Mesoscopic Systems Research Articles

Generalized Itô’s lemma and the stochastic thermodynamics of diffusion with resetting

Abstract Methods from the theory of stochastic processes are increasingly being used to extend classical thermodynamics to mesoscopic non-equilibrium systems. One characteristic feature of these systems is that averaging the stochastic entropy with respect to an ensemble of stochastic trajectories leads to a second law of thermodynamics that quantifies the degree of departure from thermodynamic equilibrium. A well known mechanism for maintaining a diffusing particle out of thermodynamic equilibrium is stochastic resetting. In its simplest form, the position of the particle instantaneously resets to a fixed position x 0 at a sequence of times generated from a Poisson process of constant rate r. Within the context of stochastic thermodynamics, instantaneous resetting to a single point is a unidirectional process that has no time-reversed equivalent. Hence, the average rate of entropy production calculated using the Gibbs–Shannon entropy cannot be related to the degree of time-reversal symmetry breaking. The problem of unidirectionality can be avoided by considering resetting to a random position or diffusion in an intermittent confining potential. In this paper we show how stochastic entropy production along sample paths of diffusion processes with resetting can be analyzed in terms of extensions of Itô’s formula for stochastic differential equations (SDEs) that include both continuous and discrete processes. First, we use the stochastic calculus of jump-diffusion processes to calculate the rate of stochastic entropy production for instantaneous resetting, and show how previous results are recovered upon averaging over sample trajectories. Second, we formulate single-particle diffusion in a switching potential as a hybrid SDE and develop a hybrid extension of Itô’s stochastic calculus to derive a general expression for the rate of stochastic entropy production. We illustrate the theory by considering overdamped Brownian motion in an intermittent harmonic potential. Finally, we calculate the average rate of entropy production for a population of non-interacting Brownian particles moving in a common switching potential. In particular, we show that the latter induces statistical correlations between the particles, which means that the total entropy is not given by the sum of the 1-particle entropies.

Autonomous tracking of honey bee behaviors over long-term periods with cooperating robots.

Digital and mechatronic methods, paired with artificial intelligence and machine learning, are transformative technologies in behavioral science and biology. The central element of the most important pollinator species-honey bees-is the colony's queen. Because honey bee self-regulation is complex and studying queens in their natural colony context is difficult, the behavioral strategies of these organisms have not been widely studied. We created an autonomous robotic observation and behavioral analysis system aimed at continuous observation of the queen and her interactions with worker bees and comb cells, generating behavioral datasets of exceptional length and quality. Key behavioral metrics of the queen and her social embedding within the colony were gathered using our robotic system. Data were collected continuously for 24 hours a day over a period of 30 days, demonstrating our system's capability to extract key behavioral metrics at microscopic, mesoscopic, and macroscopic system levels. Additionally, interactions among the queen, worker bees, and brood were observed and quantified. Long-term continuous observations performed by the robot yielded large amounts of high-definition video data that are beyond the observation capabilities of humans or stationary cameras. Our robotic system can enable a deeper understanding of the innermost mechanisms of honey bees' swarm-intelligent self-regulation. Moreover, it offers the possibility to study other social insect colonies, biocoenoses, and ecosystems in an automated manner. Social insects are keystone species in all terrestrial ecosystems; thus, developing a better understanding of their behaviors will be invaluable for the protection and even the restoration of our fragile ecosystems globally.

Impact of potential and temperature fluctuations on charge and heat transport in quantum Hall edges in the heat Coulomb blockade regime

We present a broad study of charge and heat transport in a mesoscopic system where one or several quantum Hall edge channels are strongly coupled to a floating Ohmic contact (OC). It is well-known that charge-current fluctuations emanating from the OC along the edge channels are highly susceptible to the OC charge capacitance in the heat Coulomb blockade regime (an impeded ability of the OC to equilibrate edge channels). Here, we demonstrate how potential and temperature fluctuations due to finite OC charge and heat capacities impact the heat-current fluctuations emitted from the OC. First, by assuming an infinite OC heat capacity, we show that the output heat-current noise is strongly dependent on the OC charge capacitance, following from a close relation between one-dimensional charge and heat currents. When also the OC heat capacity is finite, an interplay of potential and temperature fluctuations influences the heat transport. Concretely, we find that the effect of the charge capacitance on heat transport manifests in terms of a strongly increased energy relaxation time in the heat Coulomb blockade regime. Furthermore, we find expressions for a broad set of output observables, such as charge and heat auto- and cross correlations, as functions of input and OC fluctuations, depending on the relation between charge and energy relaxation times compared to the frequency of fluctuations and inverse (local) temperatures as well as on the number of edge channels attached to the OC. Finally, we show that a finite OC heat capacity transforms the full counting statistics of the output charge from Gaussian to non-Gaussian. Our findings provide opportunities to experimentally probe and harness the quantum nature of heat transport in strongly coupled electron circuits. Published by the American Physical Society 2024

RescuePY: Simulation-Based Rescue Response Impact Assessment

Mobility in metropolitan regions is changing. The distribution of space in cities, the design of transport modes, and the organization of mobility are being re-thought. However, no matter the changes and innovations on the way to a more sustainable future, essential constants must be upheld: In the event of minor, regionally limited emergencies, medical assistance must reach those in need quickly. When dealing with large-scale emergencies, the ability to evacuate the area promptly must be ensured. The impact analysis of mobility innovations on emergency services within urban areas so far has been based purely on empirical observations using existing data. Currently, it is only possible to analyze what-if considerations in a limited way. Nevertheless, due to the increasingly rapid changes in mobility, a comprehensive and interlinked analysis will be necessary. This is the key contribution of rescuePY: rescuePY is a simulation suite based on the mesoscopic and microscopic simulation environment hybridPY. It allows holistic and microscopic transport modeling of rescue infrastructure to quantify the impact of the mobility transition towards higher sustainability on the performance of rescue services. The main features of this software are: Rescue system assessment for strategic, long-term planning Mobility-influence studies for operative, mid-term planning Activity-based urban evacuation modeling The capabilities of rescuePY are demonstrated by two applications: a simulation- based, mesoscopic system analysis of emergency services in Munich compared to real-world data and microscopic modeling of emergency vehicles (EMVs) in different road architectures. Ongoing developments aim to improve the evaluation methodology for the aggregated impact analysis of mobility innovations on rescue response services.

Modeling temperature-dependent transport properties in dissipative particle dynamics: A top-down coarse-graining toward realistic dynamics at the mesoscale.

Dissipative particle dynamics (DPD) is a widespread computational tool to simulate the behavior of soft matter and liquids in and out of equilibrium. Although there are many applications in which the effect of temperature is relevant, most of the DPD studies have been carried out at a fixed system temperature. Therefore, this work investigates how to incorporate the effect of system temperature variation within the DPD model to capture realistic temperature-dependent system properties. In particular, this work focuses on the relationship between temperature and transport properties, and therefore, an extended DPD model for transport properties prediction is employed. Transport properties, unlike the equilibrium properties, are often overlooked despite their significant influence on the flow dynamics of non-isothermal mesoscopic systems. Moreover, before simulating the response of the system induced by a temperature change, it is important to first estimate transport properties at a certain temperature. Thus here, the same fluid is simulated across different temperature conditions using isothermal DPD with the aim to identify a temperature-dependent parametrization methodology, capable of ensuring the correctness of both equilibrium and dynamical properties. Liquid water is used as a model system for these analyses. This work proposes a temperature-dependent form of the extended DPD model where both conservative and non-conservative interaction parameters incorporate the variation of the temperature. The predictions provided by our simulations are in excellent agreement with experimental data.

Handheld structured light system for panoramic 3D measurement in mesoscale

Abstract The measurement of complete 3D topography in mesoscale plays a vital role in high-precision reverse engineering, oral medical modeling, circuit detection, etc. Traditional structured light systems are limited to measuring 3D shapes from a single perspective. How to achieve high-quality mesoscopic panoramic 3D measurement remains challenging, especially in complex measured scenarios such as dynamic measurement, scattering medium, and high reflectance. To overcome these problems, we develop a handheld mesoscopic panoramic 3D measurement system for such complex scenes together with the fast point-cloud-registration and accurate 3D-reconstruction, where a motion discrimination mechanism is designed to ensure that the captured fringe is in a quasi-stationary case by avoiding the motion errors caused during fringe scanning; a deep neural network is utilized to suppressing the fringe-degradation caused by scattering mediums resulting to significantly improves the quality of the 3D point cloud; a strategy based on phase averaging is additionally proposed to simultaneously correct the saturation-induced errors and gamma nonlinear errors. Finally, the proposed system with a multi-threaded data processing framework is further developed to verify the proposed method and the corresponding experiments verify its feasibility.

Ab initio description of monopole resonances in light- and medium-mass nuclei

Giant resonances (GRs) are a striking manifestation of collective motions in mesoscopic systems such as atomic nuclei. Until recently, theoretical investigations have essentially relied on the (quasiparticle) random phase approximation ((Q)RPA), and extensions of it, based on phenomenological energy density functionals (EDFs). As part of a current effort to describe GRs within an ab initio theoretical scheme, the present work promotes the use of the projected generator coordinate method (PGCM). This method, which can handle anharmonic effects while satisfying symmetries of the nuclear Hamiltonian, displays a favorable (i.e. mean-field-like) scaling with system’s size. Presently focusing on the isoscalar giant monopole resonance (GMR) of light- and medium-mass nuclei, PGCM’s potential to deliver wide-range ab initio studies of GRs in closed- and open-shell nuclei encompassing pairing, deformation, and shape coexistence effects is demonstrated. The comparison with consistent QRPA calculations highlights PGCM’s unique attributes and sheds light on the intricate interplay of nuclear collective excitations. The present paper is the first in a series of four and focuses on technical aspects and uncertainty quantification of ab initio PGCM calculations of GMR using the doubly open-shell 46\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{46}$$\\end{document}Ti as an illustrative example. The second paper displays results for a set of nuclei of physical interest and proceeds to the comparison with consistent (deformed) ab initio QRPA calculations. While the third paper analyzes useful moments of the monopolar strength function and different ways to access them within PGCM calculations, the fourth paper focuses on the effect of the symmetry restoration on the monopole strength function.

Competitive pricing for ride-sourcing platforms with MARL

With the advancement of autonomous driving, the competition between shared autonomous vehicles (SAVs) and the existing human-driven vehicles (HVs) ride-sourcing platforms has emerged as a prominent research area within the fields of transportation and management. Solving the pricing problem, considering the competition between HV and SAV platforms, is critical for the platform’s operation. The competitive pricing problem is hard to solve since it is a partially observable Markov decision process (POMDP), that is, only the strategies of other platforms (such as price and wage) are observable, while their states are unobservable. It is also a multivariable sequential problem pricing since spatial-temporal pricing varies with region and time. To tackle this complex problem, this paper implements the multi-agent reinforcement learning (MARL) approach of multi-agent deep deterministic policy gradient (MADDPG) to an mesoscopic simulation system. The simulation replicates the intricate dynamics within a competitive environment where SAV and HV platforms coexist. MADDPG effectively resolves the competitive pricing issue using data derived from simulation. The findings indicate that the SAV platform secures greater profits with a smaller fleet, as it eliminates the need to compensate drivers. Meanwhile, for the same reason, even with fewer vehicles, the SAV platform can set lower prices to attract more passengers and thereby generate higher profits. We further evaluate the efficacy of dynamic pricing versus spatial-temporal pricing and discover that spatial-temporal pricing yields greater profits.

Formation of two-dimensional mixed monolayers of integrated-cellulose nanofibers and starch nanoparticles using cellulose acetate and soluble starch

In this study, mixed monolayers comprising starch and cellulose nanofibers are prepared on the water surface and effectively controlled their surface morphologies. Through the spreading of cellulose acetate on the water surface, we achieve the preparation of integrated films with the spontaneous formation of nanocellulose. It is confirmed that starch, which is insoluble in the spreading solvent, is solubilized through a soluble treatment and spreads into a monolayer on the water surface. Upon mixing the nanocellulose integrated layer and starch particle layer, the morphology changes based on the mixed ratio of these components. In the monolayer film of a 1:1 mixture of cellulose acetate and soluble starch, the morphology predominantly consists of fibers, with particulate aggregates incorporated here and there. Furthermore, when the ratio changes to 1:5, the mixed monolayer transforms into a single particle layer, while the mixed monolayer evolves into a fibrous integrated film at a ratio of 5:1. Meanwhile, from the evaluation of the surface pressure-area isotherms, it is predicted that both components constitute an ideal mesoscopic miscible system. In addition, in the subphase temperature dependence of the cellulose acetate:soluble starch = 1:1 mixed monolayer, fibers are promoted under low-temperature conditions, and substantial domains are formed under high-temperature conditions. Notably, the mixed film remains amorphous at all ratios and exhibits low surface free energy, simultaneously suggesting hydrophobic and oil-repellent properties.

Ultrafast reversible phase engineering in MoTe2 thin film via polaron formation

The emergence of various polymorphs in two-dimensional transition metal dichalcogenides provides an opportunity for robust phase engineering by temperature, strain, laser irradiation, and external charge doping (Keum in Nat. Phys. 11:482, 2015; Song in Nano Lett. 16:188, 2016; Cho in Science 349:625, 2015; Kim in Nano Lett. 17:3363, 2017). This provides means to develop homojunction of metal–semiconductor, enhance mobility, reduce contact resistance, and observe novel quantum critical phenomena in mesoscopic systems. The rich physics paves the way for ultrafast light-induced switching/memory devices and optical data processing in optoelectronics. However, the fundamental temporal evolution of the laser-driven phase transformation, in particular regarding heat and charge carriers, remains elusive. We report an ultrafast reversible structural transformation in MoTe2 by coherent phonon dynamics through polaron formation at room temperature. At a high photon density, the generated coherent phonons are coupled with excitons to form polarons. The strong exciton–phonon coupling disturbs and dephases the coherent phonons of the semiconducting 2H phase in MoTe2, and generates lattice distortions to further stabilize new coherent phonons of the metallic 1T’-phase, manifested by the emergence of the corresponding phonons in each phase. This structural transformation is fully reversible within a few picoseconds by switching on/off the laser. The nonlinear response of the phonon intensity to the excited carrier density in the intermediate region indicates a gradual structural transformation through coexisting 2H and 1T’ phases.

Open dynamics of entanglement in mesoscopic bosonic systems

A key issue in quantum information is finding an adequate description of mesoscopic systems that is simpler than full quantum formalism yet retains crucial information about non-classical phenomena like entanglement. In particular, the study of fully bosonic systems undergoing open evolution is of great importance for the advancement of photonic quantum computing and communication. In this paper, we propose a mesoscopic description of such systems based on boson number correlations. This description allows for tracking Markovian open evolution of entanglement of both non-Gaussian and Gaussian states and their sub-Poissonian statistics. It can be viewed as a generalization of the reduced state of the field formalism (Alicki 2019 Entropy 21 705), which by itself does not contain information about entanglement. As our approach adopts the structure of the description of two particles in terms of first quantization, it allows for broad intuitive usage of known tools. Using the proposed formalism, we show the robustness of entanglement against low-temperature damping for four-mode bright squeezed vacuum state and beam-splitted single photon. We also present a generalization of the Mandel Q parameter. Building upon this, we show that the entanglement of the state obtained by beam splitting of a single occupied mode is inherited from sub-Poissonian statistics of the input state.

Supercurrent noise in a phase-biased superconductor-normal ring in thermal equilibrium

In superconductor-normal-superconductor (SNS) junctions, supercurrent is mediated via Andreev bound states (ABSs) controlled by the phase difference between the two superconductors. Theory has long predicted significant noise of such supercurrent in equilibrium, due to thermal excitation between the ABSs. Via the fluctuation-dissipation theorem (FDT), this leads to a finite dissipative conductance that coexists with the supercurrent but is hidden in dc measurement. Here, we directly measure the supercurrent noise at radio frequency in a phase-biased SNS ring inductively coupled to a superconducting resonator. We also measure the admittance of the same system whose real part is the dissipative conductance, and quantitatively verify the FDT relation of the SNS ring. The dissipative conductance shows a 1/T temperature dependence, in contrast to the Drude conductance of an unproximitized metal. Using linear response theory, we attribute this behavior to the enhanced current correlations due to the electron-hole symmetry imposed by the proximity effect. Our results reveal the commonly overlooked role of thermal fluctuations in superconducting hybrid systems and other mesoscopic or quantum material systems with large orbital susceptibility. Published by the American Physical Society 2024

Quantum Measurement Classification Using Statistical Learning

Interpreting the results of a quantum computer can pose a significant challenge due to inherent noise in these mesoscopic quantum systems. Quantum measurement, a critical component of quantum computing, involves determining the probabilities linked with quantum states post-multiple circuit computations based on quantum readout values provided by hardware. While there are promising classification-based solutions, they can either misclassify or necessitate excessive measurements, thereby proving to be costly. This article puts forth an efficient method to discern the quantum state by analyzing the probability distributions of data post-measurement. Specifically, we employ cumulative distribution functions to juxtapose the measured distribution of a sample against the distributions of basis states. The efficacy of our approach is demonstrated through experimental results on a superconducting transmon qubit architecture, which shows a substantial decrease (88%) in single qubit readout error compared to state-of-the-art measurement techniques. Moreover, we report additional error reduction (12%) compared to state-of-the-art measurement techniques when our technique is applied to enhance existing multi-qubit classification techniques. We also demonstrate the applicability of our proposed method for higher dimensional quantum systems, including classification of single qutrits as well as multiple qutrits.

Investigation of Conductance and Capacitance of Mesoscopic and Molecular Scale Systems

Investigation of Conductance and Capacitance of Mesoscopic and Molecular Scale Systems

Minimum Information Variability in Linear Langevin Systems via Model Predictive Control.

Controlling the time evolution of a probability distribution that describes the dynamics of a given complex system is a challenging problem. Achieving success in this endeavour will benefit multiple practical scenarios, e.g., controlling mesoscopic systems. Here, we propose a control approach blending the model predictive control technique with insights from information geometry theory. Focusing on linear Langevin systems, we use model predictive control online optimisation capabilities to determine the system inputs that minimise deviations from the geodesic of the information length over time, ensuring dynamics with minimum "geometric information variability". We validate our methodology through numerical experimentation on the Ornstein-Uhlenbeck process and Kramers equation, demonstrating its feasibility. Furthermore, in the context of the Ornstein-Uhlenbeck process, we analyse the impact on the entropy production and entropy rate, providing a physical understanding of the effects of minimum information variability control.

Temperature fluctuations in mesoscopic systems.

Temperature is a fundamental concept in thermodynamics. In macroscopic thermodynamics, systems possess their own intrinsic temperature which equals the reservoir temperature when they equilibrate. In stochastic thermodynamics for simple systems at the microscopic level, thermodynamic quantities other than temperature (a deterministic parameter of the reservoir) are stochastic. To bridge the disparity in the perspectives about temperature between the micro- and macroregimes, we assign a generic mesoscopic N-body system an intrinsic fluctuating temperature T in this work. We simplify the complicated dynamics of numerous particles to one stochastic differential equationwith respect to T, where the noise term accounts for finite-size effects arising from random energy transfer between the system and the reservoir. Our analysis reveals that these fluctuations make the extensive quantities (in the thermodynamic limit) deviate from being extensive. Moreover, we derive finite-size corrections, characterized by heat capacity of the system, to the Jarzynski equality. A possible violation of the principle of maximum work that scales with N^{-1} is also discussed. Additionally, we examine the impact of temperature fluctuations in a finite-size Carnot engine. We show that irreversible entropy production resulting from the temperature fluctuations of the working substance diminishes the average efficiency of the cycle as η_{C}-〈η〉∼N^{-1}, highlighting the unattainability of the Carnot efficiency η_{C} for mesoscopic heat engines even under the quasistatic limit. Our general framework paves the way for further exploration of nonequilibrium thermodynamics and the corresponding finite-size effects in a mesoscopic regime.

Shortcuts of Freely Relaxing Systems Using Equilibrium Physical Observables.

Many systems, when initially placed far from equilibrium, exhibit surprising behavior in their attempt toequilibrate. Striking examples are the Mpemba effect and the cooling-heating asymmetry. These anomalous behaviors can be exploited to shorten the time needed to cool down (or heat up) a system. Though, a strategy to design these effects in mesoscopic systems is missing. We bring forward a description that allows us to formulate such strategies, and, along the way, makes natural these paradoxical behaviors. In particular, we study the evolution of macroscopic physical observables of systems freely relaxing under the influence of one or two instantaneous thermal quenches. The two crucial ingredients in our approach are timescale separation and a nonmonotonic temperature evolution of an important state function. We argue that both are generic features near a first-order transition. Our theory is exemplified with the one-dimensional Ising model in a magnetic field using analytic results and numerical experiments.

Experimental study on mechanical properties and evolution of fracture failure of granite considering initial temperature damage and prefabricated fissure length

With the increasing mining depth of deep mineral resources, the influence of high temperature on the fracture failure of surrounding rock with fissures becomes increasingly serious. To investigate the fracture mechanical properties and the evolutionary characteristics of fracture process of fissured rock experiencing initial temperature damage, the three-point bending experiment was conducted on the small sized granite samples with different initial damaging temperatures and prefabricated fissure lengths by applying a μTS mesoscopic testing system. Based on the digital image correlation methods, the evolution of fracture process zone of sample was revealed. Moreover, the macroscopic fracture failure of sample was predicted by the evolution of time-varying acoustic emission b-value. The results show that with the increase of initial damaging temperature and prefabricated fissure length, the peak load of granite sample has a decreasing trend and the fracture characteristics of sample experiencing initial temperature damage gradually transform from brittleness fracture to ductility fracture, resulting in a decrease in the fracture toughness of sample. The crack tip opening displacement, the critical opening displacement and the length of fracture process zone of sample all show an increasing trend. The time interval from the time when time-varying acoustic emission b-value continues to decline to the appearance moment of macroscopic cracks is selected as the warning time of fracture failure of sample, which can conclude that the warning time increases with increasing the initial damaging temperature and prefabricated fissure length, indicating an increase in ductility and a reduction in failure suddenness of sample.

Snapshot volumetric imaging using rainbow-sheet illumination

Traditional lens-based three-dimensional imaging methods struggle with speed, spatial resolution, field of view, and depth of field (DOF). Here, we propose a volumetric imaging method that combines rainbow-sheet illumination, chromatic-aberration-induced DOF extension, and compressive hyperspectral imaging to optically section transparent objects over 200 depth slices in a single snapshot. A proof-of-concept mesoscopic system with a lateral resolution of 12.7 line pairs per millimeter and a depth resolution of roughly 140 μm in a volume of 10 × 10 × 10 mm3 is constructed. The practicality of the suggested method is demonstrated by dynamic volumetric imaging of a transparent jellyfish at a rate of 15 volumes per second.

Machine learning at the mesoscale: A computation-dissipation bottleneck.

The cost of information processing in physical systems calls for a trade-off between performance and energetic expenditure. Here we formulate and study a computation-dissipation bottleneck in mesoscopic systems used as input-output devices. Using both real data sets and synthetic tasks, we show how nonequilibrium leads to enhanced performance. Our framework sheds light on a crucial compromise between information compression, input-output computation and dynamic irreversibility induced by nonreciprocal interactions.