Charge State Research Articles

PH-Dependent Assembly and Stability of Toll-Like Receptor 3/dsRNA Signaling Complex: Insights from Constant pH Molecular Dynamics and Metadynamics Simulations.

The pH-dependent assembly of Toll-like receptors (TLRs), which triggers a threshold-like response, is a key principle in immune signaling. While crystallography has revealed the intricate structure of these assembly complexes, the mechanisms underlying their pH dependency remain unclear. Herein, constant pH simulations and metadynamics are employed to investigate the pH-dependent assembly and stability of the TLR3/dsRNA signaling complex. The findings demonstrate that system pH regulates complex assembly and stability by modulating the protonation and charge states of histidines. Histidines in TLR3 act as pH-dependent, positively charged binding sites that capture negatively charged dsRNA. Additionally, these histidines form a [H682⁺]-[E626⁻] dipole, facilitating the assembly of two TLR3 molecules into an antisymmetric dimer through dipole-dipole interactions. Surprisingly, TLR3 can shift the pKa values of key histidines from their model pKa of 6.5, increasing protonation likelihood and enhancing ligand binding. Notably, the aromatic residue Phe84, located within the dsRNA binding site [His39⁺-His60⁺-Phe84-His108⁺], alters the pKa of His60 through cation-π interactions with its protonated state. This study offers new insights into the molecular mechanisms underlying pH-dependent immune signaling via higher-order assemblies and suggests potential applications for histidine in self-assembling biomaterials.

Effect of charge state on alkaline OER activity in FeN4–Gr catalyst supported on Ni (111) substrate

Abstract The catalysts of transition metal single−atom embedded in N−doped graphene (TMN4−Gr) have attracted significant attention due to the high utilization of transition metal atoms, remarkable selectivity and tunable catalytic activity. In this study, we have employed first−principles study to investigate alkaline OER activity of FeN4−Gr system supported on a Ni (111) substrate (FeN4−Gr/Ni). The results show that the Ni substrate can significantly enhance the OER activity of FeN4−Gr catalyst. The overpotential of FeN4−Gr/Ni catalyst is 0.63 V, much lower than 0.79 V of the FeN4−Gr, which is closely related to the strong interaction from the Ni substrate. By subtracting electrons from the FeN4−Gr/Ni catalyst, a positive charge environment can be created. It is found that, between the FeN4−Gr and the Ni substrate, a significant electron transfer phenomenon is observed, and the amount can be regulated by the charge state. Besides, the charge state can obviously tune the adsorption strength of *OOH intermediate through the interaction of Ni substrate, further optimizing the OER activity thermodynamically favorable. As the charge state increases to +1.55, the overpotential decreases to 0.23 V. By analyzing the integrated crystal orbital Hamilton populations, the chemical bond strength of Fe−O is discussed. Our results reveal that the Ni substrate can significantly enhance the OER activity of the FeN4−Gr catalyst, and the charge state can also tune the overpotential, offering valuable insights for the design of high−efficiency single−atom catalysts utilizing metal substrate.

A Quantitative Method of Carbon Emission Reduction for Electrochemical Energy Storage Based on the Clean Development Mechanism

Electrochemical energy storage (EES) plays a crucial role in reducing the curtailed power from wind and solar PV power (WSP) generation and enhancing the decarbonization effects of power systems. However, research on quantifying the carbon emission reduction effects of EES methods in the engineering field is still insufficient, which constrains decision-makers from making intuitive assessments of the decarbonization effects of energy storage. Therefore, drawing on the principles of the clean development mechanism (CDM), this paper proposes a method for quantifying the carbon emission reductions of a standalone EES station. Firstly, based on the design principles of building marginal emission factor in the CDM, a method for calculating the BM weight of WSP is proposed. Secondly, three quantification methods for the carbon emission reductions of EES are presented based on the complexity of the calculations. Lastly, to analyze the impacts of different operational conditions and calculation methods on the carbon emission reduction of energy storage systems, a dispatch model is constructed for various operational scenarios. The results of the case study indicate that different calculation methods yield varying results in terms of the carbon emission reductions of energy storage systems, with the sharply value method yielding the smallest reduction and the output curve method yielding the largest reduction. Additionally, when considering the losses in the state of charge (SOC) of an energy storage system and reducing the overall output fluctuations of WSP-EES, the carbon emission reduction potential of the energy storage will decrease. This study establishes a theoretical basis for quantifying the carbon emission reductions of standalone electrochemical energy storage systems, aiding decision-makers in gaining a deeper understanding of the role of electrochemical energy storage in carbon reduction and operational value.

Low-Energy Magnetic States of Tb Adatom on Graphene.

Electronic structure and magnetic interactions of a Tb adatom on graphene are investigated from first principles using combination of density functional theory and multiconfigurational quantum chemistry techniques including spin-orbit coupling. We determine that the six-fold symmetry hollow site is the preferred adsorption site and we investigate electronic spectrum for different adatom oxidation states including Tb3+, Tb2+,Tb1+, and Tb0. For all charge states, the Tb 4f8configuration is retained with other adatom valence electrons being distributed over 5dxy, 5dx2+y2, and 6s/5d0single-electron orbitals. We find strong intra-site adatom exchange coupling that ensures that the 5d6sspins are parallel to the 4fspin. For Tb3+, the energy levels can be described by theJ= 6 multiplet split by the graphene crystal field. For other oxidation states, the interaction of 4felectrons with spin and orbital degrees of freedom of 6s5delectrons in the presence of spin-orbit coupling results in the low-energy spectrum composed closely lying effective multiplets that are split by the graphene crystal field. Stable magnetic moment is predicted for Tb3+and Tb2+adatoms due to uniaxial magnetic anisotropy and effective anisotropy barrier around 440 cm-1controlled by the temperature assisted quantum tunneling of magnetization through the third excited doublet. On the other hand, in-plane magnetic anisotropy is found for Tb1+and Tb0adatoms. Our results indicate that the occupation of the 6s5dorbitals can dramatically affect the magnetic anisotropy and magnetic moment stability of rare earth adatoms.

Electron-cloud alignment dynamics induced by an intense X-ray free-electron laser pulse: a case study on atomic argon

In an intense X-ray free-electron laser (XFEL) pulse, atoms are sequentially ionised by multiple X-ray photons. Photoionisation generally induces an alignment of the electron cloud of the produced atomic ion regarding its orbital-angular-momentum projections. However, how the alignment evolves during sequential X-ray multi-photon ionisation accompanied by decay processes has been unexplored. Here we present a theoretical prediction of the time evolution of the electron-cloud alignment of argon ions induced by XFEL pulses. To this end, we calculate state-resolved ionisation dynamics of atomic argon interacting with an intense linearly polarised X-ray pulse, which generates ions in a wide range of charge states with non-zero orbital- and spin-angular momenta. Employing time-resolved alignment parameters, we predict the existence of non-trivial alignment dynamics during intense XFEL pulses. This implies that even if initially the atomic electron cloud is perfectly spherically symmetric, X-ray multi-photon ionisation can lead to noticeable reshaping of the electron cloud.

Graphite-Based Bio-Mimetic Nanopores for Protein Sequencing and Beyond.

Protein sequencing using nanopores represents the next frontier in bio-analytics. However, linearizing unfolded proteins and controlling their translocation speed through solid-state nanopores pose significant challenges in protein sequencing. In order to address these issues, this work proposes a biomimetic graphite-based nanopore construction. These nanopores feature a nanometer-sized pore with a constriction zone, mimicking the structure of the α-hemolysin protein pore. Ourall-atom Molecular Dynamics simulations demonstrate the high practical potential of these nanopores by revealing how their charge state renders them complete ion-selective and generates an electro-osmotic flow. This study shows that this nanopore construction can detect peptides at the single amino acid level by analyzing the ionic current traces generated as peptides traverse the nanopore. The novelty of the proposed nanopore lies in its ability to modulate the hydrodynamic drag induced by electro-osmotic flow, relative to the electro-phoretic force. This investigation reveals that tuning these forces helps to linearize translocating peptides and extend the residence time of individual amino acids at the constriction zone of the pore. This significantly enhances the detection and sequencing efficiency of the pore. Furthermore, the high relevance of the proposed nanopores is underscored for seawater desalination through electrodialysis and extends to ion separation under salinitygradients.

Magnetic Circular Dichroism of Oxide Films: Study of Electronic, Magnetic and Charge States

Semiconductor materials based on ZnO and RE3+MnO3 oxides are considered as potential candidates for spintronics. This article presents the methodology and results of studying the effect of magnetic circular dichroism for Zn1-xCoxO, Zn1-x-yCoxAlyO and RE1-x 3+Ax 2+MnO3 film structures in the visible radiation range. It has been shown that the magnetic circular dichroism behavior of the manganite films reflects not only the magnetic, but also the charge component of the material. This indicates the possibility of studying the magnetic and transport characteristics of the films using the magnetic circular dichroism spectroscopy. Since the magnetic circular dichroism effect also directly probes the ground and excited electronic states of the film, it has been obtained data that update calculated parameters for describing the manganites band structure. In the case of the Zn1-xCoxO and Zn1-x-yCoxAlyO films, the magnetic circular dichroism spectroscopy acts as a tool for detecting Co nanoparticles in the solid solution matrix of ZnO:Co and ZnO:(Co+Al).

Recent Advances in NIR-Switchable Multi-Redox Systems Based on Organic Molecules.

In recent years, near-infrared (NIR) dyes, exhibiting absorption in the NIR region (750-2500 nm), have been applied to various optical applications such as security marking, photovoltaic cells and chemotherapy of deep tissues in vivo. Electrochromic systems capable of switching NIR absorption are attractive from the viewpoint of applications for material and life science, and thus several examples have been reported to date. The development of organic-based materials is needed to reduce the environmental impact and improve biocompatibility, however, the switching of NIR absorption based on redox interconversion is still a challenging issue regarding reversibility and durability during interconversion. To overcome this potential instability, several studies on organic electrochromic systems that allow ON/OFF switching of NIR absorption have been developed in recent years. In this review, we focus on redox-active well-defined small molecules that enable ON/OFF switching of NIR absorption, and present recent studies on their intrinsic electrochemical and spectroscopic properties and/or structural characterization of their charged states. We also address more sophisticated electrochromic systems that can modulate their properties in response to external stimuli such as light, heat, and electric potential.

On gravity implication in the wavefunction collapse

Abstract Inspired by an ontic view of the wavefunction in quantum mechanics and motivated
by the universal e¤ect of gravity, we discuss a possible gravity implication in the reduc-
tion scenario of the quantum state. Concretely, we investigate the stability of the spatial
superposition of a massive quantum state under the gravity e¤ect. In this context, we
argue that the stability of the spatially superposed state jYi =
nå
i=1
ai jyii, ai 2 C, depends
on its gravitational self-energy UG
Y originating from the effective mass density distribution
r m (x) through the spatially localized eigenstates jyii. We reveal that the gravitational
self-interaction between the di¤erent spacetime curvatures jGii created by the eigenstate
e¤ective masses m y leads to the reduction of the superposed state to one of the possible
localized states jyki jGki. Among others, we discuss such a gravity-driven state reduc-
tion. Then, we approach the corresponding collapse time t Collapse Y and the induced effective electric current I Y in the case of a charged state, as well as the possible detection aspects.

Regulation of CN-related optical transitions and non-radiative capture cross-section by biaxial strain in AlN

Carbon-related 4.7 eV absorption band and small in-plane strains in AlN may have some significant effects on its application in optoelectronic devices. Based on the accurate hybrid density functional calculation, we investigate the transition energy levels, photo-transition processes, and hole capture cross-sections of CN defect. We propose that the transition from −1 to 0 charge states of CN defect may be responsible for the 4.7 eV absorption band in AlN. In addition, the CN defect-related absorption and emission peaks are linearly dependent on the biaxial strain in the range of −3% to +3%, and the hole non-radiative capture rate by the CN center at the −3% biaxial strain is only 3.65% of that at the +3% biaxial strain. This work provides an effective approach for regulating the charge carrier capture ability of the defect center and improving device performance.

A Deep Learning Dependent Controller for Advanced Ultracapacitor SoC Concept to Increase Battery Life Span of Electric Vehicles

ABSTRACTOn a global scale, significant progress is being made in the field of battery technology for Electric Vehicle (EV) applications, driven by the need to combat carbon emissions and mitigate the effects of global warming. Accurately determining critical parameters, making sure battery storage system diagnosis, and functioning are correct are critical to the feasibility of EVs. However, insufficient supervision and safety measures for these storage systems may lead to serious problems like a thermal runaway, overcharging, overheating, cell imbalances, and fire hazards. To tackle these challenges, the presence of an efficient battery management system becomes paramount. By facilitating accurate monitoring, managing heat dissipation, regulating charging‐discharging procedures, guaranteeing battery safety, and offering protection measures, this system is essential to maximizing battery performance. The key intention of this innovative approach is to improve the longevity of EV batteries during extended periods of operation. By assessing vehicle velocity, remaining battery energy, and State of Charge (SoC), the proposed method effectively manages SoC in both the battery and ultracapacitor. This control is accomplished through a two‐stage convolutional neural network‐based system known as the Charge Sustain‐CNN Controller and the Charge Deplete‐CNN Controller. These controllers are fine‐tuned using the Fractional Latrans‐Hunt optimization (FLHO) algorithm to optimize the performance. The evaluation criteria encompass the battery and ultracapacitor's energy efficiency, as well as vehicle velocity. This novel approach significantly improves the energy storage system in EVs, leading to enhanced energy efficiency and prolonged battery life. Ultimately, experimental results validate the practicality and effectiveness of this developed method. Specifically, the proposed approach attained the Battery's SoC of 72.47%, 91.99%, and 82.88% for the different drive cycles including the FTP75, J1015, and UDDS, respectively.

Physics-Informed Data-Driven Approaches to State of Health Prediction of Maritime Battery Systems

Battery systems are increasingly being used for powering ocean going ships, and the number of fully electric or hybrid ships relying on battery power for propulsion and maneuvering is growing. In order to ensure the safety of such electric ships, it is of paramount importance to monitor the available energy that can be stored in the batteries, and classification societies typically require that the state of health (SOH) of the batteries can be verified by independent tests – annual capacity tests. However, this paper discusses physics-informed data-driven approaches to online diagnostics for state of health monitoring of maritime battery systems based on a combination of physical knowledge, physic-based models, insights from extensive characterization tests and operational sensor data collected from the batteries during actual operation. This represents an alternative approach to the annual capacity tests for electric ships that is found to be sufficiently robust and accurate under certain conditions. Previous attempts with purely data-driven models, including both cumulative and snapshot models, semi-supervised learning and simple models based on the state of charge did not achieve the required reliability and accuracy for them to be utilized in a ship classification perspective, as presented at previous PHM conferences. However, preliminary results from the physics-informed data-driven method presented in this paper indicate that it can be relied on for independent verification of state of health as an alternative to physical tests. It has been tested on battery cells cycled in laboratory degradation tests as well as on field returns from batteries onboard ships in service. Notwithstanding, further validation and verification of the method is recommended to further build confidence in the model predictions.

Health Indicator Development for Low-Voltage Battery Diagnostics and Prognostics in Electric Vehicles

Each electric vehicle (EV) requires a low-voltage (e.g., 12V) auxiliary battery to provide electric power to onboard electronic control units, lighting systems, and various sensors during power off. Therefore, when the low-voltage battery is in low state of health (SOH) or low state of charge (SOC), it may cause no-start events. The existing OnStar Proactive Alert service can effectively predict low SOC or low SOH events for low-voltage batteries in Internal Combustion Engine vehicles using cranking signals. However, it does not work for EVs since there is no cranking event. In this work, a diagnostic and prognostic solution for the low-voltage battery of EVs is proposed. Four novel health indicators (HIs) along with the decision-making system are developed based on equivalent circuit models. Furthermore, the selection process of appropriate HIs tailored to various operational states of the vehicle is described. The validation results based on GM test EV data have demonstrated the effectiveness and robustness of the proposed solution.

An Energy Management System for Distributed Energy Storage System Considering Time-Varying Linear Resistance

As the proportion of renewable energy in energy use continues to increase, to solve the problem of line impedance mismatch leading to the difference in the state of charge (SOC) of each distributed energy storage unit (DESU) and the DC bus voltage drop, a distributed energy storage system control strategy considering the time-varying line impedance is proposed in this paper. By analyzing the fundamental frequency harmonic components of the pulse width modulation (PWM) signal carrier of the converter output voltage and output current, we can obtain the impedance information and, thus, compensate for the bus voltage drop. Then, a novel, droop-free cooperative controller is constructed to achieve SOC equalization, current sharing, and voltage regulation. Finally, the validity of the system is verified by a hardware-in-the-loop experimental platform.

Methodology for comparative assessment of battery technologies: Experimental design, modeling, performance indicators and validation with four technologies

An increasing number of applications with diverse requirements incorporate various battery technologies. Selecting the most suitable battery technology becomes a tedious task as several aspects need to be taken into account. Two of the key aspects are the battery characteristics under temperature variations and their degradation. While numerous contributions using tailored assessment methods to evaluate both aspects for a particular application exist in the literature, a general methodology for analysis is necessary to enable a quantitative comparison between different technologies. We propose in this paper a novel methodology, based on performance indicators, to quantify the potential and limitations of a battery technology for diverse applications sharing a similar operational profile. A quantification of phenomena such as the influence of high and low temperatures on the battery, or the effect of cycling and state of charge on battery aging is obtained. In pursuit of these indicators, an experimental procedure and the fitting of aging model parameters that allow their calculation are proposed. As an additional outcome of this work, a general aging model that allows comprehensive analysis of aging behavior is developed and the trade-off between experimental time and accuracy is analyzed to find an optimal experimental time between 2 and 4 months, depending on the studied battery technology. Finally, the proposed methodology is applied to four battery technologies in order to show its potential in a real case-study.

State of Charge Estimation of Lithium Battery Utilizing Strong Tracking H-Infinity Filtering Algorithm

The ability to quickly and accurately estimate the state of charge (SOC) of lithium batteries is a key function of the battery management system (BMS). To enhance the accuracy of SOC estimation for lithium batteries, we propose a method that combines the dynamic factor recursive least squares (DFFRLS) algorithm and the strong tracking H-infinity filtering (STF-HIF) algorithm. To address the issue of fixed forgetting factors in recursive least squares (RLS) that struggle to maintain both fast convergence and stability in battery parameter identification, we introduce dynamic forgetting factors. This approach adjusts the forgetting factor based on the residuals between the model’s estimated and actual values. To improve the H-infinity filtering (HIF) algorithm’s poor performance in tracking sudden state changes, we propose a combined STF-HIF algorithm, integrating HIF with strong tracking filtering (STF). Simulation experiments indicate that, compared to the HIF algorithm, the STF-HIF algorithm achieves a maximum absolute SOC estimation error (MaxAE) of 0.69%, 0.72%, and 1.22%, with mean absolute errors (MAE) of 0.27%, 0.25%, and 0.38%, and root mean square errors (RMSE) of 0.33%, 0.30%, and 0.46% under dynamic stress testing (DST), federal urban driving schedules (FUDS), and Beijing dynamic stress testing (BJDST) conditions, respectively.

Machine Learning Applied to Lithium‐Ion Battery State Estimation for Electric Vehicles: Method Theoretical, Technological Status, and Future Development

ABSTRACTLithium‐ion batteries (LIBs) are extensively utilized in electric vehicles due to their high energy density and cost‐effectiveness. LIBs exhibit dynamic and nonlinear characteristics, which raise significant safety concerns for electric vehicles. Accurate and real‐time battery state estimation can enhance safety performance and prolong battery lifespan. With the rapid advancement of big data, machine learning (ML) holds substantial promise for state estimation. This paper systematically reviews several common ML algorithms, detailing the basic principles of each and illustrating their structures with flowcharts. We compare the advantages and disadvantages of various methods. Subsequently, we discuss feature extraction techniques employed in recent studies for estimating state of charge (SOC), state of health (SOH), state of power (SOP), and remaining useful life (RUL), as well as the application of these ML methods in state estimation. Finally, we discuss the challenges associated with using ML methods for state estimation and outline future development trends.

Charge State of Weak Polyelectrolyte Brushes Determines Salt-Dependent Swelling and Hysteretic Behavior.

We investigate the combined effects of ionizable monomer fraction f, pH, and monovalent salt concentration Cs on the swelling of weak polyelectrolyte brushes (PEBs) by using in situ ellipsometry. Our system consists of random copolymers of basic (2-(dimethylamino)ethyl acrylate, DMAEA) and neutral (2-hydroxyethyl acrylate, HEA) monomers at varying fractions of ionizable monomer. Swelling of the brushes qualitatively follows the trends predicted by scaling laws for PEBs under different charge states but quantitatively deviates at specific ionic strengths and pH values. We posit these deviations stem from the lack of excluded volume effects and assumptions of strong chain stretching in current theoretical models. Most notably, we uncover a salt-dependent, nonmonotonic hysteretic behavior as weak PEB brushes are cycled from protonated to deprotonated and back. The nonmonotonic trend of hysteresis with salt can be explained by an interplay between the protonation facilitating effects of salt in the osmotic regime and the charge screening effects in the salted regime, which make charge distribution along weak PEBs more uniform. Our results provide insight into the mechanisms that determine whether polyelectrolytes exhibit weak versus strong polyelectrolyte behavior in various environmental conditions.

Relaxed hydrodynamic theory of electrically driven nonequilibrium steady states

The capability of hydrodynamics to accurately describe slow and long-wavelength fluctuations around nonequilibrium steady states (NESS), characterized by a stationary flow of energy or matter in the presence of a driving force, remains an open question. In this study, we explicitly construct a hydrodynamic description of electrically driven nonequilibrium charged steady states in the limit in which the relaxation of the first nonhydrodynamic excitation is parametrically slow. Our approach involves introducing gapped modes and extending the effective description into a relaxed hydrodynamic theory (RHT). Leveraging the gauge-gravity duality as a tool for controlled computations within nonequilibrium systems, we establish an ultraviolet complete model for these NESS that confirms the validity of our RHT. In summary, our findings provide a concrete realization of the validity of hydrodynamics beyond thermal equilibrium, offering valuable insights into the dynamics of nonequilibrium systems. Published by the American Physical Society 2024

The additionally charged forces in the Sun-Earth and Earth-Moon systems

We build a model to describe the net charges existing in the Sun and Earth. According to statistical mechanics, electrons on average move much faster than protons and neutrons at the same temperature. Electrons escape the Sun more easily than protons and neutrons, so the Sun becomes a charged star. We estimate the maximal net charges in the Sun by using statistical mechanics first. Then, we analyze the dynamical cycles between the positive and negative charged states. At a distance far away from the Sun, the effective net charges including the leaving protons and electrons are about 6.3×109C with energies of 1 GeV initially. We also use another way based on the observations of the Earth's perihelion precession to estimate the minimum and maximum net charges between 1.15×108C and 2.80×1010C in space from the Sun to Earth. The most charged particles from the Sun to the Earth are electrons, so both the Moon and Earth are impacted by them and very possibly have the same electricity. Next, we propose new physical mechanisms causing the slowdown of the Earth's spin and propose Coulomb's repulsive force resulting in the increasing distance between the Moon and Earth. As a result, it gives the net charges of 1.11×106C surrounding the Earth and 8.29×103C surrounding the Moon. Our estimations also correspond to early works. The charges surrounding the Sun and Earth cause the Earth to be long-term accelerated in the radial direction by Coulomb's force. Finally, using the effective net charges of the Sun and Earth, we calculate the increasing distance between 11.4 m and 19.4 m on average per century if the initial radial velocities of the Earth are in between 3.59×10−9m/s and 6.12×10−9m/s, which satisfies the observed reports.