Automated Analysis of Nano‐Impact Single‐Entity Electrochemistry Signals Using Unsupervised Machine Learning and Template Matching

Abstract

Nano‐impact (NIE) (also referred to as collision) single‐entity electrochemistry is an emerging technique that enables electrochemical investigation of individual entities, ranging from metal nanoparticles to single cells and biomolecules. To obtain meaningful information from NIE experiments, analysis and feature extraction on large datasets are necessary. Herein, a method is developed for the automated analysis of NIE data based on unsupervised machine learning and template matching approaches. Template matching not only facilitates downstream processing of the NIE data but also provides a more accurate analysis of the NIE signal characteristics and variations that are difficult to discern with conventional data analysis techniques, such as the height threshold method. The developed algorithm enables fast automated processing of large experimental datasets recorded with different systems, requiring minimal human intervention and thereby eliminating human bias in data analysis. As a result, it improves the standardization of data processing and NIE signal interpretation across various experiments and applications.

Emerging Data Processing Methods for Single‐Entity Electrochemistry

“…The matching results to the validation dataset (middle) and demonstration of the identified spike event (right, red box) with the developed algorithm in comparison to the height‐threshold method (blue box). MSE is the mean squared error [12e] . Figures reproduced with permission from: b, ref.…”

“…Additionally, an advanced template‐matching approach, alongside unsupervised machine learning (ML) techniques has been used for the automated analysis of single nanoparticle (NP) collision events (Figure 5c). [12e] This method surpasses traditional manual data processing by more precisely classifying spike events into distinct categories based on their shape utilizing generated templates. Its efficiency was confirmed by applying glucose photoelectrooxidation to AuNPs and catalase collisions.…”

Recent Developments in Single-Entity Electrochemistry

“…However, emerging simulation techniques, such as random walk model and the novel application of the SPICE model, offer another direction to assess or challenge existing fundamental SEE principles and lead to new understandings of SEE. Creative ways to process and analyze SEE experimental data, such as statistical methods, time–frequency analysis, and recent artificial intelligence and ML-based recognition and classification techniques are likely to gain prominence. − A recent review on emerging data processing methods for SEE is recommended for further reading . These approaches promise to improve the reliability and interpretation of SEE signals helping to capture nuances of electrochemistry at the single-entity level.…”

A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence

“…Machine learning (ML) is becoming a critical tool in various fields of nanomaterials research, such as nanoindentation [ 317 , 318 , 319 , 320 , 321 , 322 , 323 , 324 , 325 , 326 , 327 , 328 ], nanorobotics [ 329 , 330 , 331 , 332 , 333 , 334 ], and nanosensor [ 335 , 336 , 337 , 338 , 339 , 340 ] development. Its ability to analyze and interpret complex patterns from large datasets is particularly beneficial in advancing areas like nanostructured materials analysis, nanoscale manufacturing processes, and the development of nanotechnology applications in medicine and environmental monitoring [ 327 , 341 , 342 , 343 , 344 , 345 , 346 , 347 , 348 , 349 , 350 , 351 , 352 , 353 , 354 , 355 , 356 , 357 , 358 , 359 , 360 , 361 , 362 , 363 , 364 , …”

“…Its ability to analyze and interpret complex patterns from large datasets is particularly beneficial in advancing areas like nanostructured materials analysis, nanoscale manufacturing processes, and the development of nanotechnology applications in medicine and environmental monitoring [ 327 , 341 , 342 , 343 , 344 , 345 , 346 , 347 , 348 , 349 , 350 , 351 , 352 , 353 , 354 , 355 , 356 , 357 , 358 , 359 , 360 , 361 , 362 , 363 , 364 , 365 , 366 , 367 , 368 , 369 , 370 , 371 , 372 , 373 , 374 , 375 , 376 , 377 , 378 , 379 , 380 , 381 , 382 , 383 , 384 , 385 , 386 , 387 , 388 , 389 , 390 ,…”