Laurent A. Baumes

Styrene from toluene by combinatorial catalysis

The side-chain alkylation of toluene with methanol is one alternative technology to produce styrene that has been given attention in the last few years. In the literature basic materials has been proposed as catalyst for the reaction but the real number of tested catalysts is very small and few preparation parameters have been taken into account. In this work a combinatorial approach has been used to explore the possibilities of basic zeolites to carry out such reaction. To do this, the following catalyst variables have been studied: nature of zeolite, framework composition, nature and content of compensating cation and method of incorporation (exchange, impregnation). The results obtained confirm the requirements of both basic and acid sites in the catalysts and show the compromise between these two functions. The study carried out shows that zeolite-based catalysts are still poor reactive to give the styrene/ethylbenzene yields required for converting this process in a real alternative to the existing one, based on the alkylation of benzene with ethylene, followed by the dehydrogenation of ethylbenzene to styrene.

Zeolite synthesis modelling with support vector machines: a combinatorial approach.

This work shows the application of support vector machines (SVM) for modelling and prediction of zeolite synthesis, when using the gel molar ratios as model input (synthesis descriptors). Experimental data includes the synthesis results of a multi-level factorial experimental design of the system TEA:SiO2:Na2O:Al2O3:H2O. The few parameters of the SVM model were studied and the fitting performance is compared with the ones obtained with other machine learning models such as neural networks and classification trees. SVM models show very good prediction performances and generalization capacity in zeolite synthesis prediction. They may overcome overfitting problems observed sometimes for neural networks. It is also studied the influence of the type of material descriptors used as model output.

Speedups between ×70 and ×120 for a generic local search (memetic) algorithm on a single GPGPU chip

This paper presents the first implementation of a generic memetic algorithm on one of the two GPU (Graphic Processing Unit) chips of a GTX295 gaming card. Observed speedups range between ×70 and ×120, mainly depending on the population size. An automatic parallelization of a memetic algorithm is provided through an upgrade of the EASEA language, so that the EC community can benefit from the extraordinary power of these cards without needing to program them.

Using Genetic Programming for an Advanced Performance Assessment of Industrially Relevant Heterogeneous Catalysts

Beside the ease and speed brought by automated synthesis stations and reactors technologies in materials science, adapted informatics tools must be further developed in order to handle the increase of throughput and data volume, and not to slow down the whole process. This article reports the use of genetic programming (GP) in heterogeneous catalysis. Despite the fact that GP has received only little attention in this domain, it is shown how such an approach can be turned into a very singular and powerful tool for solid optimization, discovery, and monitoring. Jointly with neural networks, the GP paradigm is employed in order to accurately and automatically estimate the whole curve “conversion vs. time” in the epoxidation of large olefins using titanosilicates, Ti-MCM-41 and Ti-ITQ-2, as catalysts. In contrast to previous studies in combinatorial materials science and high-throughput screening, it was possible to estimate the entire evolution of the catalytic reaction for unsynthesized catalysts. Consequently, the evaluation of the performance of virtual solids is not reduced to a single point (e.g., the conversion level at only one given reaction time or the initial reaction rate). The methodology is thoroughly detailed, while stressing on the comparison between the recently proposed Context Aware Crossover (CAX) and the traditional crossover operator.

Generic Local Search (Memetic) Algorithm on a Single GPGPU Chip

Memetic algorithms (MAs), evolutionary algorithms coupled with a local search routine, have been shown to be very efficient in solving a great variety of problems. This chapter presents the first implementation of a generic parallel MA on a general-purpose graphics processing unit card. An upgrade of the EASEA platform provides an automatic generation and parallelization of an MA for both novice and experienced users. Experiments on a benchmark function and a real-world problem reveal speedups ranging between × 70 and × 120, depending on population size and number of local search iterations.

A study on factors affecting the reproducibility of a chemical tongue analysis responding to amino acids.

Four fluorescent tricyclic basic dyes with two hollow organic capsules namely cucurbit[n]urils (CB[n]), n = 7 and 8, compose the chemical tongue that is examined for α-amino acids recognition. This array is able to identify and discriminate 18 α-amino acids up to 10-4 M without the need of enzyme activation. The paper shows the importance of the classification technique employed in order to reach the highest recognition rate at this concentration.

High-Performance Computing for Accelerated Zeolitic Materials Modeling

Very recently, the design and understanding of materials synthesis have received considerable attention where modeling approaches are decisive. Here, we focus on the generation of crystalline inorganic frameworks. Despite high-throughput (HT) methods having proved to be useful for the discovery of zeolites, the determination of the new phases’ structure takes up a large part of the entire process. Therefore, we show how graphic processing units (GPUs) can be used in order to speed up this mandatory step. We describe GPUs and predictive methods for phase determination. Then, we show all the details that allow us to reach a stable and robust solution with benchmark analysis and real applications for zeolites.