HS8.1.5 | Make the invisible visible- microfluidic chip applications in geosciences and ecosystem research-from surface to soil to rocks
EDI
Make the invisible visible- microfluidic chip applications in geosciences and ecosystem research-from surface to soil to rocks
Co-organized by SSS8
Convener: Sascha MüllerECSECS | Co-conveners: Hanbang Zou, Giulia Ceriotti, Edith Hammer

Understanding ecosystems and geoscience at the relevant scales is crucial to push scientific frontiers and develop new hypothesis. The last three decades and alongside with the technological progress, analytical and experimental settings were more and more fine tuned to gain a better understanding of natural processes within and across all earth science disciplines. New insights were gained on a multitude of geosphere interactions. Hereby the small-scale interaction plays an important role in answering fundamental questions in i.e., understanding transport phenomena and governing mechanisms of different physical and chemical processes in porous media, enhance understanding of organic matter dynamics in soil systems or study transient processes between roots, minerals and microbes. Additionally, in recent years microfluidic chip applications have evolved towards in-situ sensing -or environmental monitoring platforms for contaminants, nutrients or microorganisms.
Micro engineered systems such as porous media models, microfluidic chips or 3D printed micro reactors bare the great potential to simulate less accessible environments, particularly at the relevant reactive scale. The full potential in various branches of natural sciences is yet to be explored.
Therefore, this session aims to collect and connect various disciplines of natural sciences who make use of microfluidics for process understanding. But moreover, to inspire each other and create new innovative experimental setups for their own applications.
We encourage contributions focusing on the following applications of microfluidic chips:
• understand microbial and fungal dynamics in soil analogs
• study contaminant behavior in soils and groundwater aquifers at the relevant scale
• microbial dynamics at the soil/air interface
• AI- suported image analysis to enhance subsurface process understanding of microbe and contaminant dynamics

Understanding ecosystems and geoscience at the relevant scales is crucial to push scientific frontiers and develop new hypothesis. The last three decades and alongside with the technological progress, analytical and experimental settings were more and more fine tuned to gain a better understanding of natural processes within and across all earth science disciplines. New insights were gained on a multitude of geosphere interactions. Hereby the small-scale interaction plays an important role in answering fundamental questions in i.e., understanding transport phenomena and governing mechanisms of different physical and chemical processes in porous media, enhance understanding of organic matter dynamics in soil systems or study transient processes between roots, minerals and microbes. Additionally, in recent years microfluidic chip applications have evolved towards in-situ sensing -or environmental monitoring platforms for contaminants, nutrients or microorganisms.
Micro engineered systems such as porous media models, microfluidic chips or 3D printed micro reactors bare the great potential to simulate less accessible environments, particularly at the relevant reactive scale. The full potential in various branches of natural sciences is yet to be explored.
Therefore, this session aims to collect and connect various disciplines of natural sciences who make use of microfluidics for process understanding. But moreover, to inspire each other and create new innovative experimental setups for their own applications.
We encourage contributions focusing on the following applications of microfluidic chips:
• understand microbial and fungal dynamics in soil analogs
• study contaminant behavior in soils and groundwater aquifers at the relevant scale
• microbial dynamics at the soil/air interface
• AI- suported image analysis to enhance subsurface process understanding of microbe and contaminant dynamics