Charge Pattern Research Articles

Starting electro-motor loads in battery-less off-grid photovoltaics using a super-capacitor based charge-pump

Starting Electromotor (EM) loads with off-grid photovoltaics (PV) is always challenging. Because their starting current makes the PV voltage fall, leading to converter instability. A practical solution is using batteries as a backup, but the repeating inrush current lowers the battery lifespan. This paper proposes an electrical charge pump based on a super-capacitor-bank (SCB) to fix the stability issue in off-grid battery-less photovoltaics. It contains an SCB connected to the DC-link of the inverter via a bi-directional DC/DC converter. The SCB is first pre-charged from the PV. Then at the moment of starting an electromotor load (EM), it supplies the DC-link capacitor with a pulse current. The main challenge here is to control the charging/discharging current of the SCB. In the proposed method, the system model is first developed by circuit analysis. Then a pre-defined charging pattern is obtained to charge the SCB from the zero to nominal voltage with a controlled current with no transient inrush. When injecting the pulse current as a charge pump (SCB discharging mode), constant duty-cycle switching for a pre-defined period is applied to ensure the converter stability in boost mode. To get the best results, a time shift is implemented between the pulse current injection and the load start-up, as a pre-charging phase. Experimental results from a 100 w system prototype running a 70 w universal motor show that a 2 Amp/200 ms pulse current, starting 50 ms before the load start-up, keeps the DC-link voltage deviation under 10 %, and no current transient is observed.

Phosphorylation of disordered proteins tunes local and global intramolecular interactions.

Protein post-translational modifications, such as phosphorylation, are important regulatory signals for diverse cellular functions. In particular, intrinsically disordered protein regions (IDRs) are subject to phosphorylation as a means to modulate their interactions and functions. Toward understanding the relationship between phosphorylation in IDRs and specific functional outcomes, we must consider how phosphorylation affects the IDR conformational ensemble. Various experimental techniques are suited to interrogate the features of IDR ensembles; molecular simulations can provide complementary insights and even illuminate ensemble features that may be experimentally inaccessible. Therefore, we sought to expand the tools available to study phosphorylated IDRs by all-atom Monte Carlo simulations. To this end, we implemented parameters for phosphoserine (pSer) and phosphothreonine (pThr) into the OPLS version of the continuum solvent model, ABSINTH, and assessed their performance in all-atom simulations compared to published findings. We simulated short (< 20 residues) and long (> 80 residues) phospho-IDRs that, collectively, survey both local and global phosphorylation-induced changes to the ensemble. Our simulations of four well-studied phospho-IDRs show near-quantitative agreement with published findings for these systems via metrics including changes to radius of gyration, transient helicity, and persistence length. We also leveraged the inherent advantage of sequence control in molecular simulations to explore the conformational effects of diverse combinations of phospho-sites in two multi-phosphorylated IDRs. Our results support and expand on prior observations that connect phosphorylation to changes in the IDR conformational ensemble. Herein, we describe phosphorylation as a means to alter sequence chemistry, net charge and charge patterning, and intramolecular interactions, which can collectively modulate the local and global IDR ensemble features.

Intrinsically disordered region amplifies membrane remodeling to augment selective ER-phagy

Intrinsically disordered regions (IDRs) play a pivotal role in organellar remodeling. They transduce signals across membranes, scaffold signaling complexes, and mediate vesicular traffic. Their functions are regulated by constraining conformational ensembles through specific intra- and intermolecular interactions, physical tethering, and posttranslational modifications. The endoplasmic reticulum (ER)-phagy receptor FAM134B/RETREG1, known for its reticulon homology domain (RHD), includes a substantial C-terminal IDR housing the LC3 interacting motif. Beyond engaging the autophagic machinery, the function of the FAM134B-IDR is unclear. Here, we investigate the characteristics of the FAM134B-IDR by extensive modeling and molecular dynamics simulations. We present detailed structural models for the IDR, mapping its conformational landscape in solution and membrane-anchored configurations. Our analysis reveals that depending on the membrane anchor, the IDRs collapse onto the membrane and induce positive membrane curvature to varying degrees. The charge patterns underlying this Janus-like behavior are conserved across other ER-phagy receptors. We found that IDRs alone are sufficient to sense curvature. When combined with RHDs, they intensify membrane remodeling and drive efficient protein clustering, leading to faster budding, thereby amplifying RHD remodeling functions. Our simulations provide a perspective on IDRs of FAM134B, their Janus-like membrane interactions, and the resulting modulatory functions during large-scale ER remodeling.

Heterogeneity in electric taxi charging behavior: Association with travel service characteristics

A comprehensive understanding of charging behaviors among electric vehicle users is crucial for advancing green transportation and deploying effective charging infrastructure. This study conducted large-scale empirical research using data from electric taxi fleets in Shenzhen to explore the heterogeneity of charging behaviors among taxi drivers. The study hypothesized that distinct charging patterns exist within the electric taxi fleet, impacting fleet-wide fluctuations and repetitions of charging and service activities. Employing a covariate-enhanced latent profile analysis model, we examined unique charging patterns within the fleet and investigated relationships between taxi service attributes and charging behavior heterogeneity. Fleet-wide diurnal fluctuations, daily repetitions, and subgroup-specific charging patterns were identified. At the micro level, operational activity sequence similarity and between-group diversity were assessed. The findings offer valuable insights for policymakers and stakeholders involved in promoting green transportation and optimizing charging infrastructure.

Thieno[3,2-b]thiophene for the Construction of Far-Red Molecular Rotor Hemicyanines as High-Affinity DNA Aptamer Fluorogenic Reporters.

The construction of far-red fluorescent molecular rotors (FMRs) is an imperative task for developing nucleic acid stains that have superior compatibility with cellular systems and complex matrices. A typical strategy relies on the methine extension of asymmetric cyanines, which unfortunately fails to produce sensitive rotor character. To break free from this paradigm, we have synthesized far-red hemicyanines using a dimethylamino thieno[3,2-b]thiophene donor. The resultant probes, designated as ATh2Ind and ATh2Btz, possess excitation maxima (λmax) of >600 nm and have been rigorously characterized by NMR, electrochemistry, and computational methods. The dyes possess alternating charge patterns like indodicarbocyanine (Cy5), but with twisted intramolecular charge transfer (TICT) rotational barriers at 60°, akin to the classical FMR thiazole orange (TO1). ATh2Btz also displays cyanine characteristics, enhancing its response upon binding to nucleic acids and allowing for efficient staining of cellular nuclei. When binding to the DNA aptamer for quinine (MN4), ATh2Btz exhibits a Kd of 17 nM, a 660-fold light-up response, brightness (Φfl x εmax) of ∼37,000 M-1cm-1, and λex/λem of 655/677 nm. The resulting far-red DNA-based MN4-ATh2Btz platform has been termed "pomegranate."

Photovoltaic Charge Lithography on Passive Dielectric Substrates Using Fe:LiNbO3 Stamps

AbstractPhotovoltaic Fe:LiNbO3 is an outstanding material platform able to photo‐generate versatile charge patterns, useful for a broad variety of applications. However, in some cases, its photorefractive effect, light absorption, and active ferroelectric properties may interfere with the optimum operation of certain devices based on Fe:LiNbO3. Here, a novel optoelectronic method is proposed and demonstrated to transfer photovoltaic charge patterns from Fe:LiNbO3 to non‐photovoltaic passive substrates, thus removing these possible limitations. The method, denominated as photovoltaic charge lithography (PVCL), resembles the operation of a stamp and does not require external high‐voltage supplies or electron/ion beams. Upon contact between the active Fe:LiNbO3 stamp and a passive dielectric substrate, the light‐induced charge pattern can be faithfully mirrored on the passive substrate. The imprinted pattern is probed and characterized by dielectrophoretic and electrophoretic particle trapping. The results reveal that the charge builds up on the passive substrate during contact, allowing charge tunability. Moreover, arbitrary charge distributions can be flexibly tailored, using scanning laser beams or spatially structured light. Overall, PVCL opens the possibility of printing complex 1D/2D charge patterns of controlled polarity on different passive dielectric materials, enhancing the technological potential of Fe:LiNbO3 photovoltaic platforms.

Unveiling electric vehicle (EV) charging patterns and their transformative role in electricity balancing and delivery: Insights from real-world data in Sweden

Accurately estimating the charging behaviours of electric vehicles (EVs) is crucial for various applications, such as charging station planning and grid impact estimation. However, the analysis of EV charging behaviours using real-world data remains limited due to (confidential) data availability constraints. Furthermore, while existing modelling studies have demonstrated EVs as effective tools for electricity balancing and delivery between locations, their potential remains unexplored empirically. This study aims to bridge the research gap by studying EV charging behaviours and their capacity for electricity balancing and delivery. Using data from 179,665 real-world charging sessions in Sweden, we employed statistical and clustering analysis to scrutinize charging behaviours comprehensively. Synthetic weekly charging load profiles are generated for both residential areas and workplaces, considering varying charging power levels, which can be used as inputs for large-scale EV charging load modelling. Furthermore, performance indicators are proposed to quantify the potential of EVs for electricity balancing and delivery. Results show that EVs exhibit significant potential for electricity balancing (up to 51.5 kWh daily). Many EV owners underutilize their EV battery capacity, providing an opportunity for active electricity delivery across locations. This study can help understand EV charging behaviours and recognize their significant potentials for electricity regulation and integrating more renewables in the future power system.

Mesh-Free Solution of 2D Poisson Equation with High Frequency Charge Patterns Using Data-Free Physics Informed Neural Network

Abstract In this paper, we present a data-free physics-informed neural networks (PINNs) approach for solving two-dimensional (2D) Poisson equation, which is pivotal in fields such as electromagnetics, mechanical engginering, and thermodynamics. Traditional numerical method for solving this equation often require structured mesh generation such as Finite Element Method (FEM), which can be computationally expensive when dealing with high resolution Poisson Equation Solution. To address this challenge, we leverage the capabilities of PINNs capturing pattern of complex system by incorporating physical law and boundary condition as part of loss function on training model. While PINNs provide a robust framework for solving differential equations within boundary condition, they have struggle with capturing high-frequency pattern due to smooth nature of typical activation function used in neural networks. To evercome this issue, we enhance our model by incorporating Fourier Features Networks, which map inputs through a series of sinusoidal functions before feeding the input into the neural network. The result show that Fourier feature network can enhance convergence of training of PINNs model faster and obtained better result than PINNs without Fourier feature networks.

Grid-integrated solutions for sustainable EV charging: a comparative study of renewable energy and battery storage systems

IntroductionThe integration of electric vehicles (EVs) into the power network challenges the 1) grid capacity, 2) stability, and 3) management. This is due to the 1) increased peak demand, 2) infrastructure strain, and 3) intermittent charging patterns. Previous studies lack comprehensive integration of renewable energy and battery storage with EV charging.MethodsTo address these challenges, this study explores the effectiveness of incorporating renewable energy resources (RERs) and battery energy storage systems (BESS) alongside the traditional grid. The proposed study utilizes the HOMER Grid® and conducted a comprehensive analysis.ResultsThe proposed study compares two grid integrated scenarios: 1) Case-1 (grid and photovoltaic (PV) systems), and 2) Case-2 (grid, PV systems, and BESS). Both these scenarios are compared against a Base case relying solely on grid power. The evaluation employed techno-economic analysis while focusing on 1) net present cost (NPC), 2) cost of energy, and 3) annualized savings. Additionally, the proposed study analyzed 4) seasonal variations in EV charging demand, 5) grid interactions, 6) PV production, and 7) the operation of BESS in both summer and winter. The comparative analysis reveals that the Base case incurs a net present cost (NPC) of $546,977 and a cost of energy (COE) of $0.354 per kWh. In contrast, Case-1, which integrates a 100 kW PV system, shows a significantly lower NPC of -$122,962 and a reduced COE of -$0.043 per kWh, with annualized savings of $61,492. Case-2, incorporating both the 100 kW PV system and a BESS with a capacity of 9.8 kWh, has a higher NPC of $309,667 but a COE of $0.112 per kWh and provides annual savings of $51,233 compared to the Base case.DiscussionSeasonal analysis highlights that Case-2 achieves the lowest carbon emissions in summer, ranging from 2.0 to 2.5 tons, while Case-1 shows the lowest emissions in winter, ranging from 3.2 to 3.4 tons. This model 1) reduces operational costs, 2) minimizes carbon emissions, while 3) making it compelling for future energy systems in increasing EV adoption.

Research on priority scheduling strategy for smoothing power fluctuations of microgrid tie‐lines based on PER‐DDPG algorithm

AbstractThe variability of renewable energy within microgrids (MGs) necessitates the smoothing of power fluctuations through the effective scheduling of internal power equipment. Otherwise, significant power variations on the tie‐line connecting the MG to the main power grid could occur. This study introduces an innovative scheduling strategy that utilizes a data‐driven approach, employing a deep reinforcement learning algorithm to achieve this smoothing effect. The strategy prioritizes the scheduling of MG's internal power devices, taking into account the stochastic charging patterns of electric vehicles. The scheduling optimization model is initially described as a Markov decision process with the goal of minimizing power fluctuations on the interconnection lines and operational costs of the MG. Subsequently, after preprocessing the historical operational data of the MG, an enhanced scheduling strategy is developed through a neural network learning process. Finally, the results from four scheduling scenarios demonstrate the significant impact of the proposed strategy. Comparisons of reward curves before and after data preprocessing underscore its importance. In contrast to optimization results from deep deterministic policy gradient, soft actor‐critic, and particle swarm optimization algorithms, the superiority of the deep deterministic policy gradient algorithm with the addition of a priority experience replay mechanism is highlighted.

Reviewing Demand Response for Energy Management with Consideration of Renewable Energy Sources and Electric Vehicles

This review paper critically examines the role of demand response (DR) in energy management, considering the increasing integration of renewable energy sources (RESs) and the rise in electric vehicle (EV) adoption. As the energy landscape shifts toward sustainability, recognizing the synergies and challenges offered by RESs and EVs becomes critical. The study begins by explaining the notion of demand response, emphasizing its importance in optimizing energy usage and grid stability. It then investigates the specific characteristics and possible benefits of incorporating RESs and EVs into DR schemes. This assessment evaluates the effectiveness of DR techniques in leveraging the variability of renewable energy generation and managing the charging patterns of electric vehicles. Furthermore, it outlines important technological, regulatory, and behavioral impediments to DR’s mainstream adoption alongside RESs and EVs. By synthesizing current research findings, this paper provides insights into opportunities for enhancing energy efficiency, lowering greenhouse gas emissions, and advancing sustainable energy systems through the coordinated implementation of demand response, renewable energy sources, and electric vehicles.

Paradoxical peeling patterns

Processes ranging from fracture of crystals to peeling of tape have been known for many decades to emit light through a mechanism believed to be associated with electrical charging of separating surfaces. This topic, broadly termed fractoluminescence, has been proposed to be involved in several remarkable phenomena, including medical diagnostics and the generation of X-rays in the lab and earthquake lightning in nature. Here we add the paradoxical finding that two separating surfaces produce entirely different charge patterns, despite originating from the same interface. Further, we report the discovery of a rich variety of new and unexplained patterns, and we examine the hypothesis that the patterns are produced by migration of either polar or non-polar discharge ions onto contact-charged surfaces. This hypothesis may first explain prior findings that charge patterns can extend far beyond points of contact, and second suggests that the ultimate charge imparted on surfaces depends both on well-characterized mechanisms of surface potential and on highly variable discharge ions in the surrounding environment.

Am I really willing to use my electric vehicle sustainably? A study on the charging preferences of electric vehicle users

As, in most countries, electricity cannot be efficiently and practically stored at a systemic level, charging EVs during peak hours implies that additional energy has to be generated (relying almost exclusively on fossil sources) to cover the additional demand during that time window. This article reports the results of an SP-study, in which EV-owners were confronted with the option of charging their EVs at home (for a fixed known price) or at a publicly accessible charging station with charging price variability (as well as other features of EV charging). The results of the behavioral experiment show that EV-users exhibit a high willingness to accept alternative compensations for not charging EVs during peak-hours and that small monetary incentives as well as shorter access times, a guaranteed charging place, or fast charging could all efficiently promote a shift in the charging patterns toward a more sustainable behavior.

Beyond monopole electrostatics in regulating conformations of intrinsically disordered proteins.

Conformations and dynamics of an intrinsically disordered protein (IDP) depend on its composition of charged and uncharged amino acids, and their specific placement in the protein sequence. In general, the charge (positive or negative) on an amino acid residue in the protein is not a fixed quantity. Each of the ionizable groups can exist in an equilibrated distribution of fully ionized state (monopole) and an ion-pair (dipole) state formed between the ionizing group and its counterion from the background electrolyte solution. The dipole formation (counterion condensation) depends on the protein conformation, which in turn depends on the distribution of charges and dipoles on the molecule. Consequently, effective charges of ionizable groups in the IDP backbone may differ from their chemical charges in isolation-a phenomenon termed charge-regulation. Accounting for the inevitable dipolar interactions, that have so far been ignored, and using a self-consistent procedure, we present a theory of charge-regulation as a function of sequence, temperature, and ionic strength. The theory quantitatively agrees with both charge reduction and salt-dependent conformation data of Prothymosin-alpha and makes several testable predictions. We predict charged groups are less ionized in sequences where opposite charges are well mixed compared to sequences where they are strongly segregated. Emergence of dipolar interactions from charge-regulation allows spontaneous coexistence of two phases having different conformations and charge states, sensitively depending on the charge patterning. These findings highlight sequence dependent charge-regulation and its potential exploitation by biological regulators such as phosphorylation and mutations in controlling protein conformation and function.

Sequence-encoded Spatiotemporal Dependence of Viscoelasticity of Protein Condensates Using Computational Microrheology.

Many biomolecular condensates act as viscoelastic complex fluids with distinct cellular functions. Deciphering the viscoelastic behavior of biomolecular condensates can provide insights into their spatiotemporal organization and physiological roles within cells. Though there is significant interest in defining the role of condensate dynamics and rheology in physiological functions, the quantification of their time-dependent viscoelastic properties is limited and mostly done through experimental rheological methods. Here, we demonstrate that a computational passive probe microrheology technique, coupled with continuum mechanics, can accurately characterize the linear viscoelasticity of condensates formed by intrinsically disordered proteins (IDPs). Using a transferable coarse-grained protein model, we first provide a physical basis for choosing optimal values that define the attributes of the probe particle, namely its size and interaction strength with the residues in an IDP chain. We show that the technique captures the sequence-dependent viscoelasticity of heteropolymeric IDPs that differ either in sequence charge patterning or sequence hydrophobicity. We also illustrate the technique's potential in quantifying the spatial dependence of viscoelasticity in heterogeneous IDP condensates. The computational microrheology technique has important implications for investigating the time-dependent rheology of complex biomolecular architectures, resulting in the sequence-rheology-function relationship for condensates.

Discontinuous Transition in Electrolyte Flow through Charge-Patterned Nanochannels.

We investigate the flow of an electrolyte through a rigid nanochannel decorated with a surface charge pattern. Employing lattice Boltzmann and dissipative particle dynamics methods, as well as analytical theory, we show that the electrohydrodynamic coupling leads to two distinct flow regimes. The accompanying discontinuous transition between slow, ionic, and fast, Poiseuille flows is observed at intermediate ion concentrations, channel widths, and electrostatic coupling strengths. These findings indicate routes to design nanochannels containing a typical aqueous electrolyte that exhibit a digital on-off flux response, which could be useful for nanofluidics and ionotronic applications.

YAP charge patterning mediates signal integration through transcriptional co-condensates.

Transcription factor dynamics are used to selectively engage gene regulatory programs. Biomolecular condensates have emerged as an attractive signaling substrate in this process, but the underlying mechanisms are not well-understood. Here, we probed the molecular basis of YAP signal integration through transcriptional condensates. Leveraging light-sheet single-molecule imaging and synthetic condensates, we demonstrate charge-mediated co-condensation of the transcriptional regulators YAP and Mediator into transcriptionally active condensates in stem cells. IDR sequence analysis and YAP protein engineering demonstrate that instead of the net charge, YAP signaling specificity is established through its negative charge patterning that interacts with Mediator's positive charge blocks. The mutual enhancement of YAP/Mediator co-condensation is counteracted by negative feedback from transcription, driving an adaptive transcriptional response that is well-suited for decoding dynamic inputs. Our work reveals a molecular framework for YAP condensate formation and sheds new light on the function of YAP condensates for emergent gene regulatory behavior.

Benefit to Harm (B2H) Ratio for Bulk Vehicle to Grid (V2G) Services Using Lifetime Degradation Digital Twin: Emulating Various Dominant Degradation Mechanism

Vehicle-to-grid (V2G) services, utilizing electric vehicle (EV) batteries for grid support, enhance reliability and reduce peak energy demand. We present a physics-based model tuned for three different cell chemistry families and examine the influence of bulk V2G services on EV battery life. We consider various duty cycles, cell chemistries with nickel content (NMCxyz and various anode graphites), and associated dominant degradation mechanisms. To quantify the impact of offering V2G services, we introduce the V2G benefit-to-harm ratio (B2H). We define the benefit as the Ah gained with V2G and the harm as the life lost in days due to V2G, both compared to the baseline noV2G.B2H=(normalized Ah gained by 70% capacity fade) divided by (time in days reduction by the time that capacity fade has reached 70%). Note that the 70% capacity fade can be changed for various vehicle models depending on the warranty definition of the % capacity or usable battery energy (UBE) at the end of the warranty.Our findings indicate that the dominant degradation mechanism plays a crucial role in the benefit-to-harm (B2H) ratio of V2G or vehicle-to-building (V2B) [1]. Specifically, we clarify that the relative contribution of calendar aging to overall capacity loss is pivotal in determining the degradation due to V2G and possible associated benefits.Our model takes into account four degradation mechanisms based on a single-particle model. SEI growth and cathode transition metal dissolution are part of the calendar aging loss of lithium inventory (LLICal). The remaining LLI is attributed to Li-plating and mechanical degradation caused by particle cracking.We calibrate the model for three families of cell chemistries and conditions. The primary degradation mechanism for one cell family is anode mechanical degradation (LAMNeg). The second family of cells predominantly ages due to SEI layer growth, and the third family is degrading due to both mechanisms [2].We show that, in cases with a higher contribution of calendar aging to degradation (LLICal/LLI), V2G can be potentially more beneficial (in Ah gained by 70% capacity fade) than harmful (lifetime reduction in days by the time that capacity fade has reached 70%). Furthermore, we have evidence that the V2G charging pattern can have a secondary effect on the B2H ratio. For example, being a risk taker and charging the battery late or being cautious and charging it as soon as possible can lead to different degrees of degradation depending on the chemistry and conditions of the battery [3].With our multiphysics reduced order model of battery lifetime degradation, we fully explain why previous studies [4] have shown that the impact on EV battery degradation varies from inconsequential [5] to projecting a need for early battery replacement [6]. Our results clarify that battery chemistry and usage patterns are important factors in determining whether or not to utilize a vehicle for grid support, as well as the overall financial impact on the owner and OEM warranty.

Differential Effects of Sequence-Local versus Nonlocal Charge Patterns on Phase Separation and Conformational Dimensions of Polyampholytes as Model Intrinsically Disordered Proteins.

Conformational properties of intrinsically disordered proteins (IDPs) are governed by a sequence-ensemble relationship. To differentiate the impact of sequence-local versus sequence-nonlocal features of an IDP's charge pattern on its conformational dimensions and its phase-separation propensity, the charge "blockiness" κ and the nonlocality-weighted sequence charge decoration (SCD) parameters are compared for their correlations with isolated-chain radii of gyration (Rgs) and upper critical solution temperatures (UCSTs) of polyampholytes modeled by random phase approximation, field-theoretic simulation, and coarse-grained molecular dynamics. SCD is superior to κ in predicting Rg because SCD accounts for effects of contact order, i.e., nonlocality, on dimensions of isolated chains. In contrast, κ and SCD are comparably good, though nonideal, predictors of UCST because frequencies of interchain contacts in the multiple-chain condensed phase are less sensitive to sequence positions than frequencies of intrachain contacts of an isolated chain, as reflected by κ correlating better with condensed-phase interaction energy than SCD.

Semi-Covariance Coefficient Analysis of Spike Proteins from SARS-CoV-2 and Its Variants Omicron, BA.5, EG.5, and JN.1 for Viral Infectivity, Virulence and Immune Escape.

Semi-covariance has attracted significant attention in recent years and is increasingly employed to elucidate statistical phenomena exhibiting fluctuations, such as the similarity or difference in charge patterns of spike proteins among coronaviruses. In this study, by examining values above and below the average/mean based on the positive and negative charge patterns of amino acid residues in the spike proteins of SARS-CoV-2 and its current circulating variants, the proposed methods offer profound insights into the nonlinear evolving trends in those viral spike proteins. Our study indicates that the charge span value can predict the infectivity of the virus and the charge density can estimate the virulence of the virus, and both predicated infectivity and virulence appear to be associated with the capability of viral immune escape. This semi-covariance coefficient analysis may be used not only to predict the infectivity, virulence and capability of immune escape for coronaviruses but also to analyze the functionality of other viral proteins. This study improves our understanding of the trend of viral evolution in terms of viral infectivity, virulence or the capability of immune escape, which remains further validated by more future studies and statistical data.