Synergism of primary and secondary interactions in a crystalline hydrogen peroxide complex with tin.

Despite the significance of H2O2-metal adducts in catalysis, materials science and biotechnology, the nature of the interactions between H2O2 and metal cations remains elusive and debatable. This is primarily due to the extremely weak coordinating ability of H2O2, which poses challenges in characterizing and understanding the specific nature of these interactions. Herein, we present an approach to obtain H2O2-metal complexes that employs neat H2O2 as both solvent and ligand. SnCl4 effectively binds H2O2, forming a SnCl4(H2O2)2 complex, as confirmed by 119Sn and 17O NMR spectroscopy. Crystalline adducts, SnCl4(H2O2)2·H2O2·18-crown-6 and 2[SnCl4(H2O2)(H2O)]·18-crown-6, are isolated and characterized by X-ray diffraction, providing the complete characterization of the hydrogen bonding of H2O2 ligands including geometric parameters and energy values. DFT analysis reveals the synergy between a coordinative bond of H2O2 with metal cation and its hydrogen bonding with a second coordination sphere. This synergism of primary and secondary interactions might be a key to understanding H2O2 reactivity in biological systems.

Effect of Post-Implantation Heat Treatment Conditions on Photoluminescent Properties of Ion-Synthesized Gallium Oxide Nanocrystals.

A novel and promising way for creating nanomaterials based on gallium oxide is the ion synthesis of Ga2O3 nanocrystals in a SiO2/Si dielectric matrix. The properties of nanocrystals are determined by the conditions of ion synthesis-the parameters of ion irradiation and post-implantation heat treatment. In this work, the light-emitting properties of Ga2O3 nanocrystals were studied depending on the temperature and annealing atmosphere. It was found that annealing at a temperature of 900 °C leads to the appearance of intense luminescence with a maximum at ~480 nm caused by the recombination of donor-acceptor pairs. An increase in luminescence intensity upon annealing in an oxidizing atmosphere is shown. Based on data from photoluminescence excitation spectroscopy and high-resolution transmission electron microscopy, a hypothesis about the possibility of the participation of a quantum-size effect during radiative recombination is proposed. A mechanism for the formation of Ga2O3 nanocrystals during ion synthesis is suggested, which makes it possible to describe the change in the luminescent properties of the synthesized samples with varying conditions of post-implantation heat treatment.

Effective waste management in service industry: Fuzzy-based modelling approach for strategic decision-making.

Hospitals need to identify issues of greater importance on waste management because the implementation of many different strategies may lead to an unconscious increase in costs. Accordingly, the purpose of this study is to define the most effective waste management strategies in the service industry. For this purpose, a novel fuzzy decision-making model is proposed that has two different stages. In this context, six JCI-based indicators are weighted by using sine trigonometric fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) methodology. Additionally, a comparative evaluation has also been conducted with sine trigonometric fuzzy Criteria Importance Through Intercriteria Correlation (CRITIC) technique to check the reliability of the findings. On the other hand, five different strategy alternatives are selected by considering the principles of the integrated waste management hierarchy approach. These items are evaluated by considering sine trigonometric fuzzy Technique for Order Preference by Similarity (TOPSIS). On the other side, these factors are also ranked with the help of sine trigonometric fuzzy Additive Ratio Assessment (ARAS) to test the consistency of the results. The main contribution is that prior strategies can be presented to the hospitals to have appropriate waste management process by defining the most important factors. Criteria weighting and alternative ranking results are the same in all combinations. Therefore, it is seen that the proposed model creates coherent and consistent results. It is defined that efficient storage of waste is the key issue to have effective waste management process. Moreover, 'reduce' is found as the most critical stage of this process.

Comparative analysis of hydro energy determinants for European Economies using Golden Cut-oriented Quantum Spherical fuzzy modelling and causality analysis.

This article presents a comparative analysis of the determinants of hydropower for European economies using Golden Cut oriented Quantum Spherical fuzzy modelling and causality analysis in 24 European countries over the period 2001-2020. The indicators chosen for the analysis are inflation, population, GDP per capita, CO2 and hydropower consumption. The analysis shows that the selected groups of countries are characterised by an inverse relationship between GDP per capita and hydropower consumption, suggesting a bi-directional causal relationship, which also confirms the novelty of this paper. Furthermore, another analysis is carried out using the fuzzy decision-making methodology. In this framework, the directions of influence of the five selected indicators are constructed: GDP per capita (criterion 1, D = 88.656, E = 88.083), hydropower consumption (criterion 2, D = 89.471, E = 88.677), population (criterion 3, D = 87.705, E = 89.228), CO2 emissions (criterion 4, D = 88.578, E = 89.186) and inflation (criterion 5, D = 88.943, E = 88.180). The Quantum Spherical fuzzy methodology is used for this purpose. The values of D and E are measures of the sum of the rows and columns of the overall relationship matrix. Hydropower consumption is the main criterion. It is understood that two different analyses give similar results, namely the bidirectional causal relationship between criteria 1 and 2.

Enhanced charge capacity and stability of Germanium(IV) Sulfide-Based anodes through Triton X100-Assisted synthesis and polysulfide shuttle mitigation.

Highly soluble germanium oxide,an amorphous macroreticular form of germanium oxide, was used as a precursor for the deposition of GeS2on reduced graphene oxide (rGO) through a low-temperature, wet-chemistry process. Thermal treatment of the solid provided an ultrathin rGO - supported amorphous GeS2coating. The GeS2@rGO composite was tested as a lithium ion battery (LIB) anode. Leveraging the versatility of wet chemistry processing, we employed strategies initially developed for mitigating polysulfide shuttle effects in lithium-sulfur batteries to enhance anode performance. The anode exhibited exceptional stability, surpassing 1000 cycles, with charge capacities exceeding 1220 and 870 mAh.g-1 at rates of 2 and 5 A.g-1, respectively. Performance improvements were achieved by minimizing GeS2 grain size using the non-ionic surfactant Triton X-100 during synthesis and preventing polysulfide shuttle effects through a negatively charged thick glass fiber separator, fluoroethylene carbonate additive (FEC) in EC:DEC (ethylene carbonate: diethyl carbonate) solvent, and a polyacrylic acid (PAA) binder. These cumulative modifications more than tripled the charge capacity of the germanium sulfide LIB anode. Feasibility was further demonstrated through full cell studies using a LiCoO2 counter electrode.

Revisiting R&D intensity and CO2 emissions link in the USA using time varying granger causality test: 1870∼2020.

R&D intensity, per capita GDP, and per capita CO2 emissions links in the USA over the period of 1870-2020 reflects the evolution of the economic development and technology for the environment benefit. Using Time varying Granger causality, the empirical results indicate both causal links between R&D intensity and per capita CO2 emissions and between per capita GDP and per capita CO2 emissions are time varying. In addition, R&D intensity significantly affects per capita CO2 emissions since 1975, and the per capita GDP significantly influences per capita CO2 emissions since 1978. That is, these findings not only in supportive of the EKC theory, but further disentangle the subtly linkages for the R&D intensity and CO2 emissions and the per capita GDP and CO2 emissions. Finally, the policy implication is that launch the new technical innovation and increase in R&D investment to maintain its sustainable economic growth are the best government strategy to reduce CO2 emissions in the USA.

Electrical Characteristics of CMOS-Compatible SiOx-Based Resistive-Switching Devices.

The electrical characteristics and resistive switching properties of memristive devices have been studied in a wide temperature range. The insulator and electrode materials of these devices (silicon oxide and titanium nitride, respectively) are fully compatible with conventional complementary metal-oxide-semiconductor (CMOS) fabrication processes. Silicon oxide is also obtained through the low-temperature chemical vapor deposition method. It is revealed that the as-fabricated devices do not require electroforming but their resistance state cannot be stored before thermal treatment. After the thermal treatment, the devices exhibit bipolar-type resistive switching with synaptic behavior. The conduction mechanisms in the device stack are associated with the effect of traps in the insulator, which form filaments in the places where the electric field is concentrated. The filaments shortcut the capacitance of the stack to different degrees in the high-resistance state (HRS) and in the low-resistance state (LRS). As a result, the electron transport possesses an activation nature with relatively low values of activation energy in an HRS. On the contrary, Ohm's law and tunneling are observed in an LRS. CMOS-compatible materials and low-temperature fabrication techniques enable the easy integration of the studied resistive-switching devices with traditional analog-digital circuits to implement new-generation hardware neuromorphic systems.

Model of Neuromorphic Odorant-Recognition Network.

We propose a new model for a neuromorphic olfactory analyzer based on memristive synapses. The model comprises a layer of receptive neurons that perceive various odors and a layer of "decoder" neurons that recognize these odors. It is demonstrated that connecting these layers with memristive synapses enables the training of the "decoder" layer to recognize two types of odorants of varying concentrations. In the absence of such synapses, the layer of "decoder" neurons does not exhibit specificity in recognizing odorants. The recognition of the 'odorant' occurs through the neural activity of a group of decoder neurons that have acquired specificity for the odorant in the learning process. The proposed phenomenological model showcases the potential use of a memristive synapse in practical odorant recognition applications.

Organoantimony Dihydroperoxides: Synthesis, Crystal Structures, and Hydrogen Bonding Networks.

Despite growing interest in the potential applications of p-block hydroperoxo complexes, the chemistry of inorganic hydroperoxides remains largely unexplored. For instance, single-crystal structures of antimony hydroperoxo complexes have not been reported to date. Herein, we present the synthesis of six triaryl and trialkylantimony dihydroperoxides [Me3Sb(OOH)2, Me3Sb(OOH)2·H2O, Ph3Sb(OOH)2·0.75(C4H8O), Ph3Sb(OOH)2·2CH3OH, pTol3Sb(OOH)2, pTol3Sb(OOH)2·2(C4H8O)], obtained by the reaction of the corresponding dibromide antimony(V) complexes with an excess of highly concentrated hydrogen peroxide in the presence of ammonia. The obtained compounds were characterized by single-crystal and powder X-ray diffraction, Fourier transform infrared and Raman spectroscopies, and thermal analysis. The crystal structures of all six compounds reveal hydrogen-bonded networks formed by hydroperoxo ligands. In addition to the previously reported double hydrogen bonding, new types of hydrogen-bonded motifs formed by hydroperoxo ligands were found, including infinite hydroperoxo chains. Solid-state density functional theory calculation of Me3Sb(OOH)2 revealed reasonably strong hydrogen bonding between OOH ligands with an energy of 35 kJ/mol. Additionally, the potential application of Ph3Sb(OOH)2·0.75(C4H8O) as a two-electron oxidant for the enantioselective epoxidation of olefins was investigated in comparison with Ph3SiOOH, Ph3PbOOH, t-BuOOH, and H2O2.

Structure and Chemical Composition of Ion-Synthesized Gallium Oxide Nanocrystals in Dielectric Matrices.

The ion-beam synthesis of Ga2O3 nanocrystals in dielectric matrices on silicon is a novel and promising way for creating nanomaterials based on gallium oxide. This research studies the regularities of changes, depending on the synthesis regimes used, in the chemical composition of ion-implanted SiO2/Si and Al2O3/Si samples. It has been shown that the formation of Ga-O chemical bonds occurs even in the absence of thermal annealing. We also found the conditions of ion irradiation and annealing at which the content of oxidized gallium in the stochiometric state of Ga2O3 exceeds 90%. For this structure, the formation of Ga2O3 nanocrystalline inclusions was confirmed by transmission electron microscopy.

Electrochemical Behavior of Reduced Graphene Oxide Supported Germanium Oxide, Germanium Nitride, and Germanium Phosphide as Lithium-Ion Battery Anodes Obtained from Highly Soluble Germanium Oxide.

Germanium and germanium-based compounds are widely used in microelectronics, optics, solar cells, and sensors. Recently, germanium and its oxides, nitrides, and phosphides have been studied as active electrode materials in lithium- and sodium-ion battery anodes. Herein, the newly introduced highly soluble germanium oxide (HSGO) was used as a versatile precursor for germanium-based functional materials. In the first stage, a germanium-dioxide-reduced graphene oxide (rGO) composite was obtained by complete precipitation of GeO2 nanoparticles on the GO from an aqueous solution of HSGO and subsequent thermal treatment in argon at low temperature. The composition of the composite, GeO2-rGO (20 to 80 wt.% of crystalline phase), was able to be accurately determined by the HSGO to GO ratio in the initial solution since complete deposition and precipitation were achieved. The chemical activity of germanium dioxide nanoparticles deposited on reduced graphene oxide was shown by conversion to rGO-supported germanium nitride and phosphide phases. The GeP-rGO and Ge3N4-rGO composites with different morphologies were prepared in this study for the first time. As a test case, composite materials with different loadings of GeO2, GeP, and Ge3N4 were evaluated as lithium-ion battery anodes. Reversible conversion-alloying was demonstrated in all cases, and for the low-germanium loading range (20 wt.%), almost theoretical charge capacity based on the germanium content was attained at 100 mA g-1 (i.e., 2595 vs. 2465 mAh g-1 for Ge3N4 and 1790 vs. 1850 mAh g-1 for GeP). The germanium oxide was less efficiently exploited due to its lower conversion reversibility.

Market capitalization shock effects on open innovation models in e-commerce: golden cut q-rung orthopair fuzzy multicriteria decision-making analysis.

This research paper analyzes revenue trends in e-commerce, a sector with an annual sales volume of more than 340 billion dollars. The article evaluates, despite a scarcity of data, the effects on e-commerce development of the ubiquitous lockdowns and restriction measures introduced by most countries during the pandemic period. The analysis covers monthly data from January 1996 to February 2021. The research paper analyzes relative changes in the original time series through the autocorrelation function. The objects of this analysis are Amazon and Alibaba, as they are benchmarks in the e-commerce industry. This paper tests the shock effect on the e-commerce companies Alibaba in China and Amazon in the USA, concluding that it is weaker for companies with small market capitalizations. As a result, the effect on estimated e-trade volume in the USA was approximately 35% in 2020. Another evaluation considers fuzzy decision-making methodology. For this purpose, balanced scorecard-based open financial innovation models for the e-commerce industry are weighted with multistepwise weight assessment ratio analysis based on q-rung orthopair fuzzy sets and the golden cut. Within this framework, a detailed analysis of competitors should be made. The paper proves that this situation positively affects the development of successful financial innovation models for the e-commerce industry. Therefore, it may be possible to attract greater attention from e-commerce companies for these financial innovation products.

Economic modelling of electricity generation: long short-term memory and Q-rung orthopair fuzzy sets.

The main goal of this study is to evaluate the impact of population mobility on electricity generation in Russian cities in the conditions of the spread of COVID-19, and identify hotspots. Furthermore, the evaluation is also conducted using hybrid fuzzy decision-making modelling. In this context, q-ROF DEMATEL and TOPSIS methods are taken into consideration. Additionally, a comparative evaluation is also performed with the help of Intuitionistic and Pythagorean fuzzy sets. The results are quite similar that allows to conclude that the findings are reliable and coherent. The study proves the hypothesis that human behavior changed during the COVID-19 pandemic, and electricity consumption is declining in major cities around the world. The biggest fall in energy generation was in Moscow and Yekaterinburg. In St. Petersburg and Nizhny Novgorod, the fall in energy generation is no so crucial because these cities have low building density. The study uses Long Short-Term Memory models with many different parameters. The Q-Rung Orthopair Fuzzy Sets model forecasts new COVID-19 using ten parameters. This study identifies factors influencing the spread of COVID-19 based on the theory of "broken windows" and outlines directions in limiting population mobility, which can form the basis of state policy. According to the analysis the air temperature is the variable that most affects this process.

Speciation of Tellurium(VI) in Aqueous Solutions: Identification of Trinuclear Tellurates by 17O, 123Te, and 125Te NMR Spectroscopy.

Tellurates have attracted the attention of researchers over the past decade due to their properties and as less toxic forms of tellurium derivatives. However, the speciation of Te(VI) in aqueous solutions has not been comprehensively studied. We present a study of the equilibrium speciation of tellurates in aqueous solutions at a wide pH range, 2.5-15 by 17O, 123Te, and 125Te NMR spectroscopy. The coexistence of monomeric, dimeric, and trimeric oxidotellurate species in chemical equilibrium at a wide pH range has been shown. NMR spectroscopy, DFT computations, and single-crystal X-ray diffraction studies confirmed the formation and coexistence of trimeric tellurate anions with linear and triangular structures. Two cesium tellurates, Cs2[Te4O8(OH)10] and Cs2[Te2O4(OH)6], were isolated from the solution at pH 5.5 and 9.2, respectively, and studied by single-crystal X-ray diffractometry, revealing dimeric and tetrameric tellurate anions in corresponding crystal structures.

Smart city perspectives in post-pandemic governance: Externalities reduction policy.

Background: The ongoing COVID-19 quarantine restrictions have caused multiple sharp decreases in activities associated with the movement of large masses of people. The economies of regions and cities that are critically dependent on tourist flows related to various segments have suffered. This research aims to provide an economic-mathematical model of smart cities externalities' impact from the point of view of achieving social and environmental goals Methods: The objective of this study was to develop an algorithm for supporting decision-makers. Methods of mathematical modeling, statistical processing of data received in real-time, as well as methods for finding solutions by expansion into dynamic series are used, and the theory of mathematical games is applied. The theoretical mathematical model presented considers the statistical processing of data provided in real time referring to the performance indicators of megacities. Results: The activities of administrations and governments aimed at maintaining stability over the past two years have been aimed at reducing the negative impact of the pandemic. The prospect of returning to normal conditions is complicated by a number of factors. The proposed approach allows the development of the fundamental basis for making administrative decisions within individual megapolises and in environmental policy on a territory of any scale. The developed mathematical model is abstract by definition and is applied by taking into account specific tasks and criteria. Since the tasks of the administration differ depending on the region and country, the choice of criteria is set individually. Conclusions: During the period of isolation, the volume of services in the Hotel - Restaurant- Catering/Café (HORECA) segment has decreased, and personnel has also been lost. The reduced pressure on public infrastructure and the departure of migrants means that, in the long term, this work cannot be restored within a short period of time.

Integrated decision recommendation system using iteration-enhanced collaborative filtering, golden cut bipolar for analyzing the risk-based oil market spillovers.

This article is dedicated analyzing the interdependence of oil prices and exchange rate movements of oil exporting countries (the Russian ruble, Euro, Canadian dollar, Chinese yuan, Brazil real, Nigerian naira, Algerian dinar). The study also considers risk-based oil market spillovers in global crisis periods with integrated decision recommendation systems. For this purpose, a fuzzy decision-making model is created by considering the bipolar model and imputation of expert evaluations with collaborative filtering. The main contribution of this study is both its econometric analysis and evaluations based on expert opinions. This helps reach more crucial results. All three of the recent shocks (2008, 2012, 2020) in the oil market are transmitted to foreign exchange markets of oil-producing countries. At the same time, the last shock of 2020 caused by the COVID-19 pandemic has not yet been fully reflected on the Russian ruble exchange rate. Correlation parameters became weaker in the last year, as the Russian ruble correlation coefficient fluctuates between - 0.5 and 0.5. However, before 2020 the spillover effect had a higher significance (in the range from - 0.8 to - 0.1). Nigerian naira and Algerian dinar were showing almost the same movements, while the Russian Ruble was in a different trading range.

Efficiency of renewable energy plants in Russia.

The aim of this research paper is a complex assessment of the efficiency of power plants in Russia, the basis of their techno-economic processes and indicators and the development of recommendations for their practical use and improvement. Capital expenses for SWH plants with a drain-down system are 1,4-1,6 times lower than traditional SWHs with circulation systems, and their payback period does not exceed 5-5,7 years. Biogas that is obtained from specialized plants of medium and high-power costs $15,000-60,000 per cubic meter. But the price of biogas produced in low energy power plants is about $80,000-270,000, which is cheaper than natural gas. So, the use of biogas is economically justified at the moment.

Memristive Switching and Density-Functional Theory Calculations in Double Nitride Insulating Layers.

In this paper, we demonstrate a device using a Ni/SiN/BN/p+-Si structure with improved performance in terms of a good ON/OFF ratio, excellent stability, and low power consumption when compared with single-layer Ni/SiN/p+-Si and Ni/BN/p+-Si devices. Its switching mechanism can be explained by trapping and de-trapping via nitride-related vacancies. We also reveal how higher nonlinearity and rectification ratio in a bilayer device is beneficial for enlarging the read margin in a cross-point array structure. In addition, we conduct a theoretical investigation for the interface charge accumulation/depletion in the SiN/BN layers that are responsible for defect creation at the interface and how this accounts for the improved switching characteristics.

Toward Reflective Spiking Neural Networks Exploiting Memristive Devices.

The design of modern convolutional artificial neural networks (ANNs) composed of formal neurons copies the architecture of the visual cortex. Signals proceed through a hierarchy, where receptive fields become increasingly more complex and coding sparse. Nowadays, ANNs outperform humans in controlled pattern recognition tasks yet remain far behind in cognition. In part, it happens due to limited knowledge about the higher echelons of the brain hierarchy, where neurons actively generate predictions about what will happen next, i.e., the information processing jumps from reflex to reflection. In this study, we forecast that spiking neural networks (SNNs) can achieve the next qualitative leap. Reflective SNNs may take advantage of their intrinsic dynamics and mimic complex, not reflex-based, brain actions. They also enable a significant reduction in energy consumption. However, the training of SNNs is a challenging problem, strongly limiting their deployment. We then briefly overview new insights provided by the concept of a high-dimensional brain, which has been put forward to explain the potential power of single neurons in higher brain stations and deep SNN layers. Finally, we discuss the prospect of implementing neural networks in memristive systems. Such systems can densely pack on a chip 2D or 3D arrays of plastic synaptic contacts directly processing analog information. Thus, memristive devices are a good candidate for implementing in-memory and in-sensor computing. Then, memristive SNNs can diverge from the development of ANNs and build their niche, cognitive, or reflective computations.