Innovative food quality control solution: smart gas sensor technology to detect spoilage of vegetables during post-harvest storage
2025-05-29 09:58
Project goal: The aim is to reduce agricultural losses and improve the efficiency of spoilage detection using gas sensor technology in various storage conditions, at different temperatures and humidity levels.
Project description: In this project, the CuO/ZnO p-n heterostructure is used as a gas sensing material in post-harvest storage to verify the high quality of fruits and vegetables. NO, NO2, N2O, CO, CO2, H2 (target gases) and volatile organic compounds (ethanol, ethylene, toluene and acetone) affect the freshness of vegetables and fruits. Density functional theory (DFT) is used to theoretically model the interaction of surface target gases. Ab initio molecular dynamics (AIMD) is used to determine the thermal stability of the surface of a gas-sensitive material. MO methods: Principal Component Analysis (PCA), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Artificial Neural Networks (ANN) are applied to solve the problem of non-selectivity of gas sensors.
Project facilitators: PI - Dr. Baktiyar Soltabayev, Executors: Dr. Bauyrzhan Myrzakhmetov, Dr. Batukhan Tatykayev, Ms. Aizhan Rakhmanova, Dr. Amanzhol Turlybekuly, Mr. Aidarbek Nuftolla, Mr. Abylay Tangirbergen.
Realisation period: 2024-2026
PI - Dr. Baktiyar Soltabayev Executors: Dr. Bauyrzhan Myrzakhmetov Dr. Batukhan Tatykayev Ms. Aizhan Rakhmanova Dr. Amanzhol Turlybekuly Mr. Aidarbek Nuftolla Mr. Abylay Tangirbergen
Expected results: The results will be presented at international conferences for evaluating the data and developing knowledge and skills in this field. The experimental results will be published in leading international peer-reviewed journals: at least 3 (three) articles and (or) reviews in peer-reviewed scientific publications indexed in the Science Citation Index Expanded of the Web of Science database and (or) having a CiteScore percentile in the Scopus database of at least 50 (fifty); at least 1 patent for an invention (including a positive decision on it); or at least 2 (two) articles and (or) reviews in peer-reviewed scientific publications indexed in the Science Citation Index Expanded of the Web of Science database and (or) having a CiteScore percentile in the Scopus database of at least 50 (fifty), and at least 1 (one) patent included in the Derwent Innovations Index database (Web of Science, Clarivate Analytics); as well as at least 1 (one) article or review in a peer-reviewed foreign or domestic publication recommended by KOKSNVO; or at least 2 (two) articles and (or) reviews in peer-reviewed scientific publications indexed in the Science Citation Index Expanded and included in the 1st (first) and (or) 2nd (second) quartile by impact factor in the Web of Science database and (or) having a CiteScore percentile in the Scopus database of at least 65 (sixty-five); as well as at least 1 (one) article or review in a peer-reviewed foreign or domestic publication recommended by KOKSNVO; or at least 1 (one) article or review in a peer-reviewed scientific publication included in the 1st (first) or 2nd (second) quartile of the impact factor in the Web of Science database and (or) having a CiteScore percentile in the Scopus database of at least 65 (sixty-five), and at least 1 (one) patent included in the Derwent Innovations Index database (Web of Science, Clarivate Analytics); as well as at least 1 (one) article or review in a peer-reviewed foreign or domestic publication recommended by KOKSNVO; or at least 1 (one) article or review in a peer-reviewed scientific publication indexed in the Science Citation Index Expanded and included in the 1st (first) quartile by impact factor in the Web of Science database and (or) having a CiteScore percentile in the Scopus database of at least 80 (eighty); at least 1 patent for an invention (including a positive decision on it);
Methodology: Theoretical calculation of gas adsorption/desorption models of ZnO and CuO/ZnO heterostructure. Engineering of gas sensor based on ZnO and CuO/ZnO heterostructures Conducting systematic gas measurements for gas sensor devices at different gas concentrations, temperatures, and humidities. Machine learning-based identification/discrimination of different gasses such as NOx, COx, H2 at different gas concentrations, temperatures and humidities.
Co-financing: Limited Liability Partnership "Institute of Batteries"