TY - JOUR
T1 - Exploring advanced materials
T2 - Harnessing the synergy of inverse gas chromatography and artificial vision intelligence
AU - Basivi, Praveen Kumar
AU - Hamieh, Tayssir
AU - Kakani, Vijay
AU - Pasupuleti, Visweswara Rao
AU - Sasikala, G.
AU - Heo, Sung Min
AU - Pasupuleti, Kedhareswara Sairam
AU - Kim, Moon Deock
AU - Munagapati, Venkata Subbaiah
AU - Siva Kumar, Nadavala
AU - Wen, Jet Chau
AU - Kim, Chang Woo
N1 - Funding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education ( 2020R1I1A3072987 ).
Publisher Copyright:
© 2024 Elsevier B.V.
PY - 2024/4/1
Y1 - 2024/4/1
N2 - Inverse gas chromatography (IGC) has emerged as a highly sensitive, adaptable, and effective technology for material analysis. Through employing thermochemical approaches, IGC provides crucial insight into physicochemical information of materials such as dispersive surface free energy, Gibbs surface energy components and Guttamann Lewis acid-base parameters. In this comprehensive review, we delve into the historical background, instrumentation, and diverse applications of IGC. Researchers and practitioners will find valuable information on the selection and description of numerous models used in IGC experiments. The applications of IGC span various domains, including polymers, medicines, minerals, surfactants, and nanomaterials. Furthermore, IGC facilitates the measurement of important parameters such as sorption enthalpy and entropy, surface energy components (dispersive and specific), co/adhesion work, glass transition temperature, surface heterogeneity, miscibility, solubility parameters, and specific surface area. These insights contribute to a deeper understanding of material behavior and aid in the design and optimization of advanced materials. Moreover, the integration of computer vision and image processing techniques with IGC has enhanced our understanding of materials intricate surface texture, roughness, and related properties. This convergence of IGC with computer vision and artificial intelligence (AI) presents exciting opportunities for future exploration of chemical materials, opening new avenues for research and discovery. This paper not only provides a comprehensive overview of IGC, its techniques, and applications but also highlights the synergistic potential of combining IGC with AI and computer vision. The informative content and insights presented here will benefit researchers, scientists, and professionals in the field of advanced materials, enabling them to leverage IGC and AI for innovative materials discovery and development.
AB - Inverse gas chromatography (IGC) has emerged as a highly sensitive, adaptable, and effective technology for material analysis. Through employing thermochemical approaches, IGC provides crucial insight into physicochemical information of materials such as dispersive surface free energy, Gibbs surface energy components and Guttamann Lewis acid-base parameters. In this comprehensive review, we delve into the historical background, instrumentation, and diverse applications of IGC. Researchers and practitioners will find valuable information on the selection and description of numerous models used in IGC experiments. The applications of IGC span various domains, including polymers, medicines, minerals, surfactants, and nanomaterials. Furthermore, IGC facilitates the measurement of important parameters such as sorption enthalpy and entropy, surface energy components (dispersive and specific), co/adhesion work, glass transition temperature, surface heterogeneity, miscibility, solubility parameters, and specific surface area. These insights contribute to a deeper understanding of material behavior and aid in the design and optimization of advanced materials. Moreover, the integration of computer vision and image processing techniques with IGC has enhanced our understanding of materials intricate surface texture, roughness, and related properties. This convergence of IGC with computer vision and artificial intelligence (AI) presents exciting opportunities for future exploration of chemical materials, opening new avenues for research and discovery. This paper not only provides a comprehensive overview of IGC, its techniques, and applications but also highlights the synergistic potential of combining IGC with AI and computer vision. The informative content and insights presented here will benefit researchers, scientists, and professionals in the field of advanced materials, enabling them to leverage IGC and AI for innovative materials discovery and development.
KW - Bio-materials and pharmaceuticals
KW - Interfaces
KW - Lewis acid-base parameters
KW - London surface energy
KW - Materials and nanomaterials
KW - Surfaces
U2 - 10.1016/j.trac.2024.117655
DO - 10.1016/j.trac.2024.117655
M3 - (Systematic) Review article
SN - 0165-9936
VL - 173
JO - Trac-Trends in Analytical Chemistry
JF - Trac-Trends in Analytical Chemistry
M1 - 117655
ER -