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Kamiel Gabriel

Researcher at University of Ontario Institute of Technology

Publications -  92
Citations -  2491

Kamiel Gabriel is an academic researcher from University of Ontario Institute of Technology. The author has contributed to research in topics: Hydrogen production & Copper–chlorine cycle. The author has an hindex of 24, co-authored 89 publications receiving 2108 citations. Previous affiliations of Kamiel Gabriel include University of Saskatchewan.

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Recent Canadian advances in nuclear-based hydrogen production and the thermochemical Cu–Cl cycle

TL;DR: In this paper, the authors present recent Canadian advances in nuclear-based production of hydrogen by electrolysis and the thermochemical copper-chlorine (Cu-Cl) cycle, including individual process and reactor developments within the Cu-Cl cycle, thermochemical properties, advanced materials, controls, safety, reliability, economic analysis of electrolysis at off peak hours, and integrating hydrogen plants with Canada's nuclear power plants.
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Comparison of thermochemical, electrolytic, photoelectrolytic and photochemical solar-to-hydrogen production technologies

TL;DR: In this article, the authors discuss the advantages of using solar energy over other forms of energy to produce hydrogen and examine the latest research and development progress of various solar-to-hydrogen production technologies based on thermal, electrical and photon energy.
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Thermochemical hydrogen production with a copper-chlorine cycle. I: oxygen release from copper oxychloride decomposition

TL;DR: In this paper, the authors examined the thermal requirements of these steps, in efforts to recover as much heat as possible and minimize the net heat supply to the cycle, thereby improving its overall efficiency.
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Principal component analysis of the electricity consumption in residential dwellings

TL;DR: In this article, a methodology based on the latent root regression technique of Hawkins was developed to address the problem of multi-collinearities among the predictors and at the same time reduce the number of needed predictors.