SMART ANALYTICAL PLATFORMS: INTEGRATING NANOTECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR NEXT-GENERATION CHEMICAL SENSING

Abstract

Background: Traditional chemical sensing technologies are limited in sensitivity, selectivity, response time, and in detection of complex chemical analytes in the environment. Nanotechnology, when integrated with artificial intelligence (AI), is one attractive solution for creating an advanced analytical platform, which merges the specific physicochemical properties of nanomaterials with the data-driven pattern recognition and predictive capabilities of AI.

Aim: This research aimed to design, develop, and test a smart analytical platform based on nanotechnology and AI which provides a sensitive, selective, rapid, and reliable chemical sensing platform. The study also explored the viability of nanomaterial-based sensing systems with the use of machine learning to detect and identify analytes, estimate their concentration, and optimize their performance.

Methods: Nanostructured materials as chemical sensing materials — metal nanoparticles, metal oxides, carbon-based nanomaterials, and nanocomposites — were tested for chemical sensing properties. Appropriate spectroscopic, microscopic, and electrochemical methods were used to characterize the developed sensors. Responses to a number of chemical analytes were measured over a range of analyte concentrations and environmental conditions. Sensor data were combined with pattern recognition, analyte classification, and concentration prediction using AI/machine learning algorithms such as support vector machine, random forest, and artificial neural network models. Sensor characteristics were tested in terms of sensitivity, selectivity, response time, stability, detection limit, and accuracy of prediction.

Results: The integration of nanotechnology with AI was shown to yield greater sensing performance, with the nanostructured sensing interface providing higher surface-area-to-volume ratios and improved interaction with target analytes. The optimized sensing system accomplished close to 85–95% classification accuracy of the analytes, and the AI concentration model achieved a coefficient of determination (R²) between 0.90 and 0.98. The nanomaterial-based sensors showed rapid response times (around 5–30 s) compared with conventional sensing approaches and also achieved better detection limits. Among the models evaluated, the optimized artificial neural network and the ensemble-based models showed the smallest prediction error and the best discrimination between chemically similar analytes. The combined platform also exhibited good stability and repeatability of sensing performance across repeated sensing cycles.

Conclusion: This study has shown that combining nanotechnology and AI can be a highly effective approach for creating next-generation chemical sensing platforms, with great potential in terms of sensitivity, selectivity, quickness of response, and intelligent data interpretation. The envisioned solution could be applied to environmental monitoring, food safety, industrial process control, chemical analysis, and medical diagnostics. Extensive experimental validation and testing of these systems in real-world settings can contribute to developing portable, autonomous, and highly adaptive smart chemical sensing systems.