Interfacial-Assembly-Induced In Situ Transformation from Aligned 1D Nanowires to Quasi-2D Nanofilms.

As the dimensionality of materials generally affects their characteristics, thin films composed of low-dimensional nanomaterials, such as nanowires (NWs) or nanoplates, are of great importance in modern engineering. Among various bottom-up film fabrication strategies, interfacial assembly of nanoscale building blocks holds great promise in constructing large-scale aligned thin films, leading to emergent or enhanced collective properties compared to individual building blocks. As for 1D nanostructures, the interfacial self-assembly causes the morphology orientation, effectively achieving anisotropic electrical, thermal, and optical conduction. However, issues such as defects between each nanoscale building block, crystal orientation, and homogeneity constrain the application of ordered films. The precise control of transdimensional synthesis and the formation mechanism from 1D to 2D are rarely reported. To meet this gap, we introduce an interfacial-assembly-induced interfacial synthesis strategy and successfully synthesize quasi-2D nanofilms via the oriented attachment of 1D NWs on the liquid interface. Theoretical sampling and simulation show that NWs on the liquid interface maintain their lowest interaction energy for the ordered crystal plane (110) orientation and then rearrange and attach to the quasi-2D nanofilm. This quasi-2D nanofilm shows enhanced electric conductivity and unique optical properties compared with its corresponding 1D geometry materials. Uncovering these growth pathways of the 1D-to-2D transition provides opportunities for future material design and synthesis at the interface.

Quorum sensing-induced transition from colloidal waves to Turing-like patterns in chemorepulsive active colloids.

The study of active systems, especially in the presence of a chemical background field, is garnering significant attention. Traditionally, the self-propelled velocity of active colloids was assumed to be constant, independent of the local density of colloids. In this work, we introduce a chemotactic active system that features quorum sensing (QS), wherein particles act as chemorepellents. Interestingly, these particles lose their activity in regions of high local particle density. Our findings reveal that QS leads to a transition from an oscillatory colloidal wave to a Turing-like pattern, with the observation of an intermediate state. With the variation of the sensing threshold, both the mean oscillation frequency of the system and the number of clusters exhibit non-monotonic dependence. Furthermore, the QS-induced pattern differs markedly from systems without QS, primarily due to the competitive interplay between diffusion and chemotaxis. The dynamics of this phenomenon are explained using a coarse-grained mean field model.

Cascade-enhanced transport efficiency of biochemical systems.

Recent developments in nonequilibrium thermodynamics, known as thermodynamic uncertainty relations, limit the system's accuracy by the amount of free-energy consumption. A transport efficiency, which can be used to characterize the capacity to control the fluctuation by means of energy cost, is a direct result of the thermodynamic uncertainty relation. According to our previous research, biochemical systems consume much lower energy cost by noise-induced oscillations to keep almost equal efficiency to maintain precise processes than that by normal oscillations. Here, we demonstrate that the performance of noise-induced oscillations propagating can be further improved through a cascade reaction mechanism. It has been discovered that it is possible to considerably enhance the transport efficiency of the biochemical reactions attained at the terminal cell, allowing the cell to use the cascade reaction mechanism to operate more precisely and efficiently. Moreover, an optimal reaction coupling strength has been predicted to maximize the transport efficiency of the terminal cell, uncovering a concrete design strategy for biochemical systems. By using the local mean field approximation, we have presented an analytical framework by extending the stochastic normal form equation to the system perturbed by external signals, providing an explanation of the optimal coupling strength.

Crosslink-Induced Conformation Change of Intrinsically Disordered Proteins Have a Nontrivial Effect on Phase Separation Dynamics and Thermodynamics.

Biomolecule condensates formed via liquid-liquid phase separation (LLPS) play crucial roles within various cellular processes. Despite numerous theoretical and experimental discoveries, the general principle by which the protein conformation affects the propensity for LLPS remains poorly understood. Here, we systematically address this issue using a general coarse-grained model of intrinsically disordered proteins (IDPs) with different degrees of intrachain crosslinks. We find that an increased conformation collapse due to higher intrachain crosslink ratio f enhances the thermodynamic stability of protein phase separation and found the critical temperature Tc has a good scaling law with the proteins' average radius of gyration Rg. Such correlation is robust regardless of interaction types and sequence patterns. Strikingly, the growth dynamics of the LLPS process, contrary to the thermodynamic observation, is generally more favored at proteins with extended conformation. Faster condensate growing speed is again observed for higher-f collapsed IDPs, resulting altogether in a nonmonotonic dynamics as a function of f. A phenomenological understanding of the phase behavior is provided by a mean-field model with an effective Flory interaction parameter χ, which is found to have a good scaling law with conformation expansion. Our study shed lights on the general mechanism for understanding and modulation of phase separation with different conformation profiles and may provide new evidence in reconciling the contradictions in thermodynamic- and dynamic-controlled experimental LLPS observations.

Nonlinear chemical reaction induced abnormal pattern formation of chemotactic particles.

The chemotactic behavior of particles is a widespread and important phenomenon that enables them to interact with the chemical species present in the environment. These chemical species can undergo chemical reactions and even form some non-equilibrium chemical structures. In addition to chemotaxis, particles can also produce or consume chemicals, which allows them to further couple with chemical reaction fields and thus influence the dynamics of the whole system. In this paper, we consider a model of chemotactic particle coupling with nonlinear chemical reaction fields. Intriguingly, we find the aggregation of particles occurs when they consume substances and move toward high-concentration areas, which is quite counterintuitive. In addition, dynamic patterns can also be found in our system. These results imply that the interaction between chemotactic particles and nonlinear reactions can result in much novel behavior and may further extend to explain the complex phenomena in certain systems.

Improving estimation of entropy production rate for run-and-tumble particle systems by high-order thermodynamic uncertainty relation.

Entropy production plays an important role in the regulation and stability of active matter systems, and its rate quantifies the nonequilibrium nature of these systems. However, entropy production is hard to experimentally estimate even in some simple active systems like molecular motors or bacteria, which may be modeled by the run-and-tumble particle (RTP), a representative model in the study of active matters. Here we resolve this problem for an asymmetric RTP in one dimension, first constructing a finite-time thermodynamic uncertainty relation (TUR) for a RTP, which works well in the short observation time regime for entropy production estimation. Nevertheless, when the activity dominates, i.e., the RTP is far from equilibrium, the lower bound for entropy production from TUR turns out to be trivial. We address this issue by introducing a recently proposed high-order thermodynamic uncertainty relation (HTUR), in which the cumulant generating function of current serves as a key ingredient. To exploit the HTUR, we adopt a method to analytically obtain the cumulant generating function of the current we study, with no need to explicitly know the time-dependent probability distribution. The HTUR is demonstrated to be able to estimate the steady state energy dissipation rate accurately because the cumulant generating function covers higher-order statistics of the current, including rare and large fluctuations besides its variance. Compared to the conventional TUR, the HTUR could give significantly improved estimation of energy dissipation, which can work well even in the far from equilibrium regime. We also provide a strategy based on the improved bound to estimate the entropy production from a moderate amount of trajectory data for experimental feasibility.

Mode-coupling theory for the dynamics of dense underdamped active Brownian particle system.

We present a theory to study the inertial effect on glassy dynamics of the underdamped active Brownian particle (UABP) system. Using the assumption of the nonequilibrium steady-state, we obtain an effective Fokker-Planck equation for the probability distribution function (PDF) as a function of positions and momentums. With this equation, we achieve the evolution equation of the intermediate scattering function through the Zwanzig-Mori projection operator method and the mode-coupling theory (MCT). Theoretical analysis shows that the inertia of the particle affects the memory function and corresponding glass transition by influencing the structure factor and a velocity correlation function. The theory provides theoretical support and guidance for subsequent simulation work.

Phase separation of EB1 guides microtubule plus-end dynamics.

In eukaryotes, end-binding (EB) proteins serve as a hub for orchestrating microtubule dynamics and are essential for cellular dynamics and organelle movements. EB proteins modulate structural transitions at growing microtubule ends by recognizing and promoting an intermediate state generated during GTP hydrolysis. However, the molecular mechanisms and physiochemical properties of the EB1 interaction network remain elusive. Here we show that EB1 formed molecular condensates through liquid-liquid phase separation (LLPS) to constitute the microtubule plus-end machinery. EB1 LLPS is driven by multivalent interactions among different segments, which are modulated by charged residues in the linker region. Phase-separated EB1 provided a compartment for enriching tubulin dimers and other plus-end tracking proteins. Real-time imaging of chromosome segregation in HeLa cells expressing LLPS-deficient EB1 mutants revealed the importance of EB1 LLPS dynamics in mitotic chromosome movements. These findings demonstrate that EB1 forms a distinct physical and biochemical membraneless-organelle via multivalent interactions that guide microtubule dynamics.

Fast Functionalization with High Performance in the Autonomous Information Engine.

Mandal and Jarzynski have proposed a fully autonomous information heat engine, consisting of a demon, a mass, and a memory register interacting with a thermal reservoir. This device converts thermal energy into mechanical work by writing information to a memory register or, conversely, erasing information by consuming mechanical work. Here, we derive a speed limit inequality between the relaxation time of state transformation and the distance between the initial and final distributions, where the combination of the dynamical activity and entropy production plays an important role. Such inequality provides a hint that a speed-performance trade-off relation exists between the relaxation time to a functional state and the average production. To obtain fast functionalization while maintaining the performance, we show that the relaxation dynamics of the information heat engine can be accelerated significantly by devising an optimal initial state of the demon. Our design principle is inspired by the so-called Mpemba effect, where water freezes faster when initially heated.

Improved estimation for energy dissipation in biochemical oscillations.

Biochemical oscillations, regulating the timing of life processes, need to consume energy to achieve good performance on crucial functions, such as high accuracy of the phase period and high sensitivity to external signals. However, it is a great challenge to precisely estimate the energy dissipation in such systems. Here, based on the stochastic normal form theory, we calculate the Pearson correlation coefficient between the oscillatory amplitude and phase, and a trade-off relation between transport efficiency and phase sensitivity can then be derived, which serves as a tighter form than the estimator resulting from the conventional thermodynamic uncertainty relation. Our findings demonstrate that a more precise energy dissipation estimation can be obtained by enhancing the sensitivity of the biochemical oscillations. Moreover, the internal noise and amplitude power effects have also been discovered.

A Stacked Plasmonic Metamaterial with Strong Localized Electric Field Enables Highly Efficient Broadband Light-Driven CO2 Hydrogenation.

Light utilization largely governs the performance of CO2 photoconversion, whereas most of the materials that are implemented in such an application are restricted in a narrow spectral absorption range. Plasmonic metamaterials with a designable regular pattern and facile tunability are excellent candidates for maximizing light absorption to generate substantial hot electrons and thermal energy. Herein, a concept of coupling a Au-based stacked plasmonic metamaterial with single Cu atoms in alloy, as light absorber and catalytic sites, respectively, is reported for gas-phase light-driven catalytic CO2 hydrogenation. The metamaterial structure works in a broad spectral range (370-1040 nm) to generate high surface temperature for photothermal catalysis, and also induces strong localized electric field in favor of transfer of hot electrons and reduced energy barrier in CO2 hydrogenation. This work unravels the significant role of a strong localized electric field in photothermal catalysis and demonstrates a scalable fabrication approach to light-driven catalysts based on plasmonic metamaterials.

Surface Engineering on Commercial Cu Foil for Steering C2H4/CH4 Ratio in CO2 Electroreduction.

Designing catalysts with high selectivity toward C2 products in CO2 electroreduction is crucial to energy storage and sustainable development. Here, we propose a Cu foil kinetic model with abundant nanocavities possessing higher reaction rate constant k to steer the ratio of C2H4 to the competing CH4 during CO2 electroreduction. Chemical kinetic simulation demonstrates that the nanocavities could enrich the adsorbed CO surface concentration (θCOad), while the higher k helps to lower the C-C coupling barrier for CO intermediates, thus favoring the formation of C2H4. The commercial Cu foil treated with cyclic voltammetry is used to match this model, displaying a remarkable C2H4/CH4 ratio of 4.11, which is 18 times larger than that on the pristine Cu foil. This work offers a handy strategy for surface modification and provides new insights into the C-C coupling and the C2H4 selectivity in terms of mass transfer flux and energy barrier.

Theoretical modelling of liquid-liquid phase separation: from particle-based to field-based simulation.

Liquid-liquid phase separation (LLPS) has proved to be ubiquitous in living cells, forming membraneless organelles (MLOs) and dynamic condensations essential in physiological processes. However, some underlying mechanisms remain challenging to unravel experimentally, making theoretical modeling an indispensable aspect. Here we present a protocol for understanding LLPS from fundamental physics to detailed modeling procedures. The protocol involves a comprehensive physical picture on selecting suitable theoretical approaches, as well as how and what to interpret and resolve from the results. On the particle-based level, we elaborate on coarse-grained simulation procedures from building up models, identifying crucial interactions to running simulations to obtain phase diagrams and other concerned properties. We also outline field-based theories which give the system's density profile to determine phase diagrams and provide dynamic properties by studying the time evolution of density field, enabling us to characterize LLPS systems with larger time and length scales and to further include other nonequilibrium factors such as chemical reactions.

Designing circle swimmers: Principles and strategies.

Various microswimmers move along circles rather than straight lines due to their swimming mechanisms, body shapes, or hydrodynamic effects. In this paper, we adopt the concepts of stochastic thermodynamics to analyze circle swimmers confined to a two-dimensional plane and study the trade-off relations between various physical quantities, such as precision, energy cost, and rotational speed. Based on these findings, we predict principles and strategies for designing microswimmers of special optimized functions under limited energy resource conditions, which will bring new experimental inspiration for designing smart motors.

Emergent spiral vortex of confined biased active particles.

Confinement is known to have profound effects on the collective dynamics of many active systems. Here, we investigate a modeled active system in circular confinement consisting of biased active particles, where the direction of active force deviates a biased angle from the principle orientation of the anisotropic interaction. We find that such particles can spontaneously form a spiral vortex with two concentric and counter-rotating regions near the boundary. The emerged vortex can be measured by the vortex order parameter which shows nonmonotonic dependencies on both the biased angle and the strength of the anisotropic interaction. Our work can provide an understanding of such dynamic behaviors and enable different strategies for designing ordered collective behaviors.

Local Field Induced Mass Transfer: New Insight into Nano-electrocatalysis.

Unravelling the complex kinetics of electrocatalysis is essential for the design of electrocatalysts with high performance. Mass transfer and electron transfer are two primary factors that need to be optimized in order to enhance electrocatalytic reactions. The use of nanocatalysts proves to be a promising way of promoting the performance of electrocatalytic reactions, this improvement is usually attributed to their ability to enhance electron transfer. However, when catalysts are taken down to the nanoscale, their size is comparable to the thickness of an electrical double layer, so any curvature can lead to an inhomogeneous local electric field on the electrode, which then changes the mass transfer essentially. In this article, we introduce the new concept of local-field-induced mass transfer in nano-electrocatalytic systems, and provide a brief review of recent progress, revealing its effect on nano-electrocatalysis, which may bring new insight into the future design of nano-electrocatalysts.

Microchemical Engineering in a 3D Ordered Channel Enhances Electrocatalysis.

The kinetics of electrode reactions including mass transfer and surface reaction is essential in electrocatalysis, as it strongly determines the apparent reaction rates, especially on nanostructured electrocatalysts. However, important challenges still remain in optimizing the kinetics of given catalysts with suitable constituents, morphology, and crystalline design to maximize the electrocatalytic performances. We propose a comprehensive kinetic model coupling mass transfer and surface reaction on the nanocatalyst-modified electrode surface to explore and shed light on the kinetic optimization in electrocatalysis. Moreover, a theory-guided microchemical engineering (MCE) strategy has been demonstrated to rationally redesign the catalysts with optimized kinetics. Experimental measurements for methanol oxidation reaction in a 3D ordered channel with tunable channel sizes confirm the calculation prediction. Under the optimized channel size, mass transfer and surface reaction in the channeled microreactor are both well regulated. This MCE strategy will bring about a significant leap forward in structured catalyst design and kinetic modulation.

Hidden Mechanism Behind the Roughness-Enhanced Selectivity of Carbon Monoxide Electrocatalytic Reduction.

High roughness has been proved to be an effective design strategy for electrocatalyst in many systems. Especially, high selectivity of carbon monoxide reduction (CORR) in competition with the hydrogen evolution reaction has been observed on high roughness electrocatalysts. However, the two well-known mechanisms, i.e., decreasing the energy barrier of CORR and increasing local pH, failed to understand the roughness-enhanced selectivity in a recent experiment. Herein we unravel the hidden mechanism by establishing a comprehensive kinetic model for CORR on catalysts with different roughness factors. We conclude that the roughness-enhanced CORR selectivity is actually kinetic controlled by local-electric-field-directed mass transfer of adsorbed species on the electrode surface. Several ways to optimize CORR selectivity are predicted. Our work highlights the kinetics in electrocatalysis on nanocatalysts, and provides a conceptually new principle for future catalyst design.

Selectively Scissoring Hydrogen-Bonded Cytosine Dimer Structures Catalyzed by Water Molecules.

A single-molecule-level understanding of the activity of solvating water molecules in hydrogen-bonded assemblies would provide insights into the properties of the first hydration shells. Herein, we investigate the solvation of one of the DNA bases, cytosine, whose glassy-state network formed on Au(111) contains diverse types of hydrogen-bonded dimer configurations with hierarchical strengths. Upon water exposure, a global structural transformation from interwoven chain segments to extended chains was identified by scanning tunneling microscopy and atomic force microscopy. Density functional theory calculation and coarse-grained molecular dynamics simulation indicate that water molecules selectively break the weak-hydrogen-bonded dimers at T-junctions, while the stable ones within chains remain intact. The resulting hydrated chain segments further self-assemble into molecular chains by forming strong hydrogen bonds and spontaneously releasing water molecules. Such an intriguing transformation cannot be realized by thermal annealing, indicating the dynamic nature of water molecules in the regulation of hydrogen bonds in a catalytic manner.