Real-time Monitoring Research Articles

Technological advancements in drilling: A comparative analysis of onshore and offshore applications

This paper examines the technological advancements in onshore and offshore drilling operations, emphasizing their significance, challenges, and environmental considerations. Technological innovations, including automation, data analytics, and real-time monitoring, have transformed drilling practices, enhancing efficiency, safety, and environmental sustainability. By comparing onshore and offshore applications, the paper evaluates the effectiveness of these advancements in addressing the unique challenges of each environment. Key areas of focus include efficiency improvements, safety enhancements, environmental impacts, and regulatory compliance. Through case studies and analysis, the study aims to provide valuable insights into the current state of onshore and offshore drilling operations and identify opportunities for innovation and improvement. Ultimately, the findings contribute to advancing sustainable practices, mitigating environmental risks, and fostering responsible resource extraction in the oil and gas industry. This comparative study delves into the dynamic landscape of technological advancements within onshore and offshore drilling operations. It explores the evolving role of automation, data analytics, and real-time monitoring in revolutionizing drilling practices, offering insights into their implications for efficiency, safety, and environmental stewardship. By juxtaposing the unique challenges and operational requirements of onshore and offshore environments, the study underscores the importance of a nuanced understanding of technological applications. Through rigorous analysis and case studies, it illuminates the interplay between advancements and environmental considerations, shedding light on opportunities for innovation and sustainable development. The study's comprehensive scope encompasses efficiency gains, safety protocols, environmental impact mitigation, and regulatory compliance, providing a holistic view of the transformative potential of technology in the drilling industry. Ultimately, the study serves as a catalyst for informed decision-making, fostering dialogue, and driving forward-looking strategies for the future of onshore and offshore drilling operations.

AI-enhanced subsea maintenance for improved safety and efficiency: Exploring strategic approaches

As the oil and gas industry increasingly explores deeper and more remote offshore sites, the maintenance of subsea infrastructure becomes paramount. The use of Artificial Intelligence (AI) in subsea maintenance offers promising solutions to enhance safety and efficiency in these challenging environments. This review explores strategic approaches to integrating AI into subsea maintenance operations. AI facilitates predictive maintenance by analyzing vast amounts of data collected from sensors and historical maintenance records. Machine learning algorithms can detect patterns and predict equipment failures before they occur, enabling proactive maintenance scheduling. This predictive capability reduces downtime and minimizes the risk of accidents by addressing potential issues before they escalate. AI-enabled autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs) play a crucial role in subsea inspections and repairs. These AI-enhanced robots can navigate complex subsea environments, perform inspections, and execute maintenance tasks with greater precision and efficiency than human divers. By reducing the need for human intervention in hazardous environments, AI-driven AUVs and ROVs significantly improve safety. Furthermore, AI algorithms can optimize maintenance schedules based on factors such as equipment condition, environmental conditions, and operational requirements. By dynamically adjusting maintenance plans, operators can maximize equipment uptime while minimizing costs and risks. This proactive approach ensures that maintenance activities are conducted at the most opportune times, reducing the likelihood of unplanned downtime and improving overall efficiency. Moreover, AI facilitates condition-based maintenance strategies, where equipment health is continuously monitored in real-time. Sensors installed on subsea infrastructure collect data on factors such as temperature, pressure, and vibration, which is then analyzed by AI algorithms to assess equipment condition. By detecting early signs of degradation or malfunction, AI enables timely interventions, preventing costly breakdowns and ensuring optimal performance. In addition to predictive and condition-based maintenance, AI-driven analytics offer insights into operational performance and asset integrity. By analyzing data from various sources, including sensors, historical records, and operational logs, AI can identify trends, anomalies, and optimization opportunities. These insights enable operators to make data-driven decisions that enhance overall system reliability and efficiency. Strategic approaches to implementing AI in subsea maintenance require collaboration between technology providers, operators, and regulatory bodies. Establishing industry standards and guidelines for AI applications in subsea operations is crucial to ensure safety, reliability, and interoperability. Furthermore, investing in research and development to enhance AI algorithms and robotics technology is essential to unlock the full potential of AI in subsea maintenance. AI-enhanced subsea maintenance offers significant benefits in terms of safety and efficiency. By leveraging predictive analytics, autonomous robotics, and real-time monitoring, operators can optimize maintenance activities, reduce downtime, and minimize risks. Strategic approaches to integrating AI into subsea operations require collaboration, investment, and a commitment to advancing technology to meet the challenges of offshore environments.

Multifunctional gas safety monitoring and classification warning device

Due to the absence of automatic flameout and other essential functionalities in these devices, incidents such as gas leaks can easily lead to explosions. As a result, an autonomous safety monitoring and early warning device has been specifically developed for traditional gas stove equipment. This device is capable of activating a multi-tiered alarm and control mechanism when it reaches a predetermined threshold concentration. The main control of the Arduino Uno R3 microcontroller development board serves as the foundation for this device, which utilizes the MQ-5 gas sensor and 1602 liquid crystal display module to provide real-time gas concentration display. It also incorporates a sound and light alarm system that triggers when the concentration exceeds the predetermined threshold. Additionally, it employs a human infrared sensor and flame sensor as modules for fire detection and occupancy monitoring respectively. Any abnormal changes in these parameters will activate the sound and light alarm system. Equipped with an LED light and buzzer, this system provides real-time monitoring around gas stoves. Experimental results demonstrate excellent responsiveness under current conditions. Given its significant relevance in ensuring gas safety, this multifunctional gas safety monitoring device with classification early warning capabilities holds immense potential for market applications.

Health monitoring system using ECG and PPG techniques

The demand for portable health monitoring systems has grown rapidly in recent years due to the increasing prevalence of chronic diseases and the need for continuous health monitoring. This article introduces the development and implementation of a portable health monitoring system using photo plethysmography (PPG) and electrocardiogram (ECG) technologies. The system integrates the Node MCU ESP8266 microcontroller, AD8232 ECG sensor, and MAX30100 sensor to measure vital signs such as heart rate and blood oxygen saturation level. The proposed system provides a cost-effective and efficient solution for real-time health monitoring, enabling early detection of abnormalities and timely intervention. The feasibility and performance of the system were proven through experimental tests.

Q3D: a complete solution for quality control and inspection in additive manufacturing processes

Purpose This paper aims to present an integrated system designed for quality control and inspection in additive manufacturing (AM) technologies. Design/methodology/approach The study undertakes a comprehensive examination of the process in three distinct stages. First, the quality of the feedstock material is inspected during the preprocessing step. Subsequently, the main research topic of the study is directed toward the 3D printing process itself with real-time monitoring procedures using computer vision methods. Finally, an evaluation of the 3D printed parts is conducted, using measuring methods and mechanical experiments. Findings The main results of this technical paper are the development and presentation of an integrated solution for quality control and inspection in AM processes. Originality/value The proposed solution entails the development of a promising tool for the optimization of the quality in 3D prints based on machine learning algorithms.

DeepMetaDroid: Real-Time Android Malware Detection Using Deep Learning and Metadata Features

The increasing prevalence of Android malware poses significant risks to mobile devices and user privacy. The traditional detection methods have limitations in keeping up with the evolving landscape of malware attacks, necessitating the development of more effective solutions. In this paper, we present DeepMetaDroid, a real-time detection approach for Android malware that leverages metadata features. By analyzing crucial metadata, including APK size, download size, permissions, certificates, and DEX files, the proposed method enables effective identification of malware and enhances mobile security. Using deep learning techniques, a lightweight Android real-time monitoring system is equipped with the trained model. These methods include long short-term memory (LSTM), gated recurrent units (GRU), convolutional neural networks (CNN), deep neural networks (DNN), and other ensemble models. Utilizing the rectified linear unit (ReLU) as the activation function, the DNN model is constructed with 32 neurons in the input layer. A one-dimensional convolutional layer with 32 neurons and a filter size of three is used as the input layer in the CNN model. The LSTM model is designed with an input layer consisting of 16 neurons. The GRU model with 32 neurons is employed in the input layer. Additionally, ensemble models that combined several architectures were developed. The proposed method offers a faster and more scalable solution for malware detection by consuming fewer resources like memory and CPU. This work ensures device security by providing real-time monitoring on Android devices to prevent users from installing malicious applications and, thus, enhance user privacy and security.

Not All Arms of IgM Are Equal: Following Hinge-Directed Cleavage by Online Native SEC-Orbitrap-Based CDMS.

Immunoglobulins M (IgM) are key natural antibodies produced initially in humoral immune response. Due to their large molecular weights and extensive glycosylation loads, IgMs represent a challenging target for conventional mass analysis. Charge detection mass spectrometry (CDMS) may provide a unique approach to tackle heterogeneous IgM assemblies, although this technique can be quite laborious and technically challenging. Here, we describe the use of online size exclusion chromatography (SEC) to automate buffer exchange and sample introduction, and demonstrate its adaptability with Orbitrap-based CDMS. We discuss optimal experimental parameters for online SEC-CDMS experiments, including ion activation, choice of column, and resolution. Using this approach, CDMS histograms containing hundreds of individual ion signals can be obtained in as little as 5 min from single injections of <1 μg of sample. To demonstrate the unique utility of online SEC-CDMS, we performed real-time kinetic monitoring of pentameric IgM digestion by the protease IgMBRAZOR, which cleaves specifically in the hinge region of IgM. Several digestion intermediates corresponding to processive losses of F(ab')2 subunits could be mass-resolved and identified by SEC-CDMS. Interestingly, we find that for the J-chain linked IgM pentamer, cleavage of one of the F(ab')2 subunits is much slower than the other four F(ab')2 subunits, which we attribute to the symmetry-breaking interactions of the J-chain within the pentameric IgM structure. The online SEC-CDMS methodologies described here open new avenues into the higher throughput automated analysis of heterogeneous, high-mass protein assemblies by CDMS.

Real-time Continuous Glucose Monitoring in the Intensive Care Unit: The Fifth Vital Sign.

Real-time Continuous Glucose Monitoring in the Intensive Care Unit: The Fifth Vital Sign.

Augmented reality in team-based search and rescue: Exploring spatial perspectives for enhanced navigation and collaboration

Indoor search and rescue (SAR) missions frequently encounter challenges due to complex spatial layouts and conditions of high stress. Augmented Reality (AR) has emerged as a promising tool to aid SAR efforts by offering spatial information through exocentric and egocentric perspectives. Specifically, AR systems introduce an egocentric perspective, overlaying detailed spatial information onto physical environments, which holds promise for navigating the complexities of indoor SAR operations. However, the effectiveness of these AR perspectives in a team-based SAR context remains underexplored. This study introduces a multi-user AR platform that combines reality capture with synchronized real-time spatial data and real-time cognitive load monitoring module to support team-based SAR missions. A human subject experiment (N = 64) was conducted with combined variations of spatial complexity and collaboration strategies to explore the efficacy of egocentric and exocentric navigation cues in aiding multi-agent SAR tasks. In the experiment, participants were asked to search for two consecutive targets while collaborating either parallelly, or hierarchically within a given time limit. Results indicated that egocentric cues offered limited benefits in a multi-agent setup, whereas exocentric cues notably increased team performance. Hierarchically structured teams resulted in fewer errors and improved efficiency during wayfinding tasks. Cognitive analysis revealed the importance of optimizing collaborative strategies to manage the mental demands of SAR missions instead of focusing on optimizing individuals. These findings contribute to understanding AR interface design’s complex role in multi-agent SAR support, aiming to improve AR system designs for SAR scenarios.

Flexible Nanofiber Pressure Sensors with Hydrophobic Properties for Wearable Electronics

In recent years, flexible pressure sensors have received considerable attention for their potential applications in health monitoring and human–machine interfaces. However, the development of flexible pressure sensors with excellent sensitivity performance and a variety of advantageous characteristics remains a significant challenge. In this paper, a high-performance flexible piezoresistive pressure sensor, BC/ZnO, is developed with a sensitive element consisting of bacterial cellulose (BC) nanofibrous aerogel modified by ZnO nanorods. The BC/ZnO pressure sensor exhibits excellent mechanical and hydrophobic properties, as well as a high sensitivity of −15.93 kPa−1 and a wide range of detection pressure (0.3–20 kPa), fast response (300 ms), and good cyclic durability (&gt;1000). Furthermore, the sensor exhibits excellent sensing performance in real-time monitoring of a wide range of human behaviors, including mass movements and subtle physiological signals.

Self-assembled dipeptide grafted nanohybrid material strips for reliable spectroscopic and electrochemical recognition of tryptamine

According to the World Health Organization (WHO), foods high in tryptamine may cause foodborne diseases and abnormal activity in the central nervous system (CNS). The real-time monitoring of tryptamine levels is an emphasized topic among scientific community. In the present work, we report a cost-effective fluorescent paper strips-based methodology impregnated with self-assembled dipeptide-modified (DM1) zinc oxide (ZnO) i.e.DM1@ZnO for the real-time monitoring of tryptamine levels. It is based on an organic-inorganic nanohybrid material of self-assembled N-functionalised dipeptide molecule coated over the surface of ZnO thus tailoring its properties for the detection of tryptamine employing spectroscopic and electrochemical methods. The designed material exhibited a noteworthy response towards tryptamine irrespective of the presence of other biogenic amines (BAs). It selectively displayed blue fluorescence under the 365 nm UV light. On top of that, the detection of tryptamine was also corroborated by its transition to 2-oxytryptamine, as deduced from its electrochemical route. Thus, the proposed paper strip-based methodology unbolts a realistic platform for the efficient recognition of varying levels of tryptamine thus tackling the prima facie reasons for foodborne diseases.

Remote monitoring of sleep disorder using FBG sensors and FSO transmission system enabled smart vest

Optical sensors, particularly fiber Bragg grating (FBG) sensors have achieved a fast ingress into the fields of medical diagnostic and vital signs monitoring. Wearable smart textiles equipped with FBG sensors are catching huge research attention in different applications for measurement and monitoring of physiological parameters. In this paper, we report a simple technique for remote monitoring of sleep disorder using a smart vest implemented with four FBG stress sensors located at different sides of the vest and free space optics (FSO) transmission system. The sleep disorder of the patient is monitored in real time through shifts in the original Bragg wavelengths of sensors by stress loading during random changes in patient’s sleeping postures. The reflected wavelength from a stress loaded sensor at a certain posture is transmitted over 0.5 km long FSO channel towards remote medical center, photodetected, and then can be processed in a PC to record the restlessness in a certain time interval in terms of total number of times sleeping postures are changed, total time spent at a certain posture etc. To correctly detect the stress loaded FBG sensor at the medical center, various parameters of FBG sensors and demultiplexer are carefully adjusted to minimize the power leakages from unloaded sensors that may result into errors in the detection. Maximum dynamic range around 45 dB has been achieved ensuring accurate detection. This study not only provides a cost-efficient and non-intrusive solution for monitoring the sleep disorder of patients but also can be used for real-time monitoring of various other ailments, such as lung, brain, and cardiac diseases in future.

Comparative studies between humans and golden Syrian hamsters via thromboelastography.

Thromboelastography (TEG) is a widely utilized clinical testing method for real-time monitoring of platelet function and the thrombosis process. Lipid metabolism disorders are crucial risk factors for thrombosis. The lipid metabolism characteristics of hamsters resemble those of humans more closely than mice and rats, and their relatively large blood volume makes them suitable for studying the mechanisms of thrombosis related to plasma lipid mechanisms. Whole blood samples from golden Syrian hamsters and healthy humans were obtained following standard clinical procedures. TEG was employed to evaluate coagulation factor function, fibrinogen (Fib) function, platelet function, and the fibrinolytic system. The whole blood from hamster or healthy human was isolated following the clinical procedure, and TEG was employed to evaluate the coagulation factor function, Fib function, platelet function, and fibrinolytic system. Coagulation analysis used ACLTOP750 automatic coagulation analysis pipeline. Blood routine testing used XN-2000 automatic blood analyzer. TEG parameters revealed that hamsters exhibited stronger coagulation factor function than humans (reaction time [R], p = 0.0117), with stronger Fib function (alpha angle, p < 0.0001; K-time [K], p < 0.0001). Platelet function did not differ significantly (maximum amplitude [MA], p = 0.077). Hamsters displayed higher coagulation status than humans (coagulation index [CI], p = 0.0023), and the rate of blood clot dissolution in hamsters differed from that in humans (percentage lysis 30 min after MA, p = 0.02). Coagulation analysis parameters indicated that prothrombin time (PT) and activated partial thromboplastin time (APTT) were faster in hamsters than in humans (PT, p = 0.0014; APTT, p = 0.03), whereas the Fib content was significantly lower in hamsters than in humans (p < 0.0001). No significant difference was observed in thrombin time (p = 0.1949). In summary, TEG could be used to evaluate thrombosis and bleeding parameters in whole blood samples from hamsters. The platelet function of hamsters closely resembled that of humans, whereas their coagulation function was significantly stronger.

EMPOWERING FUTURE BUILDINGS WITH IOT AND 5G TECHNOLOGY

In the realm of smart buildings, the convergence of Internet of Things (IoT) and 5G technology has unlocked unprecedented opportunities for enhanced building management, efficiency, and sustainability. This project endeavors to harness the potential of IoT sensors and actuators, including LDR, DHT, gas and smoke sensors, PIR, HVAC, lights, servo motors, buzzers, and displays, integrated with Arduino Nano microcontrollers. These microcontrollers serve as end nodes, orchestrating sensor data collection and actuator control. The data collected is transmitted to a gateway device, an ESP32 microcontroller, which interfaces with a wireless Wi-Fi router for connectivity. Leveraging the ThingSpeak platform, the gateway transmits data to the cloud, facilitating real-time monitoring and analysis. This system promises to revolutionize building management by enabling remote monitoring, predictive maintenance, energy optimization, and enhanced occupant comfort and safety. Through rigorous testing and optimization, this project aims to demonstrate the feasibility and effectiveness of IoT and 5G technology integration in empowering future building infrastructures. Keywords— Heating, Ventilation, and Air Conditioning (HVAC),Light Dependent Resistor (LDR), (Digital Humidity and Temperature(DHT)

Comprehensive calibration and laboratory validation of a Micro Electromechanical System sensor–based flexible inclinometer

Abstract Real-time monitoring of slopes, tunnels, and dams is important for ensuring the long-term stability and reliability of such structures. Despite the successes of current technologies in many applications, a gap still exists in certain areas such as precision in steep slopes and complex soil conditions. This study has designed a flexible inclinometer based on an array of Micro Electromechanical System (MEMS) sensors to enhance the accuracy and flexibility of existing monitoring techniques. The inclination angle of each flexible inclinometer measurement unit was measured to monitor the horizontal or vertical displacement of the target structural body. We used the Levenberg–Marquardt (LM) algorithm for optimizing the MEMS sensors based calibration and designed multiple experiments to test the accuracy of the proposed method. Experimental results show that the calibrated flexible inclinometer measurement unit has an inclination angle of less than 0.04° and the accuracy of the flexible inclinometer lies within ±0.4 mm in the horizontal attitude and 1.6 mm in the vertical attitude. Our research has developed a novel tool for geotechnical engineering monitoring that can aid in increasing the precision of real-time assessment and prediction of structural stability.&amp;#xD;

Online boxplot derived outlier detection

AbstractOutlier detection is a widely used technique for identifying anomalous or exceptional events across various contexts. It has proven to be valuable in applications like fault detection, fraud detection, and real-time monitoring systems. Detecting outliers in real time is crucial in several industries, such as financial fraud detection and quality control in manufacturing processes. In the context of big data, the amount of data generated is enormous, and traditional batch mode methods are not practical since the entire dataset is not available. The limited computational resources further compound this issue. Boxplot is a widely used batch mode algorithm for outlier detection that involves several derivations. However, the lack of an incremental closed form for statistical calculations during boxplot construction poses considerable challenges for its application within the realm of big data. We propose an incremental/online version of the boxplot algorithm to address these challenges. Our proposed algorithm is based on an approximation approach that involves numerical integration of the histogram and calculation of the cumulative distribution function. This approach is independent of the dataset’s distribution, making it effective for all types of distributions, whether skewed or not. To assess the efficacy of the proposed algorithm, we conducted tests using simulated datasets featuring varying degrees of skewness. Additionally, we applied the algorithm to a real-world dataset concerning software fault detection, which posed a considerable challenge. The experimental results underscored the robust performance of our proposed algorithm, highlighting its efficacy comparable to batch mode methods that access the entire dataset. Our online boxplot method, leveraging dataset distribution to define whiskers, consistently achieved exceptional outlier detection results. Notably, our algorithm demonstrated computational efficiency, maintaining constant memory usage with minimal hyperparameter tuning.

IoT Based Antenna Positioning System

An IOT-based antenna’s position system aimed at optimizing connectivity. Through IoT-enabled actuators, the system enables remote adjustment of antenna orientation, managed by a central control unit. Real-time monitoring ensures accurate alignment for optimal signal reception, enhancing communication reliability. Key advantages include remote accessibility, allowing users to control antennas from anywhere with internet connectivity, and scalability, facilitating seamless integration into existing infrastructure. By leveraging IoT technology, the system empowers users with enhanced control over wireless communication networks, offering efficient and reliable antenna positioning solutions across diverse applications.

IoT Based Monitoring and Maintenance of Highway Bridges using WSN

This paper presents a WSN-based IOT-based bridge monitoring system. The automated real-time bridge health monitoring system has been made possible by sophisticated modifications to sensor technology; this system will aid in disaster management. The technology used in this development is a wireless sensor network (WSN). This system uses a variety of sensors to continually monitor the state of the bridge. These sensors include an accelerometer to identify jerks in the bridge, a vibration sensor to identify vibrations occurring on the bridge, a flex sensor to identify bends in the bridge, and a water level sensor to identify the water level. To determine the weight on the bridge, use the load sensor. The barrier is detected by an IR sensor. Only the buzzer will activate when these are high. Additionally, a microcontroller processes the data from many sensors and transmits it to a server and management system so that real-time bridge condition monitoring via a mobile device using the GSM model is possible

Novel Efficient Lipid-Based Delivery Systems Enable a Delayed Uptake and Sustained Expression of mRNA in Human Cells and Mouse Tissues

Over the past decade, mRNA-based therapy has displayed significant promise in a wide range of clinical applications. The most striking example of the leap in the development of mRNA technologies was the mass vaccination against COVID-19 during the pandemic. The emergence of large-scale technology and positive experience of mRNA immunization sparked the development of antiviral and anti-cancer mRNA vaccines as well as therapeutic mRNA agents for genetic and other diseases. To facilitate mRNA delivery, lipid nanoparticles (LNPs) have been successfully employed. However, the diverse use of mRNA therapeutic approaches requires the development of adaptable LNP delivery systems that can control the kinetics of mRNA uptake and expression in target cells. Here, we report effective mRNA delivery into cultured mammalian cells (HEK293T, HeLa, DC2.4) and living mouse muscle tissues by liposomes containing either 1,26-bis(cholest-5-en-3β-yloxycarbonylamino)-7,11,16,20-tetraazahexacosane tetrahydrochloride (2X3) or the newly applied 1,30-bis(cholest-5-en-3β-yloxycarbonylamino)-9,13,18,22-tetraaza-3,6,25,28-tetraoxatriacontane tetrahydrochloride (2X7) cationic lipids. Using end-point and real-time monitoring of Fluc mRNA expression, we showed that these LNPs exhibited an unusually delayed (of over 10 h in the case of the 2X7-based system) but had highly efficient and prolonged reporter activity in cells. Accordingly, both LNP formulations decorated with 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[amino(polyethylene glycol)-2000] (DSPE-PEG2000) provided efficient luciferase production in mice, peaking on day 3 after intramuscular injection. Notably, the bioluminescence was observed only at the site of injection in caudal thigh muscles, thereby demonstrating local expression of the model gene of interest. The developed mRNA delivery systems hold promise for prophylactic applications, where sustained synthesis of defensive proteins is required, and open doors to new possibilities in mRNA-based therapies.

Antiepileptic drug-loaded and multifunctional iron oxide@silica@gelatin nanoparticles for acid-triggered drug delivery

The current study developed an innovative design for the production of smart multifunctional core-double shell superparamagnetic nanoparticles (NPs) with a focus on the development of a pH-responsive drug delivery system tailored for the controlled release of Phenytoin, accompanied by real-time monitoring capabilities. In this regard, the ultra-small superparamagnetic iron oxide@silica NPs (IO@Si MNPs) were synthesized and then coated with a layer of gelatin containing Phenytoin as an antiepileptic drug. The precise saturation magnetization value for the resultant NPs was established at 26 emu g-1. The polymeric shell showed a pH-sensitive behavior with the capacity to regulate the release of encapsulated drug under neutral pH conditions, simultaneously, releasing more amount of the drug in a simulated tumorous-epileptic acidic condition. The NPs showed an average size of 41.04 nm, which is in the desired size range facilitating entry through the blood–brain barrier. The values of drug loading and encapsulation efficiency were determined to be 2.01 and 10.05%, respectively. Moreover, kinetic studies revealed a Fickian diffusion process of Phenytoin release, and diffusional exponent values based on the Korsmeyer-Peppas equation were achieved at pH 7.4 and pH 6.3. The synthesized NPs did not show any cytotoxicity. Consequently, this new design offers a faster release of PHT at the site of a tumor in response to a change in pH, which is essential to prevent epileptic attacks.