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Abstract: This paper presents the implementation of the recent methodology called Adaptable Time Warping (ATW) for the automatic identification of mixture of crystallographic phases from powder X-ray diffraction data, inside the framework of a new integrative platform named hIT e Q. The methodology is encapsulated into a so-called workflow, and we explore the benefits of such an environment for streamlining discovery in R&D. Beside the fact that ATW successfully identifies and classifies crystalline phases from powder XRD for the very complicated case of zeolite ITQ-33 for which has been employed a high throughput synthesis process, we stress on the numerous difficulties encountered by academic laboratories and companies when facing the integration of new software or techniques. It is shown how an integrative approach provides a real asset in terms of cost, efficiency, and speed due to a unique environment that supports well-defined and reusable processes, improves knowledge management, and handles properly multi-disciplinary teamwork, and disparate data structures and protocols. [Copyright &y& Elsevier]

The formation of mesopores in microporous zeotites 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. Its complex crystalline structure was solved with the use of automated diffraction tomography. [ABSTRACT FROM AUTHOR]

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. Here, we present a colorimetric sensor array based on supramolecular host-guest complexes of fluorescent dyes with organic capsules (cucurbiturils) for the detection of GHB., (Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

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. Because the use of high-throughput (HT) techniques for the discovery or for increasing the breadth of the synthetic routes of new microporous crystalline structures makes the reliability of search-match methods a critical factor to be assessed, a meticulous evaluation of the reliability and the robustness is provided and supported by both empirical comparisons and mathematical proof. The results offered by our methodology, which clearly outperforms the well-established solutions, open the way towards total automation of such a routine procedure, eliminating laborious and time-consuming controls, preliminary treatments, and settings. Consequently, the proposed solution is of great interest and appears to be very promising, not only because of the numerous potential applications of XRD in materials science, but also the possible expansion of the solution to several other characterization techniques.

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. However, very few new papers deal with strategies that guide exploratory studies. Mostly, traditional designs, homogeneous covering, or simple random samplings are exploited. Typical catalytic output distributions exhibit unbalanced datasets for which an efficient learning is hardly carried out, and interesting but rare classes are usually unrecognized. Here is suggested a new iterative algorithm for the characterization of the search space structure, working independently of learning processes. It enhances recognition rates by transferring catalysts to be screened from "performance-stable" space zones to "unsteady" ones which necessitate more experiments to be well-modeled. The evaluation of new algorithm attempts through benchmarks is compulsory due to the lack of past proofs about their efficiency. The method is detailed and thoroughly tested with mathematical functions exhibiting different levels of complexity. The strategy is not only empirically evaluated, the effect or efficiency of sampling on future Machine Learning performances is also quantified. The minimum sample size required by the algorithm for being statistically discriminated from simple random sampling is investigated.