The efficient dissipation of localized heat flux by convection is a key request in several engineering applications, especially electronic ones. The recent advancements in manufacturing processes are unlocking the design and industrialization of heat exchangers with unprecedented geometric characteristics and, thus, performance. In this work, laser etching manufacturing technique is employed to develop metal surfaces with designed microstructured surface patterns. Such precise control of the solid-air interface (artificial roughness) allows to manufacture metal heat sinks with enhanced thermal transmittance with respect to traditional flat surfaces. Here, the thermal performance of these laser-etched devices is experimentally assessed by means of a wind tunnel in a fully turbulent regime. At the highest Reynolds number tested in the experiments ( R e L ≈ 16 , 500 ), elliptic scale-roughened surfaces show thermal transmittances improved by up to 81% with respect to heat sinks with flat surface. At similar testing conditions, cones patterns provide an enhancement in Nusselt number and thermal transmittance of up to 102% and 357%, respectively. The latter results are correlated with the main geometric and thermal fluid dynamics descriptors of the convective heat transfer process in order to achieve a predictive model of their performance. The experimental evidence shown in this work may encourage and guide a broader use of micro-patterned surfaces for enhancing convective heat transfer in heat exchangers.
The nature of the electrode–electrolyte interface has an impact on the performance and durability of lithium-ion batteries (LIBs). The initial electrolyte’s thermodynamic instability at the anode–electrolyte interface in LIBs results in the formation of a passivation layer, called solid electrolyte interphase (SEI). The initial dense and intact layer allows Li+ transport and restricts electron tunneling, thus preventing electrolyte decomposition and ensuring the electrochemical stability of a battery. However, the growth of this layer can reduce the availability of active lithium and electrolyte, and ultimately lead to an irreversible battery capacity fade. Investigating the transport phenomena of lithium ions within SEI is crucial for understanding its formation and growth. Nonetheless, accurately describing all relevant mechanisms is challenging due to its complex and multiscale nature. An overview of current computational efforts to study Li+ transport within SEI is given in this article, ranging from electronic/atomistic scale simulations to macroscopic models. The drawbacks and advantages of the proposed numerical approaches are summarized along with the obstacles that need to be overcome to obtain accurate experimental data, identified on the basis of the most recent literature evidence. We highlight collaboration gaps between modeling and experimental approaches, as well as the urgent need for new multiscale models, to gain a better understanding of such a crucial transport phenomenon.
Gold nanoparticles (AuNPs) have received great attention in a number of fields ranging from the energy sector to biomedical applications. As far as the latter is concerned, due to rapid renal clearance and a short lifetime in blood, AuNPs are often encapsulated in a poly(lactic-co-glycolic acid) (PLGA) matrix owing to its biocompatibility and biodegradability. A better understanding of the PLGA polymers on the AuNP surface is crucial to improve and optimize the above encapsulation process. In this study, we combine a number of computational approaches to explore the adsorption mechanisms of PLGA oligomers on a Au crystalline NP and to rationalize the PLGA coating process toward a more efficient design of the NP shape. Atomistic simulations supported by a recently developed unsupervised machine learning scheme show the temporal evolution and behavior of PLGA clusterization by tuning the oligomer concentration in aqueous solutions. Then, a detailed surface coverage analysis coupled with free energy landscape calculations sheds light on the anisotropic nature of PLGA adsorption onto the AuNP. Our results prove that the NP shape and topology may address and privilege specific sites of adsorption, such as the Au {1 1 1} crystal planes in selected NP samples. The modeling-based investigation suggested in this article offers a solid platform to guide the design of coated NPs.
Lithium-ion batteries (LIBs) have become an essential technology for the green economy transition, as they are widely used in portable electronics, electric vehicles, and renewable energy systems. The solid-electrolyte interphase (SEI) is a key component for the correct operation, performance, and safety of LIBs. The SEI arises from the initial thermal metastability of the anode-electrolyte interface, and the resulting electrolyte reduction products stabilize the interface by forming an electrochemical buffer window. This article aims to make a first -- but important -- step towards enhancing the parametrization of a widely-used reactive force field (ReaxFF) for accurate molecular dynamics (MD) simulations of SEI components in LIBs. To this end, we focus on Lithium Fluoride (LiF), an inorganic salt of great interest due to its beneficial properties in the passivation layer. The protocol relies heavily on various Python libraries designed to work with atomistic simulations allowing robust automation of all the reparameterization steps. The proposed set of configurations, and the resulting dataset, allow the new ReaxFF to recover the solid nature of the inorganic salt and improve the mass transport properties prediction from MD simulation. The optimized ReaxFF surpasses the previously available force field by accurately adjusting the diffusivity of lithium in the solid lattice, resulting in a two-order-of-magnitude improvement in its prediction at room temperature. However, our comprehensive investigation of the simulation shows the strong sensitivity of the ReaxFF to the training set, making its ability to interpolate the potential energy surface challenging. Consequently, the current formulation of ReaxFF can be effectively employed to model specific and well-defined phenomena by utilizing the proposed interactive reparameterization protocol to construct the dataset. Overall, this work represents a significant initial step towards refining ReaxFF for precise reactive MD simulations, shedding light on the challenges and limitations of ReaxFF force field parametrization. The demonstrated limitations emphasize the potential for developing more versatile and advanced force fields to upscale ab initio simulation through our interactive reparameterization protocol, enabling more accurate and comprehensive MD simulations in the future.
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.