اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدChemo-Mechanical Model of SEI Growth on Silicon
51
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Silicon anodes promise high energy densities of next-generation lithium-ion batteries, but suffer from shorter cycle life. The accelerated capacity fade stems from the repeated fracture and healing of the solid-electrolyte interphase (SEI) on the silicon surface. This interplay of chemical and mechanical effects in SEI on silicon electrodes causes a complex aging behavior. However, so far, no model mechanistically captures the interrelation between mechanical SEI deterioration and accelerated SEI growth. In this article, we present a thermodynamically consistent continuum model of an electrode particle surrounded by an SEI layer. The silicon particle model consistently couples chemical reactions, physical transport, and elastic deformation. The SEI model comprises elastic and plastic deformation, fracture, and growth. Capacity fade measurements and in-situ mechanical SEI measurements provide validation for our model. For the first time, we model the influence of cycling rate on the long-term mechanical SEI deterioration and regrowth. Our model predicts the experimentally observed transition in time dependence from square-root-of-time growth during battery storage to linear-in-time growth during continued cycling. Thereby our model unravels the mechanistic dependence of battery aging on operating conditions and supports the efforts to prolong the battery life of next-generation lithium-ion batteries.
قيم البحث
اقرأ أيضاً
The solid electrolyte interphase (SEI) is detrimental for rechargeable batteries performance and lifetime. Understanding its formation requires analytical techniques that provide molecular level insight. Here dynamic nuclear polarization (DNP) is uti
lized for the first time for enhancing the sensitivity of solid state NMR (ssNMR) spectroscopy to the SEI. The approach is demonstrated on reduced-graphene oxide (rGO) cycled in Li-ion cells in natural abundance and 13C-enriched electrolyte solvents. Our results indicate that DNP enhances the signal of outer SEI layers, enabling detection of natural abundance 13C spectra from this component of the SEI at reasonable timeframes. Furthermore, 13C- enriched electrolytes measurements at 100K provide ample sensitivity without DNP due to the vast amount of SEI filling the rGO pores, thereby allowing differentiating the inner and outer SEI layers composition. Developing this approach further will benefit the study of many electrode materials, equipping ssNMR with the needed sensitivity to efficiently probe the SEI.
Ranking the binding of small molecules to protein receptors through physics-based computation remains challenging. Though inroads have been made using free energy methods, these fail when the underlying classical mechanical force fields are insuffici
ent. In principle, a more accurate approach is provided by quantum mechanical density functional theory (DFT) scoring, but even with approximations, this has yet to become practical on drug discovery-relevant timescales and resources. Here, we describe how to overcome this barrier using algorithms for DFT calculations that scale on widely available cloud architectures, enabling full density functional theory, without approximations, to be applied to protein-ligand complexes with approximately 2500 atoms in tens of minutes. Applying this to a realistic example of 22 ligands binding to MCL1 reveals that density functional scoring outperforms classical free energy perturbation theory for this system. This raises the possibility of broadly applying fully quantum mechanical scoring to real-world drug discovery pipelines.
We show, through a machine learning approach, that the equilibrium distance, harmonic vibrational frequency, and binding energy of diatomic molecules are universally related. In particular, the relationships between spectroscopic constants are valid
independently of the molecular bond. However, they depend strongly on the group and period of the constituent atoms. As a result, we show that by employing the group and period of atoms within a molecule, the spectroscopic constants are predicted with an accuracy of $lesssim 5%$. Finally, the same universal relationships are satisfied when spectroscopic constants from {it ab initio} and density functional theory (DFT) electronic structure methods are employed.
A theoretical framework is developed to describe experiments on the structure of epitaxial thin films, particularly niobium on sapphire. We extend the hypothesis of dynamical scaling to apply to the structure of thin films from its conventional appli
cation to simple surfaces. We then present a phenomenological continuum theory that provides a good description of the observed scattering and the measured exponents. Finally the results of experiment and theory are compared.
The influence of morphology on the optical properties of silver nanoparticles is studied. A general relationship between the surface plasmon resonances and the morphology of each nanoparticle is established. The optical response is investigated for c
ubes and decahedrons with different truncations. We found that polyhedral nanoparticles composed with less faces show more surface plasmon resonances than spherical-like ones. It is also observed that the vertices of the nanoparticles play an important role in the optical response, because the sharpener they become, the greater the number of resonances. For all the nanoparticles, a main resonance with a dipolar character was identified as well as other secondary resonances of less intensity. It is also found that as the nanoparticle becomes more symmetric, the main resonance is always blue shifted.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
