We propose bionanoparticles as a candidate reference material for determining the mobility of nanoparticles over the range of 6 × 10(-8)-5 × 10(-6) m(2)V(-1)s(-1). Using an electrospray differential mobility analyzer (ES-DMA), we measured the empirical distribution of several bionanoparticles. All of them show monomodal distributions that are more than two times narrower than the currently used calibration particles for mobility larger than 6 × 10(-8) m(2)V(-1)s(-1) (diameters less than 60 nm). We also present a numerical method to calculate corrected distributions of bionanoparticles by separating the contribution of the diffusive transfer function. The corrected distribution is about 20% narrower than the empirical distributions. Even with the correction, the reduced width of the mobility distribution is about a factor of 2 larger than the diffusive transfer function. The additional broadening could result from the nonuniform conformation of bionanoparticles and from the presence of volatile impurities or solvent adducts. The mobilities of these investigated bionanoparticle are stable over a range of buffer concentration and molarity, with no evidence of temporal degradation over several weeks.

Sensitivity analysis has received significant attention in engineering design. While sensitivity analysis methods can be global, taking into account all variations, or local, taking into account small variations, they generally identify which uncertain parameters are most important and to what extent their effect might be on design performance. The extant methods do not, in general, tackle the question of which ranges of parameter uncertainty are most important or how to best allocate Investments to partial uncertainty reduction in parameters under a limited budget. More specifically, no previous approach has been reported that can handle single-disciplinary multi-output global sensitivity analysis for both a single design and multiple designs under interval uncertainty. Two new global uncertainty metrics, i.e., radius of output sensitivity region and multi-output entropy performance, are presented. With these metrics, a multi-objective optimization model is developed and solved to obtain fractional levels of parameter uncertainty reduction that provide the greatest payoff in system performance for the least amount of “Investment.” Two case studies of varying difficulty are presented to demonstrate the applicability of the proposed approach.

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