Study of the diffusion properties of zeolite mixtures by combined gravimetric analysis, IR spectroscopy and inversion methods (IRIS).

We report the development of a new method of investigation of the mass transport properties of acidic zeolite-based materials aiming to overcome the limitations of classical approaches. It consists in hyphenating gravimetric analysis and infrared spectroscopy. The former allows assessing the diffusion from the gas phase to all the porosity, while IR allows for selective assessment of diffusion to the zeolite active sites located in the micropores. Furthermore, the data are processed by an original methodology allowing the recovery of the distribution of diffusion domains by inversion of the integral equations describing the uptake curves or the evolution of the infrared spectra. The combination of gravimetric analysis and IR spectroscopy makes it possible to monitor and distinguish diffusion within the various components of the material. The methodology has been applied to the isooctane uptake in the mechanical mixture of FAU and MFI zeolites. Analysis of both gravimetric uptake curves and evolving infrared spectra allows distinguishing and assigning diffusion domains to the H-FAU and H-MFI components of the mixture, with high and low effective diffusion rate constants, respectively. The advantages and limits of the methodology are discussed.

Giant and Tunable Excitonic Optical Anisotropy in Single-Crystal Halide Perovskites.

During the last years, giant optical anisotropy has demonstrated its paramount importance for light manipulation. In spite of recent advances in the field, the achievement of continuous tunability of optical anisotropy remains an outstanding challenge. Here, we present a solution to the problem through the chemical alteration of halogen atoms in single-crystal halide perovskites. As a result, we manage to continually modify the optical anisotropy by 0.14. We also discover that the halide perovskite can demonstrate optical anisotropy up to 0.6 in the visible range─the largest value among non-van der Waals materials. Moreover, our results reveal that this anisotropy could be in-plane and out-of-plane depending on perovskite shape─rectangular and square. As a practical demonstration, we have created perovskite anisotropic nanowaveguides and shown a significant impact of anisotropy on high-order guiding modes. These findings pave the way for halide perovskites as a next-generation platform for tunable anisotropic photonics.

Generalized weighted permutation entropy.

A novel heuristic approach is proposed here for time series data analysis, dubbed Generalized weighted permutation entropy, which amalgamates and generalizes beyond their original scope two well established data analysis methods: Permutation entropy and Weighted permutation entropy. The method introduces a scaling parameter to discern the disorder and complexity of ordinal patterns with small and large fluctuations. Using this scaling parameter, the complexity-entropy causality plane is generalized to the complexity-entropy-scale causality box. Simulations conducted on synthetic series generated by stochastic, chaotic, and random processes, as well as real world data, are shown to produce unique signatures in this three dimensional representation.

Elucidating the Nature of the External Acid Sites of ZSM-5 Zeolites Using NMR Probe Molecules.

The identification of acid and nonacid species at the external surface of zeolites remains a major challenge, in contrast to the extensively-studied internal acid sites. Here, it is shown that the synthesis of zeolite ZSM-5 samples with distinct particle sizes, combined with solid-state NMR and computational studies of trimethylphosphine oxide (TMPO) adsorption, provides insight into the chemical species on the external surface of the zeolite crystals. 1 H-31 P HETCOR NMR spectra of TMPO-loaded zeolites exhibit a broad correlation peak at δP ∼35-55 ppm and δH ∼5-12 ppm assigned to external SiOH species. Pore-mouth Brønsted acid sites exhibit 31 P and 1 H NMR resonances and adsorption energies close to those reported for internal acid sites interacting with TMPO. The presence of an external tricoordinate Al-Lewis site interacting strongly with TMPO is suggested, resulting in 31 P resonances that overlap with the peaks usually ascribed to the interaction of TMPO with Brønsted sites.

A New Look at Calendar Anomalies: Multifractality and Day-of-the-Week Effect.

Stock markets can become inefficient due to calendar anomalies known as the day-of-the-week effect. Calendar anomalies are well known in the financial literature, but the phenomena remain to be explored in econophysics. This paper uses multifractal analysis to evaluate if the temporal dynamics of market returns also exhibit calendar anomalies such as day-of-the-week effects. We apply multifractal detrended fluctuation analysis (MF-DFA) to the daily returns of market indices worldwide for each day of the week. Our results indicate that distinct multifractal properties characterize individual days of the week. Monday returns tend to exhibit more persistent behavior and richer multifractal structures than other day-resolved returns. Shuffling the series reveals that multifractality arises from a broad probability density function and long-term correlations. The time-dependent multifractal analysis shows that the Monday returns' multifractal spectra are much wider than those of other days. This behavior is especially persistent during financial crises. The presence of day-of-the-week effects in multifractal dynamics of market returns motivates further research on calendar anomalies for distinct market regimes.

QRNN: $q$ -Generalized Random Neural Network.

Artificial neural networks (ANNs) are widely used in applications with complex decision boundaries. A large number of activation functions have been proposed in the literature to achieve better representations of the observed data. However, only a few works employ Tsallis statistics, which has successfully been applied to various other fields. This paper presents a random neural network (RNN) with q -Gaussian activation functions [ q -generalized RNN (QRNN)] based on Tsallis statistics. The proposed method employs an additional parameter q (called the entropic index) which reflects the degree of nonextensivity. This approach has the flexibility to model complex decision boundaries of different shapes by varying the entropic index. We conduct numerical experiments to analyze the efficiency of QRNN compared with RNNs and several other classical methods. Statistical tests (Wilcoxon and Friedman) are used to validate our results and show that the QRNN performs significantly better than RNNs with different activation functions. In addition, we find that QRNN outperforms many of the compared classical methods, with the exception of support vector machines, in which case it still exhibits a substantial advantage in terms of implementation simplicity and speed.

The investigation of phenol removal from aqueous solutions by zeolites as solid adsorbents.

This work reports results on phenol adsorption from aqueous solutions on synthetic BEA (ß) and MFI (ZSM-5) zeolites, studied by heat-flow microcalorimetry. For the sake of comparison, the adsorption was performed on activated carbon, a solid customarily used for removal of phenol from water. The obtained values of heats evolved during phenol adsorption indicate the heterogeneity of active sites present on the investigated systems for the adsorption of phenol. In addition, the amounts of adsorbed pollutant were determined and presented in the form of adsorption isotherms, which were interpreted using Langmuir, Freundlich, Dubinin-Astakov and Sips' equations. The latter was found to express high level of agreement with experimental data. The results obtained in this work reveal that the adsorption of phenol on zeolites depends on both Si/Al ratio and on the pore size. Hydrophobic zeolites that possess higher contents of Si show higher affinities for phenol adsorption. Among investigated zeolites, zeolite ß possesses the highest capacity for adsorption of phenol. The possibility of regeneration of used adsorbents was investigated by thermal desorption technique. It has been shown that in the case of ß zeolite the majority of adsorbed phenol is easily released in the low temperature region.

The adsorption of nicotine from aqueous solutions on different zeolite structures.

The present work is focused on the adsorption of nicotine from aqueous solutions. Based on the data available in the literature, serious concern is claimed regarding the appearance of nicotine in ground, surface and municipal wastewaters. In order to investigate the possibility of abatement by adsorption, three different types of zeolites (BEA, MFI and HEU) have been applied as adsorbents. In addition, the adsorption was performed on activated carbon, a solid customarily used for removal of pollutants from water. The adsorption of nicotine was studied by isothermal microcalorimetry, which provided the heats evolved as a result of adsorption. The values of these heats revealed that the investigated solids are energetically heterogeneous for the adsorption of nicotine from aqueous solution. Additionally, the amounts of adsorbed pollutant were determined and presented in the form of adsorption isotherms. The obtained adsorption isotherms were interpreted using Langmuir, Freundlich, and Sips equations; the latter was found to express high level of agreement with experimental data of nicotine adsorption on the investigated solids. The possibilities to regenerate the adsorbents were examined by means of thermogravimetry coupled with mass spectrometry. From all obtained results, it was possible to distinguish zeolite BEA as a material which possesses the capacity for adsorption of nicotine comparable to that of activated carbon.