Charge Pattern Research Articles

Analysis of electro-osmotic flow by lattice Boltzmann simulation and Helmholtz-Smoluchowski formula

Numerical study of electro-osmotic flow (EOF) and theoretical exploration of relationship between EOF and electrical double layer (EDL) are carried out in the present work. Under the Poisson-Boltzmann and Debye-Hückel approximations, the analytic solution of electric potential, net charge, and flow pattern can be derived. The numerical results obtained by Poisson-Nernst-Planck (PNP) and Navier-Stokes equations are compared with the analytic solution and the solution based on the Helmholtz-Smoluchowski formula. Also, this article provides a clearer view on nondimensionalization applied on lattice Boltzmann method with different differential equation.

Joint Sensor Selection and Energy Allocation for Tasks-Driven Mobile Charging in Wireless Rechargeable Sensor Networks

Wireless power transfer (WPT) has emerged as a promising paradigm to charge devices due to the high reliability and efficiency of continuous power supply. Recent studies usually focus on relatively general charging patterns and metrics but neglect the collaborated task execution of nodes that incur charging inefficiency. In this article, we respect the energy requirement diversity among nodes to investigate the collaborated and tasks-driven mobile charging problem. Our goal is to maximize the overall task utility that concerns sensor selection and task cooperation. To address this problem, we propose a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(1-1/e)/4$ </tex-math></inline-formula> -approximation algorithm. First, we propose a novel energy allocation scheme with a specific theoretical analysis of the submodularity and gap property for the surrogate function. Then, we approximate the traveling cost to transform the formulated problem into an essentially monotone submodular function optimization subject to a general routing constraint and propose a greedy algorithm to address this problem. We conduct extensive simulations to validate our theoretical results and the results show our algorithm can achieve a near-optimal solution covering at least 84.9% of the optimal result achieved by the OPT algorithm. Furthermore, field experiments in an office room and a soccer field environment are implemented, respectively, to validate our proposed algorithm.

Synthesis of hybrid colloidal nanoparticles for a generic approach to 3D electrostatic directed assembly: Application to anti-counterfeiting

Hypothesis: The capability of making 3D directed assembly of colloidal nanoparticles on surfaces, instead of 2D one, is of major interest to generate, tailor, and enhance their original functionalities. The nanoxerography technique, i.e. electrostatic trapping of nanoparticles on charged patterns, showed such 3D assembly potentialities but is presently restricted to polarizable nanoparticles with a diameter superior to 20 nm. Hence, it should be possible to exploit a generic approach based on hybrid systems using larger nanoparticles as cargos to anchor smaller ones.Experiments: A synthesis of hybrid nanoparticles in a raspberry-like configuration was performed using 50 nm SiO2 nanoparticles and photoluminescent 3–5 nm InP@ZnS (visible emission) or PbS (infrared emission) nanoparticles. Complete topographical and photoluminescent characterizations were carried out on hybrid nanoparticle patterns assembled by nanoxerography and systematically compared to patterns obtained from single photoluminescent nanoparticles.Findings: The synthesis approach is generic. Every hybrid nanoparticle system has led to 3D assemblies with improved photoluminescent signals compared to mono/bilayered assemblies. Straightforward applications for anti-counterfeiting are illustrated. The versatility of the proposed concept is expected to be applied to other nanoparticles to make the most of their magnetic, catalytic, optical etc. properties in a wide range of applications, sensors and devices.

Power management of grid-integrated energy storage batteries with intermittent renewables

Abstract Intensive charging and discharging modes of batteries cause an adverse effect on state of health of grid-integrated energy storage batteries (GIESBs). Furthermore, uncoordinated patterns of charging and discharging the GIESBs are able to expand voltage profiles of power grids beyond their limits, while considering extra generation from photovoltaic arrays (PVs) and wind turbines (WTs). Therefore, a decentralized power management scheme is proposed in this paper to mitigate such impact on grid voltages and to improve the performance of the GIESBs with PVs and WTs. The proposed scheme develops three clusters of different objective functions to coordinate charging and discharging cycles of the GIESBs based on mixed integer linear programming, considering state of charge (SoC) and depth of discharge (DoD) of the batteries. The first cluster minimizes the power-charging cost of the GIESBs by utilizing time of use tariffs. The second cluster minimizes the charging power of the GIESBs, employing per unit generation from PVs and WTs, whereas the third cluster minimizes the discharging power of the GIESBs, using per unit consumption of residential loads. Simulation results of six scenarios show that, although the proposed scheme of the first cluster reduces operational costs of the GIESBs, it cannot maintain system voltages within the limitations. With the second and third clusters, the proposed scheme exhibits a better performance in stabilizing grid voltages, while maintaining the SoC and DoD within their boundaries to underpin state of health of the batteries.

αβDCA method identifies unspecific binding but specific disruption of the group I intron by the StpA chaperone.

Chaperone proteins—the most disordered among all protein groups—help RNAs fold into their functional structure by destabilizing misfolded configurations or stabilizing the functional ones. But disentangling the mechanism underlying RNA chaperoning is challenging, mostly because of inherent disorder of the chaperones and the transient nature of their interactions with RNA. In particular, it is unclear how specific the interactions are and what role is played by amino acid charge and polarity patterns. Here, we address these questions in the RNA chaperone StpA. We adapted direct coupling analysis (DCA) into the αβDCA method that can treat in tandem sequences written in two alphabets, nucleotides and amino acids. With αβDCA, we could analyze StpA–RNA interactions and show consistency with a previously proposed two-pronged mechanism: StpA disrupts specific positions in the group I intron while globally and loosely binding to the entire structure. Moreover, the interactions are strongly associated with the charge pattern: Negatively charged regions in the destabilizing StpA amino-terminal affect a few specific positions in the RNA, located in stems and in the pseudoknot. In contrast, positive regions in the carboxy-terminal contain strongly coupled amino acids that promote nonspecific or weakly specific binding to the RNA. The present study opens new avenues to examine the functions of disordered proteins and to design disruptive proteins based on their charge patterns.

Optimization of Variable-Current Charging Strategy Based on SOC Segmentation for Li-ion Battery

This paper presents a variable-current charging strategy of Li-ion batteries. Since the battery characteristics vary with state of charge (SOC), it is more reasonable to divide the charging process based on SOC than on cut off voltage. We find an optimal charging pattern of the proposed strategy by Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The Second-order Thevenin model of battery is established to simulate the charging process. Verification experiments are performed and results show that the obtained charging pattern has a temperature and loss reduction of 2.9 °C and 0.5% compared with constant current-constant voltage (CC-CV) strategy under the same charging time and capacity.

Compressing Double [7]Helicene by Successive Charging with Electrons

AbstractChemical reduction of a benzo‐fused double [7]helicene (1) with two alkali metals, K and Rb, provided access to three different reduced states of 1. The doubly‐reduced helicene 12− has been characterized by single‐crystal X‐ray diffraction as a solvent‐separated ion triplet with two potassium counterions. The triply‐ and tetra‐reduced helicenes, 13− and 14−, have been crystallized together in an equimolar ratio and both form the contact‐ion complexes with two Rb+ ions each, leaving three remaining Rb+ ions wrapped by crown ether and THF molecules. As structural consequence of the stepwise reduction of 1, the central axis of helicene becomes more compressed upon electron addition (1.42 Å in 14− vs. 2.09 Å in 1). This is accompanied by an extra core twist, as the peripheral dihedral angle increases from 16.5° in 1 to 20.7° in 14−. Theoretical calculations provided the pattern of negative charge build‐up and distribution over the contorted helicene framework upon each electron addition, and the results are consistent with the X‐ray crystallographic and NMR spectroscopic data.

Analytical Theory for Sequence-Specific Binary Fuzzy Complexes of Charged Intrinsically Disordered Proteins.

Intrinsically disordered proteins (IDPs) are important for biological functions. In contrast to folded proteins, molecular recognition among certain IDPs is "fuzzy" in that their binding and/or phase separation are stochastically governed by the interacting IDPs' amino acid sequences, while their assembled conformations remain largely disordered. To help elucidate a basic aspect of this fascinating yet poorly understood phenomenon, the binding of a homo or heterodimeric pair of polyampholytic IDPs is modeled statistical mechanically using cluster expansion. We find that the binding affinities of binary fuzzy complexes in the model correlate strongly with a newly derived simple "joint sequence charge decoration" parameter readily calculable from the pair of IDPs' sequence charge patterns. Predictions by our analytical theory are in essential agreement with coarse-grained explicit-chain simulations. This computationally efficient theoretical framework is expected to be broadly applicable to rationalizing and predicting sequence-specific IDP-IDP polyelectrostatic interactions.

Molecular Structure Modulated Trap Distribution and Carrier Migration in Fluorinated Epoxy Resin.

Surface charge accumulation on epoxy insulators is one of the most serious problems threatening the operation safety of the direct current gas-insulated transmission line (GIL), and can be efficiently inhibited by the surface modification technology. This paper investigated the mechanisms of fluorination modulated surface charge behaviors of epoxy resin through quantum chemical calculation (QCC) analysis of the molecular structure. The results show that after fluorination, the surface charge dissipation process of the epoxy sample is accelerated by the introduced shallow trap sites, which is further clarified by the carrier mobility model. The electron distribution probability of the highest occupied molecular orbitals (HOMO) under positive charging and the lowest unoccupied molecular orbitals (LUMO) under negative charging shows distinctive patterns. It is illustrated that electrons are likely to aggregate locally around benzenes for the positively charged molecular structure, while electrons tend to distribute all along the epoxy chain under negatively charging. The calculated results verify that fluorination can modulate surface charge behaviors of epoxy resin through redesigning its molecular structure, trap distribution and charging patterns.

MHC class I associations beyond HLA-B27: the peptide binding hypothesis of psoriatic arthritis and its implications for disease pathogenesis.

To provide an overview of the heterogeneous human leucocyte antigen (HLA) associations of psoriatic arthritis, their relationship to particular clinical features of the disease, and how a hypothesis of binding specific peptides could provide a unifying basis for this heterogeneity. There have been substantive advances in understanding the role of HLA molecules in binding self-peptides that select our repertoire of T cells, the specific peptide-binding properties of these HLA allotypes, and their crystallographic structure. These advances provide a means to envision the significance of the heterogeneous psoriatic arthritis HLA associations. The clinical relevance of these allotypes if heightened by emerging knowledge of their relationship to particular clinical features of the disease that serve as subphenotypes. We propose a peptide binding hypothesis of psoriatic arthritis based on a shared pattern of negative charge in the 'B' pocket of the HLA-B and HLA-C molecules encoded by the susceptibility allotypes. This hypothesis suggests that peptides characterized by the presence of arginine at position 2 or 3 are bound to the susceptibility allotypes and drive the T-cell clones selected on them to attack molecules containing these peptides located in sites of psoriatic arthritis inflammation.

Implications of localized charge for human influenza A H1N1 hemagglutinin evolution: Insights from deep mutational scans.

Seasonal influenza A viruses of humans evolve rapidly due to strong selection pressures from host immune responses, principally on the hemagglutinin (HA) viral surface protein. Based on mouse transmission experiments, a proposed mechanism for immune evasion consists of increased avidity to host cellular receptors, mediated by electrostatic charge interactions with negatively charged cell surfaces. In support of this, the HA charge of the globally circulating H3N2 has increased over time since its pandemic. However, the same trend was not seen in H1N1 HA sequences. This is counter-intuitive, since immune escape due to increased avidity (due itself to an increase in charge) was determined experimentally. Here, we explore whether patterns of local charge of H1N1 HA can explain this discrepancy and thus further associate electrostatic charge with immune escape and viral evolutionary dynamics. Measures of site-wise functional selection and expected charge computed from deep mutational scan data on an early H1N1 HA yield a striking division of residues into three groups, separated by charge. We then explored evolutionary dynamics of these groups from 1918 to 2008. In particular, one group increases in net charge over time and consists of sites that are evolving the fastest, that are closest to the receptor binding site (RBS), and that are exposed to solvent (i.e., on the surface). By contrast, another group decreases in net charge and consists of sites that are further away from the RBS and evolving slower, but also exposed to solvent. The last group consists of those sites in the HA core, with no change in net charge and that evolve very slowly. Thus, there is a group of residues that follows the same trend as seen for the entire H3N2 HA. It is possible that the H1N1 HA is under other biophysical constraints that result in compensatory decreases in charge elsewhere on the protein. Our results implicate localized charge in HA interactions with host cells, and highlight how deep mutational scan data can inform evolutionary hypotheses.

Distributed Electric Vehicles Charging Management Considering Time Anxiety and Customer Behaviors

This article proposes a charging management of electric vehicles (EVs) that considers time anxieties and different behaviors of EV customers. The time anxiety concept is newly presented to address some uncertain events that may happen meanwhile charging of EVs, affect their charging patterns, and prevent them from meeting their energy demands. The working principle of the concept relies on prioritizing the charging before the event occurrences, and thus changing the EV charging patterns. Based on this concept, four different EV customer behaviors are proposed and their influences are investigated. The EV charging problem is formulated as a generalized nash equilibrium (NE) game, in which each EV minimizes its charging cost given its charging requirements and the charging facility constraints. The solution is developed on the basis of receding horizon optimization and reached iteratively in a distributed manner. Detailed simulation and comparison results are introduced to verify the effectiveness of the proposed charging management with the different time-anxiety-based EV customer behaviors.

Heterogeneous surface charge confining an electrolyte solution.

The structure of dilute electrolyte solutions close to a surface carrying a spatially inhomogeneous surface charge distribution is investigated by means of classical density functional theory within the approach of fundamental measure theory. For electrolyte solutions, the influence of these inhomogeneities is particularly strong because the corresponding characteristic length scale is the Debye length, which is large compared to molecular sizes. Here, a fully three-dimensional investigation is performed, which accounts explicitly for the solvent particles, and thus provides insight into effects caused by ion-solvent coupling. The present study introduces a versatile framework to analyze a broad range of types of surface charge heterogeneities even beyond the linear response regime. This reveals a sensitive dependence of the number density profiles of the fluid components and of the electrostatic potential on the magnitude of the charge as well as on the details of the surface charge patterns at small scales.

Understanding the Long-Term Evolution of Electric Taxi Networks

Due to the ever-growing concerns over air pollution and energy security, more and more cities have started to replace their conventional taxi fleets with electric ones. Even though environmentally friendly, the rapid promotion of electric taxis raises problems to both taxi drivers and governments, e.g., prolonged waiting/charging time, unbalanced utilization of charging infrastructures, and inadequate taxi supply due to the long charging time. In this article, we conduct the first longitudinal measurement study to understand the long-term evolution of mobility and charging patterns by utilizing 5-year data from one of the largest electric taxi networks in the world, i.e., the Shenzhen electric taxi network in China. In particular, (1) we first perform an electric taxi contextualization about their operation and charging activities; (2) then we design a generic charging event extraction algorithm based on GPS data and charging station data, and (3) based on the contextualization and extracted charging activities, we perform a comprehensive measurement study called ePat to explore the evolution of the electric taxi network from the mobility and charging perspectives. Our ePat is based on 4.8 TB taxi GPS data, 240 GB taxi transaction data, and metadata from 117 charging stations, during an evolution process from 427 electric taxis in 2013 to 13,178 in 2018. Moreover, ePat also explores the impacts of various contexts and benefits during the evolution process. Our ePat as a comprehensive measurement of the electric taxi network mobility and charging evolution has the potential to advance the understanding of the evolution patterns of electric taxi networks and pave the way for analyzing future shared autonomous vehicles.

Effective electrostatic forces between two neutral surfaces with atomic scale strip shape surface charge separation

By using classical density functional theory we investigate influences of atomic scale strip shape surface charge separation on the effective electrostatic force (EEF) between two overall neutral surfaces immersed in 1:1 electrolyte solution. Main novel findings are confirmed, and summarized as follows: (i) At lower bulk concentration, the EEF is always attractive for asymmetrical surface charge pattern and repulsive for symmetrical surface charge pattern. Strength of the EEF is positively correlated with the strip shape domain width and surface charge density intensity, and generally smaller than that of the EEF between two equally and oppositely or similarly charged planar surfaces with same surface charge intensity. (ii) With increasing of the bulk concentration, the asymmetrical strip shape surface charge pattern induces two repulsion peaks, strengths of the repulsion peaks around one times and double the ion diameter surface separation are negatively and positively correlated with the strip shape domain width, respectively; the second repulsion peak position reduces with the domain charge strength. (iii) At higher bulk concentration, the symmetrical strip shape charge pattern induces two attraction wells of the EEF curve; one positioned at wall separation larger than one times and smaller than double the ion size, the other approximately 2.75 times the ion size. (iv) Considering that systems favor low-energy configurations, the present calculations provide clear evidence that effective repulsion between colloids with non-uniform surface charge distribution or charge fluctuation is less than that with uniform surface charge distribution or with fixed charges, respectively. All of the observations can be explained self-consistently by using concepts such as ion saturation adsorption capacity, arrangement compactness of the adsorption layer, formation of quasi-hydrogen-bonding, and charge reversal.

Stochastic management of hybrid AC/DC microgrids considering electric vehicles charging demands

The high growth of the automotive industry reveals the very bright future of this technology and its high penetration effects on the human society. No doubt that the random and volatile charging demand of these devices would affect the power grid optimal operation and scheduling which may be regarded as a new challenge. Therefore, this paper investigates the stochastic scheduling of hybrid AC/DC microgrids considering the plugin hybrid electric vehicles charging demands, distributed all over the grid. Three different charging patterns, called coordinated, uncoordinated and smart charging models with different characteristics for the charger type, capacity and market share are proposed. Moreover, different types of renewable energy sources including wind turbine, solar panel and fuel cell are modeled and considered in the scheduling process of the hybrid microgrid. In order to mitigate the charging effects of electric vehicles on the hybrid AC–DC microgrid operation, some remotely switches are considered in the system which make it possible for changing the topology and power flow way. In order to model the uncertainty effects, a data-driven framework based on point estimate method and support vector machine is developed. This would make it possible to extract out the standard deviation value of the uncertain parameters and reflect their impacts on the microgrid operation problem through the limited concentration points. A novel evolving solution based on flower pollination algorithm is also proposed to solve the problem optimally. An IEEE standard test system is used as the hybrid AC/DC microgrid case study to assess the performance of proposed model.

Multi-label charge predictions leveraging label co-occurrence in imbalanced data scenario

Charge prediction is to predict associated charges based on fact descriptions and plays a significant role in legal aid systems. It is a fundamental and challenging task to automatically predict charges in the multi-label classification paradigm, which is fit to real applications. Existing works either focus on balanced data scenario and multiple charges or few-shot charges with a single label. Moreover, previous models utilize special initialization with label patterns to improve the performance of the multi-label classification task, which is only applicable when there is less training data, resulting in poor robustness. To this end, a multi-task convolutional neural network combined with bidirectional long short-time memory leveraging label co-occurrence framework, called CBLLC, is introduced to predict multiple charges with article information on imbalanced data occasion. We develop a new learning mechanism to train the framework of charge and article patterns when there is a lot of training data, increasing its robustness. In CBLLC, the data preprocessing process serves to aid the training in a more generalized manner and reduce overfitting. A salient word annotation is introduced to deal with few-shot charges. A better classification result is obtained with processed data and improves the generality of the model. Experimental results of Chinese AI and Law Challenge test set show the superiority of our proposed method compared with the state-of-the-art methods. In particular, a macro-F1 score of 92.9% for charges and 86.6% for articles is achieved with co-occurrence of charges and patterns of articles.

Assessment and Mitigation of Electric Vehicle Charging Demand Impact to Transformer Aging for an Apartment Complex

Due to the increasing use of Electric Vehicles (EVs), the effect of the EV charging power demand on the reliability of the power system infrastructure needs to be addressed. In apartment complexes, which have emerged as a common residential type in metropolitan areas and highly populated districts, high charging demand could result in substantial stress to distribution networks. In this work, the effect of EV charging power demand in an apartment complex on the aging of the Distribution Transformer (DT) is studied. A methodology based on the stochastic characterization of vehicle usage profiles and user charging patterns is developed to obtain realistic EV charging demand profiles. Based on the modeled EV charging profile and the transformer thermal model, the effect of different EV penetration ratios on DT aging for an apartment complex in the Republic of Korea is studied. Results for an EV penetration ratio of up to 30% indicated that DT aging could be accelerated by up to 40%, compared to the case without EV charging. To mitigate this accelerated DT aging caused by EV charging, the effectiveness of two integration approaches of Photovoltaic (PV) sources was studied. Based on a case study that included a realistic PV generation profile, it was demonstrated that a significant contribution to DT reliability could be achieved via the operation of PV sources. A more apparent contribution of PV integration was observed with an energy storage installation at higher EV penetration ratios. At an EV penetration ratio of 30%, a maximum decrease of 41.8% in the loss-of-life probability of the DT was achieved. The effects of different PV integration approaches and power management details on DT aging were also studied. The results demonstrate that the EV charging demand could introduce a significant level of stress to DTs and that this impact can be effectively mitigated by installing PV sources. These observations are expected to contribute toward the effective planning of power system infrastructures that support the design of sustainable cities with the widespread use of EVs.

Identifying sequence perturbations to an intrinsically disordered protein that determine its phase-separation behavior.

Phase separation of intrinsically disordered proteins (IDPs) commonly underlies the formation of membraneless organelles, which compartmentalize molecules intracellularly in the absence of a lipid membrane. Identifying the protein sequence features responsible for IDP phase separation is critical for understanding physiological roles and pathological consequences of biomolecular condensation, as well as for harnessing phase separation for applications in bioinspired materials design. To expand our knowledge of sequence determinants of IDP phase separation, we characterized variants of the intrinsically disordered RGG domain from LAF-1, a model protein involved in phase separation and a key component of P granules. Based on a predictive coarse-grained IDP model, we identified a region of the RGG domain that has high contact probability and is highly conserved between species; deletion of this region significantly disrupts phase separation in vitro and in vivo. We determined the effects of charge patterning on phase behavior through sequence shuffling. We designed sequences with significantly increased phase separation propensity by shuffling the wild-type sequence, which contains well-mixed charged residues, to increase charge segregation. This result indicates the natural sequence is under negative selection to moderate this mode of interaction. We measured the contributions of tyrosine and arginine residues to phase separation experimentally through mutagenesis studies and computationally through direct interrogation of different modes of interaction using all-atom simulations. Finally, we show that despite these sequence perturbations, the RGG-derived condensates remain liquid-like. Together, these studies advance our fundamental understanding of key biophysical principles and sequence features important to phase separation.

Modified Symbiotic Organisms Search for Plug-In Hybrid Electric Vehicles through Renewable Micro-Grids

This paper uses a modern stochastic framework for studying the storage devices, PHEVs (plug-in hybrid electric vehicles), RESs (renewable energy sources) and such MGs (micro grids) for optimal management of energy while applying the popular Monte Carlo simulation technique for modelling the uncertainties of RESs and PHEVs. Prominent charging patterns namely uncontrolled, smart and controlled charging schemes are implemented for analysis of response in MGs for varied PHEV charging behaviours. Simultaneously, we also apply a powerful and robust Symbiotic Organisms Search (SOS) algorithm for analysing the uncertain parameter behaviours in natural stochastic form and the optimal MG operation. The interactions observed in natural organisms that depend on other organisms for survival are simulated by SOS. The total search ability in global and local searches is improved effectively using the modified SOS algorithm. Multiple MG test systems with varied scheduling time limits are used for examination of the proposed technique and its performance. In the presence as well as absence of PHEV charging effects, comparison of proposed technique and other algorithms are conducted with case studies under diverse conditions.