National Pingtung University of Science and Technology / Prof. Wei-Yu Chen
Pain Points Solved
This technology integrates nano carbon dot-based optical sensing with artificial intelligence (AI) for rapid identification of chemical components in mixed solutions. Designed for semiconductor cleaning waste recycling, the system evaluates whether recycled solvents meet reuse purity standards in real time.
Nano carbon dots are added to target solutions, and their interactions with chemicals generate characteristic absorption spectra. These wavelength–intensity patterns are analyzed by an AI model trained to recognize specific compounds and mixed solvent compositions. The system can identify chemicals such as water, methanol, acetic acid, and formaldehyde with high accuracy.
Compared with conventional GC methods, this technology offers several advantages:
Rapid detection: analysis can be completed within seconds to minutes.
Portable operation: compatible with handheld spectrometers for on-site measurements.
Lower operational cost: reduces dependence on expensive laboratory instruments and specialized personnel.
Mixed-solution recognition: capable of analyzing complex solvent mixtures and purity variations.
Smart process integration: suitable for automated monitoring and real-time process control.
In semiconductor manufacturing, the system can be applied to waste solvent recovery processes to quickly determine whether treated cleaning liquids satisfy re use standards. This enables reduced chemical waste, lower recycling costs, and improved circular utilization efficiency. Beyond semiconductor applications, the technology also demonstrates strong potential in chemical processing, environmental monitoring, biomedical sensing, and intelligent industrial inspection systems.
Technology Introduction
This technology combines nano carbon dots based optical sensing with AI to rapidly identify components in mixed solutions through absorption spectrum analysis. Integrated with a handheld spectrometer, the system enables real-time, portable, and low-cost detection, making it highly suitable for semiconductor cleaning waste recycling and purity monitoring.
Application Examples
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Technical Contact
Prof. Wei-Yu Chen
National Pingtung University of Science and Technology
Tel: +886 8-7703202 ext. 7292
Email: wychen@mail.npust.edu.tw