Electrochemical sensing device based on Cu/PPy heterostructure: Detection of nitrite at low reduction potentials and...

Area of Science:

• Electrochemistry
• Materials Science
• Artificial Intelligence

Background:

• Nitrite detection is crucial in environmental and biological monitoring.
• Existing methods often require high potentials or lack sensitivity.
• Developing selective and low-potential electrochemical sensors is a significant challenge.

Purpose of the Study:

• To develop a self-supporting sensing electrode for highly selective, low-potential nitrite detection.
• To investigate the synergistic effects of copper and polypyrrole on sensing performance.
• To implement a hybrid deep learning framework for enhanced nitrite concentration prediction.

Main Methods:

• Fabrication of a copper/polypyrrole/carbon cloth (Cu/PPy/CC) composite electrode via electrodeposition.
• Electrochemical characterization including cyclic voltammetry and sensitivity measurements.
• Density functional theory (DFT) calculations to understand interfacial synergy.
• Development and application of a hybrid deep learning model for signal analysis.

Main Results:

• The Cu/PPy/CC electrode demonstrated high sensitivity (7170 μA·mM⁻¹·cm⁻²), a low detection limit (0.137 μM), and a wide linear range (10-1000 μM) at -0.05 V vs. Hg/HgO.
• Excellent interference resistance and long-term stability were observed.
• DFT calculations confirmed enhanced nitrite adsorption and electron transfer due to Cu-PPy synergy.
• The hybrid deep learning model achieved a coefficient of determination of 0.9999 for concentration prediction.

Conclusions:

• The synergistic Cu/PPy composite electrode offers superior performance for low-potential nitrite detection.
• The integration of deep learning significantly improves the reliability and interpretability of electrochemical sensing.
• This research presents a novel approach for designing high-performance electrochemical sensors and intelligent algorithms.