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The microgravity environment of the Plasmakristall-4 experiment on the International Space Station provides a laboratory for exploring plasma-mediated interactions among charged dust grains in fully three-dimensional space. Away from the strong influence of Earth's gravity, the dust grains can levitate in the bulk of the plasma, where they have been observed to form extended filamentary structures aligned with the discharge tube axis. These structures can be used as a macroscopic analogue for other self-organizing systems, including electrorheological fluids and liquid crystals, and the success of the analogy depends on a thorough understanding of the mechanisms guiding the dust interaction potential. Here we present the results from molecular dynamics simulations of the ion flow past isolated dust chains within the dust cloud and the dust cloud macrostructure. Although dust grains are known to respond on the millisecond timescale, analysis reveals that periodic variations of plasma conditions on the microsecond timescale significantly affect dust structure formation. In addition to the expected formation of filamentary dust chains in the dust cloud macrostructure, dust grains in a large cloud are also observed to organize into ordered positions on the surface of nested cylinders, in agreement with experimental observations. 

This study examines the structure and stability of filamentary dusty plasmas using data from the Plasmakristall-4 (PK-4) facility on board the International Space Station. Under the action of a polarity-switched DC electric field, the dust particles in the PK-4 discharge have been found to organize into field-aligned extended filaments, which has been compared to the filamentary state in electrorheological (ER) fluids. Here we discuss how, in addition to an ER-type structural transition, the PK-4 dusty plasmas exhibit structural states reminiscent of those observed in liquid crystals (LCs) with rod-shaped molecules. We find that dust particles within the filaments are strongly coupled in a crystalline-like structure, while the coupling of particles across filaments is liquid-like. In addition to a common orientation along a director axis (nematic behavior), the dust filaments also appear to align in large-scale nested structures, or shells (smectic behavior). Finally, these filaments are found to further arrange in hexagonal patterns within the plane orthogonal to the director axis, suggesting the possibility for smectic-B and smectic-C structural states. As the observed ER and LC features of the filamentary dusty plasma states are sensitive to variations in the PK-4 discharge conditions, we argue that these dusty plasmas can provide a controlled analogous system for the study of fundamental phenomena in soft matter, such as the origins of pattern formation and universality of phase transitions. 

Glycans are one of the most widely investigated biomolecules, due to their roles in numerous vital biological processes. However, few system-independent, LC-MS/MS (liquid chromatography tandem mass spectrometry) based studies have been developed with this particular goal. Standard approaches generally rely on normalized retention times as well as m/z-mass to charge ratios of ion values. Due to these limitations, there is need for quantitative characterization methods which can be used independently of m/z values, thus utilizing only normalized retention times. As such, the primary goal of this article is to construct an LC-MS/MS based classification of the glycans derived from standard glycoproteins and human blood serum using a glucose unit index as the reference frame in the space of compound parameters. For the reference frame, we develop a closed-form analytic formula via the Green's function of a relevant convection-diffusion-absorption equation used to model composite material transport. The aforementioned equation is derived from an Einstein–Brownian motion paradigm, which provides a physical interpretation of the time-dependence at the point of observation for molecular transport in the experiment. The necessary coefficients are determined via a data-driven learning procedure. The methodology is presented in an abstractly and validated via comparison with experimental mass spectrometer data. 

The purpose of this article is to study an approximation to an abstract Bessel-type problem, which is a generalization of the extension problem associated with fractional powers of the Laplace operator. Motivated by the success of such approaches in the analysis of time-stepping methods for abstract Cauchy problems, we adopt a similar framework herein. The proposed method differs from many standard techniques, as we approximate the true solution to the abstract problem, rather than solve an associated discrete problem. The numerical method is shown to be consistent, stable, and convergent in an appropriate Banach space. These results are built upon well understood results from semigroup theory. Numerical experiments are provided to demonstrate the theoretical results.