arXiv subjects

Chao Duan

Publications and source records attributed to Chao Duan.

At least 19 recordsLinked to original sources

arXiv2026

End-Functionalized Ions Promote Stability of Highly Frustrated Phases in Diblock Copolymers

Block copolymers self-assemble into ordered nanostructures whose geometry is governed by a competition between interfacial energy and chain conformational entropy. While this competition produces a rich sequence of morphologies, topologically complex ``frustrated'' phases, such as the primitive cubic network and single networks, incur severe packing penalties and are difficult to access in neutral systems. Here we show that ions functionalized at the termini of one block in an AB diblock copolymer melt introduce a qualitatively new stabilization mechanism. Strong ion correlations drive chain-end association and generate a curvature preference toward the charged domain; the resulting tendency of end-localized ion clusters to adopt compact, curved geometries selectively favors the highly frustrated single-primitive-cubic network ($Pm\bar{3}m$) and single-gyroid network ($I$4$_1$32) over the classical phases, in a region of parameter space with a segregation strength lying below the order-disorder transition of the neutral system. Free energy decomposition reveals that the electrostatic energy, arising almost entirely from beyond-mean-field ion correlations, becomes increasingly negative with increasing interfacial curvature. In the primitive cubic network, pronounced local segregation of ions into the cylindrical struts generates compact curved clusters whose correlation energy gain more than offsets the enhanced packing frustration, so the very geometry that is the source of packing frustration in neutral systems becomes the source of its stability here. Increasing ion size weakens correlations and suppresses the network phases, consistent with experimental observations. Our results establish curvature-selective end-group association as a general principle for accessing frustrated topologies in block copolymer systems.

cond-mat.soft↗

arXiv2026

Beyond Overlap Metrics: Rewarding Reasoning and Preferences for Faithful Multi-Role Dialogue Summarization

Multi-role dialogue summarization requires modeling complex interactions among multiple speakers while preserving role-specific information and factual consistency. However, most existing methods optimize for automatic metrics such as ROUGE and BERTScore, which favor surface-level imitation of references rather than genuine gains in faithfulness or alignment with human preferences. We propose a novel framework that couples explicit cognitive-style reasoning with reward-based optimization for multi-role dialogue summarization. Our method first distills structured reasoning traces (e.g., step-by-step inferences and intermediate reflections) from a large teacher model and uses them as auxiliary supervision to initialize a reasoning-aware summarizer via staged supervised fine-tuning. It then applies GRPO with a dual-principle reward that blends metric-based signals with human-aligned criteria targeting key information coverage, implicit inference, factual faithfulness, and conciseness. Experiments on multilingual multi-role dialogue benchmarks show that our method matches strong baselines on ROUGE and BERTScore. Specifically, results on CSDS confirm the framework's stability in semantic consistency, while in-depth analysis on SAMSum demonstrates clear gains in factual faithfulness and model-based preference alignment. These findings underscore the value of reasoning-aware and preference-aware training for reliable dialogue summarization. Checkpoints and datasets are available at https://huggingface.co/collections/NebulaPixel/summorchestra-multirole-summary.

cs.CL↗

arXiv2025

Knowledge-data fusion framework for frequency security assessment in low-inertia power systems

The integration of renewable energy via power electronics is transforming power grids into low-inertia systems, heightening the risks of frequency insecurity and widespread outages. Therefore, frequency security assessment (FSA) methods are urgently needed to ensure the reliable system operation. Recently, knowledge-data fusion models attempt to address the limitations of knowledge-driven (accuracy) and data-driven (generalization) FSA methods. However, current methods remain confined to shallow knowledge-data integration due to challenges in representing heterogeneous knowledge and establishing interactive mechanisms. Here, by classifing FSA domain knowledge into physics-guided and physics-constrained categories, we propose a guided learning-constrained network (GL-CN) framework, which deeply integrates domain knowledge across both network architecture and training process. In this framework, a data-driven model with dual input channels combining graph convolutional networks (GCN) and multilayer perceptrons (MLP) is proposed to extract both nodal and system-level power system features. Furthermore, guided learning enhances model generalization through data augmentation in pre-training utilizing physics-guided knowledge, while constrained network encodes physics-constrained knowledge into the network architecture and loss function to ensure physics-consistent and robust predictions. Validated on Yunnan Provincial Power Grid in China, our method reduces FSA time from days to seconds compared to traditional simulation, achieving 98% accuracy, robustness against 39.0% knowledge error, and generalization for 40%-60% renewable penetration. This provides a solid solution for mitigating blackouts caused by frequency insecurity and offers a generalizable paradigm for broader cross-domain problems.

eess.SY↗

arXiv2025

Grid congestion stymies climate benefit from U.S. vehicle electrification

Averting catastrophic global warming requires decisive action to decarbonize key sectors. Vehicle electrification, alongside renewable energy integration, is a long-term strategy toward zero carbon emissions. However, transitioning to fully renewable electricity may take decades -- during which electric vehicles may still rely on carbon-intensive electricity. We analyze the critical role of the transmission network in enabling or constraining emissions reduction from U.S. vehicle electrification. Our models reveal that the available transmission capacity severely limits potential CO2 emissions reduction. With adequate transmission, full electrification could nearly eliminate vehicle operational CO2 emissions once renewable generation reaches the existing nonrenewable capacity. In contrast, the current grid would support only a fraction of that benefit. Achieving the full emissions reduction potential of vehicle electrification during this transition will require a moderate but targeted increase in transmission capacity. Our findings underscore the pressing need to enhance transmission infrastructure to unlock the climate benefits of large-scale electrification and renewable integration.

eess.SY↗

arXiv2025

Optimal flock formation induced by agent heterogeneity

The study of flocking in biological systems has identified conditions for self-organized collective behavior, inspiring the development of decentralized strategies to coordinate the dynamics of swarms of drones and other autonomous vehicles. Previous research has focused primarily on the role of the time-varying interaction network among agents while assuming that the agents themselves are identical or nearly identical. Here, we depart from this conventional assumption to investigate how inter-individual differences between agents affect the stability and convergence in flocking dynamics. We show that flocks of agents with optimally assigned heterogeneous parameters significantly outperform their homogeneous counterparts, achieving 20-40% faster convergence to desired formations across various control tasks. These tasks include target tracking, flock formation, and obstacle maneuvering. In systems with communication delays, heterogeneity can enable convergence even when flocking is unstable for identical agents. Our results challenge existing paradigms in multi-agent control and establish system disorder as an adaptive, distributed mechanism to promote collective behavior in flocking dynamics.

cond-mat.dis-nn↗

arXiv2024

Quantifying the Critical Micelle Concentration of Nonionic and Ionic Surfactants by Self-Consistent Field Theory

Quantifying the critical micelle concentration (CMC) and understanding its relationship with both the intrinsic molecular structures and environmental conditions are crucial for the rational design of surfactants. Here, we develop a self-consistent field theory which unifies the study of CMC, micellar structure and kinetic pathway of micellization in one framework. The long-range electrostatic interactions are accurately treated, which not only makes the theory applicable to both nonionic and ionic surfactants but also enables us to capture a variety of salt effects. The effectiveness and versatility of the theory is verified by applying it to three types of commonly used surfactants. For polyoxyethylene alkyl ethers (C$_m$E$_n$) surfactants, we predict a wide span of CMC from $10^{-6}$ to $10^{-2}$M as the composition parameters $m$ and $n$ are adjusted. For the ionic sodium dodecyl sulfate (SDS) surfactant, we show the decrease of CMC as salt concentration increases, and capture both the specific cation effect and the specific anion effect. Furthermore, for sodium lauryl ether sulfate (SLES) surfactants, we find a non-monotonic dependence of both the CMC and micelle size on the number of oxyethylene groups. Our theoretical predictions of CMC are in quantitative agreement with experimental data reported in literature for all the three types of surfactants.

cond-mat.soft↗

arXiv2024

Effects of Ionic Strength on the Morphology, Scattering, and Mechanical Response of Neurofilament-Derived Protein Brushes

Protein brushes not only play a key role in the functionality of neurofilaments but also have wide applications in biomedical materials. Here, we investigate the effect of ionic strength on the morphology of protein brushes using a continuous-space self-consistent field theory. A coarse-grained multi-block charged macromolecular model is developed to capture the chemical identity of amino acid sequences. For neurofilament heavy (NFH) brushes at pH 2.4, we predict three morphological regimes: swollen brushes, condensed brushes, and coexisting brushes which consist of both a dense inner layer and a diffuse outer layer. The brush height predicted by our theory is in good agreement with experimental data for a wide range of ionic strengths. The dramatic height decrease is a result of the electrostatic screening-induced transition from the overlapping state to the isolated state of the coexisting brushes. We also study the evolution of the scattering and mechanical responses accompanying the morphological change. The oscillation in the reflectivity spectra characterizes the existence and microstructure of the inner condensed layer, whereas the shoulder in the force spectra signifies the swollen morphology.

cond-mat.soft↗

arXiv2024

Microphase Segregation in Polyelectrolyte Brushes

Polyelectrolyte (PE) brushes have ubiquitous applications as surface modifiers which regulate various structural and dynamic properties. Here, we apply a continuous-space self-consistent field theory to study the structural heterogeneity in PE brushes induced by competing electrostatic and hydrophobic interactions. For brushes with high grafting densities, we find a series of microphase-segregated morphologies with alternating polymer-rich and polymer-poor layers in the direction normal to the substrate. The transitions between multi-layer morphologies with consecutive numbers of condensed layers as well as the melting transition to the fully swollen brush are all discontinuous. We also elucidate that the segregated layers are formed by different subpopulations of all chains, significantly different from the scenario of the pearl-necklace structure formed by a single PE in poor solvents. Furthermore, we bridge the microstructure of multi-layer brushes to experimentally measurable reflectivity spectra, where the oscillation period and amplitude in the spectra are shown to be very sensitive to the number of layers and the sharpness of the polymer-solvent interface. The multi-layer morphology predicted by our theory is in good agreement with the lamellae structure experimentally observed at hydrated Nafion film interfaces.

cond-mat.soft↗

arXiv2024

Ion Correlation-Driven Hysteretic Adhesion and Repulsion between Opposing Polyelectrolyte Brushes

Polyelectrolyte (PE) brushes are widely used in biomaterials and nanotechnology to regulate surface properties and interactions. Here, we apply the electrostatic correlation augmented self-consistent field theory to investigate the interactions between opposing PE brushes in a mixture of 1:1 and 3:1 salt solutions. Our theory predicts hysteretic feature of the normal stress induced by strong ion correlations. In the presence of trivalent ions, the force profile is discontinuous: repulsive in the compression branch and adhesive in the separation branch. The molecular origin of the hysteretic force is the coexistence of two collapsed modes: two separated condensed layer on each surface in the compression and a single bundled condensed layer in the separation. With the systematic inclusion of ion correlations, our theory fully captures the hysteretic force, adhesive separation, ``jump-in'' and ``jump-out'' features, and the ``specific ion effect'', all in good agreement with the reported experimental results.

cond-mat.soft↗

arXiv2024

A Variational Approach to Trap Macromolecules

Trapping macromolecules is impoartant for the study of their conformations, interactions, dynamics and kinetic processes. Here, we develop a variational approach which self-consistently introduces a mean force that controls the center-of-mass position and a self-adjustable harmonic potential that counters the center-of-mass fluctuation. The effectiveness and versatility of our approach is verified in three classical yet not fully understood problems in polymer physics: (1) single-chain conformation in the entire solvent regimes, (2) globule-pearl necklace-coil transition of a polyelectrolyte and (3) inter-chain interaction by simultaneously trapping two polymers. The scaling relationships and $θ$ behaviors are well captured. Conformations with large shape anisotropy appearing in charged polymers are clearly depicted. Our theoretical predictions are in quantitative agreement with experimental results reported in the literature.

cond-mat.soft↗

arXiv2024

Electrostatic Correlation Augmented Self-Consistent Field Theory and Its Application to Polyelectrolyte Brushes

Modeling ion correlations in inhomogeneous polymers and soft matters with spatially varying ionic strength or dielectric permittivity remains a great challenge. Here, we develop a new theory which systematically incorporates electrostatic fluctuations into the self-consistent field theory for polymers. Applied to polyelectrolyte brushes, the theory predicts that ion correlations induce non-monotonic change of the brush height: collapse followed by reexpansion. The scaling analysis elucidates the competition between the repulsive osmotic pressure due to translational entropy and the attraction induced by ion correlations. We also clarify the absence of causal relationship between the brush collapse-reexpansion and the inversion of the surface electrostatic potential. Furthermore, strong ion correlations can trigger microphase separation, either in the lateral direction as pinned micelles or in the normal direction as oscillatory layers. Our theoretical predictions are in good agreement with the experimental results reported in the literature.

cond-mat.soft↗

arXiv2024

On the Popov-Belevitch-Hautus tests for functional observability and output controllability

Functional observability and output controllability are properties that establish the conditions for the partial estimation and partial control of the system state, respectively. In the special case of full-state observability and controllability, the Popov-Belevitch-Hautus (PBH) tests provide conditions for the properties to hold based on the system eigenspace. Generalizations of the PBH test have been recently proposed for functional observability and output controllability, but thus far have only been proven valid for diagonalizable systems. Here, we rigorously establish the generalized PBH test for functional observability, extending its validity to a broader class of systems using Jordan decomposition. Likewise, we determine the class of systems under which the generalized PBH test is sufficient and necessary for output controllability. These results have immediate implications for observer and controller design, pole assignment, and optimal placement of sensors and drivers.

math.OC↗

arXiv2024

Duality between controllability and observability for target control and estimation in networks

Output controllability and functional observability are properties that enable, respectively, the control and estimation of part of the state vector. These notions are of utmost importance in applications to high-dimensional systems, such as large-scale networks, in which only a target subset of variables (nodes) is sought to be controlled or estimated. Although the duality between full-state controllability and observability is well established, the characterization of the duality between their generalized counterparts remains an outstanding problem. Here, we establish both the weak and the strong duality between output controllability and functional observability. Specifically, we show that functional observability of a system implies output controllability of a dual system (weak duality), and that under a certain geometric condition the converse holds (strong duality). As an application of the strong duality, we derive a necessary and sufficient condition for target control via static feedback. This allow us to establish a separation principle between the design of target controllers and the design of functional observers in closed-loop systems. These results generalize the classical duality and separation principles in modern control theory.

math.OC↗

arXiv2023

On the nature of overcharging and charge inversion in electrical double layers

Understanding overcharging and charge inversion is one of the long-standing challenges in soft matter and biophysics. To study these phenomena, we employ the modified Gaussian renormalized fluctuation theory, which allows for the self-consistent accounting of spatially varying ionic strength, as well as the spatial variations in dielectric permittivity and excluded volume effects. The underlying dependence of overcharging on the electrostatic coupling is elucidated by varying surface charge, counterion valency, and dielectric contrast. Consistent with simulations, three characteristic regimes corresponding to weak, moderate, and strong coupling are identified. Important features like the inversion of zeta potential, crowding and ionic layering at the surface are successfully captured. For weak coupling, there is no overcharging. In the moderate coupling regime, overcharging increases with surface charge. Finally, in the strong coupling regime, ionic crowding and saturation in overcharging are observed. Our theory predicts non-monotonic dependence of charge inversion on multivalent salt concentration as well as the addition of monovalent salt, in quantitative agreement with experiments.

cond-mat.soft↗

arXiv2023

Target Controllability and Target Observability of Structured Network Systems

The duality between controllability and observability enables methods developed for full-state control to be applied to full-state estimation, and vice versa. In applications in which control or estimation of all state variables is unfeasible, the generalized notions of output controllability and functional observability establish the minimal conditions for the control and estimation of a target subset of state variables, respectively. Given the seemly unrelated nature of these properties, thus far methods for target control and target estimation have been developed independently in the literature. Here, we characterize the graph-theoretic conditions for target controllability and target observability (which are, respectively, special cases of output controllability and functional observability for structured systems). This allow us to rigorously establish a weak and strong duality between these generalized properties. When both properties are equivalent (strongly dual), we show that efficient algorithms developed for target controllability can be used for target observability, and vice versa, for the optimal placement of sensors and drivers. These results are applicable to large-scale networks, in which control and monitoring are often sought for small subsets of nodes.

eess.SY↗

arXiv2023

Kinetic Pathway and Micromechanics of Vesicle Fusion/Fission

Despite the wide existence of vesicles in living cells as well as their important applications like drug-delivery, the underlying mechanism of vesicle fusion/fission remains under debate. Here, we develop a constrained self-consistent field theory (SCFT) which allows tracking the shape evolution and free energy as a function of center-of-mass separation distance. Fusion and fission are described in a unified framework. Both the kinetic pathway and the mechanical response can be simultaneously captured. By taking vesicles formed by polyelectrolytes as a model system, we predict discontinuous transitions between the three morphologies: parent vesicle with a single cavity, hemifission/hemifusion and two separated child vesicles, as a result of breaking topological isomorphism. With the increase of inter-vesicle repulsion, we observe a great reduction of the cleavage energy, indicating that vesicle fission can be achieved without hemifission, in good agreement with simulation. The force-extension relationship elucidates typical plasticity for separating two vesicles. The super extensibility in the mechanical response of vesicle is in stark contrast to soft particles with other morphologies such as cylinder and sphere.

cond-mat.soft↗

arXiv2023

Understanding the Salt Effects on the Liquid-Liquid Phase Separation of Proteins

Protein aggregation via liquid-liquid phase separation (LLPS) is ubiquitous in nature and intimately connects to many human diseases. Although it is widely known that the addition of salt has crucial impacts on the LLPS of protein, full understanding of the salt effect remains an outstanding challenge. Here, we develop a molecular theory which systematically incorporates the self-consistent field theory for charged macromolecules into the solution thermodynamics. The electrostatic interaction, hydrophobicity, ion solvation and translational entropy are included in a unified framework. Our theory fully captures the long-standing puzzles of the non-monotonic salt concentration dependence and the specific ion effect. We find that proteins show salting-out at low salt concentrations due to ionic screening. The solubility follows the inverse Hofmeister series. In the high salt concentration regime, protein remains salting-out for small ions but turns to salting-in for larger ions, accompanied by the reversal of the Hofmeister series. We reveal that the solubility at high salt concentrations is determined by the competition between the solvation energy and translational entropy of ion. Furthermore, we derive an analytical criterion for determining the boundary between the salting-in and salting-out regimes. The theoretical prediction is in quantitative agreement with experimental results for various proteins and salt ions without any fitting parameters.

cond-mat.soft↗

arXiv2022

Prevalence and scalable control of localized networks

The ability to control network dynamics is essential for ensuring desirable functionality of many technological, biological, and social systems. Such systems often consist of a large number of network elements, and controlling large-scale networks remains challenging because the computation and communication requirements increase prohibitively fast with network size. Here, we introduce a notion of network locality that can be exploited to make the control of networks scalable even when the dynamics are nonlinear. We show that network locality is captured by an information metric and is almost universally observed across real and model networks. In localized networks, the optimal control actions and system responses are both shown to be necessarily concentrated in small neighborhoods induced by the information metric. This allows us to develop localized algorithms for determining network controllability and optimizing the placement of driver nodes. This also allows us to develop a localized algorithm for designing local feedback controllers that approach the performance of the corresponding best global controllers while incurring a computational cost orders-of-magnitude lower. We validate the locality, performance, and efficiency of the algorithms in Kuramoto oscillator networks as well as three large empirical networks: synchronization dynamics in the Eastern U.S. power grid, epidemic spreading mediated by the global air transportation network, and Alzheimer's disease dynamics in a human brain network. Taken together, our results establish that large networks can be controlled with computation and communication costs comparable to those for small networks.

eess.SY↗