Machine Learning-Assisted Array-Based Biomolecular Sensing Using Surface-Functionalized Carbon Dots

Subhendu Pandit, Tuseeta Banerjee, Indrajit Srivastava, Shuming Nie, Dipanjan Pan

Research output: Contribution to journalArticlepeer-review


Fluorescent array-based sensing is an emerging differential sensing platform for sensitive detection of analytes in a complex environment without involving a conventional "lock and key" type-specific interaction. These sensing techniques mainly rely on different optical pattern generation from a sensor array and their pattern recognition to differentiate analytes. Currently emerging, compelling pattern-recognition method, Machine Learning (ML), enables a machine to "learn" a pattern by training without having the recognition method explicitly programmed into it. Thus, ML has an enormous potential to analyze these sensing data better than widely used statistical pattern-recognition methods. Here, an array-based sensor using easy-to-synthesize carbon dots with varied surface functionality is reported, which can differentiate between eight different proteins at 100 nM concentration. The utility of using machine learning algorithms in pattern recognition of fluorescence signals from the array has also been demonstrated. In analyzing the array-based sensing data, Machine Learning algorithms like "Gradient-Boosted Trees" have achieved a 100% prediction efficiency compared to inferior-performing classical statistical method "Linear Discriminant Analysis".

Original languageEnglish (US)
Pages (from-to)2730-2737
Number of pages8
JournalACS Sensors
Issue number10
StatePublished - Oct 25 2019


  • array-based sensing
  • carbon dots
  • chemical nose
  • machine learning
  • surface chemistry

ASJC Scopus subject areas

  • Bioengineering
  • Instrumentation
  • Process Chemistry and Technology
  • Fluid Flow and Transfer Processes


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