We apply the concepts of stochastic thermodynamics combined with the transition state theory to develop a framework for evaluating local heat distributions across the assemblies of interacting magnetic nanoparticles (MP) subject to time-varying external magnetic fields. We show that additivity of entropy production in the particle state-space allows separating the entropy contributions and evaluating the heat produced by the individual MPs despite interactions. Using MP chains as a model system for convenience, without losing generality, we show that the presence of dipolar interactions leads to significant heat distributions across the chains. Our study also suggests that the typically used hysteresis loops cannot be used as a measure of energy dissipation at the local particle level within MP clusters, aggregates or assemblies, and explicit evaluation of entropy production based on appropriate theory, such as developed here, becomes necessary.
Developing the non-equilibrium thermodynamics of friction is required for systematic design of low friction surfaces for a broad range of technological applications. Intuitively, the thermodynamic work done by a material sliding along a surface is expected to be partially dissipated as heat and partially transformed into the change of the internal energy of the system. However, general nonequilibrium thermodynamic principles governing this separation are presently unknown. We develop a theoretical framework based on the transition state theory combined with the conventional Prandtl-Tomlinson model, allowing to set explicit expressions for evaluating the heat dissipation and internal energy change produced during the frictional stick-slip motion of a tip of a typical friction force microscope (FFM). We use the formalism to quantify the heat dissipation for a range of parameters relevant to materials in practical applications of nanoscale friction.
In optical DNA mapping technologies sequence-specific intensity variations (DNA barcodes) along stretched and stained DNA molecules are produced. These “fingerprints” of the underlying DNA sequence have a resolution of the order one kilobasepairs and the stretching of the DNA molecules are performed by surface adsorption or nano-channel setups. A post-processing challenge for nano-channel based methods, due to local and global random movement of the DNA molecule during imaging, is how to align different time frames in order to produce reproducible time-averaged DNA barcodes. The current solutions to this challenge are computationally rather slow. With high-throughput applications in mind, we here introduce a parameter-free method for filtering a single time frame noisy barcode (snap-shot optical map), measured in a fraction of a second. By using only a single time frame barcode we circumvent the need for post-processing alignment. We demonstrate that our method is successful at providing filtered barcodes which are less noisy and more similar to time averaged barcodes. The method is based on the application of a low-pass filter on a single noisy barcode using the width of the Point Spread Function of the system as a unique, and known, filtering parameter. We find that after applying our method, the Pearson correlation coefficient (a real number in the range from -1 to 1) between the single time-frame barcode and the time average of the aligned kymograph increases significantly, roughly by 0.2 on average. By comparing to a database of more than 3000 theoretical plasmid barcodes we show that the capabilities to identify plasmids is improved by filtering single time-frame barcodes compared to the unfiltered analogues. Since snap-shot experiments and computational time using our method both are less than a second, this study opens up for high throughput optical DNA mapping with improved reproducibility.
Developing the thermodynamics of nanoscale friction is needed in a wide range of tribological applications, where the key objective is to optimally control the energy dissipation. Here we show that the modern stochastic thermodynamics allows interpreting the measurements obtained by the friction force microscopy, which is the standard tool for investigating frictional properties of materials, in terms of basic thermodynamics concepts such as the fluctuating work and entropy. We show that this allows the identification of the heat produced during the friction process as an unambiguous measure of thermodynamic irreversibility. We have applied this procedure to quantify the heat produced during the frictional sliding in a broad velocity range, and observe velocity-dependent scaling behaviour, which is useful for interpreting the experimental outcomes.
macroscopic scales. This calls for a datadriven approach implementing efficient and accurate multi-scale modelling techniques to inform and interpret laboratory experiments. These include the lateralforce atomic force microscopy (AFM), which is the key experimental tool used for quantifying the nanoscale friction processes. [4] In particular, AFM is able to record the mechanical force exerted by a crystalline surface onto a nano-scale asperity dragged on top of it. Common computational models frequently used to study the microscopic laws of friction include quantum-mechanical first principles calculations, atomistic models based on molecular dynamics, nonlinear Prandtl-Tomlinson (PT) or Frenkel-Kontrova models, agentbased earthquake models, and models based on continuum mechanics applicable at macroscopic scales. The multi-scale modeling approach requires interfacing typically two or more of these levels of modeling into a systematic framework. [5] So far, the most refined multi-scale modelling of atomic friction has combined first principles calculations with molecular dynamics methods. [6,7] While highly accurate, this methodology requires significant computational resources, which restricts its applicability to relatively short time-and length-scales. More importantly, the lack of reliable force fields for the majority of materials is a major limitation for the transferability needed for new-materials screenings.The mesoscopic to macroscopic scale range of the friction processes has been studied by bridging atomistic molecular dynamics, linear response theory, and continuum mechanics into a unified multi-scale approach. [8] However, continuum theories inherently exclude the possibility of thermal and structural fluctuations and their applicability to the nanoscale friction range becomes problematic, as it is inherently a farfrom-equilibrium phenomenon dominated by such fluctuations and size effects. [9] Instead, it is often more fruitful to employ non-equilibrium statistical mechanics combined with transition state theory or stochastic Langevin dynamics. [10] This approach has been successful in generalizing, for example, the classical PT model to describe the thermally activated nano-scale friction in AFM experiments, [11][12][13][14][15] which qualitatively captured the velocity, load, and temperature dependencies observed in experiments. [13,16,17] To advance this statistical level of modeling requires incorporating the fundamental ability to describe the nanoscale frictional behavior of specific materials, which is the main objective in this work.The main result of this work is a fully consistent thermally activated thermodynamic model, which combines mesoscopic A multi-scale computational framework suitable for designing solid lubricant interfaces fully in silico is presented. The approach is based on stochastic thermodynamics founded on the classical thermally activated 2D Prandtl-Tomlinson model, linked with first principles methods to accurately capture the properties of real materials. It allows inves...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.