High Throughput Data-Driven Design of Laser-Crystallized 2D MoS₂Chemical Sensors: A Demonstration for NO₂Detection

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 the 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 MoS₂thin 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 6000 distinct nonresonant, resonant, and polarized Raman spectra. The rapid generation of a comprehensive library of structural and compositional data elucidated important trends between structure-property processing relationships involving laser-crystallized MoS₂, including the relationships between grain size, grain orientation, and intrinsic strain. Moreover, extensive analysis of structure/property relationships allowed for intelligent design and evaluation of major contributions to device performance in MoS₂chemical sensors. In particular, it is found that NO₂sensor performance is strongly dependent on the orientation of the MoS₂grains relative to the crystal plane.