The utility of ion mobility spectrometry (IMS) for separation of mixtures and structural characterization of ions has been demonstrated extensively, including in biological and nanoscience contexts. A major attraction of IMS is its speed, several orders of magnitude greater than that of condensed-phase separations. Nonetheless, IMS combined with mass spectrometry (MS) has remained a niche technique, substantially because of limited sensitivity resulting from ion losses at the IMS-MS junction. We have developed a new electrospray ionization (ESI)-IMS-QTOF MS instrument that incorporates electrodynamic ion funnels at both front ESI-IMS and rear IMS-QTOF interfaces. The front funnel is of the novel "hourglass" design that efficiently accumulates ions and pulses them into the IMS drift tube. Even for drift tubes of 2-m length, ion transmission through IMS and on to QTOF is essentially lossless across the range of ion masses relevant to most applications. The rf ion focusing at the IMS terminus does not degrade IMS resolving power, which exceeds 100 (for singly charged ions) and is close to the theoretical limit. The overall sensitivity of the present ESI-IMS-MS system is comparable to that of commercial ESI-MS, which should make IMS-MS suitable for analyses of complex mixtures with ultrahigh sensitivity and exceptional throughput.

A theory previously developed for spectra with detector-limited (i.e., signal-independent) Gaussian-distributed noise is applied to calculate the maximal precision with which mass spectral peak parameters (mass-to-charge ratio, amplitude, width) can be determined from a discrete spectrum with source-limited Poisson-distributed noise. The precision depends in a calculable way upon the peak shape, signal-to-noise ratio, and number of data points per peak width. Those dependencies are tested by analysis of simulated data. The theory provides estimates for the precision of a repeated experiment, based on data from a single discrete mass spectrum whose parameters are extracted by a least-squares fit to a specified line shape. The relevance of the predictions to present and potential time-of-flight performance is discussed.