Binary Solvent Induced Stable Interphase Layer for Ultra-Long Life Sodium Metal Batteries.

Sodium foil, promising for high-energy-density batteries, faces reversibility challenges due to its inherent reactivity and unstable solid electrolyte interphase (SEI) layer. In this study, a stable sodium metal battery (SMB) is achieved by tuning the electrolyte solvation structure through the addition of co-solvent 2-methyl tetrahydrofuran (MTHF) to diglyme (Dig). The introduction of cyclic ether-based MTHF results in increased anion incorporation in the solvation structure, even at lower salt concentrations. Specifically, the anion stabilization capabilities of the environmentally sustainable MTHF co-solvent lead to a contact-ion pair-based solvation structure. Time-of-flight mass spectroscopy analysis reveals that a shift toward an anion-dominated solvation structure promotes the formation of a thin and uniform SEI layer. Consequently, employing a NaPF6-based electrolyte with a Dig:MTHF ratio of 50% (v/v) binary solvent yields an average Coulombic efficiency of 99.72% for 300 cycles in Cu||Na cell cycling. Remarkably, at a C/2 cycling rate, Na||Na symmetric cell cycling demonstrates ultra-long-term stability exceeding 7000 h, and full cells with Na0.44MnO2 as a cathode retain 80% of their capacity after 500 cycles. This study systematically examines solvation structure, SEI layer composition, and electrochemical cycling, emphasizing the significance of MTHF-based binary solvent mixtures for high-performance SMBs.

Discovering Content through Text Mining for a Synthetic Biology Knowledge System.

Scientific articles contain a wealth of information about experimental methods and results describing biological designs. Due to its unstructured nature and multiple sources of ambiguity and variability, extracting this information from text is a difficult task. In this paper, we describe the development of the synthetic biology knowledge system (SBKS) text processing pipeline. The pipeline uses natural language processing techniques to extract and correlate information from the literature for synthetic biology researchers. Specifically, we apply named entity recognition, relation extraction, concept grounding, and topic modeling to extract information from published literature to link articles to elements within our knowledge system. Our results show the efficacy of each of the components on synthetic biology literature and provide future directions for further advancement of the pipeline.

Atomistic Mechanisms of Binary Alloy Surface Segregation from Nanoseconds to Seconds Using Accelerated Dynamics.

Although the equilibrium composition of many alloy surfaces is well understood, the rate of transient surface segregation during annealing is not known, despite its crucial effect on alloy corrosion and catalytic reactions occurring on overlapping timescales. In this work, CuNi bimetallic alloys representing (100) surface facets are annealed in vacuum using atomistic simulations to observe the effect of vacancy diffusion on surface separation. We employ multi-timescale methods to sample the early transient, intermediate, and equilibrium states of slab surfaces during the separation process, including standard MD as well as three methods to perform atomistic, long-time dynamics: parallel trajectory splicing (ParSplice), adaptive kinetic Monte Carlo (AKMC), and kinetic Monte Carlo (KMC). From nanosecond (ns) to second timescales, our multiscale computational methodology can observe rare stochastic events not typically seen with standard MD, closing the gap between computational and experimental timescales for surface segregation. Rapid diffusion of a vacancy to the slab is resolved by all four methods in tens of nanoseconds. Stochastic re-entry of vacancies into the subsurface, however, is only seen on the microsecond timescale in the two KMC methods. Kinetic vacancy trapping on the surface and its effect on the segregation rate are discussed. The equilibrium composition profile of CuNi after segregation during annealing is estimated to occur on a timescale of seconds as determined by KMC, a result directly comparable to nanoscale experiments.

Synthetic Biology Knowledge System.

The Synthetic Biology Knowledge System (SBKS) is an instance of the SynBioHub repository that includes text and data information that has been mined from papers published in ACS Synthetic Biology. This paper describes the SBKS curation framework that is being developed to construct the knowledge stored in this repository. The text mining pipeline performs automatic annotation of the articles using natural language processing techniques to identify salient content such as key terms, relationships between terms, and main topics. The data mining pipeline performs automatic annotation of the sequences extracted from the supplemental documents with the genetic parts used in them. Together these two pipelines link genetic parts to papers describing the context in which they are used. Ultimately, SBKS will reduce the time necessary for synthetic biologists to find the information necessary to complete their designs.