFICIAL NEURAL-NETWORK, Physical Sciences, Science & Technology - Other Topics, Silicone Oils, Nanoscience & Nanotechnology, Life Sciences & Biomedicine, Instruments & Instrumentation, Micelles
T-JUNCTION, Technology, Biochemistry & Molecular Biology, MICROFLUIDICS, Science & Technology, Multidisciplinary, BUBBLES, Bayes Theorem, Analytical, Microfluidic Analytical Techniques, Biochemical Research Methods, Chemistry, Surface-Active Agents, ARTIFICIAL NEURAL-NETWORK, Physical Sciences, Science & Technology - Other Topics, Silicone Oils, Nanoscience & Nanotechnology, Life Sciences & Biomedicine, Instruments & Instrumentation, Micelles
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 38 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 1% |
