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The organic carbon (Corg) stored in seagrass meadows is globally significant and could be relevant in strategies to mitigate increasing CO2 concentration in the atmosphere. Most of that stored Corg is in the soils that underlie the seagrasses. We explored how seagrass and soil characteristics vary among seagrass meadows across the geographic range of turtlegrass (Thalassia testudinum) with a goal of illuminating the processes controlling soil organic carbon (Corg) storage spanning 23° of latitude. Seagrass abundance (percent cover, biomass, and canopy height) varied by over an order of magnitude across sites, and we found high variability in soil characteristics, with Corg ranging from 0.08 to 12.59% dry weight. Seagrass abundance was a good predictor of the Corg stocks in surficial soils, and the relative importance of seagrass-derived soil Corg increased as abundance increased. These relationships suggest that first-order estimates of surficial soil Corg stocks can be made by measuring seagrass abundance and applying a linear transfer function. The relative availability of the nutrients N and P to support plant growth was also correlated with soil Corg stocks. Stocks were lower at N-limited sites than at P-limited ones, but the importance of seagrass-derived organic matter to soil Corg stocks was not a function of nutrient limitation status. This finding seemed at odds with our observation that labile standard substrates decomposed more slowly at N-limited than at P-limited sites, since even though decomposition rates were 55% lower at N-limited sites, less Corg was accumulating in the soils. The dependence of Corg stocks and decomposition rates on nutrient availability suggests that eutrophication is likely to exert a strong influence on carbon storage in seagrass meadows.

New-type magnesium alloy with prominent solubility and mechanical property lays foundation for preparing fracturing part in petroleum extraction. Herein, Mg-xZn-Zr-SiC alloy is prepared with casting strategy. Electrochemical and compression tests are conducted to assess the feasibility as decomposable material. Morphology, composition, phase and distribution are characterized to investigate decomposition mechanism. Results indicate that floccule, substrate component and reticulate secondary phase are formed on as-prepared surface. Sample also acts out enhanced compression strength to maintain pressure and guarantee stability in dissolution process. Furthermore, as decomposition time and zinc content increase, couple corrosion intensifies, resulting in gradually enhanced decomposition rate. Rapid sample decomposition is mainly due to basal anode dissolution, micro particle exfoliation and poor decomposition resistance of corroding product. Such work shows profound significance in preparing new-type accessible alloy to ensure rapid dissolution of fracturing part and guarantee stable compression strength in oil-gas reservoir exploitation.

Aiming to elucidate the hydrogen absorption/desorption cycling properties of Mg-based alloys with in-situ formed Mg2Ni and LaHx (x = 2, 3) nanocrystallines, the hydrogen storage cycle stability, hydriding/dehydriding cycling kinetics and thermodynamic stability of the experimental alloys have been investigated in detail. The results show that the Mg–Ni–La alloys exhibit improved hydrogen storage cycling properties and can remain storage hydrogen above 5.5 wt% after 200 cycles. With the increase of cycling numbers, the dehydrogenation rates of the experimental samples increase firstly and then gradually decrease, and eventually maintain relative stable state. Microstructure observation reveals that powders sintering and hydrogen decrepitation both exist during hydrogen absorption/desorption cycles due to repeated volume expansion and contraction. Meanwhile, the in-situ formed LaHx (x = 2, 3) and Mg2Ni nanocrystallines stabilize the microstructures of the particles and hinder the powders sintering. After 200 cycles, the average particle size of the experimental samples decreases and the specific surface area apparently increases, which leads to the decomposition temperatures of MgH2 and Mg2NiH4 slightly shift to lower temperatures. Moreover, Mg2Ni and LaHx (x = 2, 3) have been proven to be stable catalysts during long-term cycling, which can still uniformly distribute within the powders after 200 cycles.

This study examines changes in productivity for seven countries including Japan and performs their decomposition into four factors from 1999 to 2019. For this purpose, this study measures total factor productivity using a combination of multiple inputs and outputs. We include a number of articles as an output, and a total amount of research and development costs as an input for production in order to incorporate innovation factors into the productivity measurement. Concretely, we measure the Hicks- Moorsteen-Bjurek (HMB) productivity index with a consideration of inefficiency by applying data envelopment analysis models. Further, the HMB productivity index is decomposed into four factors; technical change, efficiency change, scale change, and input and output mix effects components. The results indicate that Japan’s HMB productivity index for the period 1999-2019 is the third highest after Korea and Germany, which is mainly attributed to the higher technical change component that represents technological progress or frontier shift over the period.

Earthworms are ecosystem engineers associated with important soil functions. Despite the large amount of literature on earthworm ecology, relatively few studies have examined earthworms in deadwood or quantified their importance in this habitat. We investigated earthworm communities in decaying deadwood and disentangled how their community dynamics are influenced by variation in tree species, wood decomposition stage, and forest environment. Decaying logs (of standardised size) of ten common, temperate tree species were laid out to decay on the soil surface for four years. The experiment was carried out in the “tree cemetery” experiment LOGLIFE, in two contrasting temperate forests in the central Netherlands. The decaying logs yielded surprisingly rich earthworm populations, with on average 19 individuals per meter of log and in total belonging to 12 different species. Our findings highlighted that earthworm communities in deadwood in terms of composition and abundance were influenced by tree species, wood decomposition stage and forest type with different soil properties, as well as their interactions. After one and two years of decay, earthworm abundance was higher in the logs of Picea abies and relatively fast decomposing Populus spp. than in other trees, while this pattern changed after four years with higher earthworm abundance observed in the other tree species. Overall, Populus spp. supported the highest earthworm abundance, followed by Picea abies and Quercus robur. The earthworm community composition in the logs in the two forest sites had broadly similar dynamic trends of first becoming very dissimilar between one and two years, then relatively more similar from two to four years of decay, although the community composition differed between sites. The interacting influences of tree species, wood decay stage and forest environment on earthworm communities strongly suggest that diversity in deadwood resources contributes to earthworm diversity in forests. Thus, by mixing different tree species and logging gradually through the years, forest managers could enhance the diversity of this abundant and understudied component of deadwood invertebrate diversity. Further research should study the feedback loop between earthworm communities and decomposition of deadwood.

The methanol decomposition reaction has gained substantial attention due to the wide range of applications that its intermediates offer. In this work, methanol (CH3OH) decomposition on Copper-embedded graphene (CuG) surface has been investigated via density functional theory with Grimme-D2 dispersion correction. The charge density of the CuG surface has been analyzed and the redistribution of the electron density of the surface has been represented via the electron density difference (EDD) map. Moreover, the decomposition reaction mechanism of CH3OH on the CuG surface through the cleavage of C–H, O–H and C–O bonds has been investigated in detail. In the initial state, the C–O and O–H bonds of CH3OH have similar activation barriers, thereby the adsorption and degradation mechanism of the intermediate states arising through O–H bond cleavage on the CuG surface has been investigated. In addition, the charge density calculations of the transition state geometries have been conducted and examined with EDD maps. The results have revealed that the previously adsorbed oxygen molecule exhibited high catalytic activity towards O–H decomposition compared to the bare surface. The CuG surface has offered higher activity on the C–H bonds compared to the C–O bonds of the intermediate states generated by CH3OH decomposition. The results revealed that the proposed CuG structure can be utilized as an alternative electrode catalyst that can prevent the CO poisoning issue in direct methanol fuel cells. © 2021 Hydrogen Energy Publications LLC

Standing deadwood is an important structural component of forest ecosystems. Its occurrence and dynamics influence both carbon fluxes and the availability of habitats for many species. However, deadwood is greatly reduced in managed, and even in many currently unmanaged temperate forests in Europe. To date, few studies have examined how environmental factors, forest management and changing climate affect the availability of standing deadwood and its dynamics. Data from five periods of the Austrian National Forest Inventory (1981-2009) were used to (I) analyse standing deadwood volume in relation to living volume stock, elevation, eco-region, forest type, ownership and management intensity, (II) investigate the influence of forest ownership and management intensity on snag persistence and (III) define drivers of standing deadwood volume loss for seven tree genera (Abies, Alnus, Fagus, Larix, Picea, Pinus and Quercus) using tree-related, site-related and climate-related variables, and predict volume loss under two climate change scenarios. Standing deadwood volume was mainly determined by living volume stock and elevation, resulting in different distributions between eco-regions. While forest type and management intensity influenced standing deadwood volume only slightly, the latter exhibited a significant effect on persistence. Snag persistence was shorter in intensively managed forests than in extensively managed forests and shorter in private than in public forests. Standing deadwood volume loss was driven by a combination of diameter at breast height, elevation, as well as temperature, precipitation and relative humidity. Volume loss under climate change predictions revealed constant rates for moderate climate change (RCP2.6) by the end of the 21st century. Under severe climate change conditions (RCP8.5), volume loss increased for most tree genera, with Quercus, Alnus and Picea showing different predictions depending on the model used as the baseline scenario. We observed trends towards faster volume loss at higher temperatures and lower elevations and slower volume loss at high precipitation levels. The tree genera most susceptible to climate change were Pinus and Fagus, while Abies was least susceptible. Synthesis and applications. We recommend to protect standing dead trees from regular harvesting to ensure the full decomposition process. The consequences for decomposition-dependent species must be taken into account to evaluate the influences of management and climate change on standing deadwood dynamics., Journal of Applied Ecology, 60 (4), ISSN:0021-8901, ISSN:1365-2664

The core-shell metastable intermolecular composites (MIC) have attracted much attention in the past few years due to their unique properties. Here, the preparation of Al-Core heterojunction fibers using PVP as a template is proposed. The nano-Al was directly added to the precursor solution of cupric acetate monohydrate (CAM)/Polyvinylpyrrolidone (PVP), and the initial Al@CAM/PVP fibers were obtained via electrospinning. The core-shell MIC fibers are then obtained by calcining the initial fibers. The morphology, structure, and composition of Al-core MIC fibers were characterized, that the energetic fibers calcined at 300 °C, 350 °C, and 400 °C have a core-shell structure with shell compositions CuxO and PVP, CuxO and CuO, respectively. The energy release characteristics of Al-core MIC were investigated, and preliminary ignition tests were performed using an ignition temperature measuring instrument and a pulsed laser. The energetic fibers calcined at 300 °C exhibited unique properties. The decomposition of PVP in the shell layer promoted exotherm, and a low-temperature exothermic peak was shown at 372–458 °C. Lower ignition temperatures and higher flame heights were observed in the combustion tests than calcination at 350 °C and 400 °C. An unexpected result was that PVP can play a positive role in Al/CuO nanothermites. Simultaneously, this preparation method provided an idea for the integrated preparation of core-shell Al-Core MIC fibers and tuning the properties of MIC.

In space propulsion hydrogen peroxide is a “green” solution that overcomes the problem of toxicity related to hydrazine-based systems. This work aims at designing, developing, and characterizing a monopropellant thruster for small satellites, based on hydrogen peroxide. The engine is intended for use on a 12-Unit CubeSat in Low Earth Orbit. Based on mission requirements a catalytic chamber and a nozzle were designed and a ground-test breadboard was manufactured. The prototype was assembled and tested on ground and measured engine performances are discussed highlighting their dependence on the nozzle throat diameter, that primarily affects the chamber pressure. Finally, effects of the mass flow rate on the system reactivity and steady state conditions are also investigated.

We introduce a cross-entropy (CE) indicator to quantify the extent to which two input-output tables or two tables with results based on input-output analysis differ from each other. Our work deploys a unique feature of the CE indicator: it can be decomposed, allowing for matrix comparisons at various levels within one coherent framework. To illustrate the power of this approach, we apply the technique to five multi-region input-output (MRIO) tables for 2011, derived from the Eora, EXIOBASE, GTAP, OECD and WIOD databases. We make pairwise comparisons between MRIOs and between global value chain (GVC) computations based on these MRIOs. We find that answers to questions related to broader aggregates are generally quite similar, but that answers to questions at the level of single industries can be rather different across MRIOs.

This letter proposes a parallelizable algorithm for linear-quadratic model predictive control (MPC) problems with state and input constraints. The algorithm itself is based on a parallel MPC scheme that has originally been designed for systems with input constraints. In this context, one contribution of this letter is the construction of time-varying yet separable constraint margins ensuring recursive feasibility and asymptotic stability of sub-optimal parallel MPC in a general setting, which also includes state constraints. Moreover, it is shown how to tradeoff online run-time guarantees versus the conservatism that is introduced by the tightened state constraints. The corresponding performance of the proposed method as well as the cost of the recursive feasibility guarantees is analyzed in the context of controlling a large-scale mechatronic system. This is illustrated by numerical experiments for a large-scale control system with more than 100 states and 60 control inputs leading to run-times in the millisecond range.

A new NLO crystal of L-Arginine doped with potassium iodide were harvested from solution growth technique with slow evaporation method. Powder X-ray diffraction analysis exposes the structure. Presence of vibration functional groups in the grown crystal was confirmed by FTIR spectroscopy. Using the spectrum of UV–Vis-NIR the optical transmission and absorption spectra were studied. TGA-DSC studies reveal the decomposition and thermal stabilities of the grown crystals. Kurtz Perry Powder technique determines the second harmonic generation NLO behavior.

The iron and steel industries currently face the depletion of high-grade ore and high CO2 emissions. Some initiatives that effectively utilize alternative carbon sources and abundant low-grade ores become the preferable solutions. This novel study aims to utilize municipal solid waste (MSW) as a reducing agent in ironmaking using low-grade (goethite) ores. As an initial fundamental approach, the comparison of decomposition behaviors between the model and actual MSW was investigated in thermogravimetric analysis. Both model and actual MSWs mainly decompose at 300-500 degrees C. As for reduction tests, pellets containing MSWs and ores with different pretreatments were prepared. The pellets were reduced in an Ar atmosphere at different temperatures. The effect of different ores: high-grade and low-grade ones, on the decomposition of MSW and the iron reduction, were investigated. As a result, interestingly, the low-grade, goethite ore-containing pellet exhibits a more significant reduction degree than the high-grade ones. The reduction is completed in 5 minutes at 700 degrees C and above, indicating a significant reduction by the decomposed carbon. The reduction degree extends at elevated temperature, which reaches more than 94% at 900 degrees C.

Nitrogen oxides (NOx) gases, such as nitrous oxide (N2O), nitrogen oxide (NO), and nitrogen dioxide (NO2), are considered the most hazardous exhausts exhaled by industries and stationary and non-stationary application engines. Investigation of catalytic decomposition of NO has been carried out on copper ion exchanged with different bases, such as COK12, Nb2O5, Y-zeolite, and ZSM5. The catalytic decomposition of NO is widely accepted as an excellent method for the abatement of NO. However, the catalyst that achieves the highest reactivity in terms of NO decomposition is still a matter of research. The present paper aims to extend the research on the reactivity of non-noble metal-based catalysts using the direct decomposition method to remove NO from diesel engine exhaust. The reactivity of catalysts was observed in a quartz fixed bed reactor of 10 mm diameter placed in a furnace maintained at a temperature of 200 °C to 600 °C. The flow of NO was controlled by a mass flow controller, and the gas chromatography technique was used to observe the reactivity of the catalysts. Analysis showed that adding Cu to COK12, Nb2O5, Y-zeolite, and ZSM5 supports resulted in a rise in NO decomposition compared to stand-alone supports. Further experimental trials on the performance of Cu-ZSM5 at varying flow rates of NO showed that the NO decomposition activity of the catalyst was higher at lower flow rates of NO.

Biological decomposition and wildfire are two predominant and alternative processes that can mineralize organic C in forest litter. Currently, the relationships between decomposition and fire are still poorly understood. We provide an empirical test of the hypothesized decoupling of surface litter bed decomposability and flammability, and the underlying traits and trait spectra. We employed a 41-species set of gymnosperms of very broad evolutionary and geographic spread, because of the wide range of (absent to frequent) fire regimes they are associated with. We found that the interspecific pattern of mass loss proportions in a “common garden” decomposition experiment was not correlated with any of the flammability parameters and an RDA analysis also showed that the decomposability and flammability of leaf litter in litter layers were decoupled across species. This decoupling originates from the former depending mostly on size and shape spectrum traits and the latter on PES traits and those trait spectra being virtually uncorrelated. Synthesis: Our results show that, indeed, leaf litter decomposability and flammability parameters are decoupled across species, and this decoupling can be explained by their different drivers in terms of trait spectra: chemical traits for decomposability and size-shape traits for litter layer flammability.

We consider a Lévy process Y(t) that is not continuously observed, but rather inspected at Poisson( $\omega$ ) moments only, over an exponentially distributed time $T_\beta$ with parameter $\beta$ . The focus lies on the analysis of the distribution of the running maximum at such inspection moments up to $T_\beta$ , denoted by $Y_{\beta,\omega}$ . Our main result is a decomposition: we derive a remarkable distributional equality that contains $Y_{\beta,\omega}$ as well as the running maximum process $\bar Y(t)$ at the exponentially distributed times $T_\beta$ and $T_{\beta+\omega}$ . Concretely, $\overline{Y}(T_\beta)$ can be written as the sum of two independent random variables that are distributed as $Y_{\beta,\omega}$ and $\overline{Y}(T_{\beta+\omega})$ . The distribution of $Y_{\beta,\omega}$ can be identified more explicitly in the two special cases of a spectrally positive and a spectrally negative Lévy process. As an illustrative example of the potential of our results, we show how to determine the asymptotic behavior of the bankruptcy probability in the Cramér–Lundberg insurance risk model.

The plane exposure of support vitally affects the catalytic performance of the catalyst. In this work, CeO2 nanorods ((1 1 0) plane exposure), nano-octahedrons ((1 1 1) plane exposure) and nano-cubes ((1 0 0) plane exposure) were prepared as the supports of Pt/CeO2 samples to investigate the effect of CeO2 plane exposure on total toluene oxidation. Characterizations reveal that the (1 1 0) plane of CeO2 is more helpful to the dispersion of Pt species, followed by (1 1 1) face. The improved dispersion of Pt species can enhance the metal-supports interaction, which promotes the electron transfer of CeO2 carrier to Pt nanoparticles and the adsorption-activation of O2, thereby facilitating the total oxidation of toluene via the Langmuir–Hinshelwood (L-H) mechanism. Therefore, Pt/CeO2-r (nanorods) sample expresses the most excellent catalytic performance of toluene oxidation. Finally, the procedure of toluene total oxidation was studied by in-situ DRIFT. We expect that this work can contribute to the development of an effective sample for the decomposition of volatile organic compounds (VOCs).

The processes of decomposition that the body will have after the time of death are peculiar and complex. The body swells and expels gases and fluids, and the flesh decays. It also attracts many insects and scavengers. We know that these fluids are nutrients for the vegetation, and if the body is inhumed in the subsurface, they allow a rapid crop growth that remote sensors can mark. During forensic investigations, mapping the fluid migration in the subsurface can help reconstruct the genesis of a clandestine grave. Several studies show how different remote sensors and analyses can be sensitive to human burials. This paper presents a preliminary experiment studying the fluid dispersion in the subsurface using simulated body fluids in a shallow grave and detecting it through the ground penetrating radar (GPR) technique (given its ability to detect dielectric constant changes in the investigated media) and other remote sensing techniques. Although the simulation of the body fluids related to the dielectric constant was accurate and allowed us to better understand how decomposition in the subsurface does not always migrate in the way that was initially expected (toward gravity), other typical characteristics of the body fluids, other soils and external factors were left out and would be studied in future simulations.

Tensor decompositions, such as CANDECOMP/PARAFAC (CP), are widely used in a variety of applications, such as chemometrics, signal processing, and machine learning. A broadly used method for computing such decompositions relies on the Alternating Least Squares (ALS) algorithm. When the number of components is small, regardless of its implementation, ALS exhibits low arithmetic intensity, which severely hinders its performance and makes GPU offloading ineffective. We observe that, in practice, experts often have to compute multiple decompositions of the same tensor, each with a small number of components (typically fewer than 20), to ultimately find the best ones to use for the application at hand. In this article, we illustrate how multiple decompositions of the same tensor can be fused together at the algorithmic level to increase the arithmetic intensity. Therefore, it becomes possible to make efficient use of GPUs for further speedups; at the same time, the technique is compatible with many enhancements typically used in ALS, such as line search, extrapolation, and non-negativity constraints. We introduce the Concurrent ALS algorithm and library, which offers an interface to MATLAB, and a mechanism to effectively deal with the issue that decompositions complete at different times. Experimental results on artificial and real datasets demonstrate a shorter time to completion due to increased arithmetic intensity.

Polyoxymethylene dimethyl ether (PODEn, n ≥ 1) is a class of oxygenated fuels containing unique carbon-oxygen chain structure and a promising alternative fuel for diesel engines. In this study, low-temperature oxidation characteristics of PODE3 were studied experimentally and numerically. Experiments were performed in a jet-stirred reactor (JSR) at equivalence ratios of 0.5, 1.0 and 2.0, in the temperature range of 500 to 950 K, and at atmospheric pressure. Mole fractions of PODE3, O2, H2, CO, CO2, CH3OH and C1-C2 hydrocarbons were measured by gas chromatograph (GC). Experimental measurements were compared with the simulation results based on two literature low-temperature oxidation models, denoted as the He model and the Cai model, respectively. Good agreement was obtained between the measured and simulated fuel consumption profiles, while a deviation was observed between the experimental and simulation results on the mole fractions of O2 and intermediate products at medium temperatures. Reaction pathway analyses based on the two models were performed, revealing that the second O2-addition reaction pathway is more significant in the prediction by the Cai model than that by the He model. Sensitivity analyses pointed out that the most important reactions affecting fuel consumption are the H-abstraction reactions of PODE3, and the decomposition of H2O2 and the consumption of CH2O become more sensitive at medium temperatures.

No consensus has been reached on the role of reducible and irreducible supports on the reaction pathway of CO oxidation over supported metal catalysts. Here, colloidal ligand-free Pt nanoparticles were deposited on Al2O3 and CeO2 to investigate the support effect in CO oxidation by in situ diffuse reflectance Fourier-transform infrared spectroscopy (DRIFTS) and mass spectrometry (MS). On Pt/Al2O3, the carbonate-like intermediates accumulate and do not decompose to CO2 at the ignition temperature (150 °C) or above; the conversion of CO to CO2 follows classic Langmuir−Hinshelwood (L–H) mechanism with direct reaction of CO and O2 on Pt. Whereas for Pt/CeO2, the CO conversion to bidentate carbonate (b-CO32−) species followed by decomposition to CO2 is a main reaction pathway leading to its high reactivity at low temperature (75 °C). CeO2 facilitates the activation of oxygen to produce the carbonate-like intermediates and further decomposition to CO2, which resulted in a lower ignition temperature.

In a rapidly warming tundra, ecosystems will undergo major environmental changes that are predicted to significantly alter belowground processes such as decomposition of plant litter. Making use of International Tundra Experiment sites (ITEX), which were established approximately two decades ago, we examined the long-term impacts of warming on decomposition. We used the Tea Bag Index (TBI) methodology to measure the annual mass loss (%) of two tea types as a proxy for potential decomposition rates, across five tundra vegetation types. Direct effects of warming were assessed by comparing mass loss within and outside warming manipulations. Indirect effects of warming, such as those caused by warming-induced changes in plant community composition, were assessed through the relationship between mass loss of tea and local biotic and abiotic conditions. We found positive effects of warming on decomposition, although the responses varied between vegetation and tea types. Interestingly, we found support for the indirect influence of long-term warming on decomposition through warming-induced changes in the composition of plant communities. Our findings demonstrate the complexity in decomposition responses to warming across different vegetation types and highlight the importance of long-term legacies of warming in decomposition responses across the Arctic.

The cognition of active sites in the Ni-based catalysts plays a vital role and remains a huge challenge in improving catalytic performance of low temperature CO2 dry reforming of methane (LTDRM). In this work, typical catalysts of SiO2 and γ-Al2O3 supported Ni and Ni-Ce were designed and prepared. Importantly, the difference in the chemical speciations of active sites on the Ni-based catalysts is revealed by advanced characterizations and further estimates respective catalytic performance for LTDRM. Results show that larger [ Ni n 0 ] particles mixed with [Ni-O-Sin]) species on the Ni/SiO2(R) make CH4 excessive decomposition, leading to poor activity and stability. Once the Ce species is doped, however, superior activity (59.0% CH4 and 59.8% CO2 conversions), stability and high H2/CO ratio (0.96) at 600 °C can be achieved on the Ni-Ce/SiO2(R), in comparison with other catalysts and even reported studies. The improved performance can be ascribed to the formation of integral ( [ Ni n 0 ] -[CeIII-□-CeIII]) species on the Ni-Ce/SiO2(R) catalyst, containing highly dispersed [ Ni n 0 ] particles and rich oxygen vacancies, which can synergistically establish a new stable balance between gasification of carbon species and CO2 dissociation. With respect to Ni-Ce/γ-Al2O3(R), the Ni and Ce precursors are easily captured by extra-framework Aln-OH groups and further form stable isolated ( [ Ni n 0 ] -[Ni-O-Aln]) and [CeIII-O-Aln] species. In such a case, both of them preferentially accelerate CO2 adsorption and dissociation, causing more carbon deposition due to the disproportionation of superfluous CO product. This deep distinguishment of chemical speciations of active sites can guide us to further develop new efficient Ni-based catalysts for LTDRM in the future.

The fast decomposition of peroxides (M2O2, where M = H+, Li+) at room temperature has gained increased attention given its relevance, from propulsive systems to metal-O2 conversion batteries. This study describes, by a rational design of experiment, the influence of selected parameters such as: pH, initial concentration of K2CO3, precipitate aging, precipitate washing, and thermal treatment for the hydrotalcite co-precipitation, characterizing it for each synthesis variable. The results show that hydrotalcites can efficiently be converted in Co-Mn-Al spinels and as a result, a new highly active Co2Mn0.5Al0.5O4 spinel oxide was synthesized after thermal treatment. Its activity and mechanical resistance were tested in a micro-thruster reactor where it showed total decomposition (> 98%) of H2O2 and in a Li-O2 battery, with increased cyclability, been able to reduce the charging potential in ~200 mV. These encouraging results enhance the importance not only of this class of material, but also, the importance of robust synthesis methodologies for reproducible results.

In this study, several noble zeolites (SA-600A)-based catalysts supported with RuCl3.nH2O and LiOH·H2O materials were investigated for H2 production from NH3 decomposition. The synthetic catalysts were simultaneously prepared at different supported materials mixing ratios. The effect of initial mixing of Li and Ru on catalyst performance compared to simultaneous mixing was studied. All catalysts were tested for hydrogen production from NH3 decomposition at atmospheric pressure, temperature range of 200–670 oC, and gas hourly space velocity (GHSV) of 3000 h−1. It was found that the simultaneous and initial mixing of Li and Ru has the same NH3 decomposition results. The zeolite-based catalyst was enhanced with the Ru supported material mixing ratio increase. Among all the synthetic catalysts the catalyst type of 40 g (SA-600A)/5 g RuCl3.nH2O/3 g LiOH·H2O) showed the highest catalyst activity. The catalyst with a mixing ratio of 40 g (SA-600A)/5 g RuCl3.nH2O/3 g LiOH·H2O) was registered the peak NH3 conversion 99.87% at lower temperature of 490 °C.

The phosphorus-doped carbon materials as one of novel carbon catalysts towards the hydrogen evolution reaction (HER) have attracted considerable attention over the past years. However, the role of C-P species palyed in the HER activity is still not clear up to now. Phosphorus-doped carbon nanotubes (P-CNTs) were prepared by chemical vapor deposition and annealed at 900, 1000 and 1200 ℃ to remove all or parts of phosporus species, resulting in four samples with different proportions of graphite-, pyridine- and pyrrole-like P species. The correlations between their HER activity and the contents of three types of P species were investigated. Results showed that the content of graphite-like P decreased with the annealing temperature and no graphite-like P was retained at 1200℃. The HER activity increased with the annealing temperature and the one annealed at 1200 ℃ had the highest HER activity in an acid medium with an overpotential of 0.266 V at a current density of 10 mA/cm−2. Density functional theory calculations revealed that the pentagon- and nine-membered ring defects formed by the destruction of graphite-P species contributed mainly to the HER activity, which gave a deep insight into the active sites for HER.

One of the pillars of a no-tillage system is the addition of adequate amounts of residue to keep the soil continuously covered. Cover crops are a tool for supplying the demand for the permanence of residues on the soil surface and releasing nutrients to the soil. However, there is no index that relates these two factors and can reconcile the maximum permanence of crop residues in the soil with the maximum N supply via N mineralization of such residues. This study aimed to assess the effect of different cover crops on the decomposition rate of residues and N release, using the residual biomass quality index (RBQI) to evaluate cover crop systems. The study was conducted in a long-term experiment in a Latossolo Vermelho (Ferralsol, Oxisol) under no-tillage in the two agricultural years 2017/18 and 2018/19. The experiment was in a split-plot factorial scheme with eight winter cover crops and three N rates in randomized blocks with three replications. The cover crop systems were black oat (O), common vetch (V), forage radish (R), white lupine, rye, annual ryegrass, oat + vetch (O+V), and oat + vetch + radish (O+V+R). The N rates applied to the corn in succession were 0, 90, and 180 kg ha-1. The decomposition rate, remaining dry mass (RDM) on the soil surface, N release rate, and N accumulated release (NAR) were assessed using litterbags. Considering NAR and RDM evaluated for up to 105 days, the N release index (NRI) and remaining dry mass index (RDMI) were determined, and the residual biomass quality index (RBQI) was obtained using the product of these variables. The consortia O+V+R and O+V resulted in a decomposition rate and N release rate closer to the rates observed for oats and rye. The NAR was similar to that observed for Fabaceae species, and the RDM was similar or superior to that found for black oat. With these characteristics, the systems in the O+V+R and O+V consortia presented the highest values of RBQI, ranging from 0.61 to 0.90, indicating that RBQI is a potential indicator for choosing cover crop systems that promote greater sustainability of the no-tillage system. The use of N fertilizer in corn did not change the rates of decomposition and N release from the residues of cover crops.

Understanding disease dynamics is crucial for accurately predicting and effectively managing epidemic outbreaks. Mathematical modeling serves as an essential tool in such understanding. This study introduces an advanced susceptible, infected, recovered, and dead (SIRD) model that uniquely considers the evolution of the death parameter, alongside the susceptibility and infection states. This model accommodates the varying environmental factors influencing disease susceptibility. Moreover, our SIRD model introduces fractional changes in death cases, which adds a novel dimension to the traditional counts of susceptible and infected individuals. Given the model’s complexity, we employ the Laplace-Adomian decomposition method. The method allows us to explore various scenarios, including non-fuzzy non-fractional, non-fuzzy fractional, and fuzzy fractional cases. Our methodology enables us to determine the model’s equilibrium positions, compute the basic reproduction number, confirm stability, and provide computational simulations. Our study offers insightful understanding into the dynamics of pandemic diseases and underscores the critical role that mathematical modeling plays in devising effective public health strategies. The ultimate goal is to improve disease management through precise predictions of disease behavior and spread.

Understanding disease dynamics is crucial for accurately predicting and effectively managing epidemic outbreaks. Mathematical modeling serves as an essential tool in such understanding. This study introduces an advanced susceptible, infected, recovered, and dead (SIRD) model that uniquely considers the evolution of the death parameter, alongside the susceptibility and infection states. This model accommodates the varying environmental factors influencing disease susceptibility. Moreover, our SIRD model introduces fractional changes in death cases, which adds a novel dimension to the traditional counts of susceptible and infected individuals. Given the model’s complexity, we employ the Laplace-Adomian decomposition method. The method allows us to explore various scenarios, including non-fuzzy non-fractional, non-fuzzy fractional, and fuzzy fractional cases. Our methodology enables us to determine the model’s equilibrium positions, compute the basic reproduction number, confirm stability, and provide computational simulations. Our study offers insightful understanding into the dynamics of pandemic diseases and underscores the critical role that mathematical modeling plays in devising effective public health strategies. The ultimate goal is to improve disease management through precise predictions of disease behavior and spread.

The processes at the interface between ionic liquids (ILs) and metals are a key factor for understanding especially in electrochemical deposition, nanoscale tribology applications and batteries. In the present work, the interfaces of 1-butyl-1-methylpyrrolidinium bis(trifluoromethylsulfonyl)imide ([Py1,4]TFSI) and 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([EMIm]TFSI) and platinum and aluminum were investigated by depositing thin IL films and studying them with X-ray photoelectron spectroscopy (XPS) in ultrahigh vacuum. It is found that there is no evidence of a decomposition reaction of either IL on platinum; however, the imidazolium cation of [EMIm]TFSI shows a strong interaction with the surface in the monolayer regime. In contrast, [Py1,4]TFSI and [EMIm]TFSI show massive decomposition on the aluminum surface without applying any electrochemical potential. The spectra for the [TFSI]− anion components show cleavage of C-F or N-S bonds in both cases. Both cleavage of a single fluorine atom and complete cleavage were observed, leading to further decomposition reactions of the anion. Consequently, new components such as AlOOH, Al(OH)3, Al2S3, Al2(SO4)3 and AlF3 appear at the interface. In addition, there is also evidence of decomposition of the cation by the splitting off hydrogen atoms or parts of the alkyl chain in both ILs.

Nowadays, graph theory is one of the most exciting fields of mathematics due to the tremendous developments in modern technology, where it is used in many important applications. The orthogonal double cover (ODC) is a branch of graph theory and is considered as a special class of graph decomposition. In this paper, we decompose the complete bipartite graphs Kx,x by caterpillar graphs using the method of ODCs. The article also deals with constructing the ODCs of Kx,x by general symmetric starter vectors of caterpillar graphs such as stars–caterpillar, the disjoint copies of cycles–caterpillars, complete bipartite caterpillar graphs, and the disjoint copies of caterpillar paths. We decompose the complete bipartite graph by the complete bipartite subgraphs and by the disjoint copies of complete bipartite subgraphs using general symmetric starter vectors. The advantage of some of these new results is that they enable us to decompose the giant networks into large groups of small networks with the comprehensive coverage of all parts of the giant network by using the disjoint copies of symmetric starter subgraphs. The use case of applying the described theory for various applications is considered.

Wavelet decomposition in signal processing has been widely used in the literature. The popularity of machine learning (ML) algorithms is increasing day by day in agriculture, from irrigation scheduling and yield prediction to price prediction. It is quite interesting to study wavelet-based stochastic and ML models to appropriately choose the most suitable wavelet filters to predict agricultural commodity prices. In the present study, some popular wavelet filters, such as Haar, Daubechies (D4), Coiflet (C6), best localized (BL14), and least asymmetric (LA8), were considered. Daily wholesale price data of onions from three major Indian markets, namely Bengaluru, Delhi, and Lasalgaon, were used to illustrate the potential of different wavelet filters. The performance of wavelet-based models was compared with that of benchmark models. It was observed that, in general, the wavelet-based combination models outperformed other models. Moreover, wavelet decomposition with the Haar filter followed by application of the random forest (RF) model gave better prediction accuracy than other combinations as well as other individual models.

Butene is a typical component of exhaust gas in the petrochemical industry, the emission of which into the atmosphere would lead to air pollution. In this study, a tubular multilayer dielectric barrier discharge (TM-DBD) reactor was developed to decompose 1-butene at ambient pressure. The experimental results show that a decomposition efficiency of more than 99% and COx selectivity of at least 43% could be obtained at a specific energy density of 100 J/L with an inlet concentration of 1-butene ranging from 100 to 400 ppm. Increasing the volume ratio of O2/N2 from 0 to 20% and the specific energy density from 33 to 132 J/L were beneficial for 1-butene destruction and mineralization. Based on organic byproduct analysis, it was inferred that the nitrogenous organic compounds were the main products in N2 atmosphere, while alcohol, aldehyde, ketone, acid and oxirane were detected in the presence of O2. In addition, the contents of formaldehyde, acetaldehyde, ethyl alcohol, acetic acid and propionic acid increased with an increase in specific energy density, but the contents of propionaldehyde, ethyl oxirane, butyraldehyde and formic acid decreased. Three main pathways of 1-butene destruction were proposed involving Criegee intermediates and ozonolysis of the olefins, and the following degradation could be the dominant pathways rather than epoxidation. Overall, the developed TM-DBD system paved the way for scaling up the applications of plasma technology for gaseous pollutant decomposition.

The use of tank mixtures of pesticides makes it possible to increase the efficiency of chemical treatment. The aim of the study was to establish the relationship between the joint use of pesticides and the rate of decomposition of active substances. The study was carried out on the crops of spring wheat, spring barley, peas, spring rapeseed, seed potato. Chemical treatments were carried out with insecticides and fungicides – the insecticide (imidacloprid and λ-cyhalothrin), suspension concentrate; the fungicide (propiconazole), emulsifiable concentrate; the insecticide (imidacloprid), soluble concentrate; the fungicide (copper sulfate tribasic), suspension concentrate. Determination of residual amounts of active substances of pesticides was carried out using methods of gas-liquid chromatography and high-performance liquid chromatography. The acceleration of decomposition of the active substance - imidacloprid on pea crops and spring rapeseed was caused by the combined use of the insecticide (imidacloprid) and the fungicide (propiconazole). The use of the fungicide (copper sulfate tribasic) in a tank mixture with the insecticide (imidacloprid and λ-cyhalothrin) on potatoes caused a slowdown in the decomposition of the active substances imidacloprid and λ-cyhalothrin. Also, there was a change in the level of intake by plant of active substances in the first three hours after spraying, when using tank mixtures, in comparison with the separate use of compounds. The data obtained on the change in the rate of decomposition of active substances of pesticides, when they are used together in mixtures, indicate the need to continue research in this area. In this regard, it is important to study the dynamics of the decomposition of individual active substances of pesticides in plant tissues when they are used in tank mixtures, it is also necessary to conduct research using compounds most commonly used in agricultural production. Resumo A utilização de misturas em tanques de pesticidas permite aumentar a eficiência do tratamento químico. O objetivo do estudo foi estabelecer a relação entre a utilização conjunta de pesticidas e a taxa de decomposição das substâncias ativas. O estudo foi realizado nas culturas de trigo de primavera, cevada de primavera, ervilha, colza de primavera e batata-semente. Foram realizados tratamentos químicos com inseticidas e fungicidas − o inseticida (imidacloprida e valuetech-cialotrina), concentrado em suspensão; o fungicida (propiconazol), concentrado emulsionável; o inseticida (imidacloprida), concentrado solúvel; o fungicida (sulfato de cobre tribásico), concentrado em suspensão. A determinação das quantidades residuais de substâncias ativas dos pesticidas foi realizada utilizando métodos de cromatografia gás-líquido e cromatografia líquida de alta eficiência. A aceleração da decomposição da substância ativa – imidacloprida nas culturas de ervilhas e colza de primavera foi causada pela utilização combinada do inseticida (imidacloprida) e do fungicida (propiconazol). A utilização do fungicida (sulfato de cobre tribásico), que contém cobre, numa mistura de tanques com o inseticida (imidacloprida e valuetech-cialotrina) em batatas provocou um abrandamento da decomposição das substâncias ativas imidacloprida e valuetech-cialotrina. Além disso, verificou-se uma alteração no nível de ingestão por planta de substâncias ativas nas primeiras três horas após a pulverização, quando se utilizam misturas em tanques, em comparação com a utilização separada de compostos. Os dados obtidos sobre a alteração da taxa de decomposição das substâncias ativas dos pesticidas, quando utilizadas em conjunto em misturas, indicam a necessidade de prosseguir a investigação neste domínio. Por isto, é importante estudar a dinâmica da decomposição de substâncias ativas individuais de pesticidas nos tecidos vegetais quando são utilizados em misturas em tanques, e realizar pesquisas que empreguem compostos mais comuns na produção agrícola.

The accurate prediction of short-term load is crucial for the grid dispatching department in developing power generation plans, regulating unit output, and minimizing economic losses. However, due to the variability in customers’ electricity consumption behaviour and the randomness of load fluctuations, it is challenging to achieve high prediction accuracy. To address this issue, we propose an ensemble deep learning model that utilizes reduced dimensional clustering and decomposition strategies to mitigate large prediction errors caused by non-linearity and unsteadiness of load sequences. The proposed model consists of three steps: Firstly, the selected load features are dimensionally reduced using singular value decomposition (SVD), and the principal features are used for clustering different loads. Secondly, variable mode decomposition (VMD) is applied to decompose the total load of each class into intrinsic mode functions of different frequencies. Finally, an ensemble deep learning model is developed by combining the strengths of LSTM and CNN-GRU deep learning algorithms to achieve accurate load forecasting. To validate the effectiveness of our proposed model, we employ actual residential electricity load data from a province in northwest China. The results demonstrate that the proposed algorithm performs better than existing methods in terms of predictive accuracy.

In order to obtain better prediction results, this paper combines improved complete ensemble EMD (ICEEMDAN) and the whale algorithm of multi-objective optimization (MOWOA) to improve the bidirectional gated recurrent unit (BIGRU), which makes full use of original complex stock price time series data and improves the hyperparameters of the BIGRU network. To address the problem that BIGRU cannot make full use of the stationary data, the original sequence data are processed using the ICEEMDAN decomposition algorithm to derive the non-stationary and stationary parts of the data and modeled with the BIGRU and the autoregressive integrated moving average model (ARIMA), respectively. The modeling process introduces a whale algorithm for multi-objective optimization, which improves the probability of finding the best combination of parameter vectors. The R2, MAPE, MSE, MAE, and RMSE values of the BIGRU algorithm, ICEEMDAN-BIGRU algorithm, MOWOA-BIGRU algorithm, and the improved algorithm were compared. An average improvement of 14.4% over the original algorithm’s goodness-of-fit value will greatly improve the accuracy of stock price predictions.

A strong edge coloring of a graph G is a proper coloring of edges in G such that any two edges of distance at most 2 are colored with distinct colors. The strong chromatic index χs′(G) is the smallest integer l such that G admits a strong edge coloring using l colors. A K4(t)-minor free graph is a graph that does not contain K4(t) as a contraction subgraph, where K4(t) is obtained from a K4 by subdividing edges exactly t−4 times. The paper shows that every K4(t)-minor free graph with maximum degree Δ(G) has χs′(G)≤(t−1)Δ(G) for t∈{5,6,7} which generalizes some known results on K4-minor free graphs by Batenburg, Joannis de Verclos, Kang, Pirot in 2022 and Wang, Wang, and Wang in 2018. These upper bounds are sharp.

The processes of the sublimation and thermal decomposition of the 1-ethyl-3-methylimidazolium hexafluorophosphate ionic liquid (EMImPF6) were studied by a complex approach including Knudsen effusion mass spectrometry, IR and NMR spectroscopy, and quantum chemical calculations. It was established that the vapor over the liquid phase primarily consists of decomposition products under equilibrium conditions. Otherwise, the neutral ion pairs are the only vapor components under Langmuir conditions. To identify the nature of the decomposition products, an experiment on the distillation of the ionic liquid was performed and the collected distillate was analyzed. It was revealed by the IR and NMR spectroscopy that EMImPF6 decomposes to substituted imidazole-2-ylidene (C6N2H10PF5) and HF. The measured vapor pressure of C6N2H10PF5 reveals a very low activity of the decomposition products (

Short-term load forecasting is critical to ensuring the safe and stable operation of the power system. To this end, this study proposes a load power prediction model that utilizes outlier correction, decomposition, and ensemble reinforcement learning. The novelty of this study is as follows: firstly, the Hampel identifier (HI) is employed to correct outliers in the original data; secondly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to extract the waveform characteristics of the data fully; and, finally, the temporal convolutional network, extreme learning machine, and gate recurrent unit are selected as the basic learners for forecasting load power data. An ensemble reinforcement learning algorithm based on Q-learning was adopted to generate optimal ensemble weights, and the predictive results of the three basic learners are combined. The experimental results of the models for three real load power datasets show that: (a) the utilization of HI improves the model’s forecasting result; (b) CEEMDAN is superior to other decomposition algorithms in forecasting performance; and (c) the proposed ensemble method, based on the Q-learning algorithm, outperforms three single models in accuracy, and achieves smaller prediction errors.

The continuous depletion of natural resources coupled with plastics pollution, has prompted the scientific community to explore alternative biobased and/or biodegradable polymers. Poly(ethylene succinate) (PESu) is a promising substitute due to its high processability and controllable biodegradation rate. Meanwhile, hemp possesses interesting properties such as being lightweight, exhibiting excellent long-term mechanical stability, and having low carbon emissions, making it an ideal option for wood replacement. Thus, PESu/hemp fiber composites (with and without compatibilizer) were prepared novel sustainable materials with improved properties. The present study aims to investigate the thermal degradation of PESu/hemp fiber composites. More specifically, thermogravimetric analysis (TGA) and pyrolysis-gas chromatography/mass spectrometry (Py–GC/MS) were employed to examine the degradation mechanism and identify decomposition products. The isoconversional methods of Vyazovkin and Friedman, as well as the model free methods, provided comparable results. Samples without compatibilizer were characterized by a two-step Cn autocatalytic mechanism, while those with compatibilizer showed a triple Cn mechanism. The main thermal degradation pathway of the composites was the β-hydrogen scission of the polymeric backbone. In conclusion, this study provides information about the thermal behavior of PESu/hemp fiber composites useful for their application as alternative “wood plastic composites (WPCs)”.

For effective military management, the creation of complexes of automation tools, spatial information processing systems, primarily consolidated, is a priority task in the conditions of constant growth of data and requirements for their collection, transmission, storage, processing and use. The problem of consolidated processing of spatial information is related to the diversity of sources, the diversity of data formats, the diversity of acquisition and use, the diversity of processing etc. All that imply an extremely complex organizational and technical structure, a kind of ‘system of systems’. Deep Learning Machine (DLM) ensure the high accuracy of prediction. But such DLM should be matched for the military conditions of the usage where time restrictions and space lack are present. So, the effort to create a dataset for the maximum accuracy of the Deep Learning Machine rests on computing resources (in common research it is possible to overcome but in military sphere it isn’t). In military applied tasks, the criterion of time and overcoming uncertainties due to confrontation are critical. This allows us to put forward the hypothesis that it is impossible to achieve absolute accuracy in deep learning machine. Therefore, for variable tactical situations, it is advisable to create specialized datasets and achieve maximum efficiency from each iteration step (or their combination) using the decomposition method of the consolidated spatial information processing system. It is analysed the methods of scientific and technical solutions in the deep learning machine and the method of systematizing data types in existing tactical situations. In the end the detection and recognition system with a deep learning machine and a set of specialized datasets is proposed in the paper. The volume of each specialized dataset at the level of 103 enables ultra-high speed of information processing processes and allows a person to set up such a system of consolidated processing of spatial information without excessive organizational and time demands. The dataset itself is revealed. This principle of forming a dataset, or their sets, allows obtaining high-accurate and high -fast detection and recognition systems., Для ефективного військового управління створення комплексів засобів автоматизації, систем обробки просторової інформації, передусім консолідованих, є пріоритетною задачею в умовах постійного зростання даних і вимог до їх збирання, передачі, зберігання, обробки та використання. Системи глибинного навчання дають високу точність прогнозування, але потребують адаптації до військово-прикладних задач, у яких часові критерії, програмно-апаратні обмеження та долання невизначностей, зумовлених протиборством, є критичними. Використовуючи спеціалізовані датасети у каналах системи консолідованої обробки просторової інформації, її максимальну ефективність досягають через декомпозицію процесів обробки у системі з машиною глибинного навчання. Порівняно малі обсяги набору спеціалізованих датасетів уможливлюють надвисоку швидкодію процесів обробки інформації. При цьому людський фактор, необхідний для інформаційно-аналітичної роботи, залишається ключовим.

Death is a common outcome of infection, but most disease models do not track hosts after death. Instead, these hosts disappear into a void. This assumption lacks critical realism, because dead hosts can alter host–pathogen dynamics. Here, we develop a theoretical framework of carbon-based models combining disease and ecosystem perspectives to investigate the consequences of feedbacks between living and dead hosts on disease dynamics and carbon cycling. Because autotrophs (i.e. plants and phytoplankton) are critical regulators of carbon cycling, we developed general model structures and parameter combinations to broadly reflect disease of autotrophic hosts across ecosystems. Analytical model solutions highlight the importance of disease–ecosystem coupling. For example, decomposition rates of dead hosts mediate pathogen spread, and carbon flux between live and dead biomass pools are sensitive to pathogen effects on host growth and death rates. Variation in dynamics arising from biologically realistic parameter combinations largely fell along a single gradient from slow to fast carbon turnover rates, and models predicted higher disease impacts in fast turnover systems (e.g. lakes and oceans) than slow turnover systems (e.g. boreal forests). Our results demonstrate that a unified framework, including the effects of pathogens on carbon cycling, provides novel hypotheses and insights at the nexus of disease and ecosystem ecology.

The influence of the copper ion exchange protocol on SCR activity of SSZ-13 is quantified. Using the same parent SSZ-13 zeolite, four exchange protocols are used to assess how exchange protocol impacts metal uptake and SCR activity. Large differences in the SCR activity, nearly 30 percentage points at 160 degrees C at constant copper content, are observed for different exchange protocols implying that different exchange protocols lead to different copper species. Hydrogen temperature programmed reduction on selected samples and infrared spectroscopy of CO binding corroborates this conclusion as the reactivity at 160 degrees C correlates with the intensity of the IR band at 2162 cm(-1). DFT-based calculations show that such an IR assignment is consistent with CO adsorbed on a Cu(I) cation within an eight-membered ring. This work shows that SCR activity can be influenced by the ion exchange process even when different protocols lead to the same metal loading. Perhaps most interesting, a protocol used to generate Cu-MOR for methane to methanol studies led to the most active catalyst both on a unit mass or unit mole copper basis. This points to a yet not recognized means to tailor catalyst activity as the open literature is silent on this issue. US DOE by Battelle; American Chemical Society Petroleum Research Fund [55575-ND5]; DOE's Office of Biological and Environmental Research; National Science Foundation [CBET-1653561, CBET-2035280] Published version AMK and C-TK acknowledge funding from the American Chemical Society Petroleum Research Fund under award number PRF# 55575-ND5. DFS acknowledges funding from the American Chemical Society Petroleum Research Fund Award #57697-ND5 which partially supported this work. The DFT-based calculations in the paper were performed in the Environmental Molecular Sciences Laboratory (EMSL), a national scientific user facility sponsored by the DOE's Office of Biological and Environmental Research and located at PNNL. Financial support to WSU was partly provided by the National Science Foundation CAREER program under contract No. CBET-1653561. Financial support to WSU was also partly provided by the National Science Foundation under contract No. CBET-2035280. PNNL is operated for the US DOE by Battelle.

A systematic study has been carried out to investigate the neutron transmission signal as a function of sample temperature. In particular, the experimentally determined wavelength-dependent neutron attenuation spectra for a martensitic steel at temperatures ranging from 21 to 700°C are compared with simulated data. A theoretical description that includes the Debye–Waller factor in order to describe the temperature influence on the neutron cross sections was implemented in the nxsPlotter software and used for the simulations. The analysis of the attenuation coefficients at varying temperatures shows that the missing contributions due to elastic and inelastic scattering can be clearly distinguished: while the elastically scattered intensities decrease with higher temperatures, the inelastically scattered intensities increase, and the two can be separated from each other by analysing unique sharp features in the form of Bragg edges. This study presents the first systematic approach to quantify this effect and can serve as a basis , for example, to correct measurements taken during in situ heat treatments, in many cases being a prerequisite for obtaining quantifiable results.

The present paper explores the two components of the glass ceiling effect: promotion barriers for women to the executive sphere and a gender‐based differential in executive pay. The research setting is the British oil industry, which constitutes a male‐dominated sector. Analyzing both components separately, the results suggest that females are promoted more frequently to the executive ranks while they experience a pay bias compared to men. Thus, the analysis reveals that the glass ceiling is cracking in this gender‐imbalanced industry. Yet, pay discrimination still exists. However, within the narrow corridor of executives, the present study suggests that gender pay discrimination diminishes the higher one who climbs up the executive ladder. The latter finding raises the cynical question: How far up the hierarchy ladder does a woman need to climb to overcome gender‐based pay discrimination?

Reactor performance of bubbling fluidized bed (BFB) and turbulent fluidized bed (TFB) was carefully examined and systematically compared using catalytic ozone decomposition as a model reaction, based on a complete mapping of local flow structures and spatial distributions of ozone conversion and solids holdup. TFB clearly has a higher conversion and shows better reactor performance than BFB as a result of the vigorously turbulent flow and the relatively homogeneous gas–solids mixing in TEB. Besides, the intensive interaction between gas and solids in TFB leads to greater gas–solids contact efficiency of TFB over that of BFB. Due to gas bypassing and backmixing caused by bubbling behaviours and two-phase structure, BFB deviates significantly from a plug flow reactor and sometimes from a continuously stirred tank reactor. The flow structures essentially dictate the reactor performance in the low-velocity fluidized beds.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. This paper proposes an adaptive localized decision variable analysis approach under the decomposition-based framework to solve the large scale multi-objective and many objective optimization problems. Its main idea is to incorporate the guidance of reference vectors into the control variable analysis and optimize the decision variables using an adaptive strategy. Especially, in the control variable analysis, for each search direction, the convergence relevance degree of each decision variable is measured by a projection-based detection method. In the decision variable optimization, the grouped decision variables are optimized with an adaptive scalarization strategy, which is able to adaptively balance the convergence and diversity of the solutions in the objective space. The proposed algorithm is evaluated with a suite of test problems with 2-10 objectives and 200-1000 variables. Experimental results validate the effectiveness and efficiency of the proposed algorithm on the large scale multiobjective and many-objective optimization problems.

Phosphogypsum is a solid waste sourced from the wet-process phosphoric acid industry, which causes severe environmental damages. Its utilization was limited by its high decomposition temperature and high energy consumption. Herein, an Fe-P slag, which is a solid waste that mainly comprises iron phosphide (FeP) and diiron phosphide (Fe2P), can dramatically decrease the decomposition temperature of phosphogypsum. It was found that the Fe-P slag and CaSO4 can react as shown in the following reaction equation: 2Fe1.5P+3CaSO4+6CO2→Ca3(PO4)2+Fe3O4+3SO2+6CO. Its reaction mecha-nism was further determined using the thermodynamic method. It was found that CaS was the key intermediate for this reaction. The CaSO4 conversion for this method can reach approximately 97% under the optimized roasting conditions: the molar ratio between Fe1.5P and CaSO4 of 2:3, roasting temperature of 900 °C, a roasting time of 8 h.

This paper examines the efficiency and distributional effects of fuel tax and feebate policies in the automobile market. I employ a model in which households make two-stage decisions on car ownership and utilization, and I estimate model parameters by combining micro-level data from a household survey and macro-level aggregate data on the Japanese new car market from 2006 through 2013. Interestingly, several system changes in the Japanese feebate created rich variations in vehicle prices across vehicles and over time during the sample period. I use such exogenous variation to overcome the vehicle price endogeneity associated with demand estima?tion. Counterfactual analyses show that the Japanese feebate results in a significant increase in social welfare while augmenting environmental externalities. In particular, the rebound effect induced by the feebate cancels out approximately 7% of the reduction in CO2 emissions that would originally have been attained by the improvement in fuel economy. In addition, I find that the fuel tax at the current tax rate in Japan is 1.7 times less costly than the product tax, an alternative feebate scheme considered in the counterfactuals. I also find that there is no dif?ference in regressivity between the two policies in reducing negative environmental externalities by the same amount.