Systems and methods for assessing the quality of electrocatalyst-loaded disk electrodes
Systems and methods described herein relate to assessing the quality of electrocatalyst-loaded disk electrodes. In one embodiment, a system that assesses the quality of electrocatalyst-loaded disk electrodes receives one or more images of a disk electrode on which an electrocatalyst has been deposited to produce an electrocatalyst-loaded disk electrode. The system also processes the one or more images using a machine-learning-based model trained to generate a prediction of a Koutecky-Levich (K-L) quality assessment of the electrocatalyst-loaded disk electrode. The system also accepts or reject inclusion of the electrocatalyst-loaded disk electrode in an electrochemical experimentation process based, at least in part, on the prediction.
1 . A system for assessing quality of electrocatalyst-loaded disk electrodes, the system comprising:
a processor, and
a memory storing machine-readable instructions that, when executed by the processor, cause the processor to:
receive one or more images of a disk electrode on which an electrocatalyst has been deposited to produce an electrocatalyst-loaded disk electrode;
process the one or more images using a machine-learning-based model trained to generate a prediction of a Koutecky-Levich (K-L) quality assessment of the electrocatalyst-loaded disk electrode; and
accept or reject inclusion of the electrocatalyst-loaded disk electrode in an electrochemical experimentation process based, at least in part, on the prediction.
2 . The system of claim 1 , wherein the prediction pertains to an extent to which a surface of the disk electrode is covered by the electrocatalyst.
3 . The system of claim 2 , wherein the prediction is a classification of the electrocatalyst-loaded disk electrode as one of acceptable and unacceptable based on whether an estimate of the extent to which the surface of the disk electrode is covered by the electrocatalyst exceeds a predetermined threshold.
4 . The system of claim 2 , wherein the prediction is an estimate of the extent to which the surface of the disk electrode is covered by the electrocatalyst and the electrocatalyst-loaded disk electrode is accepted or rejected based on whether the estimate of the extent to which the surface of the disk electrode is covered by the electrocatalyst exceeds a predetermined tunable threshold.
5 . The system of claim 1 , wherein the one or more images are microscopic images captured using one of an optical microscope, transmission electron microscopy, and a scanning electron microscope.
6 . The system of claim 1 , wherein the electrocatalyst includes Pt nanoparticles that are used for at least an oxygen reduction reaction (ORR).
7 . The system of claim 1 , wherein the electrochemical experimentation process pertains to one or more of water electrolysis, ammonia electrolysis, ammonia synthesis, carbon dioxide electrolysis, carbon dioxide reduction, the manufacturing of fuel cells, and the manufacturing of batteries.
8 . The system of claim 1 , wherein the electrochemical experimentation process is fully automated.
9 . A non-transitory computer-readable medium for assessing quality of electrocatalyst-loaded disk electrodes and storing instructions that, when executed by a processor, cause the processor to:
receive one or more images of a disk electrode on which an electrocatalyst has been deposited to produce an electrocatalyst-loaded disk electrode;
process the one or more images using a machine-learning-based model trained to generate a prediction of a Koutecky-Levich (K-L) quality assessment of the electrocatalyst-loaded disk electrode; and
accept or reject inclusion of the electrocatalyst-loaded disk electrode in an electrochemical experimentation process based, at least in part, on the prediction.
10 . The non-transitory computer-readable medium of claim 9 , wherein the prediction pertains to an extent to which a surface of the disk electrode is covered by the electrocatalyst.
11 . The non-transitory computer-readable medium of claim 10 , wherein the prediction is a classification of the electrocatalyst-loaded disk electrode as one of acceptable and unacceptable based on whether an estimate of the extent to which the surface of the disk electrode is covered by the electrocatalyst exceeds a predetermined threshold.
12 . The non-transitory computer-readable medium of claim 10 , wherein the prediction is an estimate of the extent to which the surface of the disk electrode is covered by the electrocatalyst and the electrocatalyst-loaded disk electrode is accepted or rejected based on whether the estimate of the extent to which the surface of the disk electrode is covered by the electrocatalyst exceeds a predetermined tunable threshold.
13 . A method, comprising:
receiving one or more images of a disk electrode on which an electrocatalyst has been deposited to produce an electrocatalyst-loaded disk electrode;
processing the one or more images using a machine-learning-based model trained to generate a prediction of a Koutecky-Levich (K-L) quality assessment of the electrocatalyst-loaded disk electrode; and
accepting or rejecting inclusion of the electrocatalyst-loaded disk electrode in an electrochemical experimentation process based, at least in part, on the prediction.
14 . The method of claim 13 , wherein the prediction pertains to an extent to which a surface of the disk electrode is covered by the electrocatalyst.
15 . The method of claim 14 , wherein the prediction is a classification of the electrocatalyst-loaded disk electrode as one of acceptable and unacceptable based on whether an estimate of the extent to which the surface of the disk electrode is covered by the electrocatalyst exceeds a predetermined threshold.
16 . The method of claim 14 , wherein the prediction is an estimate of the extent to which the surface of the disk electrode is covered by the electrocatalyst and the electrocatalyst-loaded disk electrode is accepted or rejected based on whether the estimate of the extent to which the surface of the disk electrode is covered by the electrocatalyst exceeds a predetermined tunable threshold.
17 . The method of claim 13 , wherein the one or more images are microscopic images captured using one of an optical microscope, transmission electron microscopy, and a scanning electron microscope.
18 . The method of claim 13 , wherein the electrocatalyst includes Pt nanoparticles that are used for at least an oxygen reduction reaction (ORR).
19 . The method of claim 13 , wherein the electrochemical experimentation process pertains to one or more of water electrolysis, ammonia electrolysis, ammonia synthesis, carbon dioxide electrolysis, carbon dioxide reduction, and the manufacturing of fuel cells, and the manufacturing of batteries.
20 . The method of claim 13 , wherein the electrochemical experimentation process is fully automated.