This study innovates plasmonic hydrogen sensors (PHSs) by applying phase space reconstruction (PSR) and convolutional neural networks (CNNs), overcoming previous predictive and sensing limitations. Utilizing a low-cost and efficient colloidal lithography technique, palladium nanocap arrays are created and their spectral signals are transformed into images using PSR and then trained using CNNs for predicting the hydrogen level. The model achieves accurate predictions with average accuracies of 0.
View Article and Find Full Text PDFA general strategy for generating various Janus particles (JPs) based on shadow sphere lithography (SSL) by varying incident and azimuthal angles, as well as deposition numbers is introduced, forming well-identified flower-like patches on microsphere monolayers. An in-house simulation program is worked out to predict the patch morphology with complicated fabrication parameters. The predicted patch morphology matches quite well that of experimentally produced JPs.
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