Synchrotron light sources, arguably among the most powerful tools of modern scientific discov-9 ery, are presently undergoing a major transformation to provide orders of magnitude higher brightness and transverse coherence enabling the most demanding experiments. In these experiments, overall source stability will soon be limited by achievable levels of electron beam size stability, presently on the order of several microns, which is still 1-2 orders of magnitude larger than already demonstrated stability of source position and current. Until now source size stabilization has been achieved through corrections based on a combination of static predetermined physics models and lengthy calibration measurements, periodically repeated to counteract drift in the accelerator and instrumentation. We now demonstrate for the first time how application of machine learning allows for a physics-and model-independent stabilization of source size relying only on previously existing instrumentation. Such feed-forward correction based on a neural network that can be continuously online-retrained achieves source size stability as low as 0.2 µm (0.4%) rms which results in overall source stability approaching the sub-percent noise floor of the most sensitive experiments.
Rapid bulk assembly of nanoparticles into microstructures is challenging, but highly desirable for applications in controlled release, catalysis, and sensing. We report a method to form hollow microstructures via a two-stage nematic nucleation process, generating size-tunable closed-cell foams, spherical shells, and tubular networks composed of closely packed nanoparticles. Mesogen-modified nanoparticles are dispersed in liquid crystal above the nematic-isotropic transition temperature (TNI). On cooling through TNI, nanoparticles first segregate into shrinking isotropic domains where they locally depress the transition temperature. On further cooling, nematic domains nucleate inside the nanoparticle-rich isotropic domains, driving formation of hollow nanoparticle assemblies. Structural differentiation is controlled by nanoparticle density and cooling rate. Cahn-Hilliard simulations of phase separation in liquid crystal demonstrate qualitatively that partitioning of nanoparticles into isolated domains is strongly affected by cooling rate, supporting experimental observations that cooling rate controls aggregate size. Microscopy suggests the number and size of internal voids is controlled by second-stage nucleation.
When nanoparticle self-assembly takes place in an anisotropic liquid crystal environment, fascinating new effects can arise. The presence of elastic anisotropy and topological defects can direct spatial organization. An important goal in nanoscience is to direct the assembly of nanoparticles over large length scales to produce macroscopic composite materials; however, limitations on spatial ordering exist due to the inherent disorder of fluid-based methods. In this paper we demonstrate the formation of quantum dot clusters and spherical capsules suspended within spherical liquid crystal droplets as a method to position nanoparticle clusters at defined locations. Our experiments demonstrate that particle sorting at the isotropic–nematic phase front can dominate over topological defect-based assembly. Notably, we find that assembly at the nematic phase front can force nanoparticle clustering at energetically unfavorable locations in the droplets to form stable hollow capsules and fractal clusters at the droplet centers.
BackgroundDust accumulation on surfaces of critical instruments has been a major concern during lunar and Mars missions. Operation of instruments such as solar panels, chromatic calibration targets, as well as Extra Vehicular Activity (EVA) suits has been severely compromised in the past as a result of dust accumulation and adhesion. Wind storms with wind speeds of up to 70 mph have not been effective in removing significant amounts of the deposited dust. This is indeed an indication of the strength of the adhesion force(s) involved between the dust particles and the surface(s) that they have adhered to. Complications associated with dust accumulation are more severe for non-conducting surfaces and have been the focus of this work.MethodologyArgon plasma treatment was investigated as a mechanism for lowering dust accumulation on non-conducting polymeric surfaces. Polymers chosen for this study include a popular variety of silicones routinely used for space and terrestrial applications namely RTV 655, RTV 615, and Sylgard 184. Surface properties including wettability, surface potential, and surface charge density were compared before and after plasma treatment and under different storage conditions. Effect of ultraviolet radiation on RTV 655 was also investigated and compared with the effect of Ar plasma treatment.Conclusion/SignificanceGravimetric measurements proved Ar plasma treatment to be an effective method for eliminating dust adhesion to all three polymers after short periods of exposure. No physical damage was detected on any of the polymer surfaces after Ar plasma treatment. The surface potential of all three polymers remained zero up to three months post plasma exposure. Ultraviolet radiation however was not effective in reducing surface and caused damage and significant discoloration to RTV 655. Therefore, Ar plasma treatment can be an effective and non-destructive method for treating insulating polymeric surfaces in order to eliminate dust adhesion and accumulation.
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