Scalable synthesis, characterization, and DFT-machine learning modelling of Ti₃C₂ MXene quantum dots

Area of Science:

• Materials Science
• Nanotechnology
• Quantum Chemistry

Background:

• Two-dimensional Ti3C2Tx MXenes offer excellent conductivity and tunable surface chemistry.
• Reducing MXenes to zero-dimensional quantum dots (MQDs) enhances optoelectronic and electrochemical properties through quantum confinement and functionalization.

Purpose of the Study:

• To develop a scalable laboratory protocol for synthesizing high-quality MQDs.
• To investigate the impact of quantum confinement and surface functionalization on MQD properties.
• To evaluate MQDs for next-generation sensing applications.

Main Methods:

• Laboratory-scale synthesis using TMAOH.
• XRD, FTIR, XPS, Raman, PL, DLS, BET, electron microscopy, AFM, and electrochemical analyses.
• Density Functional Theory (DFT) and machine learning (ML) modeling.

Main Results:

• Successful synthesis of MQDs with quantum confinement (crystallite size ~2 nm) and enhanced surface functionalization (increased oxygen/hydroxyl groups).
• MQDs exhibited a widened electronic bandgap (1.77 to 2.32 eV), increased surface area (31.8 to 140.6 m2 g-1), and superior charge transfer and catalytic activity.
• DFT confirmed semiconducting behavior in MQDs (0.25 eV bandgap) versus metallic 2D MXenes.
• ML models identified key predictors for sensor efficiency, with MQDs outperforming MXene nanosheets.

Conclusions:

• Scalable MQD synthesis is achievable, yielding materials with significantly enhanced properties.
• Quantum confinement and surface chemistry are critical determinants of MQD performance.
• MQDs demonstrate promising potential for advanced sensing applications, driven by their unique nanoscale morphology and surface characteristics.