Dynamic Heterogeneity Research Articles

The Impact of Supersaturated Electrode on Heterogeneous Lithium Nucleation and Growth Dynamics.

The concept of a lithiophilic electrode proves inadequate in describing carbon-based electrode materials due to their substantial mismatch in surface energy with lithium metal. However, their notable capacity for lithium chemisorption can increase active lithium concentration required for nucleation and growth, thereby enhancing the electrochemical performance of lithium metal anodes (LMAs). In this study, we elucidate the effects of the supersaturated electrode which has high active lithium capacity around equilibrium lithium potential on LMAs through an in-depth electrochemical comparison using two distinct carbon electrode platforms with differing carbon structures but similar two-dimensional morphologies. In the supersaturated electrode, both the dynamics and thermodynamic states involved in lithium nucleation and growth mechanisms are significantly improved, particularly under continuous current supply conditions. Furthermore, the chemical structures of the solid-electrolyte-interface layers (SEIs) are greatly influenced by the elevated surface lithium concentration environment, resulting in the formation of more conductive lithium-rich SEI layers. The improved dynamics and thermodynamics of surface lithium, coupled with the formation of enhanced SEI layers, contribute to higher power capabilities, enhanced Coulombic efficiencies, and improved cycling performances of LMAs. These results provide new insight into understanding the enhancements in heterogeneous lithium nucleation and growth kinetics on the supersaturated electrode.

The Impact of Supersaturated Electrode on Heterogeneous Lithium Nucleation and Growth Dynamics

AbstractThe concept of a lithiophilic electrode proves inadequate in describing carbon‐based electrode materials due to their substantial mismatch in surface energy with lithium metal. However, their notable capacity for lithium chemisorption can increase active lithium concentration required for nucleation and growth, thereby enhancing the electrochemical performance of lithium metal anodes (LMAs). In this study, we elucidate the effects of the supersaturated electrode which has high active lithium capacity around equilibrium lithium potential on LMAs through an in‐depth electrochemical comparison using two distinct carbon electrode platforms with differing carbon structures but similar two‐dimensional morphologies. In the supersaturated electrode, both the dynamics and thermodynamic states involved in lithium nucleation and growth mechanisms are significantly improved, particularly under continuous current supply conditions. Furthermore, the chemical structures of the solid‐electrolyte‐interface layers (SEIs) are greatly influenced by the elevated surface lithium concentration environment, resulting in the formation of more conductive lithium‐rich SEI layers. The improved dynamics and thermodynamics of surface lithium, coupled with the formation of enhanced SEI layers, contribute to higher power capabilities, enhanced Coulombic efficiencies, and improved cycling performances of LMAs. These results provide new insight into understanding the enhancements in heterogeneous lithium nucleation and growth kinetics on the supersaturated electrode.

An ecological and evolutionary approach to arboreal communities under different environmental stress conditions

Biodiversity distribution patterns and the ecological processes underlying them reflect how organisms respond to abiotic and biotic factors that interact across both space and time. In this study, we aimed to test the hypothesis that small-scale environmental variations shape ecological and phylogenetic patterns in arboreal communities through environmental filters created by the specific conditions of each environment examined. We investigated a mosaic of Atlantic forest environments, analyzing species composition, community dynamics, and phylogenetic relationships among the various environments. This approach provided valuable insight into how environmental factors influence the ecological differentiation of these communities. Variations in conditions and resources produced environmental filters that resulted in significant small-scale heterogeneity in species composition, community dynamics, and evolutionary relationships among the environments. By considering the dynamic interations between biotic and abiotic factors, we conclude that the prevailing environmental conditions at these sites play a crucial role in shaping the behavior and distribution of the species that inhabit them.

Classification of solid and liquid structures using a deep neural network unveils origin of dynamical heterogeneities in supercooled liquids

As the temperature decreases, the dynamics of supercooled liquids significantly slow down and become increasingly heterogeneous in space. Many previous studies have found that static structures also become heterogeneous and are spatially correlated with the dynamical heterogeneity. However, there are still debates on whether the dynamical heterogeneity is controlled by the structures, and which structural order parameters should be used to describe the structural heterogeneities (if exist) in amorphous systems. The appropriate order parameter depends on the specific details of the system and needs to be determined for each system. To address this difficulty, here, we use a machine-learning-based method that was trained solely by the static structures. This method combines convolutional neural networks and gradient-weighted class activation mapping, providing interpretable characteristic structures, which can quantify the degrees of liquid-like and solid-like structures in every local part of the system. We apply this method to a canonical glass-forming system and demonstrate that particles in the liquid-like structures are mobile, while those in the solid-like structures are immobile. The present work develops a novel approach to accurately characterize amorphous structures, which will be particularly useful for systems where appropriate structural order parameters have not yet been identified.

Chlorophyll-Induced Lamellar to Nonlamellar Phase Transitions and Dynamical Heterogeneity in Plant Thylakoid Membranes.

Chlorophyll a (CLA) pigments and thylakoid membranes are crucial components of plants for photosynthesis. To understand the effect of CLA on the structure and dynamics of thylakoid membranes, coarse-grained molecular dynamics (CG MD) simulations of thylakoid membranes are performed by varying the numbers of CLA at 293 K using MARTINI-2 force fields. The membrane undergoes a lamellar to nonlamellar phase transition above a critical concentration of CLA. The CLAs dynamically form aggregates of different orders and preferentially fetch the least unsaturated nonbilayer-forming lipids around them, resulting in a nonlamellar phase with fused regions. These fused regions cause a structural arrest of CLA and lipids, inducing dynamic heterogeneity manifested by non-Gaussian parameters and van Hove correlation functions. The lamellar to nonlamellar phase transition of the membrane is associated with a drastic reduction in correlation length of the immobile CLA and lipids governed by the fused topology. Such insights into CLA-induced structural transitions in thylakoid membranes are pertinent for understanding nonphotochemical quenching mechanisms and hold promise for designing future artificial photosynthetic materials and applications in photodynamic therapy.

Quantitative proteomic mass spectrometry of protein kinases to determine dynamic heterogeneity of the human kinome.

The kinome is a dynamic system of kinases regulating signaling networks in cells and dysfunction of protein kinases contributes to many diseases. Regulation of the protein expression of kinases alters cellular responses to environmental changes and perturbations. We configured a library of 672 proteotypic peptides to quantify >300 kinases in a single LC-MS experiment using ten micrograms protein from human tissues including biopsies. This enables absolute quantitation of kinase protein abundance at attomole-femtomole expression levels, requiring no kinase enrichment and less than ten micrograms of starting protein from flash-frozen and formalin fixed paraffin embedded tissues. Breast cancer biopsies, organoids, and cell lines were analyzed using the SureQuant method, demonstrating the heterogeneity of kinase protein expression across and within breast cancer clinical subtypes. Kinome quantitation was coupled with nanoscale phosphoproteomics, providing a feasible method for novel clinical diagnosis and understanding of patient kinome responses to treatment.

Temporal generative models for learning heterogeneous group dynamics of ecological momentary assessment data.

One of the goals of precision psychiatry is to characterize mental disorders in an individualized manner, taking into account the underlying dynamic processes. Recent advances in mobile technologies have enabled the collection of ecological momentary assessments that capture multiple responses in real-time at high frequency. However, ecological momentary assessment data are often multi-dimensional, correlated, and hierarchical. Mixed-effect models are commonly used but may require restrictive assumptions about the fixed and random effects and the correlation structure. The recurrent temporal restricted Boltzmann machine (RTRBM) is a generative neural network that can be used to model temporal data, but most existing RTRBM approaches do not account for the potential heterogeneity of group dynamics within a population based on available covariates. In this paper, we propose a new temporal generative model, the HDRBM, to learn the heterogeneous group dynamics and demonstrate the effectiveness of this approach on simulated and real-world ecological momentary assessment datasets. We show that by incorporating covariates, HDRBM can improve accuracy and interpretability, explore the underlying drivers of the group dynamics of participants, and serve as a generative model for ecological momentary assessment studies.

An agent-based model of post-disaster recovery in multilayer socio-physical networks

Post-disaster recovery involves returning infrastructure to pre-disaster states or close, and humans back to communities or close, after disasters like hurricanes and earthquakes. It enables us to comprehend complex relationships between humans and infrastructure after natural hazards. Existing studies have identified individual recovery, which focuses on personal or household-level restoration, and systematic recovery, which focuses on broader community recovery processes. However, they have not described how individual recoveries contribute to the overall recovery. To close the gap, we propose an agent-based model (ABM), called Disaster Infrastructure Recovery Simulator (DIRSim). We apply DIRSim to analyze the recovery of five counties in Texas following Hurricane Harvey in 2017. Specifically, we construct a three-layer network, composed of a human layer, a social infrastructure layer, and a physical infrastructure layer, using mobile phone location data and point-of-interest foot traffic data. Based on this three-layer network, we develop the ABM to simulate how evacuated users return to their homes, and social and physical infrastructure recover using a household survey. By implementing the ABM, we unveil the heterogeneity in recovery dynamics regarding geographical locations, housing types, and income levels. Moreover, simulation results across nine scenarios quantify the positive recovery effects of social and physical infrastructure policies.

Robust Cooperative Control for Heterogeneous Uncertain Nonlinear High-Order Fully Actuated Multiagent Systems.

This research is intended to address a robust cooperative control problem of heterogeneous uncertain nonlinear high-order fully actuated multiagent systems (HUN-HOFAMASs). A nonlinear HOFA system model is used to describe the multiagent systems (MASs) with heterogeneous uncertain nonlinear dynamics, which is called the HUN-HOFAMASs. A predictive terminal sliding-mode control-based robust cooperative control scheme is presented to address this problem. In this scheme, heterogeneous nonlinear dynamics of original system are offset to establish a linear constant HOFA system with the help of full actuation feature. Then, a terminal sliding-mode variable for enhancing the system robustness is introduced to handle the uncertainties. Furthermore, a linear incremental prediction model is developed in a HOFA form by means of a Diophantine equation. According to this model, the multistep terminal sliding-mode predictions are yielded to optimize the robust cooperative control performance and compensate for the network-induced communication constraints in the feedback and forward channels. Based on a linear matrix inequality (LMI) method, a necessary and sufficient criterion is derived to discuss the simultaneous consensus and stability of closed-loop HUN-HOFAMASs. The simulation and comparison results of cooperative flying around of multiple spacecraft system are shown to illustrate the capability and advantage of the presented predictive terminal sliding-mode control for robust cooperative control.

Forest structure explains spatial heterogeneity of decadal carbon dynamics in a cool-temperate forest

Abstract Accurate evaluation of forest biomass distribution and its long-term change over wide areas is required for effective forest carbon management and prediction of landscape-scale forest dynamics. We evaluated a landscape-scale (225 km2) decadal forest carbon budget at a 1 ha spatial resolution in a cool-temperate forest, by repeating airborne laser observations 10 years apart and partitioning net forest biomass change (FBC) into growth and mortality. Using >10 000 samples, we revealed that naturally regenerated forests have large spatial heterogeneity in net biomass change, and 3/4 of the photosynthetically acquired carbon stock moved to necromass even without anthropogenic disturbances. Actual carbon residence time as living tree biomass was estimated by dividing biomass by growth or mortality rates. The residence time was 107 and 106 years, respectively with large spatial variation among stands (48 and 42 years, respectively, as the difference between 25 and 75 percentile), although studied forest stands have small variation in the forest functional type in a landscape-scale. The best predictors of subsequent decadal biomass changes were two forest structural factors, mean canopy height and canopy height variation in addition to one environmental factor, elevation. Considering the long lifetime of trees, these structural factors may be an indicator of forest soundness rather than a cause of forest growth or mortality. However, in any cases, these structural factors can be powerful predictors of subsequent FBC.

EP.08B.01 Heterogeneity of Peripheral Blood Cell Dynamics and Oncologic Outcomes Between LUAD and LUSC Receiving Neoadjuvant Immunochemotherapy

EP.08B.01 Heterogeneity of Peripheral Blood Cell Dynamics and Oncologic Outcomes Between LUAD and LUSC Receiving Neoadjuvant Immunochemotherapy

A novel method for identifying key nodes in multi-layer networks based on dynamic influence range and community importance

Identifying key nodes in multi-layer networks is a hot research topic in complex network science and has broad application prospects, such as in mining enterprises that significantly affecting multi-layer industrial chains. Unlike single-layer networks, nodes in multi-layer networks exhibit heterogeneity due to varying connections and locations. There are also correlations between different network layers, which is particularly evident in industrial chains where companies operate across multiple layers of production, supply and distribution. It is necessary to consider the impact of these layers on the global performance of key node identification. In addition, due to changes in connections, the community structure of each network layer should be different, reflecting the dynamic nature of industrial collaborations and partnerships. However, existing research lacks the model that addresses the above problems. Therefore, this paper proposes a key node identification method based on Dynamic Influence Range and Community Importance (DIRCI), using both local and global information of the multi-layer network simultaneously. DIRCI determines the importance of nodes through three centrality measures: dynamic influence range-based centrality, network layer centrality and community-based centrality. Dynamic influence range-based centrality models node heterogeneity by combining the influence range of nodes and their neighbors with lower computational costs. Network layer centrality captures the corresponding importance for different network layers. Community-based centrality comprehensively considers the importance of community, the importance of each node within the community and between different communities. Experimental results for nineteen multi-layer networks show that DIRCI achieves better performance of key node identification than the latest algorithms.

Landscape Heterogeneity and Environmental Dynamics Improve Predictions of Establishment Success of Colonising Small Founding Populations.

In long-distance dispersal events, colonising species typically begin with a small number of founding individuals. A growing body of research suggests that establishment success of small founding populations can be determined by the context of the colonisation event and the new environment. Here, we illuminate the importance of these sources of context dependence. Using a spatially explicit, temporally dynamic, mechanistic, individual-based simulator of a model amphibian species, the cane toad (Rhinella marina), we simulated colonisation scenarios to investigate how (1) the number of founding individuals, (2) the number of dispersal events, (3) landscape's spatial composition and configuration of habitats ('spatially heterogeneous landscapes') and (4) the timing of arrival with regards to dynamic environmental conditions ('dynamic environmental conditions') influence the establishment success of small founding populations. We analysed the dynamic effects of these predictors on establishment success using running-window logistic regression models. We showed establishment success increases with the number of founding individuals, whereas the number of dispersal events had a weak effect. At ≥ 20 founding individuals, propagule size swamps the effects of other factors, to whereby establishment success is near-certain (≥ 90%). But below this level, confidence in establishment success dramatically decreases as number of founding individuals decreases. At low numbers of founding individuals, the prominent predictors are landscape spatial heterogeneity and dynamic environmental conditions. For instance, compared to the annual mean, founding populations with ≤ 5 individuals have up to 18% higher establishment success when they arrive in 'packed' landscapes with relatively limited and clustered essential habitats and right before the breeding season. Accounting for landscape spatial heterogeneity and dynamic environmental conditions is integral in understanding and predicting population establishment and species colonisation. This additional complexity is necessary for advancing biogeographical theory and its application, such as in guiding species reintroduction efforts and invasive alien species management.

Influence of Density and Pressure on Glass Formation in the Kob-Andersen Model.

We systematically study glass formation in the well-known Kob-Andersen model over a wide range of densities and pressures as a basis for judging the "universality" of glass formation through a comparison to a recent systematic study of model polymeric glass-forming liquids. Our purpose is to establish general characteristics of glass formation, to identify new relations, and to discern which properties of glass-forming liquids are material-specific and which are "universal." To this end, we analyze a number of characteristic properties of glass formation, such as the structural relaxation time, self-diffusion coefficient, viscosity, characteristic temperatures, and fragility. We also consider a suite of properties presumably related to dynamic heterogeneity in an attempt to better understand its relation to structural relaxation. We demonstrate that the glassy dynamics in the Kob-Andersen model exhibit many of the essential trends observed in polymeric glass-forming liquids, pointing to a remarkable "universality" of many aspects of glass formation.

Collision-Free Formation Control for Heterogeneous Multiagent Systems Under DoS Attacks.

A safe time-varying formation (TVF) control framework is proposed in this article for heterogeneous multiagent systems under the constraints of denial of service (DoS) attacks, noncooperative dynamic obstacles, and input saturation. The framework integrates both the cyber-layer and physical-layer components to address the challenges posed by these adverse conditions. In the cyber-layer, a distributed resilient observer is provided based on a control Lyapunov function (CLF)-quadratic program (QP). This observer estimates a reference exosystem, effectively decoupling heterogeneous dynamics from unsafe networks and optimizing the system resilience against DoS attacks. At the physical-layer, for the first time, a collision-free TVF controller is presented based on the CLF-exponential control barrier function-QP. The controller guarantees high-order heterogeneous agents' operation safety under noncooperative obstacles and input saturation. The effectiveness and advantages of the proposed algorithms are verified through the comparative simulations and experiments conducted on a physical system comprising unmanned aerial vehicles and unmanned ground vehicles.

Nanophase Segregation Drives Heterogeneous Dynamics in Amphiphilic PLMA‐b‐POEGMA Block‐Copolymers with Densely Grafted Architecture

Nanophase Segregation Drives Heterogeneous Dynamics in Amphiphilic PLMA‐<i>b</i>‐POEGMA Block‐Copolymers with Densely Grafted Architecture

Heterogeneity dynamics: influencing rock type responses to overburden pressure, Permian–Triassic of the Persian Gulf

This study considers the impact of reservoir pressures on rock type variations within the Permian–Triassic formations in the Persian Gulf. Lithological composition, textures, visible porosity, pore types, and cementations of 328.85 m of carbonate rocks from Kangan and Dalan formations were determined using a polarizing microscope. Porosity, permeability, and rock types were analyzed under ambient and reservoir pressures. Findings showed limestone’s frequent rock type changes under reservoir pressure via the Winland method, while dolomite samples showed significant alterations using the flow zone indicator method. Grainstones predominated overall, persisting even amidst pressure-induced rock type shifts, indicating texture’s minimal influence. Pore throat size of low porosity samples rapidly decreased under reservoir pressure, leading to substantial rock type changes. Fewer alterations were observed based on permeability-to-porosity ratio via the flow zone indicator method. Additionally, low permeability samples exhibited varied rock types under the Winland method, while flow zone indicator method transitions mainly occurred within specific permeability ranges. In essence, this study offers a detailed understanding of pressure’s intricate interplay with rock types and properties in Persian Gulf carbonate reservoirs.

Divergence of Critical Fluctuations on Approaching Catastrophic Phase Inversion in Turbulent Emulsions.

Catastrophic phase inversion, the breakdown of a concentrated emulsion characterized by the most puzzling sudden feature, is crucial in numerous industrial applications. Here we combine well-controlled experiments and fully resolved numerical simulations to study the critical dynamics of catastrophic phase inversion in oil-water emulsions under turbulent flow as the phase-inversion volume fraction is approached. We reveal that the phase inversion is characterized by the critical power-law divergence of fluctuations in the global drag force. We determine the enhanced dynamical heterogeneity in the local droplet structures at approaching the phase inversion, and tightly connect it to the diverging drag fluctuations. Moreover, we show that near to the critical point the phase inversion is triggered as a stochastic process by large fluctuations at both large and small scales. Our findings pave the way to modeling the phase inversion process as an out-of-equilibrium critical-like phenomena.

ScMMO-atlas: a single cell multimodal omics atlas and portal for exploring fine cell heterogeneity and cell dynamics.

Single-cell multimodal sequencing parallelly captures multiple modalities of the same cell, providing unparalleled insights into cell heterogeneity and cell dynamics. For example, joint profiling of chromatin accessibility and transcriptome from the same single cell (scATAC+RNA) identified new cell subsets within the well-defined clusters. However, lack of single-cell multimodal omics (scMMO) database has led to data fragmentation, seriously hindering access, utilization and mining of scMMO data. Here, we constructed a scMMO atlas by collecting and integrating various scMMO data, then constructed scMMO database and portal called scMMO-atlas (https://www.biosino.org/scMMO-atlas/). scMMO-atlas includes scATAC+RNA (ISSAAS-seq, SNARE-seq, paired-seq, sci-CAR, scCARE-seq, 10X Multiome and so on), scRNA+protein, scATAC+proteinand scTri-modal omics data, with 3168824 cells from 27 cell tissues/organs. scMMO-atlas offered an interactive portal for visualization and featured analysis for each modality and the integrated data. Integrated analysis of scATAC+RNA data of mouse cerebral cortex in scMMO-atlas identified more cell subsets compared with unimodal omics data. Among these new cell subsets, there is an early astrocyte subset highly expressed Grm3, called Astro-Grm3. Furthermore, we identified Ex-L6-Tle4-Nrf1, a progenitor of Ex-L6-Tle4, indicating the statistical power provided by the big data in scMMO-atlas. In summary, scMMO-atlas offers cell atlas, database and portal to facilitate data utilization and biological insight.

FACIES PARTITIONING AT REGIONAL AND FIELD SCALES IN THE BARREMIAN KHARAIB‐2 CARBONATES, UAE

Carbonates in the Lower Cretaceous (Barremian to early Aptian) Kharaib Formation are reservoir rocks at giant oil fields in the UAE and Qatar. The Barremian Kharaib‐2 member (K60), the focus of this study, is in general composed of a regionally continuous succession of high‐energy, shallow‐water limestones bounded above and below by “dense” low‐energy mud‐rich strata. Despite several decades of research, conventional carbonate facies classification schemes and resulting facies groupings for the Kharaib‐2 member have failed to show a statistically acceptable correlation with core‐ and log‐derived petrophysical data. Moreover, sedimentary bodies potentially responsible for dynamic reservoir heterogeneities have not clearly been identified. This paper proposes a standardized facies classification scheme for the Kharaib‐2 carbonates based on vertical facies proportion curves (VPCs) and variogram analyses of core data to construct stratigraphic correlations at both field and regional scales. Data came from 295 cored wells penetrating the Kharaib‐2 member at ten fields in the on‐ and offshore UAE. Thin, dense intervals separating reservoir units were adopted as fourth‐order transgressive units and were used for stratigraphic correlation. Field‐scale probability maps were used to identify sedimentary bodies such as shallow‐water rudistid shoals.Regional stratigraphic correlations of the Kharaib‐2 member carbonates based on the VPCs identified variations in depositional environments, especially for the lower part of the reservoir unit; depositional facies at fields in the SE of the UAE were interpreted to be more distal compared to those at offshore fields to the NW. At a field scale, the VPCs failed to identify significant lateral variations in the carbonates. However, variogram analyses of cored wells showed spatial concentrations of specific facies in the inner ramp domain which could be correlated with high‐energy depositional bodies such as shoals dominated by rudist debris. The bodies were sinusoidal in plan view with lengths of up to 8 km and widths of ca. 1 km. Although similar‐shaped bodies with these dimensions have been reported from other carbonate depositional systems, they have not previously been reported in the Kharaib Formation. At a regional (inter‐field) scale, the stratigraphic correlation of standardized sedimentary facies remains problematic; however, mapping of facies associations and their relative proportions relative to their environments of deposition demonstrated new patterns for the stratigraphic architecture of the Kharaib‐2 member in the UAE.