Recently, biosynthesis of nanoparticles has attracted scientists' attention because of the necessity to develop new clean, cost-effective and efficient synthesis techniques. In particular, metal oxide nanoparticles are receiving increasing attention in a large variety of applications. However, up to now, the reports on the biopreparation and characterization of nanocrystalline copper oxide are relatively few compared to some other metal oxides. In this paper, we report for the first time the use of brown alga (Bifurcaria bifurcata) in the biosynthesis of copper oxide nanoparticles of dimensions 5-45 nm. The synthesized nanomaterial is characterized by UV-visible absorption spectroscopy and Fourier transform infrared spectrum analysis. X-ray diffraction confirms the formation and the crystalline nature of copper oxide nanomaterial. Further, these nanoparticles were found to exhibit high antibacterial activity against two different strains of bacteria Enterobacter aerogenes (Gram negative) and Staphylococcus aureus (Gram positive).

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The aim of this paper is to recommend a new strategy for the analytical validation based on the uncertainty profile as a graphical decision-making tool, and to exemplify a novel method to estimate the measurement uncertainty. Indeed, the innovative formula that we offer to assess the uncertainty is based on the calculation of the β-content tolerance interval. Three chemometric methodologies are exposed to build the (β, γ) tolerance interval, namely: the Satterthwaite approximation, the GPQ method (generalized pivotal confidence) and the MLS procedure (modified large simple). Furthermore, we illustrate the applicability and flexibility of the uncertainty profile to assess the fitness of the purpose of chromatographic and electrophoretic analytical methods, which use different instrumental techniques such as liquid chromatography (LC-UV, LC-MS), gas chromatography (GC-FID, GC-MS) and capillary electrophoresis (CE, CE-MS). In addition, we demonstrate here that (β, γ) tolerance intervals will provide perfect estimates of the routine uncertainty. In particular, we show that there is no difference statistically between the uncertainties estimated by our methodology as of the validation stage with those obtained from the routine phase.

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