Weakened Mn-O bond in Mn-Ce catalysts through K doping induced oxygen activation for boosting benzene oxidation at low temperatures.

K-doped Mn-Ce solid solution catalysts were synthesized using a combination of coprecipitation and hydrothermal methods, demonstrating excellent performance in benzene oxidation. The catalyst K1Ce5Mn5 exhibited comparable activity to noble metal catalysts, achieving a 90 % benzene conversion at approximately 194 ℃. Durable tests under dry and moist conditions revealed that the catalyst could maintain its activity for 50 h at 218 ℃ and 236 ℃, respectively. Characterization results indicated that the catalyst's enhanced activity resulted from the weakened Mn-O bonding caused by the introduction of K+, facilitating the activation of oxygen and its involvement in the reaction. CeOx, the main crystalline phase of Mn-Ce solid solutions, provided abundant oxygen vacancies for capturing and activating oxygen molecules for the weakened Mn-O structures. This conclusion was further supported by partial density of state analysis from density functional theory computations, revealing that the introduction of K+ weakened the orbital hybridization of Mn3d and O2p. Finally, in situ diffuse reflectance infrared Fourier-transform spectroscopy (in situ DRIFTS) studies on Ce5Mn5 and K1Ce5Mn5 catalysts suggested that the introduction of K+ promoted the conversion of adsorbed benzene. Furthermore, intermediate products were transformed more rapidly for K1Ce5Mn5 compared to Ce5Mn5.

An Improved Blind Zone Channelization Structure and Rapid Implementation Method.

The paper proposes an enhanced design for broadband digital receivers that aims to improve signal capture probability, real-time performance, and the hardware development cycle. To overcome the issue of false signals in the blind zone channelization structure, this paper introduces an improved joint-decision channelization structure that reduces channel ambiguity during signal reception. Xilinx's high-level synthesis (HLS) tools are used for accelerated algorithm implementation, and techniques such as pipelining and loop parallelization are employed to reduce system latency. The entire system is implemented on FPGA. The simulation results demonstrate that the proposed solution effectively eliminates channel ambiguity, improves algorithm implementation speed, and meets the design requirements.

Strong metal-support interactions between atomically dispersed Ru and CrO x for improved durability of chlorobenzene oxidation.

In this work, two single-atom catalysts (SACs) with atomically dispersed RuO2 supported on CrO x were successfully synthesized with a simple reduction strategy for the efficient catalytic oxidation of chlorobenzene (CB). With characterizations like Cs-corrected STEM, XPS, H2-TPR, and O2-TPD, the structure-activity relationship is addressed. The noble metal precursor Ru3+ was anchored with different oxygen species and exposed facets based on the physicochemical properties of catalyst supports. Based on the analysis results, the Ru3+ precursor could be mainly anchored into the surface lattice oxygen of Cr2O3-M over high-index facets (223) and adsorbed oxygen of Cr2O3-P over low-index facets (104), where the precursor Ru3+ was all oxidized to RuO2 when being anchored with the oxygen species of Cr2O3-M and Cr2O3-P, respectively according to XPS analysis. There is a stronger metal-support interaction (SMSI) between Ru ions and the surface lattice oxygen of Cr2O3-M, according to H2-TPR and O2-TPD characterizations. Further, the catalytic performance for CB combustion at a high space velocity of 120 000 mL (g-1 h-1) was tested, and 1RuCr2O3-M performed better than 1RuCr2O3-P in both durability and activity. This could be attributed to the SMSI between single-atom Ru and the lattice oxygen of the 1RuCr2O3-M catalyst and the abundant active sites from the exposed high-index facets. The study provided a novel synthesis strategy for Ru-based SACs with SMSI effect, and the good durability of the catalyst (1RuCr2O3-M) extended the great potential for practical application.

Refining the diagnostics of non-point source metals pollution to urban lakes based on interaction normalized PMF coupled with Bayesian network.

Spatiotemporal variability complicates source apportionment of metals in urban lakes, especially when rainfall drives urban non-point source pollution. As, Cd, Cr, Pb, Hg, Ag, Co, Cu, Fe, Mn, Ni, Sb, Sr and Zn concentrations in 648 water samples collected before and after rain in 6 urban lakes of Beijing, China were determined during 2013-2015. The response of metals concentrations after rain to the interaction between rainfall and antecedent dry days was significant. Metals concentrations were normalized pursuant to the interaction effect as the input of positive matrix factorization (PMF) to develop the interaction normalized-PMF (IN-PMF). Four primary pollution sources were diagnosed. Sediment release was considered to be the main source of Fe, Co and Ni independent of rainfall. Hg, As and some Cr associated with pesticides and fertilizers were likely to come from soil erosion and runoff from green space. It is probable that road runoff was the dominant source for heavy metals related to traffic emissions, including Pb, Cd, Cu, Sb, Mn and Zn. Cr, Sr and some Cu and Zn as key elements of rooftops can be regarded as from roof runoff. The IN-PMF lowered roof and road runoff contributions and raised the contribution of soil erosion from green space, with Pb, Sb, Cu, Zn, Cd and Mn increasing by 15.9%, 10.7%, 13.1%, 12.2%, 13.3% and 16.8%. The results shed more light on the stormwater runoff pollution mitigation on impervious surfaces and metals enrichment problems in infiltration soil on green space in the low impact development (LID) setting. The Bayesian network revealed the spatial variability of transport and fate of metal elements from land surfaces to urban lakes, supplementing the secondary pollution sources from different land use. This study will provide new insights for source apportionment of non-point source pollution under the background of sponge city construction.

The influence of rainfall and catchment characteristics on runoff generation in urban catchments-a case study in Hebi City of China.

An in-depth understanding of the rainfall-runoff process is essential for effective stormwater management. However, the understanding of the hierarchy of rainfall characteristics in terms of their importance in influencing runoff generation is limited. This paper investigates the influence of rainfall characteristics and catchment characteristics on runoff generation in urban catchments. The outcomes showed that there are 4 dominant factors affecting runoff generation: total precipitation TP and maximum 60-min rainfall intensity MAX60 are the two top-ranked factors while average rainfall intensity RI and maximum 5-min rainfall intensity MAX5 are ranked second. Additionally, compared to the moderate rainfall regime (MR), the heavy rainfall regime (HR) tends to produce higher peak flow rates, higher total inflow per unit area, and lower runoff control effect. Note that the antecedent precipitation has a more significant effect on runoff generation and is even the dominant factor when rainstorm events with daily rainfall larger than 50 mm are not considered. The results of analyzing the influence of catchment characteristics suggest that only under HR regime conditions do the catchment characteristics have an impact on runoff generation and behave as smaller catchment areas, and higher proportions of green landscapes always lead lower peak flow rates, lower total inflows per unit area, and higher runoff control effects.

Water quality responses to rainfall and surrounding land uses in urban lakes.

The effect of rainfall on the water quality of urban lakes is related to the surrounding land use types. Six lakes surrounded by business units (commercial lakes) and park green spaces (park lakes) in the central area of Beijing, China, were monitored between June 2013 and October 2015. A total of 638 water samples were obtained. The results showed that the water quality was generally worse in the rainy season than in the dry season, with the mean dissolved oxygen (DO) concentration declining by 25.1 %. Compared to the rise in DO of park lakes after rainfall, commercial lakes showed a decreased DO. The nitrate nitrogen (NO3--N) concentration decreased after rain, and that in commercial lakes decreased more than that in park lakes. Different from DO and NO3--N, the ammonia nitrogen (NH4+-N)), total suspended solids (TSS) and total phosphorus (TP) concentrations of the lakes increased obviously after rain. The values in park lakes increased 138 % (NH4+-N)), 120 % (TSS) and 69 % (TP) more than those in commercial lakes. Principal component analysis (PCA) results illustrated that green space was the main source of TSS and TP for urban lakes. Most of the nitrogen (N) comes from artificial impervious surfaces. These results will help target pollutant control for urban lakes.

Re-thinking classical mechanistic model for pollutant build-up on urban impervious surfaces.

Accurate modelling of particulates build-up process is essential for designing effective stormwater management strategies. However, current modelling practice relies on the classical 'power model' which has limitations in accounting for the variability in the build-up process. This research study investigated the relationships between influential factors of the build-up process and coefficients in the power model. The outcomes showed that the coefficient, which determines the build-up rate, is predominantly influenced by land use factors (pervious area, road area, commercial area and residential area), such that land use factors exerted 23 times more influence than the site characteristics (distance to pervious area and road surface texture depth). The coefficient, which determines how quickly build-up reaches equilibrium, was found to be equally influenced by anthropogenic activities (sweeping frequency and traffic volume) and site characteristics. Further, site characteristics were found to play a major role in generating build-up process variability with three times more influence than that of anthropogenic activities. It was found that the power model satisfactorily replicates the build-up of particles <74 µm. For the build-up of particles >74 µm, a new coefficient, namely, 'coefficient of variability' was introduced in order to improve the prediction performance (up to 17% compared to original power model). The study outcomes provide a deeper understanding into particulates build-up modelling, and can contribute to the formulation of effective stormwater treatment strategies.

Influence of traffic on build-up of polycyclic aromatic hydrocarbons on urban road surfaces: A Bayesian network modelling approach.

Due to their carcinogenic effects, Polycyclic Aromatic Hydrocarbons (PAHs) deposited on urban surfaces are a major concern in the context of stormwater pollution. However, the design of effective pollution mitigation strategies is challenging due to the lack of reliability in stormwater quality modelling outcomes. Current modelling approaches do not adequately replicate the interdependencies between pollutant processes and their influential factors. Using Bayesian Network modelling, this research study characterised the influence of vehicular traffic on the build-up of the sixteen US EPA classified priority PAHs. The predictive analysis was conditional on the structure of the proposed BN, which can be further improved by including more variables. This novel modelling approach facilitated the characterisation of the influence of traffic as a source of origin and also as a key factor that influences the re-distribution of PAHs, with positive or negative relationship between traffic volume and PAH build-up. It was evident that the re-distribution of particle-bound PAHs is determined by the particle size rather than the chemical characteristics such as volatility. Moreover, compared to commercial and residential land uses, mostly industrial land use contributes to the PAHs load released to the environment. Carcinogenic PAHs in industrial areas are likely to be associated with finer particles, while PAHs, which are not classified as human carcinogens, are likely to be found in the coarser particle fraction.