Selectivity Ratio Research Articles

Evaluating the Efficacy of Active Ingredients Used in Roach Baits against Small Hive Beetle (Aethina tumida) and Their Safety to Honey Bees (Apis mellifera).

Beekeepers need new treatment options for controlling small hive beetles (Aethina tumida), a devastating honey bee (Apis mellifera) pest. For many years, commercial beekeepers in the U.S. have used gel roach baits off-label as a method for treating SHBs. Herein, we evaluated the acute toxicity of active ingredients commonly found in gel roach baits, including abamectin, clothianidin, hydramethylnon, fipronil, and indoxacarb through topical and oral routes of exposure against SHBs and honey bees. Additionally, coumaphos, the active ingredient of the only registered in-hive control treatment for SHBs, was evaluated to provide a comparison to the gel roach bait active ingredients. Fipronil was the most toxic compound to SHBs topically (LD50 = 0.23 ng/SHB) and through pollen (LC50 = 0.06 µg/g pollen). Fipronil (LD50 = 0.31 ng/honey bee) had a selectivity ratio of 1.3, suggesting that it is more toxic to SHBs than it is to honey bees, but only to a small degree. Abamectin, clothianidin, hydramethylnon, and indoxacarb had a higher toxicity to honey bees than to SHBs through topical exposure. Our results suggest that gel roach baits and their active ingredients are toxic to honey bees and pose a serious risk to colony safety if used as in-hive treatments.

Microkinetic Molecular Volcano Plots for Enhanced Catalyst Selectivity and Activity Predictions.

Molecular volcano plots, which facilitate the rapid prediction of the activity and selectivity of prospective catalysts, have emerged as powerful tools for computational catalysis. Here, we integrate microkinetic modeling into the volcano plot framework to develop "microkinetic molecular volcano plots". The resulting unified computational framework allows the influence of important reaction parameters, including temperature, reaction time, and concentration, to be quickly incorporated and more complex situations, such as off-cycle resting states and coupled catalytic cycles, to be tackled. Compared to previous generations of molecular volcanoes, these microkinetic counterparts offer a more comprehensive understanding of catalytic behavior, in which selectivity and product ratios can be explicitly determined by tracking the evolution of each product concentration over time. This is demonstrated by examining two case studies, rhodium-catalyzed hydroformylation and metal-catalyzed hydrosilylation, in which the unique insights provided by microkinetic modeling, as well as the ability to simultaneously screen catalysts and reaction conditions, are highlighted. To facilitate the construction of these plots/maps, we introduce mikimo, a Python program that seamlessly integrates with our previously developed automated volcano builder, volcanic.

Controlling catalytic and reaction factors for regulating the distribution of aromatic products in n-butane aromatization over Ga/HZSM-5

The selective production of benzene, toluene, and xylene (BTX) has the potential to enhance the overall economics of aromatic production and subsequent processes, owing to their differing applications in the petroleum industry. Despite the economic significance of selective BTX production, the individual selectivity of BTX and the corresponding generation mechanisms have not been thoroughly investigated. Therefore, this study aims to explore the factors that affect the distribution of BTX and their respective trends of variation during n-butane aromatization over Ga/HZSM-5. This study demonstrates that selective production can be achieved by controlling the conditions that significantly influence product distribution, including the reaction temperature, extent of catalyst deactivation, and distribution of acid sites. The selectivity for benzene relative to toluene increased with increasing reaction temperature but decreased with increasing catalyst aging time. In addition, Ga/HZSM-5, which had a low Si/Al ratio, exhibited a pronounced preference for benzene. Consequently, it is possible to control benzene to toluene selectivity ratio within a range of 0.5–1.1 while maintaining over 85 % of n-butane conversion by adjusting these key factors. Further results proposed that the distinct role of protolytic cracking over Brønsted acid sites may contribute to the shift in aromatics distribution toward benzene during the series reaction, whereas the dehydrogenation over Ga Lewis acid sites plays a promotional role in the shift toward toluene.

TRPM4-Inspired Polymeric Nanochannels with Preferential Cation Transport for High-Efficiency Salinity-Gradient Energy Conversion.

Biological ion channels exhibit switchable cation transport with ultrahigh selectivity for efficient energy conversion, such as Ca2+-activated TRPM4 channels tuned by cation-π interactions, but achieving an analogous highly selective function is challenging in artificial nanochannels. Here, we design a TRPM4-inspired cation-selective nanochannel (CN) assembled by two poly(ether sulfone)s, respectively, with sulfonate acid and indole moieties, which act as cation-selective activators to manage Na+/Cl- selectivity via ionic and cation-π interactions. The cation selectivity of CNs can be activated by Na+, and thereby the Na+ transference number significantly improves from 0.720 to 0.982 (Na+/Cl- selectivity ratio from 2.6 to 54.6) under a 50-fold salinity gradient, surpassing the K+ transference number (0.886) and Li+ transference number (0.900). The TRPM4-inspired nanochannel membrane enabled a maximum output power density of 5.7 W m-2 for salinity-gradient power harvesting. Moreover, a record energy conversion efficiency of up to 46.5% is provided, superior to most nanochannel membranes (below 30%). This work proposes a novel strategy to biomimetic nanochannels for highly selective cation transport and high-efficiency salinity-gradient energy conversion.

CCD UBV(RI)KC photometry and dynamics of the open cluster NGC 1513

ABSTRACT We derive astrophysical parameters of the open cluster NGC 1513 by means of colour indices built with new CCD UBV(RI)KC photometry. Based on early-type members, the mean foreground reddening and total to selective extinction ratio are E(B − V) = 0.79 ± 0.09 mag and RV = 2.85 ± 0.05. Through the differential grid method, we derive the metal abundance [Fe/H] = −0.06 dex (Z = +0.013), which is consistent with the value [Fe/H] = −0.088 of the bright giant member – LAMOST 695710060. Z = +0.013 isochrone fit to the V × (B − V) colour–magnitude diagram leads to a turn-off age of 224 ± 27 Myr (thus an intermediate-age cluster), and a distance modulus of (V0 − MV) = 10.90 ± 0.15 mag, thus implying a distance from the Sun d = 1514 ± 105 pc. Within the uncertainties, our photometric distance is consistent with the value d = 1435 ± 14 pc from the Gaia DR3 parallax. We find signs of small mass segregation through a minimum spanning tree analysis for the 190 most massive stars, together with the rather steep mass function (χ = +2.39) slope. The high core to half-light radius ratio Rcore/Rh = 0.82, together with the compact half-light to tidal radius ratio Rh/Rt = 0.22, suggest that it is probably related to cluster-formation effects, due to little dynamical evolution, instead of driving its dynamical evolution by internal relaxation. Indeed, NGC 1513 is located in the second quadrant (ℓ = 152${_{.}^{\circ}}$59 and Galactocentric distance RGC = 9.57 kpc), which tends to minimize tidal effects by external processes and tidal disruption. Therefore, internal mass segregation effects in NGC 1513 seem to be more efficient than cluster evaporation processes. We find that NGC 1513 migrated about 0.50 kpc from its birth place.

Opportunities offered by latent-based multiblock strategies to integrate biomarkers of chemical exposure and biomarkers of effect in environmental health studies

Modern environmental epidemiology benefits from a new generation of technologies that enable comprehensive profiling of biomarkers, including environmental chemical exposure and omic datasets. The integration and analysis of large and structured datasets to identify functional associations is constrained by computational challenges that cannot be overcome using conventional regression methods. Some extensions of Partial Least Squares (PLS) regression have been developed to efficently integrate multiple datasets, including Multiblock PLS (MB-PLS) and Sequential and Orthogonalized PLS; however, these approaches remain seldom applied in environmental epidemiology. To address that research gap, this study aimed to assess and compare the applicability of PLS-based multiblock models in an observational case study, where biomarkers of exposure to environmental chemicals and endogenous biomarkers of effect were simultaneously integrated to highlight biological links related to a health outcome. The methods were compared with and without sparsity coupling two metrics to support the variable selection: Variable Importance in Projection (VIP) and Selectivity Ratio (SR). The framework was applied to a case-study dataset mimicking the structure of 36 environmental exposure biomarkers (E-block), 61 inflammation biomarkers (M-block), and their relationships with the gestational age at delivery of 161 mother-infant pairs. The results showed an overall consistency in the selected variables across models, although some specific selection patterns were identified. The block-scaled concatenation-based approaches (e.g. MB-PLS) tended to select more variables from the E-block, while these methods were unable to identify certain variables in the M-block. Overall, the number of variables selected using the SR criterion was higher than using the VIP criterion, with lower predictive performances. The multiblock models coupled to VIP, appeared to be the methods of choice for identifying relevant variables with similar statistical performances. Overall, the use of multiblock PLS-based methods appears to be a good strategy to efficiently support the variable selection process in modern environmental epidemiology.

Using Activity Measures and GNSS Data from a Virtual Fencing System to Assess Habitat Preference and Habitat Utilisation Patterns in Cattle.

There has been an increased focus on new technologies to monitor habitat use and behaviour of cattle to develop a more sustainable livestock grazing system without compromising animal welfare. One of the currently used methods for monitoring cattle behaviour is tri-axial accelerometer data from systems such as virtual fencing technology or bespoke monitoring technology. Collection and transmission of high-frequency accelerometer and GNSS data is a major energy cost, and quickly drains the battery in contemporary virtual fencing systems, making it unsuitable for long-term monitoring. In this paper, we explore the possibility of determining habitat preference and habitat utilisation patterns in cattle using low-frequency activity and location data. We achieve this by (1) calculating habitat selection ratios, (2) determining daily activity patterns, and (3) based on those, inferring grazing and resting sites in a group of cattle wearing virtual fencing collars in a coastal setting with grey, wooded, and decalcified dunes, humid dune slacks, and salt meadows. We found that GNSS data, and a measure of activity, combined with accurate mapping of habitats can be an effective tool in assessing habitat preference. The animals preferred salt meadows over the other habitats, with wooded dunes and humid dune slacks being the least preferred. We were able to identify daily patterns in activity. By comparing general trends in activity levels to the existing literature, and using a Gaussian mixture model, it was possible to infer resting and grazing behaviour in the different habitats. According to our inference of behaviour the herd predominantly used the salt meadows for resting and ruminating. The approach used in this study allowed us to use GNSS location data and activity data and combine it with accurate habitat mapping to assess habitat preference and habitat utilisation patterns, which can be an important tool for guiding management decisions.

Crown-Ether Crystal Channel Membranes with Subnanometer Pores for Selective Na+ Transport.

Emulating biological sodium ion channels to achieve high selectivity and rapid Na+ transport is important for water desalination, energy conversion, and separation processes. However, the development of artificial ion channels, especially multichannels, to achieve high ion selectivity, remains a challenge. In this work, we demonstrate the fabrication of ion channel membranes utilizing crown-ether crystals (DA18C6-nitrate crystals), which feature extremely consistent subnanometer pores. The polyethylene terephthalate (PET) membranes were initially subjected to amination, followed by the in situ growth of DA18C6-nitrate crystals to establish ordered multichannels aimed at facilitating selective Na+ conductance. These channels allow rapid Na+ transport while inhibiting the migration of other ions (K+ and Ca2+). The Na+ transport rate was 2.15 mol m-2 h-1, resulting in the Na+/K+ and Na+/Ca2+ selectivity ratios of 6.53 and 12.56, respectively. Due to the immobilization of the crown-ether ring, when the size of the transmembrane ion exceeded that of the crown-ether ring's cavity, the ions had to undergo a dehydration process to pass through the channel. This resulted in the ions encountering a higher energy barrier upon entering the channel, making it more difficult for them to permeate. However, the size of Na+ was compatible with the cavity of the crown-ether ring and was able to displace the hydrated layer effectively, facilitating selective Na+ translocation. In summary, this research offers a promising approach for the future development of functionalized ion channels and efficient membrane materials tailored for high-performance Na+ separation.

Antibacterial MXene composite with excellent U/V selectivity for uranium extraction

Uranium, as a radioactive element, is the source of utilizable energy for mankind in the future. Large quantities of uranium resources are contained in seawater, so uranium extraction from seawater is undoubtedly the most feasible method. However, the presence of competitive ions such as vanadium hinders the efficient extraction of uranium, which is currently a challenge that needs to be solved. Here, silane coupling agent (KH550) was grafted onto two-dimensional metal carbide (MXene). Subsequently, salicylaldoxime (SA) was deposited onto MXene particles using self-polymerization of dopamine (DA) to prepare PDA/SA deposited MXene composite grafted with KH550 (PDA/SA/MXene-KH550). The adsorbent exhibited excellent selectivity for uranium ions with a uranium/vanadium selectivity ratio of 4.8. In antibacterial experiments, the PDA/SA/MXene-KH550 composite showed excellent antibacterial performance against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), demonstrating the ability to adapt to complex marine microbial contamination. In addition, the maximum experimental uranium adsorption capacity of the composite was 332.3 mg/g, following the pseudo-second-order kinetic model and the Langmuir isotherm model. After five adsorption-desorption cycles, the elution efficiency of PDA/SA/MXene-KH550 for uranium can still reach 74.7 %, which demonstrated that the adsorbent had superior stability and regeneration ability, providing the possibility for actual enrichment of uranium.

Atmospheric Gas-Phase Catalyst Etching of SiO2 for Deep Microfabrication Using HF Gas and Patterned Photoresist.

Micro/nanoscale structure fabrication is an important process for designing miniaturized devices. Recently, three-dimensional (3D) integrated circuits using SiO2 via-holes interlayer filling by copper have attracted attention to extend the lifetime of Moore's law. However, the fabrication of vertical and smooth-sidewall via-hole structures on SiO2 has not been achieved using the conventional dry etching method due to the limitation of the selective etching ratio of SiO2 and hard mask materials. In this study, we developed a unique method for the deep anisotropic dry etching of SiO2 using atmospheric gas-phase HF and a patterned photoresist. The hydroxyl groups in the photoresist catalyzed the HF gas-phase dry etching of SiO2 at high-temperature conditions. Therefore, fabrication of vertical with smooth-sidewall deep microstructures was demonstrated in the photoresist-covered area on SiO2 at a processing rate of 1.3 μm/min, which is 2-3 times faster than the conventional dry etching method. Additionally, the chemical reaction pathway in the photoresist-covered area on SiO2 with HF gas was revealed via density functional theory (DFT) calculations. This simple and high-speed microfabrication process will expand the commercial application scope of next-generation microfabricated SiO2-based devices.

Synthesis of hybrid materials containing H-ZSM-5 and CeO2 for green butadiene production

Synthesis of hybrid materials combining two or more components with different properties is a commonly used strategy to obtain high-performance materials for a wide range of different applications, including catalytic reactions with a complex reaction network. In this work, two synthesis approaches are investigated to prepare hybrid materials containing two well-defined phases, i.e. H-ZSM-5 and CeO2. By dispersing nano-sized H-ZSM-5 on CeO2, and by synthesizing a core-shell catalyst with a nano H-ZSM-5 core and a ceria shell, a high intimacy between H-ZSM-5 and CeO2 was obtained, while keeping the characteristic properties of each phase. The dehydration of 1,3-butanediol is used as a test reaction to explore the potential of these materials. A ratio between the selectivities of the desired product and intermediates, i.e. 1,3-butadiene (BD), 3-buten-2-ol (3B2OL), and 2-buten-1-ol (2B1OL) to the undesired product propylene (PP) was defined: (BD+3B2OL+2B1OL)/PP. Dispersing H-ZSM-5 zeolites on a CeO2 surface was the optimal synthesis route to enhance the 1,3-butadiene selectivity, and a (BD+3B2OL+2B1OL)/PP selectivity ratio was obtained (3.9) compared to its parent material (2.1).

An auto‐configurable machine learning framework to optimize and predict catalysts for CO2 to light olefins process

AbstractThis study proposed an auto‐configurable machine learning framework based on the differential evolution algorithm (AutoML‐DE) driven by hybrid data for the screening and discovery of promising CO2 to light olefins (CO2TLO) catalysts candidates. The hybrid dataset comprises 532 experimental data from the literature and 296 simulation data. Results show that the AutoML‐DE model with extreme gradient boosting algorithms demonstrated superior performance for predicting the conversion ratio of CO2 and selectivity of light olefins (average R2 > 0.86). After identifying the input feature with the most significant impact on the output feature, the optimal AutoML‐DE model integrated with the genetic algorithm is applied to multiobjective optimization, sensitivity analysis, and prediction of new CO2TLO catalysts. The optimized Cu‐Zn‐Al/SAPO‐34 catalyst has the highest catalytic performance among the reported CO2TLO catalysts. Moreover, five new CO2TLO catalysts with higher yields are successfully predicted. However, the performance of these catalysts should be further verified by experiment.

Tuning Reinforcement Learning Parameters for Cluster Selection to Enhance Evolutionary Algorithms.

The ability to find optimal molecular structures with desired properties is a popular challenge, with applications in areas such as drug discovery. Genetic algorithms are a common approach to global minima molecular searches due to their ability to search large regions of the energy landscape and decrease computational time via parallelization. In order to decrease the amount of unstable intermediate structures being produced and increase the overall efficiency of an evolutionary algorithm, clustering was introduced in multiple instances. However, there is little literature detailing the effects of differentiating the selection frequencies between clusters. In order to find a balance between exploration and exploitation in our genetic algorithm, we propose a system of clustering the starting population and choosing clusters for an evolutionary algorithm run via a dynamic probability that is dependent on the fitness of molecules generated by each cluster. We define four parameters, MFavOvrAll-A, MFavClus-B, NoNewFavClus-C, and Select-D, that correspond to a reward for producing the best structure overall, a reward for producing the best structure in its own cluster, a penalty for not producing the best structure, and a penalty based on the selection ratio of the cluster, respectively. A reward increases the probability of a cluster's future selection, while a penalty decreases it. In order to optimize these four parameters, we used a Gaussian distribution to approximate the evolutionary algorithm performance of each cluster and performed a grid search for different parameter combinations. Results show parameter MFavOvrAll-A (rewarding clusters for producing the best structure overall) and parameter Select-D (appearance penalty) have a significantly larger effect than parameters MFavClus-B and NoNewFavClus-C. In order to produce the most successful models, a balance between MFavOvrAll-A and Select-D must be made that reflects the exploitation vs exploration trade-off often seen in reinforcement learning algorithms. Results show that our reinforcement-learning-based method for selecting clusters outperforms an unclustered evolutionary algorithm for quinoline-like structure searches.

Harnessing dimethyl ether with ultra-low-grade heat for scaling-resistant brine concentration and fractional crystallization

Solvent-driven separations may enable scalable concentration of hypersaline brines, supporting a circular resource economy from the extraction of lithium and rare earth elements from spent battery and magnet leachates. This work analyses a novel solvent-driven water extraction (SDWE) system employing dimethyl ether (DME) and ultra-low-grade heat for brine concentration and fractional crystallization. SDWE exploits DME’s unique properties: (1) a low dielectric constant that promotes water solubility over charged solutes by a factor of 103, and (2) a high volatility that facilitate efficient DME reconcentration with ultra-low-grade heat. The techno-economic viability of SDWE is assessed with a computational framework that encompasses a liquid–liquid separator and a solvent concentrator. We integrate the extended universal quasichemical model with the virial equation of state to predict the compositions of the complex three-phase DME-water mixture at vapor–liquid and liquid–liquid equilibrium. Subsequently, we optimize the thermodynamic and economic performance of SDWE, by controlling the interstage flash pressure, heat source temperature, and the number of concentrating stages. DME-based SDWE concentrates an input saline feed to 5.5 M and regenerates over 99 % of the DME using ultra-low-grade heat below 50 °C, with a DME/water selectivity ratio of 125. Our calculations reveal that optimal performance is achieved at interstage flash pressures of 0.4 – 0.5 bar for heat source temperatures between 323–373 K, with improved exergetic efficiencies at lower temperatures. At a heat source temperature of 323 K and an interstage pressure of 0.489 bar, DME-driven SDWE achieves an optimal thermodynamic efficiency of 20.5 % and a projected specific cost of US$ 1.93 m−3. These specific costs suggest that SDWE is competitive with commercialized thermal distillation technologies, while mitigating the traditional risks associated with scaling in heat and mass exchangers with hypersaline brines.

A genetic algorithm-based optimal selection and blending ratio of plastic waste for maximizing economic potential

Pyrolysis of plastic waste presents an innovative solution to convert plastics into valuable fuels like oil and gas, thereby contributing to circular economy and sustainability. The efficiency of this process highly depends on the types and compositions of plastics utilized, as each type reacts uniquely during pyrolysis, affecting fuel yield and quality. However, previous studies have not thoroughly examined the impact of plastic selection and blending ratios on the pyrolysis outcome, as this requires extensive experimentation due to the numerous possible combinations. Optimizing these factors, along with operational conditions, is essential for enhancing the economic viability of plastic recycling via pyrolysis. This study introduces an optimal selection and blending ratio for plastic waste in pyrolysis fuel production to enhance economic viability using the surrogate model-based genetic algorithm (GA). The optimization process involves (1) generating data via a process model of the plastic waste pyrolysis reactor, (2) preprocessing data, (3) developing a surrogate model based on deep neural networks (DNNs), and (4) deriving the optimal solution using GA. The results reveal an optimal blending ratio: 4 wt% LDPE, 30 wt% HDPE, 43 wt% PP, and 24 wt% PS. Additionally, the optimal pyrolysis temperature is 607 ℃. By adopting this optimal solution, the net profit could increase by 20% compared to the conventional scenario, resulting in an additional annual net profit of US$ 407,811, thus highlighting the potential this approach offers to attain maximum economic feasibility in industrial applications.

Silicon-based waveguide technology for millimeter-wave antennas

This paper describes a method for the preparation of complex structured waveguides that can be used for millimeter-wave antennas based on a bulk silicon MEMS process. In this paper, a millimeter-wave band array of ridge waveguide slit antennas with multilayer step-matched feeds is realized by this method. A bulk silicon MEMS process is used to realize the complex structure and the high precision and flatness requirements on the millimeter scale, which cannot be achieved by machining. And the step height difference of more than 100um is realized by combining multiple lithography and silicon oxide mask. The step difference is larger than that achievable by dry etching using the etching selection ratio of silicon oxide and silicon. The waveguide fabricated by this method can work well in the millimeter wave band and is used for antenna feeding, power distribution, etc. It is characterized by compact structure, high precision, and high performance.

Tuning the assembly of MgNiO2 nanoparticles-infused polysulfone membranes for efficient gas separation: The selectivity-permeability conundrum

The persistent surge in global energy demand, coupled with escalating environmental apprehensions, has underscored the imperative for adept and sustainable gas separation technologies. Mixed matrix membranes (MMMs) have surfaced as a promising avenue to confront these multifaceted challenges, synergistically harnessing the strengths of conventional polymeric membranes and nanoparticle reinforcements. In this work, MMMs have been tailored for gas separation applications by incorporating MgNiO2 nanoparticles as an additive into the polysulfone (PSf) matrix as they provide additional space (in the form of nanoscale voids) for gas diffusion, facilitating faster gas transport, promoting selective permeation of specific gases, and enhancing membrane stability, in addition to their versatility, ease of preparation, cost-effectiveness and high activity. The crystalline nature of MgNiO2 nanoparticles, as characterized by finely tuned grain sizes, has been validated by powder X-ray diffraction (XRD) analysis. Gas permeation experiments on the developed MMMs encompassing a diverse range of pure gases and gas mixtures have been performed. Among the different MMMs investigated, PSM1 with a composition of 100 mg MgNiO2 nanoparticles has yielded substantial enhancements in permeability and selectivity. The PSM1 membrane exhibits remarkable permeance of 56.9 for H2, 16.3 for CH4, and 15 GPU for CO2, having ideal selectivity ratios of 3.5 and 3.8 for H2/CH4 and H2/CO2, respectively. Finally, this work provides new directions for improving the trade of relationship between the selectivity and permeability of gas mixtures under relevant process conditions.

A Machine Learning Technique for Deriving the Optimal Mesh Size of a Gizzard Shad (Konosirus punctatus) Gillnet

Gizzard shads are facing a continual decline in population, necessitating the implementation of selective gear design for effective resource management. This study aims to prevent the bycatch of young gizzard shads, a non-target fish species, and to derive mesh sizes appropriate for fishery management. Experimental fishing (n = 11) was conducted by manufacturing gillnet fishing gear with different mesh sizes (50.5, 55.1, 60.6, and 67.3 mm) in the coastal waters of the southern Gyeongsang Province. Two methods were employed to estimate the appropriate mesh size of the shad gillnet as follows: firstly, by analyzing the selectivity curve based on body length data; secondly, by developing a complex machine learning model considering biological and economic factors. Model 1 was constructed using mesh variables to classify the score groups. As a result of this study, the total length with a 0.5 gillnet selection ratio. which was estimated to be 179.3, 195.6, 215.1, and 238.9 mm for the 50.5, 55.1, 60.6, and 67.3 mm mesh sizes, respectively. In Model 1, a mesh size of 57.85 mm or less was determined as the most appropriate mesh size. Therefore, considering both biological and economic aspects, shad gillnets should have a mesh size in the 50.5 to 55.1 mm range.

Association between skin lymphangiogenesis parameters and arterial hypertension status in patients: An observational study.

Recent studies have indicated that the skin lymphatic system and interstitium may play a role in the pathophysiology of arterial hypertension (AH). We aimed to determine whether the set of pathway parameters described previously in rodents would allow for the distinction between hypertensive and normotensive patients. Molecular and histopathological parameters from the skin and blood of patients with AH (AH group, n = 53), resistant AH (RAH group, n = 32) and control (C group, n = 45) were used, and a statistical multivariate bootstrap methodology combining partial least squares-discriminant analysis (PLS-DA) and selectivity ratio (SR) were applied. The C vs RAH model presented the best prediction performance (AUC test = 0.90) and had a sensitivity and specificity of 73.68% and 83.33%, respectively. However, the parameters selected for the C vs AH group model were the most important for the pathway described in the rodent model, i.e., greater density of the skin lymphatic vessels (D2-40 expression) and greater number of macrophages (CD68 expression), higher expression of the messenger ribonucleic acid (mRNA) of nuclear factor of activated T cells 5 (NFAT5), vascular endothelial growth factor C (VEGFC) and podoplanin (PDPN) in the skin, greater concentration of hyaluronic acid (HA) in the skin, and lower serum concentration of VEGF-C. Our study suggests that the NFAT5/VEGF-C/lymphangiogenesis pathway, previously described in rodent studies, may also be present in human HA. Further experiments are needed to confirm our findings.

Characterization of Ceria Nanoparticles as Abrasives Applied with Defoaming Polymers for CMP (Chemical Mechanical Polishing) Applications.

Chemical mechanical polishing/planarization (CMP) is an essential manufacturing process in semiconductor technologies. This method combines chemical and mechanical forces to smooth the surfaces of wafers. The effectiveness of CMP relies on a carefully chosen slurry, demanding a sophisticated manufacturing technology. This technology must seamlessly integrate both chemical composition and mechanical elements, highlighting the intricate synergy required for successful semiconductor fabrication. Particularly in milling processes, if agglomerated particles due to slurry particle corrosion are present during polishing, uneven polishing, numerous fine scratches occur, leading to an increase in roughness and a deterioration in the quality of the finished surface. In this study, to overcome the issue of particle agglomeration and uneven polishing in commonly used ceria nanoparticle slurries during CMP processes, we investigated the ceria nanoparticle behavior based on styrene-maleic acid (SMA) dispersant polymer applied with three types of defoaming polymers. The investigations are expected to open up the possibility of utilizing ceria nanoparticles with applied defoaming polymer as an abrasive for advanced CMP applications. All samples were characterized by DLS (dynamic light scattering), SEM-EDX (scanning electron microscopy-energy dispersive X-ray spectroscopy), pH, conductivity, viscosity, a 10-day stability test at 60 °C, the AF4 test, and the polishing rate efficiency test. Our research demonstrates a significant improvement achieved through the use of SMA dispersant polymer, resulting in a polishing selection ratio exceeding 80 for oxide and nitride films. The G-336 defoaming polymer utilized here is expected to serve as a viable alternative in CMP processes by providing stable uniformity.