Deciphering the Interplay between Binders and Electrolytes on the Performance of Li4Ti5O12 Electrodes for Li-Ion Batteries

Lithium titanium oxide (Li4Ti5O12, LTO) is an attractive negative electrode for the development of safe-next-generation-lithium-ion batteries (LIBs). LTO can find specific applications complementary to existing alternatives for LIBs thanks to its good rate capability at high C-rates, fast lithium intercalation, and high cycling stability. Furthermore, LIBs featuring LTO electrodes are inherently safer owing to the LTO's operating potential of 1.55 V vs. Li+/Li where the commonly used organic-based electrolytes are thermodynamically stable. Herein, we report the combined use of water-soluble sodium alginate (SA) binder and lithium bis(trifluoromethanesulfonyl)imide (LiTFSI)-tetraglyme (1m-T) electrolyte and we demonstrate the improvement of the electrochemical performance of LTO-based electrodes with respect to those operating in conventional electrolyte 1M LiPF6-ethylene carbonate: dimethyl carbonate (LP30). We also tackle the analysis of the impact of combining the binder/electrolyte on the long-term cycling performance of LTO electrodes featuring SA or conventional polyvinylidene fluoride (PVdF) as binders. Therefore, to assess the impact of the combination of binder/electrolyte on performance, we performed post-mortem characterization by ex situ synchrotron diffraction experiments of LTO electrodes after cycling in LP30 and 1m-T electrolytes

Structure and dynamics of the fullerene polymer Li4 C60 studied with neutron scattering

The two-dimensional polymer structure and lattice dynamics of the superionic conductor Li4 C60 are investigated by neutron diffraction and spectroscopy. The peculiar bonding architecture of this compound is definitely confirmed through the precise localisation of the carbon atoms involved in the intermolecular bonds. The spectral features of this phase are revealed through ab-initio lattice dynamics calculations and inelastic neutron scattering experiments. The neutron observables are found to be in very good agreement with the simulations which predict a partial charge transfer from the Li atoms to the C60 cage. The absence of a well defined band associated to one category of the Li atoms in the experimental spectrum suggests that this species is not ordered even at the lowest temperatures. The calculations predict an unstable Li sublattice at a temperature of 200 K, that we relate to the large ionic diffusivity of this system. This specificity is discussed in terms of coupling between the low frequency optic modes of the Li ions to the soft structure of the polymer.Comment: 29 pages, 13 Figure

Large scale synthesis of copper nickel alloy nanoparticles with reduced compressibility using arc thermal plasma process

Among the various methods employed in the synthesis of nanostructures, those involving high operating temperature and sharp thermal gradients often lead to the establishment of new exotic properties. Herein, we report on the formation of Cu-Ni metallic alloy nanoparticles with greatly enhanced stiffness achieved through direct-current transferred arc-thermal plasma assisted vapour-phase condensation. High pressure synchrotron X-ray powder diffraction (XRPD) at ambient temperature as well as XRPD in the temperature range 180 to 920 K, show that the thermal arc-plasma route resulted in alloy nanoparticles with much enhanced bulk modulus compared to their bulk counterparts. Such a behaviour may find an explanation in the sudden quenching assisted by the retention of a large amount of local strain due to alloying, combined with the perfect miscibility of the elemental components during the thermal plasma synthesis process

Differentially Private Model Selection with Penalized and Constrained Likelihood

In statistical disclosure control, the goal of data analysis is twofold: The released information must provide accurate and useful statistics about the underlying population of interest, while minimizing the potential for an individual record to be identified. In recent years, the notion of differential privacy has received much attention in theoretical computer science, machine learning, and statistics. It provides a rigorous and strong notion of protection for individuals' sensitive information. A fundamental question is how to incorporate differential privacy into traditional statistical inference procedures. In this paper we study model selection in multivariate linear regression under the constraint of differential privacy. We show that model selection procedures based on penalized least squares or likelihood can be made differentially private by a combination of regularization and randomization, and propose two algorithms to do so. We show that our private procedures are consistent under essentially the same conditions as the corresponding non-private procedures. We also find that under differential privacy, the procedure becomes more sensitive to the tuning parameters. We illustrate and evaluate our method using simulation studies and two real data examples

Observation of implicit complexity by non confluence

We propose to consider non confluence with respect to implicit complexity. We come back to some well known classes of first-order functional program, for which we have a characterization of their intentional properties, namely the class of cons-free programs, the class of programs with an interpretation, and the class of programs with a quasi-interpretation together with a termination proof by the product path ordering. They all correspond to PTIME. We prove that adding non confluence to the rules leads to respectively PTIME, NPTIME and PSPACE. Our thesis is that the separation of the classes is actually a witness of the intentional properties of the initial classes of programs

Intravesical Therapy for Non-Muscle-Invasive Bladder Cancer: What Is the Real Impact of Squamous Cell Carcinoma Variant on Oncological Outcomes?

Background and Objectives: To evaluate the oncological impact of squamous cell carcinoma (SCC) variant in patients submitted to intravesical therapy for non-muscle-invasive bladder cancer (NMIBC). Materials and Methods: Between January 2015 and January 2020, patients with conventional urothelial NMIBC (TCC) or urothelial NMIBC with SCC variant (TCC + SCC) and submitted to adjuvant intravesical therapies were collected. Kaplan\u2013Meier analyses targeted disease recurrence and progression. Uni-and multivariable Cox regression analyses were used to test the role of SCC on disease recurrence and/or progression. Results: A total of 32 patients out of 353 had SCC at diagnosis. Recurrence was observed in 42% of TCC and 44% of TCC + SCC patients (p = 0.88), while progression was observed in 12% of both TCC and TCC + SCC patients (p = 0.78). At multivariable Cox regression analyses, the presence of SCC variant was not associated with higher rates of neither recurrence (p = 0.663) nor progression (p = 0.582). Conclusions: We presented data from the largest series on patients with TCC and concomitant SCC histological variant managed with intravesical therapy (BCG or MMC). No significant differences were found in term of recurrence and progression between TCC and TCC + SCC. Despite the limited sample size, this study paves the way for a possible implementation of the use of intravesical BCG and MMC in NMIBC with histological variants

Combining Effects and Coeffects via Grading

This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by the Association for Computing Machinery.$\textit{Effects}$ and $\textit{coeffects}$ are two general, complementary aspects of program behaviour. They roughly correspond to computations which change the execution context (effects) versus computations which make demands on the context (coeffects). Effectful features include partiality, non-determinism, input-output, state, and exceptions. Coeffectful features include resource demands, variable access, notions of linearity, and data input requirements. The effectful or coeffectful behaviour of a program can be captured and described via type-based analyses, with fine grained information provided by monoidal effect annotations and semiring coeffects. Various recent work has proposed models for such typed calculi in terms of $\textit{graded (strong) monads}$ for effects and $\textit{graded (monoidal) comonads}$ for coeffects. Effects and coeffects have been studied separately so far, but in practice many computations are both effectful and coeffectful, e.g., possibly throwing exceptions but with resource requirements. To remedy this, we introduce a new general calculus with a combined $\textit{effect-coeffect system}$. This can describe both the $\textit{changes}$ and $\textit{requirements}$ that a program has on its context, as well as interactions between these effectful and coeffectful features of computation. The effect-coeffect system has a denotational model in terms of effect-graded monads and coeffect-graded comonads where interaction is expressed via the novel concept of $\textit{graded distributive laws}$. This graded semantics unifies the syntactic type theory with the denotational model. We show that our calculus can be instantiated to describe in a natural way various different kinds of interaction between a program and its evaluation context.Orchard was supported by EPSRC grant EP/M026124/1 and EP/K011715/1 (whilst previously at Imperial College London), Katsumata by JSPS KAKENHI grant JP15K00014, Uustalu by Estonian Min. of Educ. and Res. grant IUT33-13 and Estonian Sci. Found. grant 9475. Gaboardi’s work was done in part while at the University of Dundee, UK supported by EPSRC grant EP/M022358/1