Jun 22 – 25, 2025
University of South Dakota
US/Central timezone

Laser-Induced Graphene-Based Electrochemical Sensors: Advancing Nutrient Sensing, Health Monitoring, and Environmental Diagnostics

Jun 24, 2025, 4:24 PM
24m
Room 216 MUC

Room 216 MUC

The Role of LLMs, Scientific ML, and Data-Driven Approaches in Materials Innovation Parallel Session H: AI and Materials Innovation for Sensing, Alloys, and Device Modeling

Speaker

Gustavo Leite Miliao (Iowa State University)

Description

Laser-Induced Graphene (LIG) offers a cost-effective, scalable platform for electrochemical sensors, driving advancements in environmental monitoring, agriculture, and health diagnostics. Our studies focus on recent developments in LIG-based ion-selective electrodes for nutrient sensing in soil, supporting precision agriculture, and for non-invasive monitoring of metabolites and electrolytes in sweat, enhancing sports performance and health tracking. Additionally, we explore the functionalization of LIG with platinum nanoparticles and the surface tunability of electrodes to improve sensitivity for saliva analysis and nitrite detection in food safety. We also introduce Laser-Induced Graphene Microfluidic Integrated Sensors (LIGMIS), which combine microfluidics with LIG electrodes for real-time ion detection with high selectivity and low detection limits. These sensors enable multiplexed electrochemical detection of pesticides and ions in environmental water monitoring. Incorporating hydrophobic surface tuning and polyethyleneimine coatings, LIGMIS sensors demonstrate enhanced performance and long-term stability across a range of applications, from agriculture to wearable biosensing. This scalable, low-cost approach provides a promising solution for decentralized monitoring in precision agriculture, environmental, and health diagnostics. Looking ahead, we aim to further enhance the selectivity and electrochemical performance of the sensors through the integration of artificial intelligence (AI), which can potentially optimize sensor design by improving stability, sensitivity, and selectivity through data-driven insights.

Primary author

Gustavo Leite Miliao (Iowa State University)

Co-authors

Dr Carmen Gomes (Iowa State University) Dr Jonathan Claussen (Iowa State University)

Presentation materials

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