A Novel Approach for Classifying Monoamine Neurotransmitters by Applying Machine Learning on UV Plasmonic-Engineered Auto Fluorescence Time Decay Series (AFTDS)

Nanoscale Advances

This study introduces a hybrid approach integrating advanced plasmonic nanomaterials and machine learning (ML) for high-precision biomolecule detection.

Published

  1. Mohammadi, M.
  2. Najafzadehkhoei, S.Population Health Sciences, University of Utah
  3. Vega Yon, G. G.University of UtahiD
  4. Wang, Y.

DOI•Publisher / Preprint

Abstract

This study introduces a hybrid approach integrating advanced plasmonic nanomaterials and machine learning (ML) for high-precision biomolecule detection. We leverage aluminum concave nanocubes (AlCNCs) as an innovative plasmonic substrate to enhance the native fluorescence of neurotransmitters, including dopamine (DA), norepinephrine (NE), and 3,4-dihydroxyphenylacetic acid (DOPAC). AlCNCs amplify weak fluorescence signals, enabling probe-free, label-free detection and differentiation of these molecules with great sensitivity and specificity. To further improve classification accuracy, we employ ML algorithms, with Long Short-Term Memory (LSTM) networks playing a central role in analyzing time-dependent fluorescence data. Comparative evaluations with k-nearest neighbors (KNN) and Random Forest (RF) demonstrate the superior performance of LSTM in distinguishing neurotransmitters. The results reveal that AlCNC substrates provide up to a 12-fold enhancement in fluorescence intensity for DA, 9-fold for NE, and 7-fold for DOPAC compared to silicon substrates. At the same time, ML algorithms achieve classification accuracy exceeding 89%. This interdisciplinary methodology bridges the gap between nanotechnology and ML, showcasing the synergistic potential of AlCNC-enhanced native fluorescence and ML in biosensing. The framework paves the way for probe-free, label-free biomolecule profiling, offering transformative implications for biomedical diagnostics and neuroscience research.

Cite

@article{mohammadiNovelApproachClassifying2025c,
  title = {A Novel Approach for Classifying Monoamine Neurotransmitters by Applying Machine Learning on UV Plasmonic-Engineered Auto Fluorescence Time Decay Series (AFTDS)},
  author = {{Mohammadi}, {Mohammad} and {Najafzadehkhoei}, {Sima} and {Vega Yon}, {George G.} and {Wang}, {Yunshan}},
  journaltitle = {Nanoscale Advances},
  volume = {7},
  number = {24},
  pages = {8013–8022},
  doi = {10.1039/D5NA00416K},
  url = {https://xlink.rsc.org/?DOI=D5NA00416K},
  issn = {2516-0230},
}

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