Environmental Applications Research Articles

A stacking machine‐learning based method for accelerating magnetic coupler design with ferrite cores in inductive power transfer applications

AbstractThis paper presents a magnetic coupler design method for WPT systems based on stacking machine‐learning algorithms. A synthetic dataset generated by ANSYS Maxwell is used for training and evaluating machine‐learning models. Stacking technology effectively combines the results from multiple models and make best predictions. The designed model allows us to obtain the optimal values of the coil inner radius and number of turns , when other coil design parameters such as the coil outer radius , the wire diameter , and the coil inductance , are given based on the application environment. The proposed method provides practical solutions meeting design requirements quickly, easily accommodating magnetic couplers with ferrite cores, and showing advantages in designing magnetic couplers with optimal power transfer efficiency.

Implementation of Text to Image using Diffusion Model

Text-to-image generation is a transformative field in artificial intelligence, aiming to bridge the semantic gap between textual descriptions and visual representations. This presents a comprehensive approach to tackle this challenging task. Leveraging the advancements in deep learning, natural language processing (NLP), and computer vision, this proposes a cutting-edge model for generating high-fidelity images from textual prompts. Trained on a vast and varied dataset of written descriptions and related images, this model combines an image decoder and a text encoder within a hierarchical framework. To enhance realism, this incorporates attention mechanisms and fine-grained semantic parsing. The model's performance is rigorously evaluated through both quantitative metrics and qualitative human assessments. Results demonstrate its ability to produce visually compelling and contextually accurate images across various domains, from natural scenes to specific object synthesis. This further explores applications in creative content generation, design automation, and virtual environments, showcasing the potential impact of our approach. Additionally, this releases a user-friendly API, empowering developers and designers to integrate our model into their projects, and fostering innovation and creativity. Key Words: image generation model, Deep learning, Natural language processing.

Solmers: Versatile hybrid resins for nanometric 3D printing of silica-based photonic components

Owning to their intrinsic properties, silica-based glasses are widely used in various technological fields, especially in photonics. However, high degree of flexibility is yet challenging in realization of next-generation miniaturized optical components. In this work, we develop an approach based on ‶Solmers″ hybrid resins allowing versatile two-photon polymerization 3D printing of silica glasses with 23 nm resolution, doping with Germanium and/or rare-earths elements. Other dopants such as gold nanoparticles were also incorporated for localized metallization. After 3D printing and sintering (1100–1300 °C), high optical quality glasses with low surface roughness (<0.2 nm) were obtained. Structural analyses confirmed the amorphous structure of silica glasses. Various mono- or multi-materials microstructures were successfully fabricated on fused silica substrates. Besides, this approach was extended to the functionalization of optical fibers for optical sensing applications in harsh environment (1000 °C). Compared to organic or organic-inorganic materials, these dense silica-based glasses with enhanced optical and structural properties will open new avenues for the development of emerging advanced optical components.

Review on Porous Asphalt Pavements: A Comprehensive Resolution for Stormwater Management and Applications in Current Built Environment

Review on Porous Asphalt Pavements: A Comprehensive Resolution for Stormwater Management and Applications in Current Built Environment

Data Collection in Areas without Infrastructure Using LoRa Technology and a Quadrotor

The use of sensor networks in monitoring applications has increased; they are useful in security, environmental, and health applications, among others. These networks usually transmit data through short-range stations, which makes them attractive for incorporation into applications and devices for use in places without access to satellite or mobile signals, for example, forests, seas, and jungles. To this end, unmanned aerial vehicles (UAVs) have attractive characteristics for data collection and transmission in remote areas without infrastructure. Integrating systems based on wireless sensors and UAVs seems to be an economical and easy-to-use solution. However, the main difficulty is the amount of data sent, which affects the communication time and even the flight status of the UAV. Additionally, factors such as the UAV model and the hardware used for these tasks must be considered. Based on those difficulties mentioned, this paper proposes a system based on long-range (LoRa) technology. We present a low-cost wireless sensor network that is flexible, easy to deploy, and capable of collecting/sending data via LoRa transceivers. The readings obtained are packaged and sent to a UAV. The UAV performs predefined flights at a constant height of 30 m and with a direct line-of-sight (LoS) to the stations, during which it collects information from two data stations, concluding that it is possible to carry out a correct data transmission with a flight speed of 10 m/s and a transmission radius of 690 m for a group of three packages confirmed by 20 messages each. Thus, it is possible to collect data from routes of up to 8 km for each battery charge, considering the return of the UAV.

Biosurfactant production by Bacillus amyloliquefaciens, characterization and its potential applications

Aim: To identify indigenous bacteria responsible for producing biosurfactants in soil contaminated by oil and to investigate their potential uses in the fields of pharmaceuticals and environmental applications. Methodology: The microorganisms of oil-contaminated soil were isolated, screened, and identified using 16S rRNA gene sequencing. Bacillus amyloliquefaciens was utilized to produce biosurfactants, and were characterized by UV, TLC, FTIR, HPTLC, and GCMS analysis. Antibacterial activity, plant growth promotion, biocompatibility, and wound healing activity of surfactant were performed. Results: Six bacterial cultures (B1 to B6) were isolated from the soil samples collected from various locations in Kerala and Tamil Nadu. Among the isolates screened for biosurfactant production, Bacillus amyloliquefaciens exhibited the highest capacity for biosurfactant production, confirmed through 16S rRNA gene sequencing. The biosurfactant produced was identified as surfactin of lipopeptide nature, characterized by various analytical techniques: UV absorption at 400 nm, TLC and HPTLC (Rf value of 0.58), FTIR (prominent peak at 3819 cm-1 corresponding to -OH groups), GCMS with a maximum retention time of 12.03. Surfactin effectively inhibited pathogenic bacteria, enhanced plant growth, was biocompatible, and enhanced the rate of cell migration towards wound scratch. Interpretation: The current study revealed that Bacillus amyloliquefaciens, isolated from oil-contaminated soil, is capable of producing biosurfactant surfactin. Additionally, it unveiled a broad spectrum of applications for surfactin, encompassing wound healing, biocompatibility, antimicrobial activity, and promotion of plant growth. Key words: Antibacterial activity, Biocompatibility, Bacillus amyloliquefaciens, Biosurfactant, Wound healing

Engineered biochar/hydrochar derived from organic wastes for energy, environmental, and agricultural applications

Engineered biochar/hydrochar derived from organic wastes for energy, environmental, and agricultural applications

Validation and Application of the Diffusive Gradients in Thin-Films Technique for In Situ Measurement of β-Blocker Drugs in Waters and Sediments

The occurrence of β-blocker drugs in aquatic environments worldwide has caused increasing attention to their threat to human health in recent years. It is essential to monitor these widely prescribed pharmaceuticals in natural waters and sediments, helping us investigate their potential risk to humans and ecosystems. In this study, a passive sampling technique, diffusive gradients in thin-films (DGT), was systematically developed for eight frequently detected β-blockers. The effective capacities of target compounds were large enough for the devices to deploy for several weeks. The uptake of all compounds was linearly correlated with deployment times during the 7-day laboratory experiment and agreed well with the theoretical line, except for several compounds (e.g., ATL) due to their relatively slow uptake rate. The performance of most compounds was independent of varying pH values and organic matter contents; only a few compounds were affected, while the application in high-salinity environments needs to be conducted with caution. Field deployments of DGT to detect β-blockers in situ in rivers and sediments proved that DGT is an effective tool to monitor β-blocker drugs and their fate in the natural aquatic environment, while DGT probes can provide information for us to investigate the biogeochemical processes occurred in sediment, especially at the sediment–water interface. This novel approach will help us understand the behaviour of β-blocker drugs in the aquatic environment, assess their risks, finally protect human health and maintain the sustainable development of the ecosystem.

Generation and classification of patch-based land use and land cover dataset in diverse Indian landscapes: a comparative study of machine learning and deep learning models.

In the context of environmental and social applications, the analysis of land use and land cover (LULC) holds immense significance. The growing accessibility of remote sensing (RS) data has led to the development of LULC benchmark datasets, especially pivotal for intricate image classification tasks. This study addresses the scarcity of such benchmark datasets across diverse settings, with a particular focus on the distinctive landscape of India. The study entails the creation of patch-based datasets, consisting of 4000 labelled images spanning four distinct LULC classes derived from Sentinel-2 satellite imagery. For the subsequent classification task, three traditional machine learning (ML) models and three convolutional neural networks (CNNs) were employed. Despite facing several challenges throughout the process of dataset generation and subsequent classification, the CNN models consistently attained an overall accuracy of 90% or more. Notably, one of the ML models stood out with 96% accuracy, surpassing CNNs in this specific context. The study also conducts a comparative analysis of ML models on existing benchmark datasets, revealing higher prediction accuracy when dealing with fewer LULC classes. Thus, the selection of an appropriate model hinges on the given task, available resources, and the necessary trade-offs between performance and efficiency, particularly crucial in resource-constrained settings. The standardized benchmark dataset contributes valuable insights into the relative performance of deep CNN and ML models in LULC classification, providing a comprehensive understanding of their strengths and weaknesses.

Electrochemical detection platform based on graphene-zinc oxide composite nanomaterials for 17β-estradiol determination in milk

This research presents the development of an innovative electrochemical detection platform using a graphene-zinc oxide (G-ZnO) nanocomposite for the quantification of 17β-estradiol in milk. The G-ZnO nanocomposite, synthesized via a facile one-pot method, exhibited a unique wrinkled structure with a high specific surface area of 143 m²/g. Characterization through SEM, TEM, and XRD confirmed the effective integration of ultrafine ZnO nanoparticles onto graphene sheets. The electrochemical performance of the G-ZnO modified glassy carbon electrode was evaluated, revealing a notably low detection limit of 8.3 nM for 17β-estradiol, a significant improvement over traditional sensors. The sensor demonstrated a wide linear range of 0.05–100 μM and exceptional reproducibility with less than 3.5 % RSD over repeated measurements. Recovery studies in spiked milk samples yielded highly accurate results, with recovery percentages ranging from 97.8 % to 104.6 %. These findings underscore the potential of G-ZnO nanocomposites in creating sensitive, efficient, and reliable sensors for healthcare and environmental applications.

Analysis of GPS Technology in Surveying and Mapping Engineering Technology

With the rapid advancement of digital and information technology, global positioning system (GPS) technology has seen increasing utilization in surveying and mapping engineering, extending its application across land, ocean, and various other domains. By analyzing the technical means of GPS in surveying and mapping engineering, understanding the characteristics and key technologies in different application environments, and exploring the application process and key technical means, accurate control can be effectively realized. Based on this, this paper mainly analyzes the specific application of GPS technology in surveying and mapping engineering technology for reference.

Investigation of the Stability Mechanism of Stimulus-Responsive Wormlike Micelle-CO2 Foams in the Oil Phase.

Foam flooding is an important tool for reservoir development. This study aims to further investigate the interaction between stimulus-responsive wormlike micelle (WLM)-CO2 foams and crude oil. We performed micromorphology experiments as our major studies and used molecular dynamics simulations as an auxiliary tool for interfacial analysis. We utilized foam generation, liquid separation, and defoaming as the entry points of experimental research and energy as the quantitative assessment index to investigate the dynamic process of the action of different oil contents and oil phase types in a DOAPA@NaSal-H+ foam system. We also examined the role of NaSal in the generation and development of the foam system. Results indicated that the law of crude oil's effect on foam could be summarized as "low contents are beneficial and high contents are harmful." In addition, although the DOAPA@NaSal-H+ foam system has high compatibility for saturated and aromatic hydrocarbons, it is highly suitable for application in reservoir environments with relatively high asphaltene and resin contents. Through combined experimental and simulation approaches, we clarified the law governing the stability of the DOAPA@NaSal-H+ foam system in different oil-containing environments, identified the key role of NaSal, and provided a reference for the targeted application of the DOAPA@NaSal-H+ foam system in different oil reservoirs.

A deep learning based approach for image retrieval extraction in mobile edge computing

AbstractDeep learning has been widely explored in 5G applications, including computer vision, the Internet of Things (IoT), and intermedia classification. However, applying the deep learning approach in limited-resource mobile devices is one of the most challenging issues. At the same time, users’ experience in terms of Quality of Service (QoS) (e.g., service latency, outcome accuracy, and achievable data rate) performs poorly while interacting with machine learning applications. Mobile edge computing (MEC) has been introduced as a cooperative approach to bring computation resources in proximity to end-user devices to overcome these limitations. This article aims to design a novel image reiterative extraction algorithm based on convolution neural network (CNN) learning and computational task offloading to support machine learning-based mobile applications in resource-limited and uncertain environments. Accordingly, we leverage the framework of image retrieval extraction and introduce three approaches. First, privacy preservation is strict and aims to protect personal data. Second, network traffic reduction. Third, minimizing feature matching time. Our simulation results associated with real-time experiments on a small-scale MEC server have shown the effectiveness of the proposed deep learning-based approach over existing schemes. The source code is available here: https://github.com/jamalalasadi/CNN_Image_retrieval.

Photostability and photodynamic antimicrobial profile of dye extracts from four (4) plants: prospects for eco-friendly low-cost food disinfection and topical biomedical applications.

In this study, photostability and photodynamic antimicrobial performance of dye extracts from Hibiscus sabdariffa (HS) calyces, Sorghum bicolor (SB) leaf sheaths, Lawsonia inermis (LI) leaves and Curcuma longa (CL) roots were investigated in Acetate-HCl (AH) Buffer (pH 4.6), Tris Base-HCl (TBH) Buffer (pH 8.6), distilled water (dH2O), and Phosphate Buffer Saline (PBS, pH 7.2) using Bacillus subtilis as model for gram positive bacteria, Escherichia coli as model for gram negative bacteria, phage MS2 as model for non-envelope viruses and phage phi6 as model for envelope viruses including SARS CoV-2 which is the causative agent of COVID-19. Our results showed that the photostability of the dye extracts is in the decreasing order of LI > CL > SB > HS. The dye extract-HS is photostable in dH2O but bleaches in buffers-AH, TBH and PBS. The rate of bleaching is higher in AH compared to in TBH and PBS. The bleaching and buffers affected the photodynamic and non-photodynamic antimicrobial activity of the dye extracts. The photodynamic antibacterial activity of the dye extracts is in the decreasing order of CL > HS > LI > SB while the non-photodynamic antibacterial activity is in the decreasing order of LI > CL > HS > SB. The non-photodynamic antiviral activity pattern observed is the same as that of non-photodynamic antibacterial activity observed. However, the photodynamic antiviral activity of the dye extracts is in the decreasing order of CL > LI > HS > SB. Given their performance, the dye extracts maybe mostly suitable for environmental applications including fresh produce and food disinfection, sanitation of hands and contact surfaces where water can serve as diluent for the extracts and the microenvironment is free of salts.

Experimental Examination of Additively Manufactured Patterns on Structural Nuclear Materials for Digital Image Correlation Strain Measurements

Abstract Background There are a limited number of commercially available sensors for monitoring the deformation of materials in-situ during harsh environment applications, such as those found in the nuclear and aerospace industries. Such sensing devices, including weldable strain gauges, extensometers, and linear variable differential transformers, can be destructive to material surfaces being investigated and typically require relatively large surface areas to attach (&gt; 10 mm in length). Digital image correlation (DIC) is a viable, non-contact alternative to in-situ strain deformation. However, it often requires implementing artificial patterns using splattering techniques, which are difficult to reproduce. Objective Additive manufacturing capabilities offer consistent patterns using programmable fabrication methods. Methods In this work, a variety of small-scale periodic patterns with different geometries were printed directly on structural nuclear materials (i.e., stainless steel and aluminum tensile specimens) using an aerosol jet printer (AJP). Unlike other additive manufacturing techniques, AJP offers the advantage of materials selection. DIC was used to track and correlate strain to alternative measurement methods during cyclic loading, and tensile tests (up to 1100 µɛ) at room temperature. Results The results confirmed AJP has better control of pattern parameters for small fields of view and facilitate the ability of DIC algorithms to adequately process patterns with periodicity. More specifically, the printed 100 μm spaced dot and 150 μm spaced line patterns provided accurate measurements with a maximum error of less than 2% and 4% on aluminum samples when compared to an extensometer and commercially available strain gauges. Conclusion Our results highlight a new pattern fabrication technique that is form factor friendly for digital image correlation in nuclear applications.

Amphibious Multifunctional Hydrogel Flexible Haptic Sensor with Self-Compensation Mechanism.

In recent years, hydrogel-based wearable flexible electronic devices have attracted much attention. However, hydrogel-based sensors are affected by structural fatigue, material aging, and water absorption and swelling, making stability and accuracy a major challenge. In this study, we present a DN-SPEZ dual-network hydrogel prepared using polyvinyl alcohol (PVA), sodium alginate (SA), ethylene glycol (EG), and ZnSO4 and propose a self-calibration compensation strategy. The strategy utilizes a metal salt solution to adjust the carrier concentration of the hydrogel to mitigate the resistance drift phenomenon to improve the stability and accuracy of hydrogel sensors in amphibious scenarios, such as land and water. The ExpGrow model was used to characterize the trend of the ∆R/R0 dynamic response curves of the hydrogels in the stress tests, and the average deviation of the fitted curves ϵ¯ was calculated to quantify the stability differences of different groups. The results showed that the stability of the uncompensated group was much lower than that of the compensated group utilizing LiCl, NaCl, KCl, MgCl2, and AlCl3 solutions (ϵ¯ in the uncompensated group in air was 276.158, 1.888, 2.971, 30.586, and 13.561 times higher than that of the compensated group in LiCl, NaCl, KCl, MgCl2, and AlCl3, respectively; ϵ¯ in the uncompensated group in seawater was 10.287 times, 1.008 times, 1.161 times, 4.986 times, 1.281 times, respectively, higher than that of the compensated group in LiCl, NaCl, KCl, MgCl2 and AlCl3). In addition, for the ranking of the compensation effect of different compensation solutions, the concentration of the compensation solution and the ionic radius and charge of the cation were found to be important factors in determining the compensation effect. Detection of events in amphibious environments such as swallowing, robotic arm grasping, Morse code, and finger-wrist bending was also performed in this study. This work provides a viable method for stability and accuracy enhancement of dual-network hydrogel sensors with strain and pressure sensing capabilities and offers solutions for sensor applications in both airborne and underwater amphibious environments.

Selenium doping to improve internal electric field for excellent photocatalytic efficiency of g-C3N4 nanosheets

Graphitic carbon nitride is a promising photocatalyst to address environmental issues and energy applications. Herein, we develop an efficient defect-containing functionalized system of 2D selenium doped g-C3N4 porous nanosheets via one pot with two-step temperature for the first time. During heat treatment, Se-infused g-C3N4 nanosheets loosen stacking and form thinner nanosheets, which could effectively shorten the electronic path of charge migration. The introduction of nitrogen vacancies creates a midgap energy state below the CB, which improves the light-harvesting efficiency. Moreover, the dual effect of N vacancy and Se doping provides a driving force for the dissociation of excitons and achieves an effective spatial charge separation. High specific surface area (104.1 m2/g), suitable bandgap (2.65 eV), and good photocurrent response (0.45 µA cm−2) help to enhance the photocatalytic efficiency of 1-SeCN remarkably. Henceforth, high photocatalytic H2 production rate of 5441 µmol g−1 h−1 and CO evolution rate of 31.5 µmol g−1 h−1 verifies the effective Se doping enhances the photocatalytic efficiency of g-C3N4. Furthermore, DFT calculations are consistent with experimental results and this facile engineering strategy provides a scalable way to develop a novel, efficient g-C3N4-based photocatalyst for H2 production and CO2 reduction.

Biogenically synthesized aluminum oxide nanoparticles via phyllanthus emblica seeds for photocatalytic applications

In this study, Phyllanthus emblica (amla) seed extract was employed to biosynthesize Aluminum oxide nanoparticles (Al2O3 NPs) as efficient stabilizers and bio-reducing agents. The successful synthesis of Al2O3 NPs was confirmed and characterized by UV–Vis spectroscopy (UV/Vis), Fourier Transform Infrared Spectroscopy (FT-IR), scanning electron microscopy (SEM) and X-ray diffraction spectroscopy (XRD). Antibacterial activity of these environmental friendly Al2O3 NPs was evaluated through a disk diffusion method, demonstrating their potential as antimicrobial agents against pathogens, including gram-positive and gram-negative bacteria. Additionally, Al2O3 NPs exhibited substantial photocatalytic activity, achieving a remarkable 95.06% degradation of 4-nitroaniline (4-NA) under UV light irradiation. Furthermore, Al2O3 NPs served as efficient catalysts in catalytic biomass pyrolysis, boosting bio-oil yields by 47.4%. The resulting bio-oil was analyzed using FT-IR spectroscopy. This research emphasizes the significance of biomass waste in producing biofuel as well as disinfection and environmental remediation applications, offering sustainable solutions for various challenges.

Alkaline earth metal ions affect the dissolution of kaolinite-bearing olivine and its carbon storage effects

Olivine dissolution applications in coastal environments hold great promise for both ocean alkalinity enhancement and carbon dioxide storage. Yet the dissolution mechanism of olivine and the influence of metal ions, such as Mg2+ and Ca2+, remain unclear. Moreover, the weathering products, such as clay minerals are usually mixed with olivine, and the influence of the associated minerals on the dissolution also unexplored. In this study, an 80-day dissolution experiment was performed in Ca2+-free artificial seawater (ASW-Ca) and ASW lacking both Ca2+ and Mg2+ (ASW-CaMg), to elucidate the mechanism of olivine dissolution and the impact of Ca2+ and Mg2+ on the presence of an associated clay mineral, kaolinite. To evaluate the mineral properties before and after dissolution, X-ray diffraction spectroscopy, scanning electron microscopy, and energy-dispersive X-ray spectroscopy analyses were used. Etch pits were observed on olivine's surface as well as its reduced crystallinity. The atomic ratio of Mg/Si in olivine decreased from 1.49 ± 0.56 in the raw samples to 1.29 ± 0.53 in the samples obtained from ASW-CaMg after undergoing the 80-day dissolution, whereas it was much higher, at 1.85 ± 0.32, in the samples from ASW-Ca. The increasing Mg/Si was attributed to the Mg2+ adsorption on the surface of olivine, which inhibited the release of structural Mg2+. Hence, Mg2+ was a key environmental factor influencing the process of olivine dissolution, and estuaries, which have low Mg2+ content and low pH may be the promising areas for the olivine application. Moreover, in ASW-CaMg, the total alkalinity (TA), dissolved inorganic carbon (DIC) concentration, and charge concentration of alkaline earth metal ions increased by 3392 ± 28, 2922 ± 3, and 3740 ± 91 μmol kg−1, respectively, compared to the data from the blank experiment. Notably, TA and DIC had strong linear relationships with the alkaline earth metal ions (Mg2+ and Ca2+). The release of free alkaline earth metal cations during olivine dissolution was a controlling factor for long-term carbon dioxide storage in ASW-CaMg. Finally, during the 80-day dissolution experiment, no dissolution of the associated kaolinite was observed, however, kaolinite may influence the olivine dissolution by adsorbing the released Si.

A scientometric analysis of multicriteria decision-making research

ABSTRACT Multi-criteria decision-making (MCDM) is an important decision-making technique for incorporation into Decision Support Systems (DSS). This paper uses bibliographic techniques to characterise the MCDM research field. This research identifies the historical origin of MCDM in both Operations Research/Management Science (OR/MS) and Computer Science. This research identifies the increasing importance of environmental and medical applications in addition to traditional engineering and business applications. It also examines the core literature and the extent to which newer papers are cited.