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Enhanced collection of fine particles in a cyclone using ultrasonic vapor with surfactants
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Comparing the interactions between particulate matter and cloud properties over two populated cities in Texas using WRF-Chem fine-resolution modeling
Accurate modeling of aerosol-cloud interactions is essential for reliable weather and air quality simulations, given their significant impact on precipitation patterns, cloud dynamics, and aerosol distributions. This study employed the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to examine the impact of enhanced meteorological simulations, achieved through advanced microphysics parameterization supported by data assimilation techniques, on air quality across Texas on August 19 and 20, 2022. We tested four distinct configurations: (1) the Morrison two-moment bulk microphysics scheme, (2) Morrison's with observation nudging, (3) the Spectral Bin Microphysics (SBM), and (4) SBM with observation nudging. While the SBM scheme is known for its detailed representation of aerosol-cloud interactions, our focus was on how improvements in meteorological accuracy translate to more precise air quality simulations. Our findings demonstrated a progressive improvement in simulation accuracy, starting with the Morrison's scheme and further enhanced by adopting the SBM scheme, complemented by incorporating observation nudging. Specifically, the combination of the SBM scheme and the nudging substantially enhanced the model's ability to capture convective precipitation events, as shown by better alignment with NEXRAD radar reflectivity, with R increasing from –0.21 to 0.82, IOA from 0.10 to 0.87, and NMB decreasing from 99% to 34% in Houston. The enhanced meteorology translated into more accurate PM 2.5 concentration simulations, particularly through the more accurate representation of aerosol washout during precipitation events. In Houston, the SBM scheme with nudging improved the model's PM 2.5 simulations significantly, with NMB decreasing from –20% to 5% and IOA improving from 0.43 to 0.61. In San Antonio, improvements were also notable, with NMB improved from –27% to –22%, R increased from 0.48 to 0.82, and IOA increased from 0.66 to 0.86. Furthermore, our results underscore the crucial role of accurate meteorological simulations in refining our understanding of aerosol behaviors in relation to precipitation patterns, directly enhancing the reliability and effectiveness of air quality modeling.
The pectin puzzle: Decoding the fine structure of rhamnogalacturonan-I (RG-I) in Arabidopsis thaliana uncovers new pectin features
Pectin is generally divided into four distinct structural categories, namely homogalacturonan, xylogalacturonan, rhamnogalacturonan I (RG-I) and rhamnogalacturonan II. While much of the structural diversity of homogalacturonan, xylogalacturonan and rhamnogalacturonan II has been elucidated, the structural features of RG-I are less well understood. In this work, we employed multiple complementary analytical techniques to present a detailed structural analysis of RG-I in the model species Arabidopsis thaliana . Starting with highly purified RG-I from different Arabidopsis tissues, we employed comparative linkage and nuclear magnetic resonance analysis along with mass spectrometry analysis of enzymatically digested RG-I oligosaccharides. Besides the presence of the canonical α-1,5-arabinan, β-1,4-galactan, β-1,6-galactan and arabinogalactan RG-I side chains of varying lengths, we show that a large portion of the β-1,6-galactan is terminated by either 4-O-methyl β-glucuronic acid (GlcA) residues or, to a smaller degree, β-GlcA that lacks the Me-ether group. Importantly, O-acetylation of RG-I GalA residues is a minor modification while 10 % of the backbone Rha residues are 3-O-acetylated, and most of the acetylated Rha is additionally branched with β-galactose substituents. Taken together, the combined results of these different analytical techniques present the most comprehensive structural overview of Arabidopsis thaliana RG-I to date.
From inert gas to fertilizer, fuel and fine chemicals: N 2 reduction and fixation
The 100th anniversary of a leading nitrogen fixation technology developer like CASALE SA is a reason to reflect over the 20th century successful solution of the problem of world food supply, and to look out for solutions for sustainable developments with respect to ammonia production. Here, we review the role of nitrogen as essential chemical constituent in photosynthesis and biology, and component of ammonia as it is used as fertilizer for primary production by photosynthesis for farming and food supply and its future role as energy carrier. While novel synthesis methods and very advanced synchrotron based x-ray analytical techniques are being developed, we feel it is important to refer to the historical and economical context of nitrogen. The breaking of the N≡N triple bond remains a fundamental chemical and energetic problem in this context. We review the electrochemical ammonia synthesis as an energetically and environmentally benign method. Two relatively novel X-ray spectroscopy methods, which are relevant for the molecular understanding of the catalysts and biocatalysts, i.e. soft X-ray absorption spectroscopy and nuclear resonant vibration spectroscopy are presented. We illustrate the perceived reality in fertilizer usage on the field, and fertilizer production in the factory complex with photos and thus provide a contrast to the academic view of the molecular process of ammonia function and production.
Microscopic characterization of alkali-silica reaction (ASR) affected recycled concrete mixtures induced by reactive coarse and fine aggregates
Alkali-silica reaction (ASR) is one of the most detrimental distress mechanisms leading to early deterioration of concrete infrastructure, creating large amounts of construction and demolition waste which however can be transformed into recycled concrete aggregates (RCA). Yet, as ASR is an ongoing distress mechanism, its potential of reoccurrence in RCA could cause adverse effects. Investigation of the crack propagation of RCA concrete revealed that the distress features vary widely thus, indicating different distress mechanisms in all RCA concrete. Concrete made of slightly damaged RCA shows the lease number of cracks yet, wider cracks at high expansion levels. Additionally, the distress in slightly damaged RCA concrete made of reactive coarse aggregate and RCA concrete made of reactive sand, regardless of original damage extent, is governed by cracks propagating through the residual cement paste whereas cracks propagate through the new cement paste in severely damaged RCA made of reactive coarse aggregate.
Fine-tuning catalytic selectivity by modulating catalyst-environment interactions: CO 2 hydrogenation over Pd-based catalysts
Capturing catalytic behaviors under operational conditions is pivotal to gaining a mechanistic understanding and promoting the design of robust catalysts. The challenge lies in the difficulty of monitoring real-time surface dynamics driven by catalyst-environment interactions. Here, in this work, we introduce a framework based on density functional calculations and kinetic modeling. This framework significantly improves the accuracy of theoretical models’ descriptions of experimental observations by quantifying environmental impacts on surface phases and active sites. CO 2 hydrogenation over Pd-based catalysts is taken as a showcase. The observed selectivity variations of Pd and Pd-M bimetallic catalysts strongly correlate with hydrogen coverage maintained under typical CO 2 hydrogenation conditions. By reducing the amount of surface hydrogen, the selectivity tuned effectively from formic acid toward CO and methanol. This study not only deepens the comprehension of dynamics of active sites under active chemical conditions but also introduces an alternative opportunity for catalytic tuning by modulating catalyst-environment interactions.
Oxybarometry and valence quantification based on microscale X-ray absorption fine structure (XAFS) spectroscopy of multivalent elements
Abstract not provided
From observation to replication: machine-learning-driven quantification and replication of fine-scale fish kinematics and behavior
Long-term quantification of fish behavior is essential for aquatic ecology, wildlife telemetry, and biomechanical device development. However, the observation duration required to obtain reliable behavioral and kinematic metrics remains unclear, and few tools exist to physically reproduce natural swimming motion for controlled experimentation. We address these challenges by developing a generalizable framework that models behavioral reliability (Spearman–Brown reliability index) as a function of observation duration and derives metric-specific monitoring thresholds. Using juvenile white sturgeon (Acipenser transmontanus) as a case study, we demonstrate that the minimum duration needed for reliable estimates varies substantially across kinematic features: to exceed a reliability of 0.8, total distance traveled requires 12 days, average curvature (mm?¹) 15 days, tail-beat frequency (Hz) 8 days, and average speed (body length/s) 17 days. We further bridge digital analysis and physical testing by developing a hardware-in-the-loop simulator that reconstructs machine-learning-derived swimming kinematics with high fidelity (correlation coefficient 0.98–0.99, RMSE 1.22–1.27 mm over a 5-minute segment). This platform enables realistic, repeatable motion stimuli for evaluating aquatic sensing technologies and bio-integrated devices under controlled conditions. Together, these contributions provide a scalable approach for designing long-term behavioral studies and a data-driven connection between ecological observation and robotic experimentation.
The relationship between selenium retention and fine particles removal during coal combustion
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Enrichment mechanism of arsenic in fine ash deposits during co-combustion of rice husk and coal
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Fine-tuned order-order phase transitions in giant surfactants via interfacial engineering
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Fine-scale cation dynamics and control by Rhodonia placenta and Pleurotus ostreatus during wood decay
Fungi are the primary decomposers of wood across the globe. They redistribute the vast pool of wood carbon to other parts of the carbon cycle using mechanisms that also have intriguing potential in bioconversion and biotechnology applications. To make a living in the relatively sparse microenvironment of wood, these filamentous fungi must import many of their needs, including cations. Understanding the timing of cation import can help us validate the functions of cations in wood decay and create a clearer understanding of these complex wood degradation mechanisms. In this study, we resolved cation timing dynamics across space for two fungi (brown rot fungus Rhodonia placenta and white rot fungus Pleurotus ostreatus) using a wood wafer system. We found some expected patterns of the cation dynamics for both fungi, and a clear role for iron at the early stages of brown rot decay. On the other hand, the lack of an increase in manganese during initial white rot decay was surprising. Unexpectedly, we also saw a spike in copper during early brown rot decay that demands more investigation as a potential player in the brown rot mechanism.