José S. Torrecilla

Estimation of toxicity of ionic liquids in Leukemia Rat Cell Line and Acetylcholinesterase enzyme by principal component analysis, neural networks and multiple lineal regressions

Multiple linear regression (MLR), radial basis network (RB), and multilayer perceptron (MLP) neural network (NN) models have been explored for the estimation of toxicity of ammonium, imidazolium, morpholinium, phosphonium, piperidinium, pyridinium, pyrrolidinium and quinolinium ionic liquid salts in the Leukemia Rat Cell Line (IPC-81) and Acetylcholinesterase (AChE) using only their empirical formulas (elemental composition) and molecular weights. The toxicity values were estimated by means of decadic logarithms of the half maximal effective concentration (EC(50)) in microM (log(10)EC(50)). The models performances were analyzed by statistical parameters, analysis of residuals and central tendency and statistical dispersion tests. The MLP model estimates the log(10)EC(50) in IPC-81 and AchE with a mean prediction error less than 2.2 and 3.8%, respectively.

Artificial sensing intelligence with silicon nanowires for ultraselective detection in the gas phase.

The use of molecularly modified Si nanowire field effect transistors (SiNW FETs) for selective detection in the liquid phase has been successfully demonstrated. In contrast, selective detection of chemical species in the gas phase has been rather limited. In this paper, we show that the application of artificial intelligence on deliberately controlled SiNW FET device parameters can provide high selectivity toward specific volatile organic compounds (VOCs). The obtained selectivity allows identifying VOCs in both single-component and multicomponent environments as well as estimating the constituent VOC concentrations. The effect of the structural properties (functional group and/or chain length) of the molecular modifications on the accuracy of VOC detection is presented and discussed. The reported results have the potential to serve as a launching pad for the use of SiNW FET sensors in real-world counteracting conditions and/or applications.

A neural network approach for thermal/pressure food processing

Abstract High-pressure processing is an interesting technology for the food industry that offers some important advantages compared to thermal processing. But, the results obtained after a pressure treatment depend as much on the applied pressure as the temperature during the treatment. Modelling the thermal behaviour of foods during high-pressure treatments using physical-based models is a really hard task. The main difficulty is the almost complete lack of values for thermophysical properties of foods under pressure. In this work, an artificial neural network (ANN) was carried out to evaluate its capability in predicting process parameters involved in thermal/pressure food processing. The ANN was trained with a data file composed of: applied pressure, pressure increase rate, set point temperature, high-pressure vessel temperature, ambient temperature and time needed to re-equilibrate temperature in the sample after pressurisation. When ANN was trained, it was able to predict accurately this last variable. Then, it becomes a useful alternative to physical-based models for process control since thermophysical properties of products implied are not needed in modellisation.

Optimising an artificial neural network for predicting the melting point of ionic liquids

We present an optimised artificial neural network (ANN) model for predicting the melting point of a group of 97 imidazolium salts with varied anions. Each cation and anion in the model is described using molecular descriptors. Our model has a mean prediction error of 1.30%, a regression coefficient of 0.99 and a mean P-value of 0.92. The ANNs prediction performance depends mainly on the anion size. In particular, the prediction error decreases as the anion size increases. The high statistical relevance makes this model a useful tool for predicting the melting points of imidazolium-based ionic liquids.

A quantum-chemical-based guide to analyze/quantify the cytotoxicity of ionic liquids

A COSMO-RS descriptor (Sσ-profile) has been used in quantitative structure–activity relationship studies (QSARs) based on a neural network for the prediction of the toxicological effect of ionic liquids (ILs) on a leukemia rat cell line (LogEC50 IPC-81) for a wide variety of compounds including imidazolium, pyridinium, ammonium, phosphonium, pyrrolidinium and quinolinium ILs. Sσ-profile is a two-dimensional quantum-chemical parameter capable of characterising the electronic structure and molecular size of cations and anions. By using a COSMO-RS descriptor for a training set of 105 compounds (96 ILs and 9 closely related salts) with known biological activities (experimental LogEC50 IPC-81 values), a reliable neural network was designed for the systematic analysis of the influence of structural IL elements (cation side chain, head group, anion type and the presence of functional groups) on the cytotoxicity of ∼450 IL compounds. The Quantitative Structure–Activity Map (QSAM), a new concept developed here, was proposed as a valuable tool for (i) the molecular understanding of IL toxicity, by relating Log EC50 IPC-81 parameters to the electronic structure of compounds given by quantum-chemical calculations; and (ii) the sustainable design of IL products with low toxicity, by linking the chemical structure of counterions to the predictions of IL cytotoxicity in handy contour plots. As a principal contribution, quantum-chemical-based QSAM guides allow the analysis/quantification of the non-linear mixture effects of the toxicophores constituting the IL structures. Based on these favorable results, the QSAR model was applied to estimate IL cytotoxicities in order to screen commercially available compounds with comparatively low toxicities.

A Highly Sensitive Diketopyrrolopyrrole‐Based Ambipolar Transistor for Selective Detection and Discrimination of Xylene Isomers

An ambipolar poly(diketopyrrolopyrrole-terthiophene)-based field-effect transistor (FET) sensitively detects xylene isomers at low ppm levels with multiple sensing features. Combined with pattern-recognition algorithms, a sole ambipolar FET sensor, rather than arrays of sensors, can discriminate highly similar xylene structural isomers from one another.

Silicon Nanowire Sensors Enable Diagnosis of Patients via Exhaled Breath

Two of the biggest challenges in medicine today are the need to detect diseases in a noninvasive manner and to differentiate between patients using a single diagnostic tool. The current study targets these two challenges by developing a molecularly modified silicon nanowire field effect transistor (SiNW FET) and showing its use in the detection and classification of many disease breathprints (lung cancer, gastric cancer, asthma, and chronic obstructive pulmonary disease). The fabricated SiNW FETs are characterized and optimized based on a training set that correlate their sensitivity and selectivity toward volatile organic compounds (VOCs) linked with the various disease breathprints. The best sensors obtained in the training set are then examined under real-world clinical conditions, using breath samples from 374 subjects. Analysis of the clinical samples show that the optimized SiNW FETs can detect and discriminate between almost all binary comparisons of the diseases under examination with >80% accuracy. Overall, this approach has the potential to support detection of many diseases in a direct harmless way, which can reassure patients and prevent numerous unpleasant investigations.

Estimation with neural networks of the water content in imidazolium-based ionic liquids using their experimental density and viscosity values

A multilayer perceptron neural network (NN) model has been created for the estimation of the water content present in the following ionic liquids (ILs): 1-butyl-3-methylimidazolium tetrafluoroborate, 1-butyl-3-methylimidazolium methylsulfate, 1,3-dimethylimidazolium methylsulfate and 1-ethyl-3-methylimidazolium ethylsulfate. To achieve this goal, their density and viscosity values were used. The experimental values of these physicochemical properties, employed to design the NN model, were measured and registered at 298.15K. They were determined at different relative humidity values ranging from 11.1 to 84.3%. The estimated results were then compared with the experimental measurements of the water content, which were carried out by the Karl Fischer technique, and the difference between the real and estimated values was less than 0.05 and 3.1% in the verification and validation processes, respectively. In addition, an external validation process was developed using four bibliographical references. In this case, the mean prediction error was less than 6.3%. In light of these results, the NN model shows an acceptable goodness of fit, sufficient robustness, and an adequate estimative capacity to determine the water content inside the studied range of the ILs analyzed.

A Novel Method To Quantify the Adulteration of Extra Virgin Olive Oil with Low-Grade Olive Oils by UV−Vis

A simple and novel method to quantify adulterations of extra virgin olive oil (EVOO) with refined olive oil (ROO) and refined olive-pomace oil (ROPO) is described here. This method consists of calculating chaotic parameters (Lyapunov exponent, autocorrelation coefficients, and two fractal dimensions, CPs) from UV-vis scans of adulterated EVOO samples. These parameters have been successfully linearly correlated with the ROO or ROPO concentrations in 396 EVOO adulterated samples. By an external validation process, when the adulterating agent concentration is less than 10%, the integrated CPs/UV-vis model estimates the adulterant agent concentration with a mean correlation coefficient (estimated versus real concentration of low grade olive oil) greater than 0.97 and a mean square error of less than 1%. In light of these results, this detector is suitable not only to detect adulterations but also to measure impurities when, for instance, a higher grade olive oil is transferred to another storage tank in which lower grade olive oil was stored that had not been adequately cleaned.

Estimation of ternary liquid–liquid equilibria for arene/alkane/ionic liquid mixtures using neural networks

Neural network models have been explored for the prediction of the liquid-liquid equilibrium data and aromatic/aliphatic selectivity values. Four ternary systems composed of toluene, heptane, and the ionic liquids 1-ethyl-3-methylimidazolium ethylsulfate, or 1,3-dimethylimidazolium methylsulfate were investigated at 313.2 and 348.2 K.