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As a direct result of laboratory sample manipulation required to identify microplastics (MPs) within a given matrix, some MPs are inevitably lost. The extent of this loss can be quite significant and varies greatly depending on the sample matrix, choice of protocol and target MPs in question. Defining analytical MP recovery is therefore a critical component in ensuring the quality of MP protocols. The potential relationship between particle size and recovery rate has been widely discussed but remains uncertain. To determine whether MP loss correlated with particle size, three aliquots of polyethylene fragments in the 5–50 µm size range and three aliquots of polypropylene fragments in the 50–500 µm size range, were consecutively transferred back and forth from filter to liquid. After each individual transfer the analytical recovery within specified size groups, was evaluated by applying high-resolution darkfield microscopy. Average recovery across the entire size range was estimated at 80% with a standard deviation (std. dev.) of 26%. Notably, particle coverage on filters (A%) showed a more significant impact on recovery than particle size. Maintaining A% below 5% on filters for microscopic analysis is advised to prevent excessive loss due to particle agglomeration. To determine whether the use of red polyethylene fragments in the 5–50 µm size range in combination with darkfield microscopy could potentially improve MP recovery evaluation in environmental samples, three aliquots of 0.5 g of dry brown trout muscle tissue were spiked and treated according to a relevant protocol. This size-discriminating approach accurately determined average recovery at 52% with a std. dev. of 4% and demonstrated the potential for correction of the concentration enhancement of smaller MPs resulting from particle breakup during sample pre-treatment, which would otherwise lead to overestimation of smaller size fractions. Highlights • Recovery experiment shows no evidence of increasing microplastic loss with decreasing particle size, in the 5–500 µm range. • Investigating colored PE micro-fragments under darkfield illumination enables size-discriminating recovery estimations of microplastics ≥ 5 µm. • Particle area coverage should not exceed 5% of filters intended for microscopic analysis of microplastics (< 500 µm). • For increased relevance to environmental conditions, fragments should replace spheres or beads in microplastic recovery experiments.

Organic Light‐Emitting Diodes To prevent short‐circuit defects of OLEDs caused by dust particles on substrates, Yoshiyuki Suzuri and colleagues report on the particle coverage properties of the spin‐coated hole injection layer in article number 2201695. Quantitatively contaminating the substrate surface with size‐controlled SiO2 particles, PEDOT:PSS was spin‐coated, resulting in coverage up to SiO2 particles 10 to 20 times larger than the film thickness.

In this work, the changes in surface roughness and thickness of particles have been investigated after coating with polymers that result in hydrophobicity after a given time period. The study fundamentally investigates the evolution of these surface properties from the instant the particles are coated. Six chemical agents have been used on near-spherical glass beads and the changes in surface profiles monitored. Surface roughness was quantified using the power spectral density method and the surface thickness of the coatings was determined by a new technique which involved calculating the change in the representative radius of asperity. Results showed that the coating process altered the surface roughness and thickness of particles irrespective of chemicals used. The time-dependency of the coating process is illustrated and it was observed that fluctuations in both surface roughness and thickness lessened after a time period of 30 min. Depending on the chemical agent used, either an overall roughening or softening was recorded at 60 min and the values of surface thickness showed increases between 71 and 256 nm. By analyzing the evolution of surface roughness and thickness at the particle level following coating, this study demonstrated the intricate link between surface properties and chemistry in inducing functional properties on particles. Graphical Abstract

Short‐circuit defects caused by microscale dust particles in organic light‐emitting diodes (OLEDs) cause a decrease in production yield and hinder cost reduction. An organic layer coating by solution process is used to prevent short‐circuit defects of particles on a substrate. In this study, the coverage properties of a coated organic layer on size‐controlled particles are revealed. The surface of the substrate with size‐controlled SiO2 particles with a diameter of 0.2–5 µm is quantitatively contaminated, and the particle coverage properties of the solution‐processed hole injection layer are investigated. From the results of the leakage current measurement and cross‐sectional observation by a transmission electron microscope, it is observed that devices with 50 nm‐spin‐coated poly (3,4‐ethylenedioxythiophene): poly(styrene sulfonate) can cover SiO2 particles up to 1 µm in diameter without any increase in leakage current. It is revealed that larger‐sized particles cause electric defects, albeit with a low probability, owing to the larger space under the particles. To fabricate OLEDs with a high yield, the shape of the coverage at the bottom of the particle is important in preventing electric defects. The results of this study are useful not only for OLEDs but also for printed and coated devices. To prevent short‐circuit defects caused by dust particles on substrates, the particle coverage properties of the spin‐coated hole injection layer are reported. Quantitatively contaminating the substrate surface with size‐controlled SiO2 particles, poly(3,4‐ethylenedioxy‐thiophene):poly(styrene sulfonate) is spin‐coated, resulting in coverage up to SiO2 particles 10–20 times larger than the film thickness.

Nanoparticles are recognized as highly attractive tunable materials for designing field-effect biosensors with enhanced performance. In this work, we present a theoretical model for electrolyte-insulator-semiconductor capacitors (EISCAP) decorated with ligand-stabilized charged gold nanoparticles. The charged AuNPs are taken into account as additional, nanometer-sized local gates. The capacitance-voltage (C–V) curves and constant-capacitance (ConCap) signals of the AuNP-decorated EISCAPs have been simulated. The impact of the AuNP coverage on the shift of the C–V curves and the ConCap signals was also studied experimentally on Al–p-Si–SiO2 EISCAPs decorated with positively charged aminooctanethiol-capped AuNPs. In addition, the surface of the EISCAPs, modified with AuNPs, was characterized by scanning electron microscopy for different immobilization times of the nanoparticles.

Positioning, coverage, and energy efficiency are essential for developing next-generation intelligent sensor networks. In wireless sensor networks (WSNs), the random deployment of sensor nodes (SNs) frequently results in suboptimal area coverage and excessive energy consumption, primarily due to overlapping sensing regions and redundant data transmissions. This research presents a Particle Swarm Optimization (PSO) algorithm to optimize the deployment of electronic information sensing nodes. The focus is on maximizing the monitored area while minimizing energy usage. A Scalable coverage-based particle swarm optimization (SCPSO) algorithm integrates a probabilistic coverage model based on Euclidean distance to detect coverage gaps and guide the optimal positioning of nodes, ensuring that each target within the region of interest is covered by at least one sensor. Data preprocessing, including Z-score normalization and Independent Component Analysis (ICA), ensures feature scaling and dimensionality reduction for improved model performance, enabling effective optimization. Experimental results under different key metrics included coverage rate (CR) for various numbers of nodes (0.9971) with 50 nodes, deployment (99.95%) with the best coverage, and computation time (0.008s), indicating significant performance improvements under optimized deployment configurations. These results highlight the effectiveness of swarm intelligence methods in enabling energy-efficient, performance-optimized deployment of electronic information sensing systems in intelligent WSNs.

Wireless Sensor Networks (WSNs) are large-scale and high-density networks that typically have coverage area overlap. In addition, a random deployment of sensor nodes cannot fully guarantee coverage of the sensing area, which leads to coverage holes in WSNs. Thus, coverage control plays an important role in WSNs. To alleviate unnecessary energy wastage and improve network performance, we consider both energy efficiency and coverage rate for WSNs. In this paper, we present a novel coverage control algorithm based on Particle Swarm Optimization (PSO). Firstly, the sensor nodes are randomly deployed in a target area and remain static after deployment. Then, the whole network is partitioned into grids, and we calculate each grid’s coverage rate and energy consumption. Finally, each sensor nodes’ sensing radius is adjusted according to the coverage rate and energy consumption of each grid. Simulation results show that our algorithm can effectively improve coverage rate and reduce energy consumption.

In this study, the conceptual design and physics simulations of a near-field Water-based Liquid Scintillator (WbLS) detector placed 100 m from the Akkuyu Nuclear Power Plant (ANPP), currently under construction and aiming at being Turkey’s first nuclear power plant, is presented. The ANPP is an excellent opportunity for neutrino studies and the development of an R &D program for neutrino detectors in Turkey. The Reactor Neutrino Experiments of Turkey (RNET) program includes a compact detector with a 2.5-ton volume of WbLS and a ∼ 30% photo-coverage, and the program is planned to be expanded with a medium-size 30-ton detector that will be an international testbed for WbLS and new detector technologies through low energy neutrino studies. In the following, the focus will be on the smaller ∼2.5 ton detector, instrumented with 8-in. high quantum efficiency PMTs and two layers of cosmic veto paddles, covering all sides of the detector, to track and veto cosmic particles. Inverse Beta Decay (IBD) events from electron anti-neutrinos generated in the reactor core are simulated using the RAT-PAC simulation package and several liquids with different percentages of Liquid Scintillator (LS) and Gadolinium (Gd) are investigated.

Ru/TiO2 catalysts are well known for their high activity in the hydrogenation of CO2 to CH4 (the Sabatier reaction). This activity is commonly attributed to strong metal–support interactions (SMSIs), associated with reducible oxide layers partly covering the Ru-metal particles. Moreover, isothermal rates of formation of CH4 can be significantly enhanced by the exposure of Ru/TiO2 to light of UV/visible wavelengths, even at relatively low intensities. In this study, we confirm the significant enhancement in the rate of formation of methane in the conversion of CO2, e.g., at 200 °C from ~1.2 mol gRu−1·h−1 to ~1.8 mol gRu−1·h−1 by UV/Vis illumination of a hydrogen-treated Ru/TiOx catalyst. The activation energy does not change upon illumination—the rate enhancement coincides with a temperature increase of approximately 10 °C in steady state (flow) conditions. In-situ DRIFT experiments, performed in batch mode, demonstrate that the Ru–CO absorption frequency is shifted and the intensity reduced by combined UV/Vis illumination in the temperature range of 200–350 °C, which is more significant than can be explained by temperature enhancement alone. Moreover, exposing the catalyst to either UV (predominantly exciting TiO2) or visible illumination (exclusively exciting Ru) at small intensities leads to very similar effects on Ru–CO IR intensities, formed in situ by exposure to CO2. This further confirms that the temperature increase is likely not the only explanation for the enhancement in the reaction rates. Rather, as corroborated by photophysical studies reported in the literature, we propose that illumination induces changes in the electron density of Ru partly covered by a thin layer of TiOx, lowering the CO coverage, and thus enhancing the methane formation rate upon illumination.

Bluetongue (BT) is a hemorrhagic non-contagious, biting midge-transmitted disease of wild and domestic ruminants that is caused by bluetongue virus (BTV). Annual vaccination plays a pivotal role in BT disease control in endemic regions. Due to safety concerns of the current BTV multivalent live attenuated vaccine (LAV), a safe efficacious new generation subunit vaccine such as a plant-produced BT virus-like particle (VLP) vaccine is imperative. Previously, homogenous BTV serotype 8 (BTV-8) VLPs were successfully produced in Nicotiana benthamiana plants and provided protective immunity in sheep. In this study, combinations of BTV capsid proteins from more than one serotype were expressed and assembled to form chimaeric BTV-3 and BTV-4 VLPs in N. benthamiana plants. The assembled homogenous BTV-8, as well as chimaeric BTV-3 and chimaeric BTV-4 VLP serotypes, were confirmed by SDS-PAGE, Transmission Electron microscopy (TEM) and protein confirmation using liquid chromatography-mass spectrometry (LC-MS/MS) based peptide sequencing. As VP2 is the major determinant eliciting protective immunity, the percentage coverage and number of unique VP2 peptides detected in assembled chimaeric BT VLPs were used as a guide to assemble the most appropriate chimaeric combinations. Both plant-produced chimaeric BTV-3 and BTV-4 VLPs were able to induce long-lasting serotype-specific neutralizing antibodies equivalent to the monovalent LAV controls. Antibody levels remained high to the end of the trial. Combinations of homogenous and chimaeric BT VLPs have great potential as a safe, effective multivalent vaccine with the ability to distinguish between vaccinated and infected individuals (DIVA) due to the absence of non-structural proteins.