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Uncertainty evaluation in the estimates of isotopic abundances and atomic weight of any element: a unique application of the theory of uncertainty for derived results

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 Added by B. P. Datta
 Publication date 2020
  fields Physics
and research's language is English
 Authors B. P. Datta




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It has been previously shown that any measurement system specific relationship (SSR)/ mathematical-model Y_d = f_d ({X_m}) or so is bracketed with certain parameters which should prefix the achievable-accuracy/ uncertainty (e_d^Y) of a desired result y_d. Here we clarify how the element-specific-expressions of isotopic abundances and/ or atomic weight could be parametrically distinguished from one another, and the achievable accuracy be even a priori predicted. It is thus signified that, irrespective of whether the measurement-uncertainty (u_m) could be purely random by origin or not, e_d^Y should be a systematic parameter. Further, by property-governing-factors, any SSR should belong to either variable-independent (F.1) or -dependent (F.2) family of SSRs/ models. The SSRs here are shown to be the members of the F.2 family. That is, it is pointed out that, and explained why, the uncertainty (e) of determining an either isotopic abundance or atomic weight should vary, even for any given measurement-accuracy(s) u_m(s), as a function of the measurable-variable(s) X_m(s). However, the required computational-step has been shown to behave as an error-sink in the overall process of indirect measurement in question.



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369 - B. P. Datta 2011
Any physiochemical variable (Ym) is always determined from certain measured variables {Xi}. The uncertainties {ui} of measuring {Xi} are generally a priori ensured as acceptable. However, there is no general method for assessing uncertainty (em) in the desired Ym, i.e. irrespective of whatever might be its system-specific-relationship (SSR) with {Xi}, and/ or be the causes of {ui}. We here therefore study the behaviors of different typical SSRs. The study shows that any SSR is characterized by a set of parameters, which govern em. That is, em is shown to represent a net SSR-driven (purely systematic) change in ui(s); and it cannot vary for whether ui(s) be caused by either or both statistical and systematic reasons. We thus present the general relationship of em with ui(s), and discuss how it can be used to predict a priori the requirements for an evaluated Ym to be representative, and hence to set the guidelines for designing experiments and also really appropriate evaluation models. Say: Y_m= f_m ({X_i}_(i=1)^N), then, although: e_m= g_m ({u_i}_(i=1)^N), N is not a key factor in governing em. However, simply for varying fm, the em is demonstrated to be either equaling a ui, or >ui, or even <ui. Further, the limiting error (d_m^(Lim.)) in determining an Ym is also shown to be decided by fm (SSR). Thus, all SSRs are classified into two groups: (I) the SSRs that can never lead d_m^(Lim.) to be zero; and (II) the SSRs that enable d_m^(Lim.) to be zero. In fact, the theoretical-tool (SSR) is by pros and cons no different from any discrete experimental-means of a study, and has resemblance with chemical reactions as well.
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