New eco-friendly low-cost binders for Li-ion anodes

In the production of commercial Li-ion batteries, the active materials slurries are generally prepared using polyvinylidene fluoride (PVdF) as binder because of its good adhesion properties and electrochemical stability. Unfortunately, there are some disadvantages related to the use of PVdF: the most important is the use of toxic and environmentally unfriendly solvents, such as N-methyl-pyrrolidone (NMP), and the second is the high costs. In the light of these considerations, it seemed straightforward to investigate the suitability of some water-soluble, inexpensive, and eco-friendly materials to test as alternative binders (sodium alginate, chitosan tragacanth gum, gelatin). The rheological properties of these materials have been investigated in addition to the electrochemical characterization. Furthermore, graphite electrodes with PVdF, carboxymethyl cellulose (CMC), and styrene-butadiene rubber (SBR) binders have been considered for sake of comparison. We found that some of these water-soluble binders, besides good electrochemical performances, showed a high adhesion to the current collector and a good electrochemical stability under the experimental conditions employed, which makes them interesting for the next generation of Li-ion batteries.     

Synthesis of 1-(2-Fluorophenyl)pyrazoles by 1,3-Dipolar Cycloaddition of the Corresponding Sydnones

3-Arylsydnones bearing fluorine and bromine atoms on the benzene ring were synthesized from N-nitroso-2-fluorophenylglycines and characterized by NMR spectroscopy. These were employed further in synthesis of the corresponding 1-(2-fluorophenyl)pyrazoles by 1,3-dipolar cycloaddition reaction with dimethyl acetylenedicarboxylate (DMAD) as activated dipolarophile. The sydnones as reaction intermediates were characterized by single crystal X-ray diffraction analysis showing interesting features such as halogen bonding as an important interaction in modeling the crystal structure.     

Effects of structural properties of silicon carbide-derived carbons on their electrochemical double-layer capacitance in aqueous and organic electrolytes

High surface area silicon carbide-derived carbons (Si-CDCs) synthesized by chlorination of beta silicon carbide (βSiC) with two different particle sizes (6 μm and 50 nm) show different porosities with graphitic structure. Transmission electron microscopy, Raman spectroscopy and argon (Ar) and carbon dioxide (CO₂) sorption analyses are used to examine the textural properties of the Si-CDCs. The results show that the particle size of the precursor affects the surface area and porosity of carbons. Furthermore, an additional heat treatment of the Si-CDC with 50-nm particle size for 24 h at 1,000 °C results in a collapse of the pore structure and reduces the surface area. The capacitive behaviours are investigated in H₂SO₄ and in tetraethyl ammonium tetrafluoroborate (TEABF₄)/acetonitrile (AN). The electrochemical performance of the Si-CDCs is influenced by the particle size, surface area, pore volume and pore size distribution. The Si-CDCs exhibit capacitances in 1 M H₂SO₄ of up to 179 F g^?1 and very stable charge–discharge performance over 5,000 cycles. This study shows the crucial importance of ultramicropores less than 1 nm combined with nanosized particles for achieving high capacitance in aqueous electrolyte. Moreover, the graphitic degree at the surface of the Si-CDCs enhances considerably the rate capability and stability in both electrolytes.     

Forcing operators on MTL‐algebras

We study the forcing operators on MTL‐algebras, an algebraic notion inspired by the Kripke semantics of the monoidal t ‐norm based logic (MTL). At logical level, they provide the notion of the forcing value of an MTL‐formula. We characterize the forcing operators in terms of some MTL‐algebras morphisms. From this result we derive the equality of the forcing value and the truth value of an MTL‐formula (© 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Encapsulation of Risperidone by Methylated β-Cyclodextrins: Physicochemical and Molecular Modeling Studies

Risperidone (RSP) is an atypical antipsychotic drug which acts as a potent antagonist of serotonin-2 (5TH2) and dopamine-2 (D2) receptors in the brain; it is used to treat schizophrenia, behavioral and psychological symptoms of dementia and irritability associated with autism. It is a poorly water soluble benzoxazole derivative with high lipophilicity. Supramolecular adducts between drug substance and two methylated β-cyclodextrins, namely heptakis(2,6-di-O-methyl)-β-cyclodextrin (DM-β-CD) and heptakis(2,3,6-tri-O-methyl)-β-cyclodextrin (TM-β-CD) were obtained in order to enhance RSP solubility and improve its biopharmaceutical profile. The inclusion complexes were evaluated by means of thermoanalytical methods (TG—thermogravimetry/DTG—derivative thermogravimetry/HF—heat flow), powder X-ray diffractometry (PXRD), universal-attenuated total reflectance Fourier transform infrared (UATR-FTIR), UV spectroscopy and saturation solubility studies. Job’s method was employed for the determination of the stoichiometry of the inclusion complexes, which was found to be 2:1 for both guest–host systems. Molecular modeling studies were carried out for an in-depth characterization of the interaction between drug substance and cyclodextrins (CDs). The physicochemical properties of the supramolecular systems differ from those of RSP, demonstrating the inclusion complex formation between drug and CDs. The RSP solubility was enhanced as a result of drug encapsulation in the CDs cavity, the higher increase being obtained with DM-β-CD as host; the guest–host system RSP/DM-β-CD can thus be a starting point for further research in developing new formulations containing RSP, with enhanced bioavailability.     

Support vector machine learning with an evolutionary engine

The paper presents a novel evolutionary technique constructed as an alternative of the standard support vector machines architecture. The approach adopts the learning strategy of the latter but aims to simplify and generalize its training, by offering a transparent substitute to the initial black-box. Contrary to the canonical technique, the evolutionary approach can at all times explicitly acquire the coefficients of the decision function, without any further constraints. Moreover, in order to converge, the evolutionary method does not require the positive (semi-)definition properties for kernels within nonlinear learning. Several potential structures, enhancements and additions are proposed, tested and confirmed using available benchmarking test problems. Computational results show the validity of the new approach in terms of runtime, prediction accuracy and flexibility.     

Double-layer capacitance of waste coffee ground activated carbons in an organic electrolyte

Using ZnCl₂ activation we prepared a series of carbon electrodes from waste coffee grounds to study the effect of mesopores on double-layer capacitance in a tetraethyl ammonium tetrafluoroborate/acetonitrile electrolyte. The activated carbon with the largest mesopore volume achieved an energy density of 34 Wh kg^?1 at low current loads, and significantly retained an energy density of 16.5 Wh kg^?1 and specific capacitance of more than 100 F g^?1 at fast charge–discharge rates (20 A g^?1). The effect of mesopores on capacitance at fast charge–discharge rates is discussed.