Effect of the Electrolyte Formulation on the Performances of Edlc and Li-Ion Batteries

One of the most important components in electrochemical storage devices (batteries and supercapacitors) is undoubtedly the electrolyte. The basic function of any electrolyte in these systems is the transport of ions between the positive and negative electrodes. In addition, electrochemical reactions occurring at each electrode/electrolyte interface are the origin of the current generated by storage devices. In other words, performances (capacity, power, efficiency and energy) of electrochemical storage devices are strongly related to the electrolyte properties, as well as, to the affinity for the electrolyte to selected electrode materials. Indeed, the formulation of electrolyte presenting good properties, such as high ionic conductivity and low viscosity, is then required to enhance the charge transfer reaction at electrode/electrolyte interface (e.g. charge accumulation in the case of Electrochemical Double Layer Capacitor, EDLC). For practical and safety considerations, the formulation of novel electrolytes presenting a low vapor pressure, a large liquid range temperature, a good thermal and chemical stabilities is also required.

This lecture will be focused on the effect of the electrolyte formulation on the performances of electrochemical storage devices (Li-ion batteries and supercapacitors). During which, a summary of the physical, thermal and electrochemical data obtained by our group, recently, on the formulation of novel electrolyte-based on the mixture of an ionic liquid (such as EmimNTf2 and Pyr14NTf2) and carbonate or dinitrile solvents will be presented and commented. The impact of the electrolyte formulation on the storage performances of EDLC and Li-ion batteries will be also discussed to further understand the relationship between electrolyte formulation and electrochemical performances. This talk will also be an opportunity to further discuss around the effects of additives (SEI builder: fluoroethylene carbonate and vinylene carbonate), ionic liquids, structure and nature of lithium salt (LiTFSI vs LiPF6) on the cyclability of negative electrode to then enhance the electrolyte formulation. For that, our recent results on TiSnSb and graphite negative electrodes will be presented and discussed, for example 1,2.

1-C. Marino, A. Darwiche1, N. Dupré, H.A. Wilhelm, B. Lestriez, H. Martinez, R. Dedryvère, W. Zhang, F. Ghamouss, D. Lemordant, L. Monconduit “ Study of the Electrode/Electrolyte Interface on Cycling of a Conversion Type Electrode Material in Li Batteries” J. Phys.chem. C, 2013, 117, 19302-19313

2- Mouad Dahbi, Fouad Ghamouss, Mérièm Anouti, Daniel Lemordant, François Tran-Van “Electrochemical lithiation and compatibility of graphite anode using glutaronitrile/dimethyl carbonate mixtures containing LiTFSI as electrolyte” 2013, 43, 4, 375-385.

Select, Link and Rank: Diversified Query Expansion and Entity Ranking using Wikipedia

A search query, being a very concise grounding of user intent, could potentially have many possible interpretations. Search engines hedge their bets by diversifying top results to cover multiple such possibilities so that the user is likely to be satisfied, whatever be her intended interpretation. Diversified Query Expansion is the problem of diversifying query expansion suggestions, so that the user can specialize the query to better suit her intent, even before perusing search results. We propose a method, Select-Link-Rank, that exploits semantic information from Wikipedia to generate diversified query expansions. SLR does collective processing of terms and Wikipedia entities in an integrated framework, simultaneously diversifying query expansions and entity recommendations. SLR starts with selecting informative terms from search results of the initial query, links them to Wikipedia entities, performs a diversity-conscious entity scoring and transfers such scoring to the term space to arrive at query expansion suggestions. Through an extensive empirical analysis and user study, we show that our method outperforms the state-of-the-art diversified query expansion and diversified entity recommendation techniques.

Automated Equation Formulation for Causal Loop Diagrams

The annotation of Business Dynamics models with parameters and equations, to simulate the system under study and further evaluate its simulation output, typically involves a lot of manual work. In this paper we present an approach for automated equation formulation of a given Causal Loop Diagram (CLD) and a set of associated time series with the help of neural network evolution (NEvo). NEvo enables the automated retrieval of surrogate equations for each quantity in the given CLD, hence it produces a fully annotated CLD that can be used for later simulations to predict future KPI development. In the end of the paper, we provide a detailed evaluation of NEvo on a business use-case to demonstrate its single step prediction capabilities.

Clustering-based Query Routing in Cooperative Semi-structured Peer to Peer Networks

We consider the problem of resource selection in clustered Peer-to-Peer Information Retrieval (P2P IR) networks with cooperative peers. The clustered P2P IR framework presents a significant departure from general P2P IR architectures by employing clustering to ensure content coherence between resources at the resource selection layer, without disturbing document allocation. We propose that such a property could be leveraged in resource selection by adapting well-studied and popular inverted lists for centralized document retrieval. Accordingly, we propose the Inverted PeerCluster Index (IPI), an approach that adapts the inverted lists, in a straightforward manner, for resource selection in clustered P2P IR. IPI also encompasses a strikingly simple peer-specific scoring mechanism that exploits the said index for resource selection. Through an extensive empirical analysis on P2P IR testbeds, we establish that IPI competes well with the sophisticated state-of-the-art methods in virtually every parameter of interest for the resource selection task, in the context of clustered P2P IR.