Archive for November, 2019

How does LiNO3 Make Lithium–Sulfur Batteries Long-Lasting?

Lithium–sulfur (Li–S) batteries are rechargeable batteries with elemental sulfur and metallic lithium as the cathode and anode, respectively. These batteries are promising electrochemical energy storage devices because their energy densities are three to five times higher than those of Li-ion batteries. Unfortunately, the practicality of Li–S batteries is hindered by their short lifetimes due to two processes that occur on the Li anode surface: the growth of Li dendrites and the irreversible polysulfide reduction. Adding LiNO3 into battery electrolytes has proven to be useful to prolong battery lifetimes, but the underlying mechanism is uncertain.

In Chemical Communications (doi: 10.1039/c9cc06504k), Sawangphruk and coworkers from Vidyasirimedhi Institute of Science and Technology, Thailand have offered valuable insights to settle the dispute over the effects of LiNO3. The researchers performed theoretical reactive molecular dynamics simulations and elucidated two roles of LiNO3 in Li–S batteries.

The first discovery was that LiNO3 promoted the formation of smooth, double-layered solid electrolyte interfaces (SEIs) on the Li surface. SEIs are thin layers composed of electrolyte-decomposition products, including Li-containing organic compounds and inorganic salts. By simulating the charge distribution near a Li metal surface, the authors mapped the Li-Li radial pair distribution profiles in three phases (Fig. 1a). The similarity between the profiles of the dense phase (the Li metal) and the nest phase evidenced the presence of an amorphous, Li-containing layer atop the Li metal surface. Beyond this amorphous layer was a liquid-like film with Li element distributed homogenously. This double-layered SEI altered the kinetics of Li deposition onto the Li surface upon charging, resulting in smooth and dense SEIs (Figs. 1b and c) that avoided Li dendrite formation.

Figure 1. (a) Li-Li radial pair distribution functions of the dense phase (Li metal), nest phase (the layer atop Li), and disperse phase (the outermost layer). (b and c) Top-view scanning electron microscopy images of the Li metal surface in (b) LiNO3-free and (c) LiNO3-containing electrolytes. Both electrolytes had lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) as a solute, and 1,3-dioxolane (DOL) and 1,2-dimethoxyethane (DME) as solvents.

Another effect of LiNO3 was to capture polysulfide compounds. Through their simulations, the authors deduced the reaction pathways involving the electrolyte molecules, LiNO3 or LiClO4 additives, and lithium polysulfide compounds (Fig. 2a). The concentration of LixNOy, the reduction products of LiNO3 when contacted Li metal, in the LiNO3-containing electrolyte was much higher than those in the additive-free and LiClO4-containing electrolytes. First-principle calculations proved that the highly electro-negative N and O atoms in LixNOy could capture lithium polysulfides via dipole-dipole interactions. This process reduced the likelihood of polysulfide reduction on Li that passivated anodes.

Figure 2. (a) A scheme of the reaction pathways involving the electrolyte, additive, and polysulfide molecules. (b) Product distributions in electrolytes without additives and with LiNO3 or LiClO4.

LiNO3 elongates the lifetimes of Li–S batteries by forming smooth SEIs to impede Li dendrite formation, while maintaining the reactivity of Li anodes by capturing lithium polysulfides.

 

To find out more, please read:

Insight into the Effect of Additives Widely Used in Lithium–Sulfur Batteries

Salatan Duangdangchote, Atiweena Krittayavathananon, Nutthaphon Phattharasupakun, Nattanon Joraleechanchai, and Montree Sawangphruk

Chem. Commun., 2019, 55, 13951-13954

Tianyu Liu acknowledges John Elliott of Virginia Tech, the U.S., for his careful proofreading of this post.

About the blogger:

Tianyu Liu obtained his Ph.D. (2017) in Chemistry from the University of California, Santa Cruz, in the United States. He is passionate about the communication of scientific endeavors to both the general public and other scientists with diverse research expertise to introduce cutting-edge research to broad audiences. He is a blog writer for Chem. Commun. and Chem. Sci. More information about him can be found at http://liutianyuresearch.weebly.com/.

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Designing Syntheses with Machine Learning

I don’t know if you’ve looked at the structure of pharmaceuticals recently, but most novel drugs are rather complicated. Identifying promising new targets is just the start for synthetic chemists; they then need to figure out how to use a series of reactions to take simple (and commercially available) molecules and transform them into a new drug. They also must predict all possible side reactions and products given a set of reaction conditions, particularly when a range of functional groups are involved. Historic approaches involved manual curation of reaction rules, limited by personal experience and the state of the accessed chemical literature. Newer approaches seek to create templates directly from data but are defined by available data sets and cannot reliably extrapolate. The emergence of machine learning offers the opportunity to move beyond traditional templating and atom mapping of reactants to products. It also offers to take full advantage of novel technologies and address problems with dataset bias and ineffective modeling systems.

In a collaboration between academics in the UK and industrial scientists in the US, researchers used Molecular Transformer, an attention-based machine translation model, to perform both reaction prediction and retrosynthesis analysis after training on a publicly available dataset. Instead of atom mapping, which moves atoms from the reactants to the products, Molecular Transformer (MT) relies on SMILES text strings, which represent structures in a line format. A unique aspect of this work is the validation and training performed using proprietary data of drug targets from Pfizer. They used three datasets: the first a literature standard from the US Patent and Trade Office (USPTO), the second from internal medicinal chemistry projects in Pfizer, and the final a diverse range of 50,000 reactions from US patents (USPTO-R). Building on previous research from the authors, they trained the MT on both the Pfizer data and the initial USPTO data sets. They found that the Pfizer data provided the most accurate product predictions and that the MT could also return a confidence rating to determine the probability the prediction is correct.

Figure 1. Sample syntheses predicted by Molecular Transformer for various bioactive molecules of interest.

While synthesis predictions can easily be checked, it’s harder to confirm accuracy with retrosynthesis since there is not a single correct answer. The researchers used the broad USPTO-R to train MT, which consistently outperformed both a benchmark template-based program and another literature machine learning method also trained on USPTO-R. When tested on the Pfizer dataset, the MT performed best with 31.5% accuracy despite the datasets coming from different regions of chemical space (which increased to 91% when MT was trained on Pfizer data). Figure 1 shows several predicted routes for the synthesis of bioactive molecules as predicted by MT, which generally agree with established syntheses. These data suggest the highly generalizable nature of MT as a tool for developing novel pharmaceutically interesting molecules.

To find out more, please read:

Molecular Transformer unifies reaction prediction and retrosynthesis across pharma chemical space

Alpha A. Lee, Qingyi Yang, Vishnu Sresht, Peter Bolgar, Xinjun Hou, Jacquelyn L. Klug-McLeod and Christopher R. Butler

Chem. Commun., 2019, 55, 12152-12155.

About the blogger:

Beth Mundy is a PhD candidate in chemistry in the Cossairt lab at the University of Washington in Seattle, Washington. Her research focuses on developing new and better ways to synthesize nanomaterials for energy applications. She is often spotted knitting in seminars or with her nose in a good book. You can find her on Twitter at @BethMundySci.

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The Cram Lehn Pedersen Prize in Supramolecular Chemistry


 

The International Committee of the International Symposium on Macrocyclic and Supramolecular Chemistry is pleased to invite nominations for the Cram Lehn Pedersen Prize for young supramolecular chemists.

The Cram Lehn Pedersen Prize, named in honor of the winners of the 1987 Nobel Prize in Chemistry, will recognise significant original and independent work in supramolecular chemistry.

Those who were awarded their PhD on or after 1st January 2009 (or who have an award of PhD date together with allowable career interruptions* that would be commensurate with award of their PhD on or after 1st January 2009) are eligible for the 2020 award. The winner will receive a prize of £2000 and free registration for the ISMSC meeting in Sydney, Australia. In addition to giving a lecture at ISMSC, a short lecture tour will be organized after the meeting in consultation with the Editor of Chemical Communications, the sponsor of the award.

Nomination Details

You may nominate yourself, but a nomination letter is recommended. Nomination materials should include: CV, list of publications (divided into publications from your PhD and post-doc, and those from your independent work), and be sent to Prof. Roger Harrison (ISMSC Secretary) at roger_harrison@byu.edu by 31st December 2019.

*Allowable career interruptions include primary caregiver’s responsibilities, illness, disability or parental leave and must be outlined in a cover letter with supporting documentation. See  https://www.chem.byu.edu/faculty-and-staff/resources/international-symposium-on-macrocyclic-and-supramolecular-chemistry/awards/ for specific details.

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Strengthening Li+-Coordination Decelerates Li-Dendrite Growth in Li-Metal Batteries

Lithium-metal batteries are a family of rechargeable batteries with higher charge-storage capacities than those of lithium-ion batteries. The boosted charge-storage performance of lithium-metal batteries is rooted in its anode material – Li metal, as it possesses an ultrahigh theoretical capacity (3860 mAh/g). However, the growth of dendrites on Li surfaces during charging could short-circuit batteries, cause combustion, and trigger explosions.

A research group led by Feng Li at the Institute of Metal Research, Chinese Academy of Sciences, recently devised a strategy to suppress the notorious Li dendrite growth in lithium-metal batteries. By tuning the composition of the electrolytes, the authors strengthened the coordination between Li+ and electrolyte solvents, which slowed the growth of Li dendrites. This work has been published in Chemical Communications (doi: 10.1039/C9CC07092C).

The researchers introduced an electrolyte additive, tetraethylene glycol dimethyl ether (TEGDME), as a coordination ligand to Li+. Compared to other components in the electrolyte, i.e., 1,2-dimethoxyethane (DME) and 1,3-dioxolane (DOL), TEGDME contains more oxygen atoms that can form multiple, robust coordination bonds with Li+. Specifically, density functional theory calculations showed that the binding energy between Li+ and electrolyte molecules increased by 0.31 eV after introducing TEGDME, reaching an absolute value of 4.93 eV. The enhanced binding force made the separation of Li+ from TEGDME (a prerequisite for Li-dendrite growth) energetically consuming and kinetically sluggish (Figure 1). These characteristics could decelerate Li-dendrite formation and elongate battery lifetimes.

Figure 1. Lithium-dendrite growth in different electrolytes: (a) weak coordination with Li+ promotes fast dendrite growth while (b) strong coordination with Li+ decelerates dendrite formation.

To confirm the above idea, the authors assembled lithium batteries with TEGDME+DME+DOL or DME+DOL electrolytes. Cycling stability tests demonstrated that the battery with the TEGDME-added electrolyte survived 60 charge-discharge cycles at a current density of 1C, whereas the capacity of the battery without TEGDME rapidly decayed beyond 30 cycles under identical testing conditions (Figure 2a). Scanning electron microscopy images revealed that the number of rod-shaped Li dendrites on the anode in the TEGDME-added electrolyte (Figure 2c) was less than that in the TEGDME-free electrolyte (Figure 2b), further confirming that the enhanced cycling stability resulted from the Li-dendrite suppressing effect of TEGDME.

Figure 2. (a) Cycling stability performance of lithium-metal batteries with two different electrolytes. The cathode material in both batteries was lithium iron phosphate (LFP). (b and c) SEM images of the Li anode surface after charging in (b) DME+DOL and (c) DME+DOL+TEGDME electrolytes.

This work highlights the importance of tailoring the electrolyte composition for preserving the stability and safety of lithium-metal batteries.

 

To find out more, please read:

Suppressing Lithium Dendrite Formation by Slowing Its Desolvation Kinetics

Huicong Yang, Lichang Yin, Huifa Shi, Kuang He, Hui-Ming Cheng, and Feng Li

Chem. Commun., 2019, doi: 10.1039/C9CC07092C

Tianyu Liu acknowledges Xiaozhou Yang of Virginia Tech, the U.S., for his careful proofreading of this post.

About the blogger:

Tianyu Liu obtained his Ph.D. (2017) in Chemistry from the University of California, Santa Cruz, in the United States. He is passionate about the communication of scientific endeavors to both the general public and other scientists with diverse research expertise as a way to introduce cutting-edge research to broad audiences. He is a blog writer for Chem. Commun. and Chem. Sci. More information about him can be found at http://liutianyuresearch.weebly.com/.

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MOF-Derived Solid-State Lithium-Oxygen Batteries

Just in case you weren’t aware, it turns out that lithium-based batteries are kind of a big deal. While the Nobel-winning batteries have already revolutionized consumer electronics, further development requires batteries with even higher energy densities. Enter: lithium-oxygen batteries (LOBs) with theoretical energy densities of 3500 W h/kg. LOBs come in non-aqueous, aqueous, hybrid, and solid-state varieties based on their electrolytes. Given the previous safety issues for lithium-based batteries with liquid electrolytes (remember the exploding phones?), solid-state electrolytes have attracted substantial research attention. Specifically, Li1+xAlxGe2x(PO4)3, or LAGP, shows promise given its high Li+ transport number and electrochemical stability over a wide window. These solid-state electrolytes need to be combined with new catalytically active high surface area cathode materials that will not react with the lithium and degrade, a persistent issue with MOFs.

Figure 1. Schematic of an assembled all solid-state lithium-oxygen battery.

Researchers in China and Japan have combined LAGP electrolyte with NiCo2O4 (NCO) nanoflakes as the catalytically active cathode material. They then assembled full solid-state batteries, the structure of which is shown in Figure 1, for electrochemical and stability testing. The LAGP was prepared using previously established methods and found to exhibit the expected high stability and lithium mobility. To prepare the nanoflakes, the researchers annealed cobalt-based MOFs on a sacrificial carbon substrate then dipped them in a Ni(NO3)2 solution for nickel doping and annealed once more. This leaves the final nanostructured metal oxide, with the elemental composition confirmed by TEM elemental mapping. As a conveniently freestanding electrode material, the nanoflakes were then loaded in as the cathode.

Once assembled, the researchers tested the full all solid-state LOBs for stability and performance. They demonstrated high discharge capacity and electron transfer efficiency with charge and discharge potentials well within the electrochemical window of the LAGP electrolyte. These are attributable to the high lithium ion mobility and the porous bimetallic nature of the cathode. To confirm that the incorporation of nickel impacted the overall device performance, the pure cobalt nanoflakes were used as the cathode.

Figure 2. Cycling performance of cobalt (left) and cobalt-nickel cathodes (right) at a current density of 100 mA/g.

As seen in Figure 2, the cobalt-only batteries exhibit significant capacity loss in only 35 cycles whereas the NCO cathodes showed no degradation after 90 cycles. While cycling the NCO electrodes, the reversible formation of Li2O2, a common discharge product, occurred in the open pores of the cathode. These pores allow the 500 nm Li2O2 particles to form and dissolve without disrupting the structure of the cathode and give a more stable battery. This research brings completely solid-state lithium-oxygen batteries one step closer to reality.

To find out more, please read:

All solid-state lithium–oxygen batteries with MOF-derived nickel cobaltate nanoflake arrays as high-performance oxygen cathodes

Hao Gong, Hairong Xue, Xueyi Lu, Bin Gao, Tao Wang, Jianping He and Renzhi Ma

Chem. Commun., 2019, 55, 10689-10692.

About the blogger:

Beth Mundy is a PhD candidate in chemistry in the Cossairt lab at the University of Washington in Seattle, Washington. Her research focuses on developing new and better ways to synthesize nanomaterials for energy applications. She is often spotted knitting in seminars or with her nose in a good book. You can find her on Twitter at @BethMundySci.

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