Browsing by Author "Li, S. C."

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    ArticlePublicationOpen Access
    Flux melting of metal–organic frameworks
    (Royal Society of Chemistry, 2019-03-28) Longley, L.; Collins, S. M.; Li, S. C.; Smales, G. L.; Fındıkçı, İlknur Eruçar; Qiao, A.; Hou, J.; Doherty, C. M.; Thornton, A. W.; Hill, A. J.; Yu, X.; Terrill, N. J.; Smith, A. J.; Cohen, S. M.; Midgley, P. A.; Keen, D. A.; Telfer, S. G.; Bennett, T. D.; Mechanical Engineering; FINDIKÇI, Ilknur Eruçar
    Recent demonstrations of melting in the metal-organic framework (MOF) family have created interest in the interfacial domain between inorganic glasses and amorphous organic polymers. The chemical and physical behaviour of porous hybrid liquids and glasses is of particular interest, though opportunities are limited by the inaccessible melting temperatures of many MOFs. Here, we show that the processing technique of flux melting, borrowed' from the inorganic domain, may be applied in order to melt ZIF-8, a material which does not possess an accessible liquid state in the pure form. Effectively, we employ the high-temperature liquid state of one MOF as a solvent for a secondary, non-melting MOF component. Differential scanning calorimetry, small- and wide-angle X-ray scattering, electron microscopy and X-ray total scattering techniques are used to show the flux melting of the crystalline component within the liquid. Gas adsorption and positron annihilation lifetime spectroscopy measurements show that this results in enhanced, accessible porosity to a range of guest molecules in the resultant flux melted MOF glass.
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    Machine learning based activity learning for behavioral contexts in Internet of things (IoT)
    (Springer Nature, 2020-12) Safyan, M.; Sarwar, S.; Ul Qayyum, Z.; Iqbal, M.; Li, S. C.; Kashif, Muhammad; Kashif, Muhammad
    Ontology based activity learning models play a vital role in diverse fields of Internet of Things (IoT) such as smart homes, smart hospitals or smart communities etc. The prevalent challenges with ontological models are their static nature and inability of self-evolution. The models cannot be completed at once and smart home inhabitants cannot be restricted to limit their activities. Also, inhabitants are not predictable in nature and may perform "Activities of Daily Life (ADL)" not listed in ontological model. This gives rise to the need of developing an integrated framework based on unified conceptual backbone (i.e. activity ontologies), addressing the lifecycle of activity recognition and producing behavioral models according to inhabitant's routine. In this paper, an ontology evolution process has been proposed that learns particular activities from existing set of activities in daily life (ADL). It learns new activities that have not been identified by the recognition model, adds new properties with existing activities and learns inhabitant's newest behavior of performing activities through Artificial Neural Network (ANN). The better degree of true positivity is evidence of activity recognition with detection of noisy sensor data. Effectiveness of proposed approach is evident from improved rate of activity learning, activity detection and ontology evolution.