Thermal stability and surface acidity of mesoporous silica doubly doped by incorporation of sulfate and zirconium ions

A sulfated Si-Zr-MCM-41 solid with highly ordered mesostructure was synthesized through a templated synthesis route where the CTAB surfactant was used as template. During the synthesis procedure, various amounts of (NH₄)₂SO₄ were added into the mixed solution of Zr and Si precursors to in situ sulfate the MCM-41 solids, aiming to enhance the acidity and thermal stability. The resultant materials showed a long-range ordered hexagonal arrangement with high surface area larger than 797 m²/g and an average pore size distributed at approximate 2.5-2.8 nm. The high-resolution TEM observations confirmed that the order of the mesostructure gained when the molar ratio of SO₄²⁻/(ZrO₂ + SiO₂) increased from 0.1 to 0.3 but decreased as it reached 0.5, which is consistent with the results of ²⁹Si MAS-NMR and XRD analysis. Compared to Si-MCM-41, the (Q² + Q³)/Q⁴ ratio derived from the NMR spectra of the Zr-doped sample was higher, indicating that zirconium atoms were incorporated into the silica framework. Unexpectedly, in situ sulfation does not enhance the surface Brönsted acidity, most likely due to the sulfur retained within the bulk of the materials; however, it indeed improved the thermal stability of the solid and long-range order of the structure.     

Effect of Tin Precursor on the Catalytic Properties of Pt-Sn/Al2O3 Sol-Gel Prepared Catalysts

Tin-alumina supports were prepared by cogelation of aluminum tri-sec-butoxide and tetrabutyltin or tin tetrachloride. Aqueous or acetone platinum solutions were used for impregnation. The results show that the specific surface area and the acidity of the catalysts were only slightly modified by the metal precursors used. On cyclohexane dehydrogenation andn-hexane conversion, high selectivity to olefins and low cracking products were detected.     

New insights on the mechanism of oxidation of D-galacturonic acid by hypervalent chromium

The pollutant Cr(VI) is known to be very carcinogenic. In conditions of excess of Cr(VI), oxidation of D-galacturonic acid (Galur), the major metabolite of pectin, yields d-galactaric acid (Galar) and Cr(III). The redox reaction takes place through a multistep mechanism involving formation of intermediate Cr(II/IV) and Cr(V) species. The mechanism combines one- and two-electron pathways for the reduction of Cr(IV) by the organic substrate: Cr(VI)→ Cr(IV)→ Cr(II) and Cr(VI)→ Cr(IV)→ Cr(III). This is supported by the observation of the optical absorption spectra of Cr(VI) esters, free radicals, CrO(2)(2+) (superoxoCr(III) ion) and oxo-Cr(V) complexes. Cr(IV) cannot be directly detected; however, formation of CrO(2)(2+) provides indirect evidence for the intermediacy of Cr(II/IV). Cr(IV) reacts with Galur much faster than Cr(V) and Cr(VI) do. The analysis of the reaction kinetics via optical absorption spectroscopy shows that the Cr(IV)-Galur reaction rate inversely depends on [H(+)]. Nevertheless, high [H(+)] still does not facilitate accumulation of Cr(IV) in the Cr(VI)-Galur mixture. Cr(VI) and the intermediate Cr(V) react with Galur at comparable rates; therefore the build-up and decay of Cr(V) accompany the decay of Cr(VI). The complete rate laws for the Cr(VI), Cr(V) and Cr(IV)-Galur redox reaction are here derived in detail. Furthermore, the nature of the five-co-ordinated oxo-Cr(V) bischelate complexes formed in Cr(VI)-Galur mixtures at pH 1-5 is investigated using continuous-wave and pulsed electron paramagnetic resonance (EPR) and density functional theory (DFT).     

Nonnegative matrices with prescribed spectrum and elementary divisors

In this paper we give new sufficient conditions for the existence and construction of nonnegative matrices with prescribed elementary divisors, which drastically improve and contain some of the previous known conditions. We also show how to perturb complex eigenvalues of a nonnegative matrix while keeping its nonnegativity. These results allow us, under certain conditions, to easily decide if a given list is realizable with prescribed elementary divisors.     

A generalized Beraha conjecture for non-planar graphs

We study the partition function _ZG(nk,k)(Q,v)$Z_{G(nk,k)}(Q,v)$ of the Q  -state Potts model on the family of (non-planar) generalized Petersen graphs G(nk,k)$G(nk,k)$. We study its zeros in the plane (Q,v)$(Q,v)$ for 1?k?7$1?k?7$. We also consider two specializations of _ZG(nk,k)$Z_{G(nk,k)}$, namely the chromatic polynomial _PG(nk,k)(Q)$P_{G(nk,k)}(Q)$ (corresponding to v=−1$v=-1$), and the flow polynomial _ΦG(nk,k)(Q)${\Phi }_{G(nk,k)}(Q)$ (corresponding to v=−Q$v=-Q$). In these two cases, we study their zeros in the complex Q  -plane for 1?k?7$1?k?7$. We pay special attention to the accumulation loci of the corresponding zeros when n→∞$n\to \infty$. We observe that the Berker–Kadanoff phase that is present in two-dimensional Potts models, also exists for non-planar recursive graphs. Their qualitative features are the same; but the main difference is that the role played by the Beraha numbers for planar graphs is now played by the non-negative integers for non-planar graphs. At these integer values of Q, there are massive eigenvalue cancellations, in the same way as the eigenvalue cancellations that happen at the Beraha numbers for planar graphs.     

Determination of convex functions via subgradients of minimal norm

Mathematical Programming - We show, in Hilbert space setting, that any two convex proper lower semicontinuous functions bounded from below, for which the norm of their minimal subgradients...     

A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score

Electric power forecasting plays a substantial role in the administration and balance of current power systems. For this reason, accurate predictions of service demands are needed to develop better programming for the generation and distribution of power and to reduce the risk of vulnerabilities in the integration of an electric power system. For the purposes of the current study, a systematic literature review was applied to identify the type of model that has the highest propensity to show precision in the context of electric power forecasting. The state-of-the-art model in accurate electric power forecasting was determined from the results reported in 257 accuracy tests from five geographic regions. Two classes of forecasting models were compared: classical statistical or mathematical (MSC) and machine learning (ML) models. Furthermore, the use of hybrid models that have made significant contributions to electric power forecasting is identified, and a case of study is applied to demonstrate its good performance when compared with traditional models. Among our main findings, we conclude that forecasting errors are minimized by reducing the time horizon, that ML models that consider various sources of exogenous variability tend to have better forecast accuracy, and finally, that the accuracy of the forecasting models has significantly increased over the last five years.