Machine learning-guided the fabrication of nanozyme based on highly-stable violet phosphorene decorated with phosphorus-doped hierarchically porous carbon microsphere for portable intelligent sensing of mycophenolic acid in silage

Artificial enzyme innovations in electrochemical devices: advancing wearable and portable sensing technologies

“…The meticulous detailing of the VP-PCM material's preparation, characterization, and its performance metrics in MPA detection further enriches the discourse. 98…”

Machine learning in nanozymes: from design to application

“…For example, Ge et al constructed highly stable violet phosphorene decorated with phosphorus-doped hierarchically porous carbon microspheres (VP-PCMs) based on ML-guided experimental data. 40 The current values generated by VP-PCMs under different concentrations of violet phosphorene (VP) and porous carbon microspheres (PCMs) were collected for meconium phenolic acid (MPA) detection. Employing this data, a random forest (RF) model was developed to predict the optimal concentration of VP and PCM during VP-PCM synthesis.…”

Rational Integration of Nanozyme Probe and Smartphone Device for On‐Site Analysis

“…[79][80][81] Recently, multiple machine learning algorithms have been exploited to help constructing nanozyme-based sensors. [82][83][84] By virtue of machine learning, nanozyme sensors with polytype of output signals, meanwhile the signals could be processed simultaneously within one smartphone, are highly expected, yet underdeveloped. It is due to machine learning algorithms rely on huge computing data, which is still difficult to be undertaken by smartphones at present.…”