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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. [Display omitted] • This review paper offers an extensive exploration of the historical background, instrumentation, and diverse applications of Inverse Gas Chromatography (IGC). • It provides valuable information for researchers and practitioners by presenting various models employed in IGC experiments. • The applications of IGC covered in this review span multiple domains like polymers, medicines, minerals, surfactants, and nanomaterials. • These insights contribute to a deeper understanding of material behavior and aid in the design and optimization of advanced materials. • A complete overview of IGC, its techniques, and applications but also the synergistic potential of combining IGC with AI and computer vision. [ABSTRACT FROM AUTHOR]

Over the last four decades, studies have investigated the incorporation of Artificial Intelligence (AI) into education. A recent prominent AI-powered technology that has impacted the education sector is ChatGPT. This article provides a systematic review of 14 empirical studies incorporating ChatGPT into various educational settings, published in 2022 and before the 10th of April 2023—the date of conducting the search process. It carefully followed the essential steps outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines, as well as Okoli's (Okoli in Commun Assoc Inf Syst, 2015) steps for conducting a rigorous and transparent systematic review. In this review, we aimed to explore how students and teachers have utilized ChatGPT in various educational settings, as well as the primary findings of those studies. By employing Creswell's (Creswell in Educational research: planning, conducting, and evaluating quantitative and qualitative research [Ebook], Pearson Education, London, 2015) coding techniques for data extraction and interpretation, we sought to gain insight into their initial attempts at ChatGPT incorporation into education. This approach also enabled us to extract insights and considerations that can facilitate its effective and responsible use in future educational contexts. The results of this review show that learners have utilized ChatGPT as a virtual intelligent assistant, where it offered instant feedback, on-demand answers, and explanations of complex topics. Additionally, learners have used it to enhance their writing and language skills by generating ideas, composing essays, summarizing, translating, paraphrasing texts, or checking grammar. Moreover, learners turned to it as an aiding tool to facilitate their directed and personalized learning by assisting in understanding concepts and homework, providing structured learning plans, and clarifying assignments and tasks. However, the results of specific studies (n = 3, 21.4%) show that overuse of ChatGPT may negatively impact innovative capacities and collaborative learning competencies among learners. Educators, on the other hand, have utilized ChatGPT to create lesson plans, generate quizzes, and provide additional resources, which helped them enhance their productivity and efficiency and promote different teaching methodologies. Despite these benefits, the majority of the reviewed studies recommend the importance of conducting structured training, support, and clear guidelines for both learners and educators to mitigate the drawbacks. This includes developing critical evaluation skills to assess the accuracy and relevance of information provided by ChatGPT, as well as strategies for integrating human interaction and collaboration into learning activities that involve AI tools. Furthermore, they also recommend ongoing research and proactive dialogue with policymakers, stakeholders, and educational practitioners to refine and enhance the use of AI in learning environments. This review could serve as an insightful resource for practitioners who seek to integrate ChatGPT into education and stimulate further research in the field. [ABSTRACT FROM AUTHOR]

Much more than their machinic reality, current iterations of AI rely on imagined divisions of human and non-human properties and skills that have genealogical ties to colonization. For this reason, research efforts have recently been made to historicize these imaginaries, connecting them to colonial ideals that delegate black and brown colonized people into the realm of the non-human. Atanasoski and Vora (Surrogate humanity. Race, robots and the politics of technological futures, Duke, Durham and London, 2019) have called this a "surrogate humanity", where narratives of autonomous technologies function to disappear precisely the formerly colonized peoples that are enveloped in its production process. At the same time, the gendered and racialized roots of this maternal figure represents an opportunity to uncover and critique the invisibilization of embodied resources necessary to produce AI, precarious bodies labouring to produce algorithmic infrastructures in a manner that can be considered in a genealogy of carework and reproduction. These genealogies complicate the detachment suggested by the surrogate figure and go beyond it to proclaim a more generative function of the relationship between the black maternal figure and AI. The article analyses Tabita Rezaire's multi-media artwork Sugar Walls Teardom to think through decolonial and queer renderings of the black female bodies upon which technological imaginaries rest, to extend beyond AI surrogacy and towards notions of kinship, care and world-making by producing an AI aesthetics that is relational, embodied, and celebratory of other ways of liveness. [ABSTRACT FROM AUTHOR]