Fuel cells (FC) produce electricity in a continuous mode through a catalytic reaction and have many possible applications, as for example, in the transportation sector substituting the combustion engines. These devices can be regarded as a free emission technology if the fuel used in them is obtained in a renewable mode, such as hydrogen from the reforming of light alcohols obtained from biomass fermentation or gasification. In fact, proton exchange membrane fuel cells (PEMFC) use hydrogen as fuel that, in turn, has to be free of carbon monoxide (CO) since the later chemical species poisons the platinum based catalyst applied in the electrochemical process. This review aims at clarifying how multicomponent catalysts can be used in the hydrogen production from light alcohols reforming to overcome the limitations of current catalysts. Specifically, their low thermal stability, the CO formation that is not suitable for FC use, the carbon (coke) production that poisons the reforming catalyst, or byproducts (i.e. CH4) generation that reduces the hydrogen amount produced. Special emphasis is paid to the applicability of theoretical methods for the study and development of improved multicomponent catalysts for light alcohols reforming.

The applicability of deep eutectic solvents is determined by their physicochemical properties. In turn, the properties of eutectic mixtures are the result of the components' molar ratio and chemical composition. Owing to the relatively low viscosities displayed by alcohol-based deep eutectic solvents (DESs), their application in industry is more appealing. Modeling the composition-property relationships established in polyalcohol-based mixtures is crucial for both understanding and predicting their behavior. In this work, a physicochemical property-structure comparison study is made between four choline chloride polyalcohol-based DESs, namely, ethaline, propeline, propaneline, and glyceline. Physicochemical properties obtained from molecular dynamic simulations are compared to experimental data, whenever possible. The simulations cover the temperature range from 298.15 to 348.15 K. The simulated and literature experimental data are generally in good agreement for all the studied DESs. Structural properties, such as radial and spatial distribution functions, coordination numbers, hydrogen bond donor (HBD)-HBD aggregate formation, and hydrogen bonding are analyzed in detail. The higher prevalence of HBD:HBD and HBD:anion hydrogen bonds is likely to be the major reason for the relatively high density and viscosity of glyceline as well as for lower DES self-diffusions.

The ever-increasing presence of contaminants in environmental waters is an alarming issue, not only because of their harmful effects in the environment but also because of their risk to human health. Pharmaceuticals and pesticides, among other compounds of daily use, such as personal care products or plasticisers, are being released into water bodies. This release mainly occurs through wastewater since the treatments applied in many wastewater treatment plants are not able to completely remove these substances. Therefore, the analysis of these contaminants is essential but this is difficult due to the great variety of contaminating substances. Facing this analytical challenge, electrochemical sensing based on molecularly imprinted polymers (MIPs) has become an interesting field for environmental monitoring. Benefiting from their superior chemical and physical stability, low-cost production, high selectivity and rapid response, MIPs combined with miniaturized electrochemical transducers offer the possibility to detect target analytes in-situ. In most reports, the construction of these sensors include nanomaterials to improve their analytical characteristics, especially their sensitivity. Moreover, these sensors have been successfully applied in real water samples without the need of laborious pre-treatment steps. This review provides a general overview of electrochemical MIP-based sensors that have been reported for the detection of pharmaceuticals, pesticides, heavy metals and other contaminants in water samples in the past decade. Special attention is given to the construction of the sensors, including different functional monomers, sensing platforms and materials employed to achieve the best sensitivity. Additionally, several parameters, such as the limit of detection, the linear concentration range and the type of water samples that were analysed are compiled.

The inhibitors of two isoforms of mitogen-activated protein kinase-interacting kinases (i.e., MNK-1 and MNK-2) are implicated in the treatment of a number of diseases including cancer. This work reports, for the first time, a multi-target (or multi-tasking) in silico modeling approach (mt-QSAR) for probing the inhibitory potential of these isoforms against MNKs. Linear and non-linear mt-QSAR classification models were set up from a large dataset of 1892 chemicals tested under a variety of assay conditions, based on the Box-Jenkins moving average approach, along with a range of feature selection algorithms and machine learning tools, out of which the most predictive one (>90% overall accuracy) was used for mechanistic interpretation of the likely inhibition of MNK-1 and MNK-2. Considering that the latter model is suitable for virtual screening of chemical libraries-i.e., commercial, non-commercial and in-house sets, it was made publicly accessible as a ready-to-use FLASK-based application. Additionally, this work employed a focused kinase library for virtual screening using an mt-QSAR model. The virtual hits identified in this process were further filtered by using a similarity search, in silico prediction of drug-likeness, and ADME profiles as well as synthetic accessibility tools. Finally, molecular dynamic simulations were carried out to identify and select the most promising virtual hits. The information gathered from this work can supply important guidelines for the discovery of novel MNK-1/2 inhibitors as potential therapeutic agents.

<jats:p>The tau protein is considered an important qualitative and quantitative biomarker for Alzheimer’s disease in its asymptomatic phase. In 2011, biomarkers were suggested by the National Institute on Aging-Azheimer’s Association as a new criterion for the early diagnosis of Alzheimer’s disease. Thus, highlighting the non-existence of theoretical research on the subject, we investigated the binding interaction properties between phosphorylated tau protein and a theoretically modeled ligands constituted by the fullerol functionalized with radiopharmaceuticals from an in silico approach via molecular docking and density functional theory (DFT) ab initio computational simulation. The results demonstrated that the ligand with the greatest affinity-based binding energy to the protein was fullerol + F-THK5105. However, all systems were considered promising for the development of a potential diagnostic nanomarker. These theoretical results could efficiently contribute to reduce the time and the cost for future experimental preclinical studies and open new opportunities toward molecular recognition in nanomedicine.</jats:p>

Simple Summary: Carbon nanotubes are revolutionary materials with applications in a lot of different areas. However, there is a rising concern regarding unlikely toxicity effects these materials may trigger. Due to this, the main aim of this paper is to develop a comprehensive approach to study toxicity effect of carbon nanotubes on the mitochondria F0F1-ATPase. We have employed a combination of experimental and computational study. In so doing, we have combined in vitro inhibition responses in submitochondrial particles with docking elastic network models, fractal surface analysis, and Nano-quantitative structure toxicity relationship models (Nano-QSTR models). Results show that this method may be used for the fast prediction of the nanotoxicity induced by single walled carbon nanotubes (SWCNT), avoiding time- and money-consuming techniques, and may open new avenues toward to the better understanding and prediction of new nanotoxicity mechanisms. Single-walled carbon nanotubes can induce mitochondrial F0F1-ATPase nanotoxicity through inhibition. To completely characterize the mechanistic effect triggering the toxicity, we have developed a new approach based on the combination of experimental and computational study, since the use of only one or few techniques may not fully describe the phenomena. To this end, the in vitro inhibition responses in submitochondrial particles (SMP) was combined with docking, elastic network models, fractal surface analysis, and Nano-QSTR models. In vitro studies suggest that inhibition responses in SMP of F0F1-ATPase enzyme were strongly dependent on the concentration assay (from 3 to 5 mu g/mL) for both pristine and COOH single-walled carbon nanotubes types (SWCNT). Besides, both SWCNTs show an interaction inhibition pattern mimicking the oligomycin A (the specific mitochondria F0F1-ATPase inhibitor blocking the c-ring F0 subunit). Performed docking studies denote the best crystallography binding pose obtained for the docking complexes based on the free energy of binding (FEB) fit well with the in vitro evidence from the thermodynamics point of view, following an affinity order such as: FEB (oligomycin A/F0-ATPase complex) = -9.8 kcal/mol > FEB (SWCNT-COOH/F0-ATPase complex) = -6.8 kcal/mol similar to FEB (SWCNT-pristine complex) = -5.9 kcal/mol, with predominance of van der Waals hydrophobic nano-interactions with key F0-ATPase binding site residues (Phe 55 and Phe 64). Elastic network models and fractal surface analysis were performed to study conformational perturbations induced by SWCNT. Our results suggest that interaction may be triggering abnormal allosteric responses and signals propagation in the inter-residue network, which could affect the substrate recognition ligand geometrical specificity of the F0F1-ATPase enzyme in order (SWCNT-pristine > SWCNT-COOH). In addition, Nano-QSTR models have been developed to predict toxicity induced by both SWCNTs, using results of in vitro and docking studies. Results show that this method may be used for the fast prediction of the nanotoxicity induced by SWCNT, avoiding time- and money-consuming techniques. Overall, the obtained results may open new avenues toward to the better understanding and prediction of new nanotoxicity mechanisms, rational drug design-based nanotechnology, and potential biomedical application in precision nanomedicine.

Quantitative structure activity relationships (QSAR) modelling is a well-known computational tool, often used in a wide variety of applications. Yet one of the major drawbacks of conventional QSAR modelling is that models are set up based on a limited number of experimental and/or theoretical conditions. To overcome this, the so-called multitasking or multitarget QSAR (mt-QSAR) approaches have emerged as new computational tools able to integrate diverse chemical and biological data into a single model equation, thus extending and improving the reliability of this type of modelling. We have developed QSAR-Co-X, an open source python-based toolkit (available to download at ) for supporting mt-QSAR modelling following the Box-Jenkins moving average approach. The new toolkit embodies several functionalities for dataset selection and curation plus computation of descriptors, for setting up linear and non-linear models, as well as for a comprehensive results analysis. The workflow within this toolkit is guided by a cohort of multiple statistical parameters and graphical outputs onwards assessing both the predictivity and the robustness of the derived mt-QSAR models. To monitor and demonstrate the functionalities of the designed toolkit, four case-studies pertaining to previously reported datasets are examined here. We believe that this new toolkit, along with our previously launched QSAR-Co code, will significantly contribute to make mt-QSAR modelling widely and routinely applicable.

Determination of antibiotics in environmental waters is an important global issue. Although furazolidone (FZD) was banned from use in food-producing animals, owing to its mutagenic and carcinogenic effects, this antibiotic has been illegally used across the world and its presence in environment have being noted. In this work, the first selective molecularly imprinted polymer (MIP) was developed for electrochemical detection of FZD. It was constructed based on the modification of the traditional carbon paste electrode (CPE) with MIP microparticles, followed by introduction of multi-walled carbon nanotubes (MWCNTs). Quantum mechanical (QM) calculations and molecular dynamics (MD) simulations were performed to allow rational selection of an appropriate functional monomer and to simulate the best pre-polymerisation conditions, respectively. The MIP were synthetized by polymerization using 2-acrylamido-2-methyl-1-propanesulfonic acid (AMPS) as monomer and FZD as template molecule. The MIP microparticles were then incorporated on CPE-MWCNTs and the electrochemical analysis of FZD were evaluated by differential pulse voltammetry (DPV). After optimisation of experimental conditions, the MIP-CPE-MWCNTs sensor exhibited a good linear response over the concentration range of 0.01 mu M to 1 mu M with a correlation coefficient of 0.9995. The limit of detection (LOD) was found to be 0.03 mu M (S/N = 3). Due to high imprinting efficiency the sensor displayed selectivity to recognise FZD molecules and it was successfully applied in water samples where excellent recovery values (over 90 %) were obtained. The proposed sensor provides an efficient and promising sustainable strategy for monitorisation of FZD in environmental waters.