Detection and quantification limits (LOD and LOQ) are two fundamental elements of method validation. Rigorous statistical definitions exist, but in HPLC they could not be implemented. Nevertheless there are several estimation methods for these limits. The most commonly used is the signal-to-noise ratio criterion. Others are based on the dispersion characteristics of the regression line, either simple or weighted. For LOQ, Eurachem proposed an alternate approach based on the use of a target value for the area RSD. Since official guidelines imposed no particular modus operandi, an experimental methodology was set up to investigate the compatibility of the different approaches and their respective reliabilities. Several samples prepared in a concentration range close to the limits were analyzed. It appeared that, both for values and their reliabilities, the different approaches were far from equivalent. In our opinion, the best way to handle the problem of detection and quantification limits was a methodology based on the use of the residual standard deviation of a weighted regression for LOD and on a Eurachem approach for LOQ. Values obtained by these means had the advantage of being reliable, i.e., with a small dispersion, and were still compatible with those obtained with the usual signal-to-noise ratio approach.
Electroslag remelting (ESR) is widely used for the production of high-value-added alloys such as special steels or nickel-based superalloys. Because of high trial costs and the complexity of the mechanisms involved, trial-and-error-based approaches are not well suited for fundamental studies or for optimization of the process. Consequently, a transient-state numerical model has been developed that accounts for electromagnetic phenomena and coupled heat and momentum transfers in an axisymmetrical geometry. The model simulates the continuous growth of the electroslag-remelted ingot through a mesh-splitting method. In addition, solidification of the metal is modeled by an enthalpy-based technique. A turbulence model is implemented to compute the motion of liquid phases (slag and metal), while the mushy zone is described as a porous medium the permeability of which varies with the liquid fraction, thus enabling accurate calculation of solid/liquid interaction. The coupled partial differential equations (PDEs) are solved using a finite-volume technique. The computed results are compared to the experimental observation of an industrial remelted ingot; the melt pool depth and shape, in particular, are investigated, in order to validate the model. These results provide valuable information about the process performance and the influence of the operating parameters. In this way, we present an example of a model used as a support in analyzing the influence of the electrode fill ratio.
In order to accurately evaluate the performances of any electrolyte medium, a clear concept of selectivity in capillary electrophoresis and related electroseparation techniques is proposed. Selectivity is defined as the ratio of the affinity factors of both analytes for a separating agent (phase, pseudophase, or complexing agent present in the background electrolyte). When in the presence of a complexing agent and if only 1:1 complexation occurs, selectivity corresponds to the ratio of the apparent binding constants and is independent of the concentration of the complexing agent. This concept is illustrated through the separations of neutral and anionic enantiomers in the presence of a cationic cyclodextrin, the mono(6-amino-6-deoxy)-β-cyclodextrin, as a chiral complexing agent. The values obtained for different pairs of enantiomers are discussed with regard to the functional groups that distinguish them. When the analytes have the same mobilities in free solution and in their complexed form, then the resolution equation developed in micellar electrokinetic chromatography may be applied and optimum conditions (affinity factors, chiral agent concentration) can be predicted.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.