Dry impregnation in fluidized bed: Drying and calcination effect on nanoparticles dispersion and location in a porous support

The synthesis of metal nanoparticles dispersed inside the grains of a porous inorganic support was carried out by ‘‘dry impregnation’’ in a fluidized bed. The principle of this technique consists in the spraying of a solution containing a metal source into a hot fluidized bed of porous particles. The metal source can be of different nature such as metal salts, organometallic precursors or colloidal solutions. The experimental results obtained from iron oxide deposition on a porous silica gel as support, constitute the core of this article but others results concerning the deposition of rhodium from a colloidal suspension containing preformed rhodium nanoparticles are also described. More precisely, this study aims to understand the effect of the bed temperature during the impregnation step, the initial particle porosity and the calcination operating protocol on the metallic nanoparticles dispersion and location in the silica porous particles. The so-obtained products were characterized by various techniques in order to determine their morphology, their surface properties and the dispersion of the nanoparticles inside the support. The results showed that, under the chosen operating conditions, the deposit efficiency is close to 100% and the competition between the drying rate, depending on the process-related variables, and the capillary penetration rate, depending on the physicochemical-related variables, controls the deposit location. A quasi uniform deposit inside the support particles is observed for soft drying. The metal nanoparticles size is controlled by the pore mean diameter of the support as well as the calcination operating protocol

Synthesis of Supported Catalysts by Dry Impregnation in Fluidized Bed

The synthesis of catalytic or not composite materials by dry impregnation in fluidized bed is described. This process can be carried out under mild conditions from solutions of organometallic precursors or colloidal solutions of preformed nanoparticles giving rise to reproducible metallic nanoparticles containing composite materials with a high reproducibility. The adequate choice of the reaction conditions makes possible to deposit uniformly the metal precursor within the porous matrix or on the support surface. When the ratio between the drying time and the capillary penetration time (tsec/tcap) is higher than 10, the impregnation under soft drying conditions leads to a homogeneous deposit inside the pores of the particles of support. The efficiency of the metal deposition is close to 100%, and the size of the formed metal nanoparticles is controlled by the pores diameter. Finally, some of the presented composite materials have been tested as catalysts: iron-based materials were used in carbon-nanotubes synthesis, while Pd and Rh composite materials have been investigated in hydrogenation reactions

Transference Principles for Log-Sobolev and Spectral-Gap with Applications to Conservative Spin Systems

We obtain new principles for transferring log-Sobolev and Spectral-Gap inequalities from a source metric-measure space to a target one, when the curvature of the target space is bounded from below. As our main application, we obtain explicit estimates for the log-Sobolev and Spectral-Gap constants of various conservative spin system models, consisting of non-interacting and weakly-interacting particles, constrained to conserve the mean-spin. When the self-interaction is a perturbation of a strongly convex potential, this partially recovers and partially extends previous results of Caputo, Chafa\"{\i}, Grunewald, Landim, Lu, Menz, Otto, Panizo, Villani, Westdickenberg and Yau. When the self-interaction is only assumed to be (non-strongly) convex, as in the case of the two-sided exponential measure, we obtain sharp estimates on the system's spectral-gap as a function of the mean-spin, independently of the size of the system.Comment: 57 page

Maximizing the Conditional Expected Reward for Reaching the Goal

The paper addresses the problem of computing maximal conditional expected accumulated rewards until reaching a target state (briefly called maximal conditional expectations) in finite-state Markov decision processes where the condition is given as a reachability constraint. Conditional expectations of this type can, e.g., stand for the maximal expected termination time of probabilistic programs with non-determinism, under the condition that the program eventually terminates, or for the worst-case expected penalty to be paid, assuming that at least three deadlines are missed. The main results of the paper are (i) a polynomial-time algorithm to check the finiteness of maximal conditional expectations, (ii) PSPACE-completeness for the threshold problem in acyclic Markov decision processes where the task is to check whether the maximal conditional expectation exceeds a given threshold, (iii) a pseudo-polynomial-time algorithm for the threshold problem in the general (cyclic) case, and (iv) an exponential-time algorithm for computing the maximal conditional expectation and an optimal scheduler.Comment: 103 pages, extended version with appendices of a paper accepted at TACAS 201

Towards a unified theory of Sobolev inequalities

We discuss our work on pointwise inequalities for the gradient which are connected with the isoperimetric profile associated to a given geometry. We show how they can be used to unify certain aspects of the theory of Sobolev inequalities. In particular, we discuss our recent papers on fractional order inequalities, Coulhon type inequalities, transference and dimensionless inequalities and our forthcoming work on sharp higher order Sobolev inequalities that can be obtained by iteration.Comment: 39 pages, made some changes to section 1

Stochastic Invariants for Probabilistic Termination

Termination is one of the basic liveness properties, and we study the termination problem for probabilistic programs with real-valued variables. Previous works focused on the qualitative problem that asks whether an input program terminates with probability~1 (almost-sure termination). A powerful approach for this qualitative problem is the notion of ranking supermartingales with respect to a given set of invariants. The quantitative problem (probabilistic termination) asks for bounds on the termination probability. A fundamental and conceptual drawback of the existing approaches to address probabilistic termination is that even though the supermartingales consider the probabilistic behavior of the programs, the invariants are obtained completely ignoring the probabilistic aspect. In this work we address the probabilistic termination problem for linear-arithmetic probabilistic programs with nondeterminism. We define the notion of {\em stochastic invariants}, which are constraints along with a probability bound that the constraints hold. We introduce a concept of {\em repulsing supermartingales}. First, we show that repulsing supermartingales can be used to obtain bounds on the probability of the stochastic invariants. Second, we show the effectiveness of repulsing supermartingales in the following three ways: (1)~With a combination of ranking and repulsing supermartingales we can compute lower bounds on the probability of termination; (2)~repulsing supermartingales provide witnesses for refutation of almost-sure termination; and (3)~with a combination of ranking and repulsing supermartingales we can establish persistence properties of probabilistic programs. We also present results on related computational problems and an experimental evaluation of our approach on academic examples.Comment: Full version of a paper published at POPL 2017. 20 page

Automated Unbounded Analysis of Cryptographic Constructions in the Generic Group Model

We develop a new method to automatically prove security statements in the Generic Group Model as they occur in actual papers. We start by defining (i) a general language to describe security definitions, (ii) a class of logical formulas that characterize how an adversary can win, and (iii) a translation from security definitions to such formulas. We prove a Master Theorem that relates the security of the construction to the existence of a solution for the associated logical formulas. Moreover, we define a constraint solving algorithm that proves the security of a construction by proving the absence of solutions. We implement our approach in a fully automated tool, the $gga^{\infty}$ tool, and use it to verify different examples from the literature. The results improve on the tool by Barthe et al. (CRYPTO\u2714, PKC\u2715): for many constructions, $gga^{\infty}$ succeeds in proving standard (unbounded) security, whereas Barthe\u27s tool is only able to prove security for a small number of oracle queries

A formally verified compiler back-end

This article describes the development and formal verification (proof of semantic preservation) of a compiler back-end from Cminor (a simple imperative intermediate language) to PowerPC assembly code, using the Coq proof assistant both for programming the compiler and for proving its correctness. Such a verified compiler is useful in the context of formal methods applied to the certification of critical software: the verification of the compiler guarantees that the safety properties proved on the source code hold for the executable compiled code as well

Two remarks on generalized entropy power inequalities

This note contributes to the understanding of generalized entropy power inequalities. Our main goal is to construct a counter-example regarding monotonicity and entropy comparison of weighted sums of independent identically distributed log-concave random variables. We also present a complex analogue of a recent dependent entropy power inequality of Hao and Jog, and give a very simple proof.Comment: arXiv:1811.00345 is split into 2 papers, with this being on