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\"Guide to Spacecraft Charging Effects is a single reference source containing both theory of spacecraft charging and suggested practical detailed spacecraft design requirements and procedures to minimize the effects of spacecraft charging and to limit the effects of the resulting electrostatic discharge.This book contains virtually the whole body of spacecraft charging knowledge as of today, moving from first principles for the beginner to intermediate and more advanced concepts.? Many equations are present to provide a good theoretical background,?as well as numerous charts, graphs, figures, tables, and photos to summarize and illustrate the theoretical background in a practical presentation. Numerous appendices expand on the main text, a well thought-out index gives quick access to important concepts, and an extensive list of references provides further avenues of research for those wishing to extend their knowledge.Much of the environmental data and material response information has been adapted from published and unpublished scientific literature for use in this document.? It is the book form of the recently issued NASA Technical Handbook NASA-HDBK-4002A, March 3, 2011 (by the same authors).? In particular, this book can be used as the textbook form of that Handbook and its earlier sources, NASA Technical Paper 2361, 1984, and NASA Technical Handbook NASA-HDBK-4002, 1999 (both co-authored by the current authors).Since the original writing of the 2361 and 4002, there have been many developments in the understanding of spacecraft charging issues and mitigation solutions, as well as advanced technologies needing new mitigation solutions.? Solar cell technology, especially higher voltage arrays have been found to need new design approaches; these are described in detail in this new book.? Information about the space plasma environment has been studied more thoroughly; that information is in this new book.? New analytic computer codes have been developed to help analyze spacecraft charging; they are described and listed in this new book.? Spacecraft anomalies and failures have emphasized certain designs that are now known to be of greater importance than others; that knowledge is incorporated in this new book\"--Provided by publisher.

The spontaneous electrification of surfaces and interfaces is a widespread phenomenon that produces unexpected effects in chemical reactivity and mass charge transfer, revealed in abundant literature over the past twenty years. The pervasive presence of electrostatic charges originates from many sources, including friction, mechanochemical reactions, phase change, flexoelectricity, and others. Since fused deposition modeling undergoes most well-known electrification mechanisms, it would be not surprising that 3D-printed objects display large amounts of charge. Here we uncover the hitherto unexplored realm of electrostatic charging in 3D printing, underscores the impact of printing parameters on charge generation in polymers. Substrates, printing speed, temperature, and printing direction each exert distinct impacts on charge buildup, depending upon the material used for printing. We also develop simple protocols employing common multimeters for charge monitoring, while substrates subjected to corona charging or triboelectrification demonstrate effective methods for charge control or mitigation. An original development is achieved by demonstrating the ability to print quasi-electrets, indicating a potential revolution in electret technology. The implications of these findings establish the groundwork for advancements in 3D printing technology and electrostatics, creating new scientific opportunities for a better understanding of matter. In this work, authors investigate how printing parameters like speed, temperature, and direction affect electrostatic charge in 3Dprinted polymers. They develop protocols for charge monitoring and control, demonstrating the potential to print quasi-electrets and offering insights into electrostatics during 3D printing.

Bilayer graphene twisted by a small angle shows a significant charge modulation away from neutrality, as the charge in the narrow bands near the Dirac point is mostly localized in a fraction of the Moiré unit cell. The resulting electrostatic potential leads to a filling-dependent change in the low-energy bands, of a magnitude comparable to or larger than the bandwidth. These modifications can be expressed in terms of new electron–electron interactions, which, when expressed in a local basis, describe electron-assisted hopping terms. These interactions favor superconductivity at certain fillings.

The hypervariable residues that compose the major part of proteins’ surfaces are generally considered outside evolutionary control. Yet, these “nonconserved” residues determine the outcome of stochastic encounters in crowded cells. It has recently become apparent that these encounters are not as random as one might imagine, but carefully orchestrated by the intracellular electrostatics to optimize protein diffusion, interactivity, and partner search. The most influential factor here is the protein surface-charge density, which takes different optimal values across organisms with different intracellular conditions. In this study, we examine how far the net-charge density and other physicochemical properties of proteomes will take us in terms of distinguishing organisms in general. The results show that these global proteome properties not only follow the established taxonomical hierarchy, but also provide clues to functional adaptation. In many cases, the proteome–property divergence is even resolved at species level. Accordingly, the variable parts of the genes are not as free to drift as they seem in sequence alignment, but present a complementary tool for functional, taxonomic, and evolutionary assignment.

Machine learning potentials have become an important tool for atomistic simulations in many fields, from chemistry via molecular biology to materials science. Most of the established methods, however, rely on local properties and are thus unable to take global changes in the electronic structure into account, which result from long-range charge transfer or different charge states. In this work we overcome this limitation by introducing a fourth-generation high-dimensional neural network potential that combines a charge equilibration scheme employing environment-dependent atomic electronegativities with accurate atomic energies. The method, which is able to correctly describe global charge distributions in arbitrary systems, yields much improved energies and substantially extends the applicability of modern machine learning potentials. This is demonstrated for a series of systems representing typical scenarios in chemistry and materials science that are incorrectly described by current methods, while the fourth-generation neural network potential is in excellent agreement with electronic structure calculations. Machine learning potentials do not account for long-range charge transfer. Here the authors introduce a fourth-generation high-dimensional neural network potential including non-local information of charge populations that is able to provide forces, charges and energies in excellent agreement with DFT data.

This research aims to validate an electrostatics characterization device to better understand the process of static charge generation in textile materials and to see how different factors affect it. This electrostatic device offers a variety of settings for controlling sample electrostatic activation and has a sample size range of up to one square meter. It can move in both horizontal and vertical directions in a controlled manner, providing a variety of possibilities for testing the effect of various movement features on electrostatic charge formation. Not only the textile polymer but also the motion characterizations influence the generation of electrostatic charges in textiles. The influence of frequency, pressure, dwell time between moves, test duration, effect of different sample sizes, and amplitude of movement on electrostatic charge generation was studied in greater detail. Two different parameters of the electrostatic waveform (peak voltage and peak-to-peak voltage) were investigated. The generation of electrostatic charges is proportional to the peak voltage and peak-to-peak voltage of the electrostatic waveform. Overall electrostatic charge generation increases with increasing frequency, stepping height, applied pressure at the same frequency, and sample size, but decreases with increasing dwell time between moves at the same frequency. The charge also increases with test duration until a saturation point is reached.

Machine learning has the potential to revolutionize the field of molecular simulation through the development of efficient and accurate models of interatomic interactions. Neural networks can model interactions with the accuracy of quantum mechanics-based calculations, but with a fraction of the cost, enabling simulations of large systems over long timescales. However, implicit in the construction of neural network potentials is an assumption of locality, wherein atomic arrangements on the nanometer-scale are used to learn interatomic interactions. Because of this assumption, the resulting neural network models cannot describe long-range interactions that play critical roles in dielectric screening and chemical reactivity. Here, we address this issue by introducing the self-consistent field neural network — a general approach for learning the long-range response of molecular systems in neural network potentials that relies on a physically meaningful separation of the interatomic interactions — and demonstrate its utility by modeling liquid water with and without applied fields. Machine learning-based neural network potentials often cannot describe long-range interactions. Here the authors present an approach for building neural network potentials that can describe the electronic and nuclear response of molecular systems to long-range electrostatics.

Various physical tweezers for manipulating liquid droplets based on optical, electrical, magnetic, acoustic, or other external fields have emerged and revolutionized research and application in medical, biological, and environmental fields. Despite notable progress, the existing modalities for droplet control and manipulation are still limited by the extra responsive additives and relatively poor controllability in terms of droplet motion behaviors, such as distance, velocity, and direction. Herein, we report a versatile droplet electrostatic tweezer (DEST) for remotely and programmatically trapping or guiding the liquid droplets under diverse conditions, such as in open and closed spaces and on flat and tilted surfaces as well as in oil medium. DEST, leveraging on the coulomb attraction force resulting from its electrostatic induction to a droplet, could manipulate droplets of various compositions, volumes, and arrays on various substrates, offering a potential platform for a series of applications, such as high-throughput surface-enhanced Raman spectroscopy detection with single measuring time less than 20 s.

Scaling of silicon (Si) transistors is predicted to fail below 5-nanometer (nm) gate lengths because of severe short channel effects. As an alternative to Si, certain layered semiconductors are attractive for their atomically uniform thickness down to a monolayer, lower dielectric constants, larger band gaps, and heavier carrier effective mass. Here, we demonstrate molybdenum disulfide (MoS₂) transistors with a 1-nm physical gate length using a single-walled carbon nanotube as the gate electrode. These ultrashort devices exhibit excellent switching characteristics with near ideal subthreshold swing of ~65 millivolts per decade and an On/Off current ratio of ~10⁶. Simulations show an effective channel length of ~3.9 nm in the Off state and ~1 nm in the On state.

Electrostatics and van der Waals (vdW) interactions are two major components of intermolecular weak interactions. Electrostatic potential has been a very popular function in revealing electrostatic interaction between the system under study and other species, while the role of vdW potential was less recognized and has long been ignored. In this paper, we explicitly present definition of vdW potential, describe its implementation details, and demonstrate its important practical values by several examples. We hope this work can arouse researchers’ attention to the vdW potential and promote its application in the studies of weak interactions. Calculation, visualization, and quantitative analysis of the vdW potential have been supported by our freely available code Multiwfn ( http://sobereva.com/multiwfn ).