Framework Of Stochastic Thermodynamics Research Articles

Variational time reversal for free-energy estimation in nonequilibrium steady states.

Studying the structure of systems in nonequilibrium steady states necessitates tools that quantify population shifts and associated deformations of equilibrium free-energy landscapes under persistent currents. Within the framework of stochastic thermodynamics, we establish a variant of the Kawasaki-Crooks equality that relates nonequilibrium free-energy corrections in overdamped Langevin systems to heat dissipation statistics along time-reversed relaxation trajectories computable with molecular simulation. Using stochastic control theory, we arrive at a general variational approach to evaluate the Kawasaki-Crooks equality and use it to estimate distribution functions of order parameters in specific models of driven and active matter, attaining substantial improvement in accuracy over simple perturbative methods.

Thermodynamics of a collisional quantum-dot machine: the role of stages

Abstract Sequential (or collisional) engines have been put forward as an alternative candidate for the realisation of reliable engine setups. Despite this, the role of the different stages and the influence of the intermediate reservoirs is not well understood. We introduce the idea of conveniently adjusting/choosing intermediate reservoirs at engine devices as a strategy for optimizing its performance. This is done by considering a minimal model composed of a quantum-dot machine sequentially exposed to various reservoirs at each stage, and for which thermodynamic quantities (including power and efficiency) can be obtained exactly from the framework of stochastic thermodynamics, irrespective the number of stages. Results show that a significant gain can be obtained by increasing the number of stages and conveniently choosing their parameters.

General theory for localizing the where and when of entropy production meets single-molecule experiments

The laws of thermodynamics apply to biophysical systems on the nanoscale as described by the framework of stochastic thermodynamics. This theory provides universal, exact relations for quantities like work, which have been verified in experiments where a fully resolved description allows direct access to such quantities. Complementary studies consider partially hidden, coarse-grained descriptions, in which the mean entropy production typically is not directly accessible but can be bounded in terms of observable quantities. Going beyond the mean, we introduce a fluctuating entropy production that applies to individual trajectories in a coarse-grained description under time-dependent driving. Thus, this concept is applicable to the broad and experimentally significant class of driven systems in which not all relevant states can be resolved. We provide a paradigmatic example by studying an experimentally verified protein unfolding process. As a consequence, the entire distribution of the coarse-grained entropy production rather than merely its mean retains spatial and temporal information about the microscopic process. In particular, we obtain a bound on the distribution of the physical entropy production of individual unfolding events.

Estimate of entropy production rate can spatiotemporally resolve the active nature of cell flickering

We use the short-time inference scheme [Manikandan , ], obtained within the framework of stochastic thermodynamics, to infer a lower bound to entropy production rate from flickering data generated by interference reflection microscopy of HeLa cells. We can clearly distinguish active cell membranes from their adenosine-triphosphate-depleted selves and even spatiotemporally resolve activity down to the scale of about 1 µm. Our estimate of activity is . Published by the American Physical Society 2024

Entropy production of multivariate Ornstein-Uhlenbeck processes correlates with consciousness levels in the human brain.

Consciousness is supported by complex patterns of brain activity which are indicative of irreversible nonequilibrium dynamics. While the framework of stochastic thermodynamics has facilitated the understanding of physical systems of this kind, its application to infer the level of consciousness from empirical data remains elusive. We faced this challenge by calculating entropy production in a multivariate Ornstein-Uhlenbeck process fitted to Functional magnetic resonance imaging brain activity recordings. To test this approach, we focused on the transition from wakefulness to deep sleep, revealing a monotonous relationship between entropy production and the level of consciousness. Our results constitute robust signatures of consciousness while also advancing our understanding of the link between consciousness and complexity from the fundamental perspective of statistical physics.

Revisiting the Monge Problem in the Landauer Limit

We discuss the Monge problem of mass transportation in the framework of stochastic thermodynamics and revisit the problem of the Landauer limit for finite-time thermodynamics, a problem that got the interest of Krzysztof Gawedzki in the last years. We show that restricted to one dimension, optimal transportation is efficiently solved numerically by well-known methods from differential equations. We add a brief discussion about the relevance this has on optimising the processing in modern computers.

Skewness and kurtosis in stochastic thermodynamics

The thermodynamic uncertainty relation is a prominent result in stochastic thermodynamics that provides a bound on the fluctuations of any thermodynamic flux, also known as current, in terms of the average rate of entropy production. Such fluctuations are quantified by the second moment of the probability distribution of the current. The role of higher order standardized moments such as skewness and kurtosis remains largely unexplored. We analyze the skewness and kurtosis associated with the first passage time of thermodynamic currents within the framework of stochastic thermodynamics. We develop a method to evaluate higher order standardized moments associated with the first passage time of any current. For systems with a unicyclic network of states, we conjecture upper and lower bounds on skewness and kurtosis associated with entropy production. These bounds depend on the number of states and the thermodynamic force that drives the system out of equilibrium. We show that these bounds for skewness and kurtosis do not hold for multicyclic networks. We discuss the application of our results to infer an underlying network of states.

Maximal power for heat engines: Role of asymmetric interaction times

The performance of endoreversible thermal machines operating at finite power constitutes one of the main challenges of nonequilibrium classical and quantum thermodynamics, engineering and others. We introduce the idea of adjusting the interaction time asymmetry in order to optimize the engine performance. We consider one of the simplest thermal machines, composed of a quantum dot interacting sequentially with two different reservoirs of heat and particles. Distinct optimization protocols are analyzed in the framework of stochastic thermodynamics. Results reveal that asymmetric interaction times play a fundamental role in enhancing the power output and that maximizations can provide an increase larger than 25\% the symmetric case. As an extra advantage, efficiencies at maximum power are slightly greater than the endoreversible Curzon-Ahlborn efficiency for a broad range of reservoir temperatures.

Underdamped stochastic thermodynamic engines in contact with a heat bath with arbitrary temperature profile.

We study thermodynamic processes in contact with a heat bath that may have an arbitrary time-varying periodic temperature profile. Within the framework of stochastic thermodynamics, and for models of thermodynamic engines in the idealized case of underdamped particles in the low-friction regime subject to a harmonic potential, we derive explicit bounds as well as optimal control protocols that draw maximum power and achieve maximum efficiency at any specified level of power.

Stochastic Energetics and Thermodynamics at Small Scales

At very small scales, thermodynamic energy exchanges like work and heat become comparable to the thermal energy of the system, which leads to unusual phenomena like the transient violations of the second law. We explore the generic characters of such systems using the framework of stochastic thermodynamics and provide a preliminary overview of the basic concepts. Here, we have attempted to put into simple terms some actively pursued topics like the arrow of time, effect of information gain on second law, explanation of the origin of life using Crooks theorem, and the thermodynamic uncertainty relations.

Stochastic Discrete Time Crystals: Entropy Production and Subharmonic Synchronization

Discrete time crystals are periodically driven systems that display spontaneous symmetry breaking of time translation invariance in the form of indefinite subharmonic oscillations. We introduce a thermodynamically consistent model for a discrete time crystal and analyze it using the framework of stochastic thermodynamics. In particular, we evaluate the rate of energy dissipation of this many-body system of interacting noisy subharmonic oscillators in contact with a heat bath. The mean-field model displays the phenomenon of subharmonic synchronization, which corresponds to collective subharmonic oscillations of the individual units. The 2D model does not display synchronization but it does show a time-crystalline phase, which is characterized by a power-law behavior of the number of coherent subharmonic oscillations with system size. This result demonstrates that the emergence of coherent oscillations is possible even in the absence of synchronization.

Maximal power output of a stochastic thermodynamic engine

Classical thermodynamics aimed to quantify the efficiency of thermodynamic engines, by bounding the maximal amount of mechanical energy produced, compared to the amount of heat required. While this was accomplished early on, by Carnot and Clausius, the more practical problem to quantify limits of power that can be delivered, remained elusive due to the fact that quasistatic processes require infinitely slow cycling, resulting in a vanishing power output. Recent insights, drawn from stochastic models, appear to bridge the gap between theory and practice in that they lead to physically meaningful expressions for the dissipation cost in operating a thermodynamic engine over a finite time window. Indeed, the problem to optimize power can be expressed as a stochastic control problem. Building on this framework of stochastic thermodynamics we derive bounds on the maximal power that can be drawn by cycling an overdamped ensemble of particles via a time-varying potential while alternating contact with heat baths of different temperature (Tc cold, and Th hot). Specifically, assuming a suitable bound M on the spatial gradient of the controlling potential, we show that the maximal achievable power is bounded by M8(ThTc−1). Moreover, we show that this bound can be reached to within a factor of (ThTc−1)∕(ThTc+1) by operating the cyclic thermodynamic process with a quadratic potential.

Quantum Otto engine working with interacting spin systems: Finite power performance in stochastic thermodynamics.

A quantum Otto engine using two-interacting spins as its working medium is analyzed within framework of stochastic thermodynamics. The time-dependent power fluctuations and average power are explicitly derived for a complete cycle of engine operation. We find that the efficiency and power fluctuations are affected significantly by interparticle interactions, but both of them become interaction-independent under maximal power via optimizing the external control parameter. The behavior of the efficiency at maximum power is further explained by analyzing the optimal protocol of the engine.

Impact of Interparticle Interaction on Thermodynamics of Nano-Channel Transport of Two Species.

Understanding the function and control of channel transport is of paramount importance for cell physiology and nanotechnology. In particular, if several species are involved, the mechanisms of selectivity, competition, cooperation, pumping, and its modulation need to be understood. What lacks is a rigorous mathematical approach within the framework of stochastic thermodynamics, which explains the impact of interparticle in-channel interactions on the transport properties of the respective species. To achieve this, stochastic channel transport of two species is considered in a model, which different from mean field approaches, explicitly conserves the spatial correlation of the species within the channel by analysis of the stochastic dynamics within a state space, the elements of which are the channel’s spatial occupation states. The interparticle interactions determine the stochastic transitions between these states. Local flow and entropy production in this state space reveal the respective particle flows through the channel and the intensity of the Brownian ratchet like rectifying forces, which these species exert mutually on each other, together with its thermodynamic effectiveness and costs. Perfect coupling of transport of the two species is realized by an attractive empty channel and strong repulsive forces between particles of the same species. This confines the state space to a subspace with circular topology, in which the concentration gradients as thermodynamic driving forces act in series, and channel flow of both species becomes equivalent. For opposing concentration gradients, this makes the species with the stronger gradient the driving, positive entropy producing one; the other is driven and produces negative entropy. Gradients equal in magnitude make all flows vanish, and thermodynamic equilibrium occurs. A differential interparticle interaction with less repulsive forces within particles of one species but maintenance of this interaction for the other species adds a bypass path to this circular subspace. On this path, which is not involved in coupling of the two species, a leak flow of the species with less repulsive interparticle interaction emerges, which is directed parallel to its concentration gradient and, hence, produces positive entropy here. Different from the situation with perfect coupling, appropriate strong opposing concentration gradients may simultaneously parallelize the flow of their respective species, which makes each species produce positive entropy. The rectifying potential of the species with the bypass option is diminished. This implies the existence of a gradient of the other species, above which its flow and gradient are parallel for any gradient of the less coupled species. The opposite holds for the less coupled species. Its flow may always be rectified and turned anti-parallel to its gradient by a sufficiently strong opposing gradient of the other one.

Thermodynamic costs of dynamic function in active soft matter

Living matter combines complex structures and dissipative processes to achieve dynamic functions that rely on material organization in space and time. In this Review, we discuss recent progress in creating synthetic material systems capable of four such functions–keeping time, powering motion, building structures, and making copies. Chemical oscillators coordinate the temporal activity of material assemblies; molecular motors and active colloids convert chemical energy into mechanical forces and motions; chemical activation of self-assembling components provides temporal control over dissipative structures; information-rich nanomaterials replicate their structures in exponential fashion. These and other dynamic functions cannot be achieved at thermodynamic equilibrium but instead require flows of energy and matter to create and maintain spatiotemporal order. Such systems are captured within the framework of stochastic thermodynamics, which describes the fluctuating thermodynamic quantities of driven systems. Even far from equilibrium, these quantities obey universal relations, which establish fundamental trade-offs between the rate of energy dissipation and performance metrics such as precision, efficiency, and speed. For each function considered, we present a simple kinetic model that offers general insights that inform the design and creation of dissipative material systems capable of dynamic functions. Overall, we aim to bridge experimental efforts in active soft matter and theoretical advances from stochastic thermodynamics to inform future research on material systems inspired by living matter.

Bounds on current fluctuations in periodically driven systems

Small nonequilibrium systems in contact with a heat bath can be analyzed with the framework of stochastic thermodynamics. In such systems, fluctuations, which are not negligible, follow universal relations such as the fluctuation theorem. More recently, it has been found that, for nonequilibrium stationary states, the full spectrum of fluctuations of any thermodynamic current is bounded by the average rate of entropy production and the average current. However, this bound does not apply to periodically driven systems, such as heat engines driven by periodic variation of the temperature and artificial molecular pumps driven by an external protocol. We obtain a universal bound on current fluctuations for periodically driven systems. This bound is a generalization of the known bound for stationary states. In general, the average rate that bounds fluctuations in periodically driven systems is different from the rate of entropy production. We also obtain a local bound on fluctuations that leads to a trade-off relation between speed and precision in periodically driven systems, which constitutes a generalization to periodically driven systems of the so called thermodynamic uncertainty relation. From a technical perspective, our results are obtained with the use of a recently developed theory for 2.5 large deviations for Markov jump processes with time-periodic transition rates.

Fluctuation-Dissipation Relations Far from Equilibrium.

Near equilibrium, where all currents of a system vanish on average, the fluctuation-dissipation relation (FDR) connects a current's spontaneous fluctuations with its response to perturbations of the conjugate thermodynamic force. Out of equilibrium, fluctuation-response relations generally involve additional nondissipative contributions. Here, in the framework of stochastic thermodynamics, we show that an equilibriumlike FDR holds for internally equilibrated currents, if the perturbing conjugate force only affects the microscopic transitions that contribute to the current. We discuss the physical requirements for the validity of our result and apply it to nanosized electronic devices.

Maxwell’s demon and the management of ignorance in stochastic thermodynamics

It is nearly 150 years since Maxwell challenged the validity of the second law of thermodynamics by imagining a tiny creature who could sort the molecules of a gas in such a way that would decrease entropy without exerting any work. The demon has been discussed largely using thought experiments, but it has recently become possible to exert control over nanoscale systems, just as Maxwell imagined, and the status of the second law has become a more practical matter, raising the issue of how measurements manage our ignorance in a way that can be exploited. The framework of stochastic thermodynamics extends macroscopic concepts such as heat, work, entropy and irreversibility to small systems and allows us explore the matter. Some arguments against a successful demon imply a second law that can be suspended indefinitely until we dissipate energy in order to remove the records of his operations. In contrast, under stochastic thermodynamics, the demon fails because on average, more work is performed upfront in making a measurement than can be extracted by exploiting the outcome. This requires us to exclude systems and a demon that evolve under what might be termed self-sorting dynamics, and we reflect on the constraints on control that this implies while still working within a thermodynamic framework.

Double quantum dot coupled to a quantum point contact: a stochastic thermodynamics approach

We study the nonequilibrium properties of an electronic circuit composed of a double quantum dot (DQD) channel capacitively coupled to a quantum point contact (QPC) within the framework of stochastic thermodynamics. We show that the transition rates describing the dynamics satisfy a nontrivial local detailed balance and that the statistics of energy and particle currents across both channels obeys a fluctuation theorem. We analyze two regimes where the device operates as a thermodynamic machine and study its output power and efficiency fluctuations. We show that the electrons tunneling through the QPC without interacting with the DQD have a strong effect on the device efficiency.

Stochastic efficiency for effusion as a thermal engine

The stochastic efficiency of effusion as a thermal engine is investigated within the framework of stochastic thermodynamics. Explicit results are obtained for the probability distribution of the efficiency both at finite times and in the asymptotic regime of large deviations. The universal features, derived in Verley et al. (Nat. Commun., 5 (2014) 4721), are reproduced. The effusion engine is a good candidate for both the numerical and experimental verification of these predictions.