Selecting strong Brønsted acid zeolites through screening from a database of hypothetical frameworks.

The database of prospective zeolites () has been screened in search of feasible zeolites with the condition of having at least one strong Brønsted site. Several criteria of zeolite feasibility have been tested using energetic and structural concepts, allowing a fast elimination of unsuitable candidates. Based on improved definitions to count and enumerate rings in zeolites, Brønsted acidity has been assessed in a fast albeit inaccurate way, by calculating a structural descriptor related to ammonia desorption energy. In each zeolite, the value of this descriptor was calculated for all the possible centres where a Brønsted acid site can be located. Ranking each zeolite through the value of the strongest candidate acid site allowed obtaining a selection of potentially strong acid zeolites. With further selection criteria, a final short list of 12 structures was obtained, where accurate calculations using periodic DFT indicate that 6 of them must contain a Brønsted site of very strong acidity.

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.

Synthesis and structure determination of the hierarchical meso-microporous zeolite ITQ-43.

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. Its complex crystalline structure was solved with the use of automated diffraction tomography.

Boosting theoretical zeolitic framework generation for the determination of new materials structures using GPU programming.

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. A case study is presented.

A colorimetric sensor array for the detection of the date-rape drug γ-hydroxybutyric acid (GHB): a supramolecular approach.

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.

Design of a full-profile-matching solution for high-throughput analysis of multiphase samples through powder X-ray diffraction.

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.

Integrating chemists preferences for shape-similarity clustering of series.

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., conversion) over time is expected to be analyzed. The mathematical tool has been developed to compare and group curves taking into account their shape. The strategy, which consists on combining a hierarchical clustering with the k-means algorithm, is described and compared with both algorithms used separately. The hybridization is shown to be of great interest. Then, a second application mode of the proposed methodology is presented. Once meaningful clusters according to chemist's preferences and goals are successfully achieved, the induced model may be used in order to automatically classify new experimental results. The grouping of the new catalysts tested for the Heck coupling reaction between styrene and iodobenzene verified the set of criteria "defined" during the initial clustering step, and facilitated a quick identification of the catalytic behaviors following user's preferences.

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.

MAP: an iterative experimental design methodology for the optimization of catalytic search space structure modeling.

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.