Despite the sustained use of forcefield methodologies to study SiO(2) polymorphs few reviews on the subject are available in the literature. The present study is an attempt to help fill this gap, focusing on classical forcefields used to reproduce and predict properties of pure silica zeolites (or zeosils) such as cell parameters, SiO distance and especially pore size. Instead of an exhaustive study we have focused on an application where diffusion of hydrocarbons makes important the use of pure silica zeolites. A particular area of interest is small pore zeosils containing 8-rings as the largest window, which are industrially interesting for their ability to perform kinetic separations of mixtures of C3 hydrocarbon molecules whose dimensions are of similar characteristics. A set of forcefields have been selected from the literature to analyze their accuracy and transferability when predicting structural, mechanical and dynamical properties of small pore pure silica zeolites and their performance at selective diffusion of C3 hydrocarbons.

Traditional potato growth models evidence certain limitations, such as the cost of obtaining the input data required to run the models, the lack of spatial information in some instances, or the actual quality of input data. In order to address these issues, we develop a model to predict potato yield using satellite remote sensing. In an effort to offer a good predictive model that improves the state of the art on potato precision agriculture, we use images from the twin Sentinel 2 satellites (European Space Agency-Copernicus Programme) over three growing seasons, applying different machine learning models. First, we fitted nine machine learning algorithms with various pre-processing scenarios using variables from July, August and September based on the red, red-edge and infra-red bands of the spectrum. Second, we selected the best performing models and evaluated them against independent test data. Finally, we repeated the previous two steps using only variables corresponding to July and August. Our results showed that the feature selection step proved vital during data pre-processing in order to reduce multicollinearity among predictors. The Regression Quantile Lasso model (11.67% Root Mean Square Error, RMSE; R 2 = 0.88 and 9.18% Mean Absolute Error, MAE) and Leap Backwards model (10.94% RMSE, R 2 = 0.89 and 8.95% MAE) performed better when predictors with a correlation coefficient > 0.5 were removed from the dataset. In contrast, the Support Vector Machine Radial (svmRadial) performed better with no feature selection method (11.7% RMSE, R 2 = 0.93 and 8.64% MAE). In addition, we used a random forest model to predict potato yields in Castilla y León (Spain) 1-2 months prior to harvest, and obtained satisfactory results (11.16% RMSE, R 2 = 0.89 and 8.71% MAE). These results demonstrate the suitability of our models to predict potato yields in the region studied.The use of new technologies, such as satellite data, Geographic Information Systems (GIS) or Global Positioning Systems (GPS), can improve crop yield production and its quality [1], helping to secure food supply for the future as well as reducing the negative impacts resulting from agricultural practices [11]. More specifically, satellite remote sensing data has many applications in agriculture: soil property detection [12], crop type classification [13], crop yield forecast [14], crop health monitoring [15], soil moisture retrieval [16] or weather data assessment [17]. Remote sensing offers vast amounts of information which can be considered big data [18], and can help to improve crop modelling and decision-making. Big data has been described by Wolfert et al. [19] as "massive volumes of data with a wide variety that can be captured, analysed and used for decision-making", with said authors expecting big data to have a major impact on the agricultural sector. In order to improve the use of this data, given its size and variety, machine learning has emerged as an appropriate tool to identify rules and patterns in datasets [20], in addition to autonomously...

The confinement effects upon hydrogen adsorption in Cu(II)-paddle wheel containing metal-organic frameworks (MOFs) were evaluated and rationalized in terms of the structural properties (cavity types and pore diameters) of PCN-12, HKUST-1, MOF-505, NOTT-103 and NOTT-112. First-principles calculations were employed to identify the strongest adsorption positions at the paddle wheel inorganic building unit (IBU). The adsorption centres due to confinement were located through analysis of 3D occupancy maps obtained from the hydrogen trajectories computed via molecular dynamics simulations. It was found that the confinement enhances the adsorption on the weakest adsorption centres around the IBU in regions close to the narrowest windows and promotes the formation of new adsorption regions into the small cavities.Our results indicate that at low pressure, the high H 2 uptake in these materials is partly due to the presence of small cavities (5.3-8.5 Å ) or narrow windows where the long-range contribution to the adsorption becomes important. Conversely, confinement effects in cavities with diameters >12 Å were not observed.

Computational screening throughout a database containing ∼138 000 metal-organic frameworks (MOFs) has been performed to select candidate structures for hydrogen storage. A total of 231 structures (of which 79 contain paddle-wheel units) have been selected that meet the gravimetric and volumetric targets at 100 atm and 77 K. Grand Canonical Monte Carlo simulations have been performed to calculate the isotherms and select structures which meet the targets at 50 atm, and also to check the adsorption in the low pressure regime (1 atm). From this a reduced set of 18 structures has been analysed in more detail, regarding not only gravimetric and volumetric uptake but also pore size distribution and pore volume. A few structures with 3% gravimetric uptake at 1 atm and 77 K perform at the best level found so far.

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Machine learning approach to locate desert locust breeding areas based on ESA CCI soil moisture," J. Appl.Abstract. Desert locusts have attacked crops since antiquity. To prevent or mitigate its effects on local communities, it is necessary to precisely locate its breeding areas. Previous works have relied on precipitation and vegetation index datasets obtained by satellite remote sensing. However, these products present some limitations in arid or semiarid environments. We have explored a parameter: soil moisture (SM); and examined its influence on the desert locust wingless juveniles. We have used two machine learning algorithms (generalized linear model and random forest) to evaluate the link between hopper presences and SM conditions under different time scenarios. RF obtained the best model performance with very good validation results according to the true skill statistic and receiver operating characteristic curve statistics. It was found that an area becomes suitable for breeding when the minimum SM values are over 0.07 m 3 ∕m 3 during 6 days or more. These results demonstrate the possibility to identify breeding areas in Mauritania by means of SM, and the suitability of ESA CCI SM product to complement or substitute current monitoring techniques based on precipitation datasets. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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