Publications by authors named "Laurent A Baumes"

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.

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The formation of mesopores in microporous zeolites is generally performed by postsynthesis acid, basic, and steam treatments. The hierarchical pore systems thus formed allow better adsorption, diffusion, and reactivity of these materials. By combining organic and inorganic structure-directing agents and high-throughput methodologies, we were able to synthesize a zeolite with a hierarchical system of micropores and mesopores, with channel openings delimited by 28 tetrahedral atoms.

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Evolutionary algorithms have proved to be efficient for solving complicated optimization problems. On the other hand, the many-core architecture in graphical cards "General Purpose Graphic Processing Unit" (GPGPU) offers one of the most attractive cost/performance ratio. Using such hardware, the manuscript shows how an efficiently implemented genetic algorithm with a simple fitness function allows boosting the determination of zeolite structures.

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γ-Hydroxybutyric acid (GHB), a colourless, odourless and tasteless chemical, has become one of the most dangerous illicit drugs of abuse today. At low doses, this drug is a central nervous system depressant that reduces anxiety and produces euphoria and relaxation, sedating the recipient. There is an urgent need for simple, easy-to-use sensors for GHB in solution.

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Few solutions that aim to identify crystalline materials from the analysis of powder X-ray diffraction (XRD) data have been reported to date. A careful inspection of the powder XRD data, and the corresponding highlight of specific failures when it has been used for the determination of the crystallographic phases of zeolites among mixtures, has allowed the creation of the recently proposed strategy: adaptable time warping (ATW). Herein, the design process is thoroughly detailed in a step-by-step manner, which allows a deep understanding of the motivations, improvements, and the resulting remarkable properties of our methodology.

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This study shows how chemistry knowledge and reasoning are taken into account for building a new methodology that aims at automatically grouping data having a chronological structure. We consider combinatorial catalytic experiments where the evolution of a reaction (e.g.

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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.

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One of the main problems in high-throughput research for materials is still the design of experiments. At early stages of discovery programs, purely exploratory methodologies coupled with fast screening tools should be employed. This should lead to opportunities to find unexpected catalytic results and identify the "groups" of catalyst outputs, providing well-defined boundaries for future optimizations.

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