Search Results (169)

As the global demand for electronic equipment continues to grow, many devices are being replaced more frequently, resulting in a rapid rise in electronic waste (e-waste), now the fastest growing waste stream worldwide. Motivated by this, the objective of this study is to present an environmentally friendly method to recycle acrylonitrile–butadiene–styrene (ABS), one of the most common e-waste plastics, by using it for asphalt production. In contrast to earlier methods of plastic-modified asphalt production involving complex pretreatments or complimentary additives unsuitable for plant-scale use, this study aims to demonstrate a practical, low-cost solution through the use of carbon black. This approach included physically pretreating ABS plastics for size reduction and incorporating waste tire-derived carbon black to promote effective dispersion in asphalt during wet modification. The rheological properties of the e-waste-modified asphalt were subsequently assessed. The test results indicated that recycling ABS plastics with a blending content of 5% alongside 5% carbon black can enhance cold-weather cracking resistance and high-temperature anti-rutting performance of asphalt. The enhancement can be attributed to the proper preparation procedures of ABS plastics and the addition of carbon black, which can further improve the performance by promoting the proper dispersion of plastic particles in asphalt. The outcome of this study indicates that recycling e-waste plastics through asphalt production can lead to more green and sustainable asphalt construction, reduce total construction costs, and most importantly enhance performance. Full article

Belle II is a particle physics experiment working at an high luminosity collider within a hard irradiation environment. The Time-Of-Propagation detector, aimed at the charged particle identification, surrounds the Belle II tracking detector on the barrel part. This detector is composed by 16 modules, each module contains a finely fused silica bar, coupled to microchannel plate photomultiplier tube (MCP-PMT) photo-detectors and readout by high-speed electronics. The MCP-PMT lifetime at the nominal collider luminosity is about one year, this is due to the high photon background degrading the quantum efficiency of the photocathode. An alternative to these MCP-PMTs is multi-pixel photon counters (MPPC), known as silicon photomultipliers (SiPM). The SiPMs, in comparison to MCP-PMTs, have a lower cost, higher photon detection efficiency and are unaffected by the presence of a magnetic field, but also have a higher dark count rate that rapidly increases with the integrated neutron flux. The dark count rate can be mitigated by annealing the damaged devices and/or operating them at low temperatures. We tested SiPMs, with different dimensions and pixel sizes from different producers, to study their time resolution (the main constraint that has to satisfy the photon detector) and to understand their behavior and tolerance to radiation. For these studies we irradiated the devices to radiation up to $5\times {10}^{11}1$ MeV neutrons equivalent (neq) per cm² fluences; we also started studying the effect of annealing on dark count rates. We performed several measurements on these devices, on top of the dark count rate, at different conditions in terms of overvoltage and temperatures. These measurements are: IV-curves, amplitude spectra, time resolution. For the last two measurements we illuminated the devices with a picosecond pulsed laser at very low intensities (with a number of detected photons up to about twenty). We present results mainly on two types of SiPMs. A new SiPM prototype developed in collaboration with FBK with the aim of improving radiation hardness, is expected to be delivered in September 2025. Full article

To solve the problem of high cost and low efficiency of measuring equipment in traditional distribution network fault location, a fault section location and line selection strategy combining dynamic binary particle swarm optimization (DBPSO) configuration and fuzzy C-means (FCM) clustering is proposed in this paper. Firstly, the DBPSO algorithm is used to optimize the configuration scheme of the distributed voltage and current sensing device, which reduces the number of measuring devices and system cost on the premise of ensuring the global observability of the distribution network. When a fault occurs in the distribution network, the sensor device based on optimal configuration collects fault feature data, combines it with the FCM clustering algorithm to classify nodes according to fault feature similarity, and divides the most significant fault-affected section as the core fault area. Further, by calculating the Euclidean distance between each node in the fault section and the cluster center, the fault line is accurately identified. Finally, a fault simulation model based on an IEEE 11-node system is constructed to verify the effectiveness of the proposed method. The results show that, compared with the traditional fault section location and route selection strategy, this method can reduce the number of measurement devices optimally configured by 19–36% and significantly reduce the number of algorithm iterations. In addition, it can realize rapid fault location and precise line screening at a low equipment cost under multiple fault types and different fault locations, which significantly improves fault location accuracy while reducing economic investment. Full article

Despite the convenience of solar potential and the magnitude of energy received by the Earth from the sun, solar photovoltaic systems have failed to meet the growing energy demand. This can be attributed to various factors such as low cell efficiency, environmental conditions, and improper tracking of operating points, which further worsen the system’s performance. Various advanced metaheuristic-based Maximum Power Point Tracking (MPPT) techniques were reported in the literature. Most available techniques were designed and tested in subscription-based/paid software such as MATLAB/Simulink, PSIM simulator, etc. Due to this, the simulation and analysis of these MPPT algorithms for developing and underdeveloped countries added an extra economic burden. Many open-source PV libraries are computationally intensive, lack active support, and prove impractical for MPPT testing on resource-constrained hardware. Their complexity and absence of optimization for edge devices limit their viability for the edge device. This issue is addressed in this research by designing a robust framework using an open-source programming language i.e., Python. For demonstration purposes, we simulated and analyzed a solar PV system and benchmarked its performance against the JAP6 solar panel. We implemented multiple metaheuristic MPPT algorithms including Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO), evaluating their efficacy under both Standard Test Conditions (STC) and complex partial shading scenarios. The results obtained validate the feasibility of the implementation in Python. Therefore, this research provides a comprehensive framework that can be utilized to implement sophisticated designs in a cost-effective manner for developing and underdeveloped nations. Full article

Reliable cosmic-ray measurements require a thorough understanding of the detector used. It is especially important when detectors are very simple like the scintillator detectors considered in this work, which provide only information about the amplitude of the signal generated by a detected particle. Arrays of these devices can work in coincidental setups to detect Extensive Air Showers caused by high-energy primary cosmic rays. Due to their low cost and simple design, they can be used as elements of large detector networks needed for the search for global correlations in the cosmic rays. To be able to interpret data collected by those arrays, extensive simulations of such detectors are necessary to determine their efficiency of detection of different types of particles. This work presents the results of analysis of such simulations performed using the Geant4 software (v1.1.2). The analysis results lead to the conclusion that detectors feature almost maximal (close to 100%) efficiency for the detection of cosmic-ray muons and electrons with momenta greater than 0.03 GeV/c. Their sensitivity to low-energy electrons and photons is lower but not negligible and has to be properly taken into account during the interpretation of collected data. Full article

Particulate matter (PM) is recognized as a leading health risk factor worldwide, causing adverse effects for people in living and working environments. During the COVID-19 pandemic, it was shown that ultrafine particles (UFP) and PM concentrations, may have played an important role in the transmission of SARS-CoV-2. This study aims to investigate whether the mechanical ventilation system installed as a COVID-19 mitigation measure in a university dining hall can be effectively and sustainably used to improve indoor UFP exposure levels, integrated with a continuous low-cost sensor monitoring system. Measurements of particle number concentration (PNC), average diameter (D_avg), and Lung Deposited Surface Area (LDSA) were performed over three working days divided into ten homogeneous daily time slots (from 12:00 am to 11:59 pm) using high-frequency (1 Hz) real-time devices. PM and other indoor pollutants (CO₂ and TVOC) were monitored using low-cost handheld sensors. Indoor PNC (Dp < 700 nm) increased and showed great variability related to dining activities, reaching a maximum average PNC level of 30,000 part/cm³ (st. dev. 16,900). D_avg (Dp < 300 nm) increased during lunch and dinner times, from 22 nm at night to 48 nm during post-dinner recovery activities. Plasma-based filter technology reduced average PNC (Dp < 700 nm) by up to three times, effectively mitigating UFP concentrations in indoor environments, especially during dining hall access periods. It could be successfully adopted also after the pandemic emergency, as a sustainable health and safety control measure to improve UFPs exposure levels. Full article

The introduction of side-emitting lenses into white light-emitting diodes (LEDs) has enabled thin panel lighting technology based on LED technology, but also presents the disadvantage of low color rendering due to insufficient red components in the spectra of typical white LEDs. Additional application of remote quantum dot (QD) components such as QD films or caps presents the issues of increased numbers of components and higher costs. In this study, we incorporated red QDs directly into a lens placed on white LEDs and analyzed the effects of QD lenses on the optical characteristics of a lighting device through experiments and simulations. By incorporating red CdSe/ZnS QDs into UV-curable resin to fabricate QD lenses and applying them to white LEDs, we significantly improved the color rendering index and were able to adjust the correlated color temperature over a wide range between 2700 and 9900 K. However, as the concentration of QDs in the lens increased, scattering by the QD particles was enhanced, strengthening the Lambertian distribution in the intensity plot. Following the development of optical models for QD lenses under experimental conditions, comprehensive optical simulations of white LED lighting systems revealed that increasing the device height proved more effective than modifying TiO₂ scattering particle concentration in the diffuser plate for mitigating QD-induced bright spots and enhancing illumination uniformity. Full article

Addressing hazards from dangerous pollutants requires specialized techniques and risk-control strategies, including detection, neutralization and disposal of contaminants. Smart polymers, designed for specific contaminants, provide powerful solutions for hazardous compound challenges. Their remarkable performance capabilities and potential applications present exciting opportunities for further exploration and development in this field. This editorial aims to provide a comprehensive overview of smart materials with unique features and emerging polymeric technologies that are being developed for isolation, screening, removal, and decontamination of hazardous compounds (e.g., heavy metals, pharmaceutically active contaminants, hormones, endocrine-disrupting chemicals, pathogens, and energetic materials). It highlights recent advancements in synthesis methods, characterization, and the applications of molecularly imprinted polymers (MIPs), along with alternative smart polymeric platforms including hydrogels, ion-imprinted composites, screen-printed electrodes, nanoparticles, and nanofibers. MIPs offer highly selective recognition properties, reusability, long-term stability, and low production costs. Various MIP types, including particles and films, are used in applications like sensing/diagnostic devices for hazardous chemicals, biochemicals, pharmaceuticals, and environmental safety. Full article

Clean water scarcity in developing countries like South Africa poses significant health risks. This study investigated the effectiveness of a simple water purification device using Moringa oleifera Lam. seeds as active beads, offering a novel, low-cost, and sustainable solution for water treatment in resource-limited settings. The device combined M. oleifera seed powder with activated charcoal and cotton wool, providing a locally adaptable and environmentally friendly solution. Water samples were collected from three sites along the Pienaars River during winter and summer, and M. oleifera seeds were ground into three particle sizes (710 µm, 1000 µm, and 2000 µm) for testing. Results showed that the device significantly reduced microbial loads, with the total coliforms decreasing by 60–85%, E. coli by 50–75%, Salmonella spp. by 40–70%, and Shigella spp. by 30–65% across sampling points. However, filtered samples still exceeded the WHO and SANS guidelines, with microbial counts remaining above 0 CFU/100 mL. Physicochemical properties, including pH (6.02–7.73), electrical conductivity (17.8–109.5 mS/m), and ion concentrations (e.g., nitrate: 0.21–39.55 mg/L; chloride: 8.57–73.55 mg/L), complied with the SANS 241:2015 and WHO drinking water standards. The finest particle size (710 µm) demonstrated the highest microbial reduction and increased magnesium concentrations by up to 30%. Seasonal variations influenced the performance, with summer samples showing a better microbial removal efficiency (70–85%) compared to winter (50–70%). This study highlights the potential of M. oleifera-based filtration as a low-cost, sustainable solution for reducing microbial contamination, though further refinement is needed to meet drinking water standards. This research introduces a novel approach to water purification by combining M. oleifera seed powder with activated charcoal and cotton wool, providing a locally adaptable and environmentally friendly solution. The findings contribute to the development of scalable, natural water treatment systems for resource-limited communities. Full article

In view of the problems of low work efficiency and high operating costs caused by manual harvesting of Chinese cabbage in China, in this study, a Chinese cabbage harvester with agronomic integrity was designed. The harvester is mainly composed of a crawler chassis, a drawing device, a flexible clamping device, a cutting device, and a horizontal delivery device. Firstly, physical properties of Chinese cabbage such as diameter, plant height, weight, and drawing rate of Chinese cabbage were measured and analyzed to provide necessary basic data for the design of the harvester. Secondly, simulation tests were conducted on the Chinese cabbage harvesting process; a 3D model of Chinese cabbage using SolidWorks 2022 was established and filled with particles using the three-layer stacking method. At the same time, SolidWorks was applied to simplify the model of the Chinese cabbage harvester. The belt of the machine model was set as a flexible body through RecurDyn 2023 software and coupled with EDEM 2022 for simulation analysis. Based on single factor tests, the BBD model was applied to conduct multi-factor response surface analysis on the above factor levels. The optimal working conditions of the harvester were obtained as follows: the rotating speed of the cutting device was 207.85 r/min, the rotating speed of the flexible clamping conveyor belt was 165.51 r/min, the rotating speed of the drawing device was 102.38 r/min, and the machine walking speed was 1.37 km/h. The qualified rate of Chinese cabbage harvesting was the highest, achieving a maximum theoretical value of 97.91%. Field validation tests were conducted on the designed Chinese cabbage harvester. Based on the actual operating conditions of the Chinese cabbage harvester and the simulated operating parameters, the optimal parameter combination was finally determined as follows: rotating speed of the root cutting device was 200 r/min, rotating speed of the flexible clamping conveyor belt was 160 r/min, rotating speed of the drawing device was 100 r/min, and machine walking speed was 1.4 km/h, respectively. Through field verification tests, the highest qualified rate of Chinese cabbage harvesting reached 93.19%, showing a good harvesting effect, which approximates the simulated optimal qualified rate of 97.91%, meeting the mechanized harvesting demand of Chinese cabbage. This study provides reference to the further design and development of Chinese cabbage harvesters in the future. Full article

Dielectrophoresis at the nanoscale has gained significant attention in recent years as a low-cost, rapid, efficient, and label-free technique. This method holds great promise for various interdisciplinary applications related to micro- and nanoscience, including biosensors, microfluidics, and nanomachines. The innovation and development of such devices and platforms could promote wider applications in the field of nanotechnology. This review aims to provide an overview of recent developments and applications of nanoparticle dielectrophoresis, where at least one dimension of the geometry or the particles being manipulated is equal to or less than 100 nm. By offering a theoretical foundation to understand the processes and challenges that occur at the nanoscale—such as the need for high field gradients—this article presents a comprehensive overview of the advancements and applications of nanoparticle dielectrophoresis platforms over the past 15 years. This period has been characterized by significant progress, as well as persistent challenges in the manipulation and separation of nanoscale objects. As a foundation for future research, this review will help researchers explore new avenues and potential applications across various fields. Full article

Obtaining wide energy-gap semiconductor ultra-thin films is an important aspect for their application in sulfide-based solar cells. By reducing the optical losses associated with light reflection and exhibiting absorption edge shifts towards short wavelengths, these layers can optimize the amount of photons interacting with the active photovoltaic material, which increases the conversion efficiency of the solar cell. Ultra-thin CdS films were prepared by a low-cost chemical synthesis and the impact of silver doping on the optical, structural, and morphological properties was evaluated. SEM micrographs revealed that the layers are ultra-thin, homogeneous and uniform, with a reduction in particle size with increasing doping concentration. X-ray diffraction data confirmed the crystallization of CdS in the hexagonal phase for all prepared samples. A low concentration contributed to the formation of Ag₂S in the monoclinic phase according to the diffractograms. The optical properties of the thin films revealed an absorption edge shift that increased the CdS band gap from 2.267 ± 0.007 to 2.353 ± 0.005 eV with increasing doping concentration, improving the spectral transmittance response. These results make these layers particularly useful for implementation in next-generation flexible photovoltaic devices. Full article

Edge computing, characterized by its proximity to users and fast response times, is considered one of the key technologies for addressing low-latency demands in the future. An appropriate edge server deployment strategy can reduce costs for service providers and improve the quality of service for users. However, most previous studies have focused on server coverage or deployment solution consumption time, often neglecting the most critical aspect: minimizing user-request response latency. To address this, we propose an edge deployment strategy based on the queuing search algorithm (QSA), which models the edge deployment problem as a multi-constrained nonlinear optimization problem. The QSA mimics the logic of human queuing behavior and has the ability to perform faster global searches while avoiding local optima. Experimental results show that, compared to the genetic algorithm, simulated annealing algorithm, particle swarm optimization, and other recent algorithms, the average number of “hopping” iterations in QSA is 0.1 to 0.6 times fewer than in the other algorithms. Additionally, QSA is particularly suitable for edge computing environments with a large number of users and devices. Full article

This study calibrates an affordable, solar-powered LoRaWAN air quality monitoring prototype using the research-grade Palas Fidas Frog sensor. Motivated by the need for sustainable air quality monitoring in smart city initiatives, this work integrates low-cost, self-sustaining sensors with research-grade instruments, creating a cost-effective hybrid network that enhances both spatial coverage and measurement accuracy. To improve calibration precision, the study leverages the Super Learner machine learning technique, which optimally combines multiple models to achieve robust PM (Particulate Matter) monitoring in low-resource settings. Data was collected by co-locating the Palas sensor and LoRaWAN devices under various climatic conditions to ensure reliability. The LoRaWAN monitor measures PM concentrations alongside meteorological parameters such as temperature, pressure, and humidity. The collected data were calibrated against precise PM concentrations and particle count densities from the Palas sensor. Various regression models were evaluated, with the stacking-based Super Learner model outperforming traditional approaches, achieving an average test R² value of 0.96 across all target variables, including 0.99 for PM_2.5 and 0.91 for PM_10.0. This study presents a novel approach by integrating Super Learner-based calibration with LoRaWAN technology, offering a scalable solution for low-cost, high-accuracy air quality monitoring. The findings demonstrate the feasibility of deploying these sensors in urban areas such as the Dallas-Fort Worth metroplex, providing a valuable tool for researchers and policymakers to address air pollution challenges effectively. Full article

Biosensing shows promise in detecting cancer, renal disease, and other illnesses. Depending on their transducing processes, varieties of biosensors can be divided into electrochemical, optical, piezoelectric, and thermal biosensors. Advancements in material production techniques, enzyme/protein designing, and immobilization/conjugation approaches can yield novel nanoparticles with further developed functionality. Research in cutting-edge biosensing with multifunctional nanomaterials, and the advancement of practical biochip plans utilizing nano-based sensing material, are of current interest. The miniaturization of electronic devices has enabled the growth of ultracompact, compassionate, rapid, and low-cost sensing technologies. Some sensors can recognize analytes at the molecule, particle, and single biological cell levels. Nanomaterial-based sensors, which can be used for biosensing quickly and precisely, can replace toxic materials in real-time diagnostics. Many metal-based NPs and nanocomposites are favorable for biosensing. Through direct and indirect labeling, metal-oxide NPs are extensively employed in detecting metabolic disorders, such as cancer, diabetes, and kidney-disease biomarkers based on electrochemical, optical, and magnetic readouts. The present review focused on recent developments across multiple biosensing modalities using metal/metal-oxide-based NPs; in particular, we highlighted the specific advancements of biosensing of key nanomaterials like ZnO, CeO₂, and TiO₂ and their applications in disease diagnostics and environmental monitoring. For example, ZnO-based biosensors recognize uric acid, glucose, cholesterol, dopamine, and DNA; TiO₂ is utilized for SARS-CoV-19; and CeO₂ for glucose detection. Full article