High throughput data-driven design of laser crystallized 2D MoS2 chemical sensors
Drake Austin, Paige Miesle, Deanna Sessions, Michael Motala, David, Moore, Griffin Beyer, Adam Miesle, Andrew Sarangan, Amritanand Sebastian,, Saptarshi Das, Anand Puthirath, Xiang Zhang, Jordan Hachtel, Pulickel Ajayan,, Tyson Back, Peter Stevenson, Michael Brothers, Steven Kim

TL;DR
This study demonstrates a high throughput, data-driven approach to design and analyze laser-crystallized 2D MoS2 chemical sensors, revealing key structure-property-performance relationships through extensive Raman spectroscopy data.
Contribution
The paper introduces a high throughput Raman spectroscopy method to analyze laser-crystallized MoS2, linking structural variations to sensor performance, which is a novel approach in 2D material device design.
Findings
Sensor performance depends on grain orientation.
Laser crystallization creates diverse structural regions.
Comprehensive data enables predictive design of sensors.
Abstract
High throughput characterization and processing techniques are becoming increasingly necessary to navigate multivariable, data-driven design challenges for sensors and electronic devices. For two-dimensional materials, device performance is highly dependent upon a vast array of material properties including number of layers, lattice strain, carrier concentration, defect density, and grain structure. In this work, laser-crystallization was used to locally pattern and transform hundreds of regions of amorphous MoS2 thin films into 2D 2H-MoS2. A high throughput Raman spectroscopy approach was subsequently used to assess the process-dependent structural and compositional variations for each illuminated region, yielding over 5500 distinct non-resonant, resonant, and polarized Raman spectra. The rapid generation of a comprehensive library of structural and compositional data elucidated…
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Taxonomy
TopicsGas Sensing Nanomaterials and Sensors · Machine Learning in Materials Science · 2D Materials and Applications
