Current Limitations Research Articles

An enhanced broadband piezoelectric energy harvester via elastic amplification structure for multidirectional vibration

Abstract Vibration energy harvesting using a piezoelectric mechanism has significant potential for powering wireless sensors. However, most current vibration energy harvesters face limitations such as bidirectionality, narrow bandwidth, and high operating frequencies. To address these issues, we propose an enhanced broadband piezoelectric energy harvester utilizing an elastic amplification structure for multidirectional vibration (EB-PVEH). By utilizing the multidirectional rotation capacity of the excitation block and the amplified foundation excitation provided by springs, the EB-PVEH effectively captures broadband vibrations in 2D space under low-frequency excitation. Additionally, its design features long-term durability, as the piezoelectric beams are smoothly excited by the pendulum-induced motion of the block without a tip mass. The practical feasibility and the impact of structural parameters on the output behavior of EB-PVEH were investigated through theoretical analysis and experimental testing. The results revealed that the introduction of springs dynamically amplified the harnessed electrical power output. Moreover, EB-PVEH could harvest the multidirectional vibration, and it exhibited different power-generating characteristics in various directions. Furthermore, the resonance frequency could be efficiently tuned by adjusting the flexible arm length and proof mass, with different optimal arm lengths identified for each vibration direction to maximize working bandwidth. The harvester achieved an optimal output power of 3.98 mW. Practical applications, such as charging a capacitor by driving an e-bike or a bike, demonstrate the potential of the proposed harvester to provide power for micro-electrical devices.

The Evolution of Data Visualization Tools and Their Impact on Business Decision-Making : A Historical Analysis

This article examines the transformative evolution of data visualization tools and their profound impact on business decision-making processes. Through a comprehensive analysis of historical developments, theoretical frameworks, and technological advancements, the article explores how visualization tools have progressed from basic manual charts to sophisticated AI-powered analytical platforms. The investigation encompasses the cognitive foundations of visual information processing, the emergence of first-generation Business Intelligence tools, and the current landscape of modern visualization platforms. Particular attention is given to the integration of artificial intelligence in data visualization, including automated generation, pattern recognition, and predictive analytics capabilities. The article presents both quantitative and qualitative assessments of these tools' impact on organizational decision-making, highlighting significant improvements in decision speed, accuracy, and user engagement. The article reveal substantial benefits in terms of ROI, decision-making efficiency, and organizational adoption patterns. The article concludes by examining emerging trends, including the potential of extended reality technologies and edge computing, while acknowledging current limitations and providing recommendations for future research directions in this rapidly evolving field.

Exploring the Origins of Hexaploid Wheats: Typification of Archaeological Triticum vulgare var. antiquorum and Description of Modern Triticum sphaerococcum subsp. antiquorum (Poaceae: Triticeae)

This study addresses a critical issue in plant taxonomy and phylogeny: the relationship between archaeological materials and potentially analogous living populations. Given the current limitations in definitively establishing the identity between archaeological and contemporary materials, we propose an intermediate approach. This approach serves as a useful framework while scientific methods advance towards definitively assessing whether an archaeological wheat sample, approximately 5000 years old from Central Europe, belongs to the same species as a modern wheat currently endemic to Central Asia. This approach consolidates the taxonomic validity of both archaeological and living materials, allowing them to be treated as distinct taxa while preserving the possibility of future identification convergence. Triticum vulgare var. antiquorum, an archaeobotanical small-grained, free-threshing wheat, was originally described in 1865. The 1982 discovery of morphologically similar living wheat in Tajikistan raised questions about their taxonomic relationship. Our study reviews the nomenclature of both taxa, designating an illustration from the original description of T. vulgare var. antiquorum as the lectotype to align with the traditional concept of the name. We address the ambiguity surrounding “Triticum antiquorum” as used by Russian agronomists and botanists, proposing a more precise circumscription within the current systematic framework of the genus based on cytogenetic data. Consequently, we describe a new taxon, Triticum sphaerococcum subsp. antiquorum. The holotype, selected from material with available cytogenetic data and grown from Professor Udachin’s original Pamir (Tajikistan) collection, is preserved in the N.I. Vavilov All-Russian Institute of Plant Genetic Resources (WIR) collection. It is deposited at the I.M. Krasnoborov Herbarium of Central Siberian Botanical Garden SB RAS (NS), with an isotype at the WIR. This taxonomic revision and new subspecies designation provide a robust framework for reconciling archaeological and contemporary wheat diversity, advancing our understanding of wheat evolution and agricultural history.

Abstract 4116141: Von Willebrand Factor Inhibition by BB-031 Prevents and Thrombolyses Clots in a Murine Model of Venous Thrombosis

Introduction: Venous thromboembolism (VTE) manifesting as deep vein thrombosis and pulmonary embolus results in ~300,000 US deaths annually. Risk factors include obesity and current therapy limitations leave ~ 50% of patients with no effective treatment. Circulating von Willebrand Factor (VWF) levels portend poor clinical outcomes. BB-031, an aptamer-based VWF inhibitor with prophylactic and arterial thrombolytic efficacy may translate to VTE. Methods: Jugular vein occlusion (JVO) was induced by FeCl 3 either before or after intravenous treatment in 14 week old male and female C57/BL6 wild type (WT) or male diet-induced obese (DIO) mice. 5.0 mg/kg BB-031 was administered as a bolus, 10.0 mg/kg rtPA with 10% bolus and remaining 90% over 60 minutes, 400 U heparin as a bolus, or vehicle. Doppler jugular vein flow velocity was recorded from baseline to determine patency 30 minutes after injury. Thrombolytic clots were stabilized for 20 minutes. Results: Prophylactic mice occluded ~20 minutes after FeCl 3 application with rtPA=18.32 +/- 6.54 min, heparin 17.14 +/- 8.53 min, and vehicle 17.29 +/- 7.26 min. Thrombosis did not occur with BB-031 treatment (n=4-12/group). Treatment after JVO resulted in no difference compared to vehicle in male WT mice, but BB-031-treated WT females resulted in 76.09% recanalization 35 minutes after occlusion compared to rtPA (9.64%, p<0.0001), heparin (30.95%, p=0.7306), or vehicle (22.07%, p<0.0001), (n=5/group). Male DIO mice significantly reperfused with BB-031 (100%) vs rtPA (10.52%), heparin (4.54%), and vehicle (22.50%), p<0.0001, (n=4-6/group). Conclusion: VWF inhibition by BB-031 maintains patency in a VTE murine model offering future therapeutic alternatives.

Model and Ensemble Indicator-Guided Assessment of Robust, Exploitable Groundwater Resources for Denmark

Groundwater constitutes 99% of the Earth’s liquid freshwater and is crucial for human health, economic development, and ecosystem sustainability. This study assesses groundwater sustainability in Denmark by employing a comprehensive hydrological model and a set of ensemble indicators. The paper describes the methodology and the results based on nine selected indicators. Three indicators focus on recharge capture and aquifer sustainability, one focuses on groundwater level and wetland capture, two focus on baseflow and drainage flow capture, and three focus on eco flow capture. Our findings highlight that while overall exploitable groundwater resources are estimated at 1.1 billion m3/year, significant regional disparities exist, with certain areas, notably Zealand, facing over-exploitation rates exceeding 250% of sustainable limits. The indicators developed not only provide a framework for assessing current groundwater resource limits, but also serve as a basis for future monitoring and adaptive management strategies. This research underscores the need for stakeholder engagement and integrated approaches to ensure the sustainability of groundwater resources in the face of growing anthropogenic pressures and climate change. Our work contributes to the ongoing discourse on sustainable water management and offers a robust methodology for assessing groundwater sustainability.

Common open source WebGIS platform for spatial and non-spatial data to enhance health services in Sri Lanka

This study explores the transformative potential and current limitations of Geographic Information Systems (GIS) in the context of Sri Lanka's health sector and broader development initiatives. Despite the global GIS market's expected growth to billions by 2025, driven by its adoption in infrastructure, urban planning, and the integration with emerging technologies, its application in critical areas such as public health, disaster management, and citizen services remains underutilized. This underutilization stems from a lack of ground-level information, limited knowledge of GIS capabilities, restricted access to essential geospatial data, and the high cost of GIS software. This paper discusses the significant hurdles in obtaining updated and comprehensive citizen, household, and health-related information, which impedes effective planning and decision-making within the health sector of Sri Lanka. It further examines the initiatives by the Ministry of Health, supported by the Asian Development Bank, to implement a GIS-based Health Mapping System focused on infrastructure and inventory management of health facilities, yet lacking in integrating critical citizen data for demand analysis of health services. The study underscores the necessity for a comprehensive "Geographical Citizen Information System for the Health Sector in Sri Lanka" akin to those in developed countries, to host, process, and analyze spatial and non-spatial information for enhanced policymaking and development planning. Through an analysis of current challenges and the exploration of potential solutions, this paper advocates for an integrated approach to harness the full capabilities of GIS technologies, thereby enabling more informed and effective governance in Sri Lanka’s Health sector.

Bioinspired Passive Cooling Hydrogel for Visualizing Hygroscopicity and Desorption Process

AbstractSelf‐hygroscopic hydrogels, characterized by high evaporation enthalpy, cooling efficiency, and self‐regulating properties, have garnered significant attention. However, most current research focuses on enhancing the hygroscopic and desorption performance, often overlooking the importance of monitoring the self‐regulation process, which limits its further application. Advanced visualization technologies, such as in situ electrical impedance tomography, low‐field nuclear magnetic resonance, and hyperspectral imaging, offer potential insights into this behavior, yet they often require additional devices, incur high costs, and involve complex sample preparation processes. Therefore, drawing inspiration from nature, humidity‐color‐sensitive hydrogels (HCSHs) strategy is proposed for visualized cooling. Benefiting from the strong polar responsiveness of the aggregation‐induced emission (AIE) molecules, the hydrogel's fluorescence significantly changes with varying interior water content, thereby its self‐regulation process is monitored easily. Further, the obtained hydrogel could be applied in the electronic device cooling owing to the polymer skeletons’ high swelling ratio, strong adhesion, and excellent self‐hygroscopic properties. This strategy overcomes current limitations in the visual technology of self‐hygroscopic materials and provides new insights into intelligent thermal management for electronic devices.

Recent progress in atmospheric modeling over the Andes – part I: review of atmospheric processes

The Andes is the longest mountain range in the world, stretching from tropical South America to austral Patagonia (12°N-55°S). Along with the climate differences associated with latitude, the Andean region also features contrasting slopes and elevations, reaching altitudes of more than 4,000 m. a.s.l., in a relatively narrow crosswise section, and hosts diverse ecosystems and human settlements. This complex landscape poses a great challenge to weather and climate simulations. The interaction of the topography with the large-scale atmospheric motions controls meteorological phenomena at scales of a few kilometers, often inadequately represented in global (grid spacing ∼200–50 km) and regional (∼50–25 km) climate simulations previously studied for the Andes. These simulations typically exhibit large biases in precipitation, wind and near-surface temperature over the Andes, and they are not suited to represent strong gradients associated with the regional processes. In recent years (∼2010–2024), a number of modeling studies, including convection permitting simulations, have contributed to our understanding of the characteristics and distribution of a variety of systems and processes along the Andes, including orographic precipitation, precipitation hotspots, mountain circulations, gravity waves, among others. This is Part I of a two-part review about atmospheric modeling over the Andes. In Part I we review the current strengths and limitations of numerical modeling in simulating key atmospheric-orographic processes for the weather and climate of the Andean region, including low-level jets, downslope winds, gravity waves, and orographic precipitation, among others. In Part II, we review how climate models simulate surface-atmosphere interactions and hydroclimate processes in the Andes Cordillera to offer information on projections for land-cover/land-use change or climate change. With a focus on the hydroclimate, we also address some of the main challenges in numerical modeling for the region.

Ambient air pollution exposure and incidence of cataract surgery: The prospective 3City‐Alienor study

AbstractPurposeCataract, the leading cause of blindness worldwide, is a multifactorial disease involving oxidative stress mechanisms. The aim of our study was to investigate the relationship between air pollution exposure and the incidence of cataract surgery.MethodsThe 3C‐Alienor study is a population‐based cohort of residents of Bordeaux, France, aged 65 years or more, recruited in 1999–2000 and followed every 2–3 years until 2017. Cataract surgery was self‐reported and checked at slit‐lamp by trained professionals. Average air pollution exposure (particulate matter ≤2.5 μm (PM2.5), black carbon (BC), nitrogen dioxide (NO2)) in the 10 years preceding baseline was estimated at the participants' geocoded residential address, using temporally adjusted land use regression. Associations of 10‐year average air pollution exposure with incidence of cataract were estimated using Cox proportional hazard models adjusted for confounders.ResultsThe study included 829 subjects without cataract surgery prior to inclusion; the mean age at inclusion was 72.6 years (standard deviation (SD): 4.2) and 61% were women. The median (Interquartile‐range (IQR)) follow‐up duration was 14.1 (6.4) years during which 507 participants underwent cataract surgery. Exposure to a concentration ≥40 μg/m3 of NO2 (the current regulatory limit value in Europe) was associated with incident cataract surgery (HR = 1.46, CI (1.16, 1.84), p = 0.001). No statistically significant association was found with PM2.5 and BC.ConclusionLong‐term exposure to a NO2 concentration ≥ 40 μg/m3 was associated with an increased incidence of cataract surgery. Complying with current European air pollution standards could reduce cataract surgery costs and improve population quality of life.

Integrating Artificial Intelligence Across Medical Clinics: Strengthening Collaborative Efforts for Improved Patient Outcomes

The integration of Artificial Intelligence (AI) across medical clinics holds transformative potential for enhancing patient outcomes through improved collaboration. This review examines the applications of AI in fostering inter-clinic connectivity, with a focus on diagnostics, treatment planning, and patient management. By utilizing AI-powered data-sharing platforms, predictive analytics, and telemedicine solutions, clinics can collaborate more efficiently, ensuring continuity of care and reducing diagnostic errors. Despite these advancements, challenges persist, including data privacy, interoperability, and resistance to AI adoption among clinical staff. This review highlights case studies showcasing successful AI-enabled collaborations and proposes future directions to address current limitations. Overall, AI's role in linking medical clinics underscores its potential to revolutionize patient care by bridging gaps in communication and decision-making processes.

Unveiling the power of artificial intelligence for image‐based diagnosis and treatment in endodontics: An ally or adversary?

AbstractBackgroundArtificial intelligence (AI), a field within computer science, uses algorithms to replicate human intelligence tasks such as pattern recognition, decision‐making and problem‐solving through complex datasets. In endodontics, AI is transforming diagnosis and treatment by applying deep learning algorithms, notably convolutional neural networks, which mimic human brain function to analyse two‐dimensional (2D) and three‐dimensional (3D) data.ObjectivesThis article provides an overview of AI applications in endodontics, evaluating its use in 2D and 3D imaging and examining its role as a beneficial tool or potential challenge.MethodsThrough a narrative review, the article explores AI's use in 2D and 3D imaging modalities, discusses their limitations and examines future directions in the field.ResultsAI significantly enhances endodontic practice by improving diagnostic accuracy, workflow efficiency, and treatment planning. In 2D imaging, AI excels at detecting periapical lesions on both periapical and panoramic radiographs, surpassing expert radiologists in accuracy, sensitivity and specificity. AI also accurately detects and classifies radiolucent lesions, such as radicular cysts and periapical granulomas, matching the precision of histopathology analysis. In 3D imaging, AI automates the segmentation of fine structures such as pulp chambers and root canals on cone‐beam computed tomography scans, thereby supporting personalized treatment planning. However, a significant limitation highlighted in some studies is the reliance on in vitro or ex vivo datasets for training AI models. These datasets do not replicate the complexities of clinical environments, potentially compromising the reliability of AI applications in endodontics.DiscussionDespite advancements, challenges remain in dataset variability, algorithm generalization, and ethical considerations such as data security and privacy. Addressing these is essential for integrating AI effectively into clinical practice and unlocking its transformative potential in endodontic care. Integrating radiomics with AI shows promise for enhancing diagnostic accuracy and predictive analytics, potentially enabling automated decision support systems to enhance treatment outcomes and patient care.ConclusionsAlthough AI enhances endodontic capabilities through advanced imaging analyses, addressing current limitations and fostering collaboration between AI developers and dental professionals are essential. These efforts will unlock AI's potential to achieve more predictable and personalized treatment outcomes in endodontics, ultimately benefiting both clinicians and patients alike.

Advances in CAR NK Cell Therapy for Targeting and Eradicating Circulating Tumor Cells: Challenges and Solutions for Aging Patients Mini Review

Chimeric Antigen Receptor (CAR) Natural Killer (NK) cells represent a promising advancement in cancer immunotherapy, particularly for targeting circulating tumor cells (CTCs) and preventing metastasis. This review examines the latest developments in CAR NK cell therapy, including diverse NK cell sources, genetic engineering techniques, and dual mechanisms of action. Targeting CTCs with CAR NK cells shows significant potential in aggressive cancers like triple-negative breast cancer (TNBC) and pancreatic cancer. The impact of aging on NK cell function, especially regarding cytotoxicity, cytokine secretion, and persistence, poses challenges for elderly patients, but strategies such as interleukin-15 and metabolic interventions offer potential solutions. The review also addresses current limitations, such as poor persistence in immunosuppressive microenvironments and low solid tumor infiltration, while proposing combination therapies to enhance effectiveness. Although still in earlier clinical stages compared to CAR T cells, CAR NK cells’ safety profile and MHC-independent recognition mechanisms make them a versatile therapeutic option. Future directions include optimizing NK cell persistence, improving infiltration, and developing age-adapted therapies for elderly patients. _________________________________________________________________________________________ Keywords: CAR NK cells, CAR T cells, Circulating tumor cells (CTCs), Cell Therapy, Aging

Vessel Trajectory Prediction Based on AIS Data: Dual-Path Spatial–Temporal Attention Network with Multi-Attribute Information

With the rapid growth of the global shipping industry, the increasing number of vessels has brought significant challenges to navigation safety and management. Vessel trajectory prediction technology plays a crucial role in route optimization and collision avoidance. However, current prediction methods face limitations when dealing with complex vessel interactions and multi-dimensional attribute information. Most models rely solely on global modeling in the temporal dimension, considering spatial interactions only later, failing to capture dynamic changes in trajectory interactions at different time points. Additionally, these methods do not fully utilize the multi-attribute information in AIS data, and the simple concatenation of attributes limits the model’s potential. To address these issues, this paper proposes a dual spacial–temporal attention network with multi-attribute information (DualSTMA). This network models vessel behavior and interactions through two distinct paths, comprehensively considering individual vessel intentions and dynamic interactions. Moreover, we divide vessel attributes into dynamic and static categories, with dynamic attributes fused during feature preprocessing, and with static attributes being controlled through a gating mechanism during spatial interactions to regulate the importance of neighboring vessel features. Benchmark tests on real AIS data show that DualSTMA significantly outperforms existing methods in prediction accuracy. Ablation studies and visual analyses further validate the model’s reliability and advantages.

Changes in lipid abundance are associated with disease progression and treatment response in chronic Trypanosoma cruzi infection

BackgroundChagas disease, caused by the parasite Trypanosoma cruzi, is a zoonosis that affects more than seven million people. Current limitations on the diagnosis of the disease hinder the prognosis of patients and the evaluation of treatment efficacy, slowing the development of new therapeutic options. The infection is known to disrupt several host metabolic pathways, providing an opportunity for the identification of biomarkers.MethodsThe metabolomic and lipidomic profiles of a cohort of symptomatic and asymptomatic patients with T. cruzi infection and a group of uninfected controls were analysed using liquid chromatography/mass spectrometry. Differences among all groups and changes before and after receiving anti-parasitic treatment across those with T. cruzi infection were explored.ResultsThree lipids were found to differentiate between symptomatic and asymptomatic participants: 10-hydroxydecanoic acid and phosphatidylethanolamines PE(18:0/20:4) and PE(18:1/20:4). Additionally, sphinganine, 4-hydroxysphinganine, hexadecasphinganine, and other sphingolipids showed post-treatment abundance similar to that in non-infected controls.ConclusionsThese molecules hold promise as potentially useful biomarkers for monitoring disease progression and treatment response in patients with chronic T. cruzi infection.Graphical

Synthetic Biology in T-cell Engineering Research

Synthetic biology has emerged as a transformative discipline, enabling precise genetic and functional reprogramming of cellular systems. In T-cell engineering, it offers groundbreaking potential to revolutionize immunotherapy by endowing T cells with enhanced specificity, adaptability, and resilience against complex diseases such as cancer and autoimmune disorders. By integrating advanced genome-editing tools like CRISPR-Cas9 with modular synthetic constructs, researchers can design T cells with bespoke functionalities, such as tunable antigen recognition, controlled cytokine release, and resistance to immunosuppressive tumor microenvironments. This approach not only overcomes the limitations of conventional T-cell therapies but also facilitates the development of novel therapeutic paradigms, including "smart" cellular systems capable of sensing and responding to dynamic biological cues. Furthermore, synthetic circuits allow for the incorporation of logic-gated mechanisms to minimize off-target effects and enhance therapeutic precision. Despite these advancements, challenges remain in optimizing safety, scalability, and regulatory compliance. This research aims to explore the intersection of synthetic biology and T-cell engineering, highlighting cutting-edge methodologies, therapeutic applications, and emerging trends. By addressing current limitations and envisioning future possibilities, this work seeks to contribute to the growing body of knowledge driving synthetic biology toward clinical and industrial breakthroughs in cellular immunotherapy.

Ultrasound-triggered nano delivery of lenvatinib for selective immunotherapy treatment against hepatocellular carcinoma

Although there have been remarkable advances in the treatment of hepatocellular carcinoma (HCC), the prognosis remains poor in those with advanced stage and more effective therapeutic options are urgently needed. Lenvatinib (Len), a multiple receptor tyrosine kinase inhibitor, is an emerging molecular targeted agent for HCC, whose immunomodulatory activities have been investigated. However, Len utilization is limited because of its low metabolic stability, poor bioavailability and dose-dependent toxicity, rendering its direct use insufficient for immune modulation. Stimuli-responsive nanoparticles, which are drug-targeted delivery platforms for on-demand drug release, facilitate deeper and uniform tumour penetration, providing alternative solutions to overcome current limitations. Ultrasound (US) exhibits superior tissue penetration abilities and can produce reactive oxygen species (ROS) at the tumour site to treat deeper tumours. In addition, US serves as an excellent and selective drug delivery mediator for tumour treatment. Herein, we designed US-triggered lenvatinib nanoparticles (Len-RNPs) for selective drug delivery that utilize US-triggered ROS to induce in situ oxidation reactions, resulting in nanoparticle disintegration. Len-RNPs mitigate the toxicity of Len and effectively trigger robust systemic anti-tumour immune responses in a H22 tumour model, resulting in a tumour suppression rate of 95.7%, with 60% of mice being cured. Our study elucidates a novel and precise strategy of combining Len and US therapy for enhanced HCC immunotherapy.

Analysis of the Principle of Jet Engine and State-of-art Application

Abstract. In 1903, the Wright Brother made the dream of flight come true. Contemporarily, as the development of modern technology, aviation has become a major type of daily transportation. The jet engines are the magic that makes the airplanes soar high in the blue sky. As the jet engine is a major invention in human history that brought humans to the sky, it is meaningful to gig into the principle behind the engines. In this article, the principle of 5 jet engines are explained. There are benefits but also impacts due to the human beings. Environmental problems are caused by the exhausted gases, healthy of people who work and live near the airstrips and airports are threatened. Different types of jet engine produced by different companies latest are enumerated and stated with detailed data such as thrust and bypass ratio. According to the analysis, the current limitations and challenges of the development with the future prospect are stated.

Modeling and Analysis of Micro-Turbine Integrating PV Generation Systems

Abstract. As a matter of fact, in the emerging field of distributed generation, micro turbines (MT) are seen as having significant potential in power generation especially in recent years. In reality, it can provide flexibility as well as robustness for PV generation system with great performances. Therefore, with this in mind, this paper proposes a hybrid power system integrating MT into PV generation system. To be specific, this study investigates as well as discusses the operating principles of MT. On this basis, a coordinated PQ control strategy for the hybrid PVMT power generation system is proposed. At the same time, the inertia enhancement brought by the MT is analyzed validating the potential and feasibility of the proposed strategy. According to the analysis, the current limitations as well as future improvements are discussed in the meantime. Overall, these results shed light on guiding further exploration of MT integrating PV generation systems and pave a novel path for designs.

An overview of outdoor low-cost gas-phase air quality sensor deployments: current efforts, trends, and limitations

Abstract. We reviewed 60 sensor networks and 17 related efforts (sensor review papers and data accessibility projects) to better understand the landscape of stationary low-cost gas-phase sensor networks deployed in outdoor environments worldwide. This study is not exhaustive of every gas-phase sensor network on the globe but rather exists to categorize types of sensor networks by their key characteristics and explore general trends. This also exposes gaps in monitoring efforts to date, especially regarding the availability of gas-phase measurements compared to particulate matter (PM) and geographic coverage gaps (the Global South, rural areas). We categorize ground-based networks that measure gas-phase air pollutants into two main subsets based on their deployment type – quasi-permanent (long term) and campaign (short to medium term) – and explore commonplace practices, strengths, and weaknesses of stationary monitoring networks. We conclude with a summary of cross-network unification and quality control efforts. This work aims to help scientists looking to build a sensor network explore best practices and common pathways and aid end users in finding low-cost sensor datasets that meet their needs.

Electrochemical Methods for Nutrient Removal in Wastewater: A Review of Advanced Electrode Materials, Processes, and Applications

In response to the increasing global water demand and the pressing environmental challenges posed by climate change, the development of advanced wastewater treatment processes has become essential. This study introduces novel electrochemical technologies and examines the scalability of industrial-scale electrooxidation (EO) methods for wastewater treatment, focusing on simplifying processes and reducing operational costs. Focusing on the effective removal of key nutrients, specifically nitrogen and phosphorus, from wastewater, this review highlights recent advancements in electrode materials and innovative designs, such as high-performance metal oxides and carbon-based electrodes, that enhance efficiency and sustainability. Additionally, a comprehensive discussion covers a range of electrochemical methods, including electrocoagulation and electrooxidation, each evaluated for their effectiveness in nutrient removal. Unlike previous studies, this review not only examines nutrient removal efficiency, but also assesses the industrial applicability of these technologies through case studies, demonstrating their potential in municipal and industrial wastewater contexts. By advancing durable and cost-effective electrode materials, this study emphasizes the potential of electrochemical wastewater treatment technologies to address global water quality issues and promote environmental sustainability. Future research directions are identified with a focus on overcoming current limitations, such as high operational costs and electrode degradation, and positioning electrochemical treatment as a promising solution for sustainable water resource management on a larger scale.