System and method for detecting hydrogen peroxide using magnetically-driven iron oxide nanozymes

Abstract

The present disclosure provides a system and method for rapid and accurate detection of hydrogen peroxide (H₂O₂) using a colorimetric reaction with an ABTS buffer solution on a digital microfluidic platform. In the present disclosure, catalytic magnetic nanoparticles and a rotating magnetic field accelerate the reaction, while an RGB sensor detects the color change. A mobile application may analyze the data using advanced algorithms to determine H₂O₂ concentration. This system offers high accuracy, speed, and portability for medical, industrial, and environmental applications.

Description

DescriptionTitle of Invention: System and Method for Detecting Hydrogen Peroxide Using Magnetically-Driven Iron Oxide Nanozymes Technical Field

[0001] The present invention relates to the fields of chemical analysis and microfluidic technology. More specifically, it pertains to systems and methods for the detection and analysis of hydrogen peroxide using nanozymes and digital microfluidics. The invention is particularly suited for applications in clinical diagnostics, environmental monitoring, and industrial processes requiring precise and rapid hydrogen peroxide detection.Background Art

[0002] The detection of hydrogen peroxide (H2O2) is crucial across diverse sectors, including clinical diagnostics, environmental monitoring, and food safety. In clinical diagnostics, monitoring blood hydrogen peroxide levels is vital for diagnosing and managing oxidative stress-related diseases like cancer, cardiovascular diseases, and neurodegenerative disorders. Elevated H2O2levels indicate oxidative damage to cells and tissues, providing essential insights for disease prognosis and therapeutic strategies.

[0003] Hydrogen peroxide is an important marker of oxidative stress and environmental pollution in environmental monitoring. It is naturally produced through processes like photochemical reactions and microbial metabolism but can also result from anthropogenic sources such as industrial emissions and wastewater discharge. Detecting H2O2is crucial for assessing pollution in water, soil, and air, enabling effective environmental monitoring and remediation efforts.

[0004] In the food industry, hydrogen peroxide's strong oxidizing properties make it an essential agent for disinfection and preservation. It is widely used to ensure food safety and extend product shelf life. Accurate detection of residual H2O2ensures product quality and compliance with safety regulations governing food processing and storage. There are numerous analytical methods for detecting hydrogen peroxide, each offering specific advantages and limitations. Common techniques include electrochemical detection, where hydrogen peroxide undergoes catalyticoxidation at electrodes, and colorimetric assays that rely on chromogenic reactions. Additionally, chemiluminescence methods utilize the emission of light during H2O2- driven reactions. However, these techniques often face challenges such as limited sensitivity, interference from other reactive species, and the need for complex instrumentation.

[0005] Numerous analytical methods have been developed for detecting hydrogen peroxide, each offering distinct advantages and limitations. Common approaches include electrochemical detection, where hydrogen peroxide undergoes catalytic oxidation at electrodes, and colorimetric assays based on chromogenic reactions. Chemiluminescence methods, which rely on light emission from H2O2-driven reactions, are also widely employed. However, these methods often face challenges such as limited sensitivity, interference from reactive species, and the need for complex instrumentation.

[0006] Traditional methods such as titrimetric analysis, spectrophotometry, and electrochemical techniques have been extensively used due to their accuracy and sensitivity. However, these approaches frequently require complex instrumentation and tedious sample preparation, making them less suitable for routine or on-site analysis. For example, titrimetric analysis, while reliable, is time-consuming and necessitates skilled analysts, limiting its practicality for high-throughput applications.

[0007] Spectrophotometry, which relies on the formation of colored complexes during reactions between H2O2and specific reagents, offers high sensitivity in detecting absorbance changes. However, this method can be prone to inaccuracies due to interference from other substances present in complex samples, potentially leading to false positives or erroneous quantifications.

[0008] Electrochemical methods, such as amperometry and voltammetry, are commonly used to measure the oxidation or reduction of hydrogen peroxide at electrode surfaces. These techniques enable rapid and highly sensitive detection with low detection limits. Despite their advantages, electrochemical methods often require sophisticated instrumentation, frequent calibration, and high maintenance, limiting their feasibility for portable or on-site applications.

[0009] In recent years, portable colorimetric sensing devices have gained attention as a practical option for the rapid and simple detection of various analytes, including hydrogen peroxide. Colorimetry is based on the quantitative analysis of light absorption by colored complexes formed through chemical reactions. The simplicity and user-friendly nature of colorimetric methods make them ideal for onsite analysis without the need for specialized equipment. However, traditional colorimetric techniques also face limitations, such as the requirement for analytespecific chromogenic reagents or dyes, the use of large sample volumes, and, critically, a lack of selectivity due to potential interference from other reactive substances in the sample.Summary of Invention

[0010] This summary is intended to provide an overview of the subject matter of the present disclosure, and is not intended to identify essential elements or key elements of the subject matter, nor is it intended to be used to determine the scope of the claimed implementations. The proper scope of the present disclosure may be ascertained from the claims set forth below in view of the detailed description and the drawings.

[0011] In a general aspect, the present disclosure relates to a hydrogen peroxide detection system (10) and method designed to accurately measure hydrogen peroxide concentration in a solution using a digital microfluidic platform. The hydrogen peroxide detection system (10) can include a digital microfluidic platform (200) configured to manipulate fluid droplets, comprising a first droplet (202) containing a buffer solution comprising ABTS (2,2 ' -azino-bis(3- ethylbenzothiazoline-6-sulfonate) and a second droplet (203) containing a hydrogen peroxide solution, wherein the digital microfluidic platform (200) may include patterned electrodes for precise droplet movement and merging.

[0012] In one implementation, iron oxide nanoparticles (204) can integrate into the first droplet (202), wherein the iron oxide nanoparticles (204) exhibit catalytic properties to enhance the reaction rate between the hydrogen peroxide and the buffer solution,

[0013] In one implementation, at least one magnetic actuator (300), positioned beneath the digital microfluidic platform (200), can be configured to generate a rotational magnetic field for inducing movement of the iron oxide nanoparticles(204) within a merged droplet (205) formed by the first and second droplets, ensuring thorough mixing of the solutions.

[0014] In one aspect, a color sensor (400) strategically positioned to detect the color change resulting from the reaction between the hydrogen peroxide and the buffer solution in the merged droplet (205) wherein the digital microfluidic platform (200) and the sensor (400) are enclosed by a lid (102) that aligns flush with a working surface (104).

[0015] In another aspect of the present disclosure, the hydrogen peroxide detection system (10) may further comprise a data processing module, comprising a wireless communication unit for transmitting the detected data from the color sensor (400) and a machine learning-enabled device configured to analyze the transmitted data.Technical Problem

[0016] The technical problem that this invention aims to solve stems from the significant limitations of existing methods for hydrogen peroxide (H2O2) detection, particularly in terms of sensitivity, selectivity, ease of use, portability, and the ability to perform rapid, real-time analysis. These limitations are problematic in various fields, including clinical diagnostics, environmental monitoring, and industrial applications. The technical problem can be expanded and detailed as follows:

[0017] Traditional methods for hydrogen peroxide detection, such as titrimetric analysis, spectrophotometry, and electrochemical techniques, often suffer from limited sensitivity. For example, while spectrophotometry offers high sensitivity, it is prone to interference from other substances in the sample, leading to inaccuracies. Moreover, many traditional methods frequently require large sample volumes and specialized reagents to detect low concentrations of hydrogen peroxide, which limits their effectiveness in detecting trace amounts of H2O2in complex environments like biological fluids or environmental samples.

[0018] Many traditional detection methods, particularly spectrophotometric and colorimetric approaches, lack the necessary selectivity to accurately measure hydrogen peroxide in complex sample matrices. Interfering substances — such as other reactive oxygen species, organic compounds, or ions — often compete with or obscure the detection of H2O2, resulting in false positives or inaccurate quantification. This lack of selectivity compromises the reliability of these methods,especially in applications requiring high accuracy, such as medical diagnostics or pollution monitoring.

[0019] Classical methods such as electrochemical detection (e.g., amperometry and voltammetry) and titrimetric analysis rely on sophisticated and bulky instrumentation, which limits their portability and adaptability for field or point-of- care (POC) applications. These instruments require regular calibration and maintenance, making them impractical for routine or on-site analysis where rapid results are needed. Moreover, the complexity of these systems necessitates skilled operators, further restricting their use in low-resource settings or by non-specialists.

[0020] The sample preparation and detection processes in traditional methods are often tedious and time-consuming. Techniques like titrimetric analysis require a skilled analyst, and many spectrophotometric methods demand extensive sample preparation to avoid interference, both of which delay the acquisition of results. In clinical and environmental applications where rapid decision-making is crucial, such delays are a significant disadvantage, making these methods unsuitable for real-time or high-throughput applications.

[0021] Natural enzyme-based detection systems, commonly used in hydrogen peroxide assays, are expensive to produce and require strict storage conditions to maintain enzyme activity. These enzymes are sensitive to temperature, pH, and other environmental factors, limiting their stability and lifespan. Furthermore, the purification and extraction processes involved in obtaining natural enzymes significantly increase the overall cost of the detection system. This presents a problem for large-scale or cost-sensitive applications, such as environmental monitoring or industrial quality control.

[0022] Many traditional hydrogen peroxide detection systems are not designed for portability or on-site analysis. For instance, electrochemical methods require stationary, lab-based equipment, while titrimetric methods are inherently nonportable due to their reliance on time-consuming procedures and large reagent volumes. These limitations prevent the use of such systems in point-of-care diagnostics or in-field testing, where portability, ease of use, and real-time detection are essential.

[0023] While microfluidics has emerged as a promising technology for miniaturizing detection systems, the integration of traditional methods with microfluidic platforms has been challenging. Classical assays are not easily adaptable to small volumes and often require specialized equipment and reagents, making it difficult to leverage the full advantages of microfluidic technology (e.g., reduced reagent consumption, faster processing times, and higher efficiency). This problem is exacerbated by the difficulty in manipulating traditional enzymes or bulky detection reagents within microfluidic systems.

[0024] Existing hydrogen peroxide detection methods like colorimetric are often slow to provide results, which can be problematic in applications requiring real-time monitoring, such as in clinical diagnostics or environmental pollution tracking. Methods like titrimetric and spectrophotometric analysis involve multiple steps, each contributing to delays in obtaining actionable data. This limits their use in scenarios where immediate detection and response are crucial.

[0025] Traditional detection systems are not designed to take advantage of modern, portable technologies, such as smartphone-based diagnostics and machine learning for real-time data analysis. These technologies have become increasingly important for point-of-care diagnostics and fieldwork, where results need to be generated quickly and easily interpreted by non-specialists. The lack of integration with portable devices and automated data analysis tools further limits the utility of existing hydrogen peroxide detection methods in today’s rapidly evolving technological landscape.

[0026] Recent advancements in nanotechnology have paved the way for the development of nanoparticle-based colorimetric sensors. These sensors act as signal amplifiers, enhancing both the sensitivity and selectivity of the assays. By incorporating nanoparticles into the detection systems, it becomes possible to achieve more accurate and reliable measurements, even in complex sample environments. This new generation of nano-enabled sensors offers promising solutions for overcoming the limitations of classical colorimetric methods, providing a more robust platform for hydrogen peroxide detection across diverse applications.

[0027] Eminent metal nanoparticles, such as gold and silver, have been extensively used in colorimetric sensing due to their unique optical and catalytic properties.This can be attributed to their exceptional sensitivity, linked to their tunable plasmonic properties when exposed to minute concentrations of analytes. These nanoparticles exhibit surface plasmon resonance, which enhances the optical signal upon interaction with target molecules, making them highly effective for sensitive detection methods.Solution to Problem

[0028] The present invention addresses the challenges of rapid and accurate hydrogen peroxide detection by introducing an innovative system that integrates iron oxide nanoparticles with digital microfluidic technology and advanced data analysis methods. Iron oxide nanoparticles, with their dual functionality, act as nanozyme catalysts, leveraging their peroxidase-like activity to accelerate the oxidation of ABTS buffer in the presence of hydrogen peroxide. Simultaneously, their rotational motion — driven by a magnetic field generated by an induction motor — enhances particle collisions within the solution, significantly boosting reaction efficiency. This synergistic effect dramatically reduces reaction times from 10 minutes to less than one minute, far surpassing conventional methods. The inherent advantages of iron oxide nanoparticles, including stability, biocompatibility, low-cost synthesis, and magnetic properties, make them ideal for integration into digital microfluidic devices. Digital microfluidics provides precise control of small fluid volumes on a hydrophobic surface via electrical actuation, enabling efficient droplet merging and mixing. This system’s closed design ensures reliable fluid manipulation, eliminates external light interference, prevents data loss, and captures all colorimetric changes resulting from the reaction via an integrated optical sensor. By combining the high catalytic sensitivity of iron oxide nanoparticles and magnetically stirring method with the precise fluid control offered by digital microfluidics, the invention creates a portable, efficient, and user-friendly sensor platform. This system supports real-time detection and quantification of hydrogen peroxide with high sensitivity and accuracy. Furthermore, integrating smartphone-based machine learning analysis enables real-time data processing and interpretation, resulting in a cost-effective solution for diverse applications such as point-of-care diagnostics, environmental monitoring, and industrial quality control. This innovative approach, which combines catalytic enhancement, advanced fluid manipulation, and robust data analysis in a closed system,represents a significant advancement in hydrogen peroxide detection technology, offering speed, sensitivity, and practicality across various fields.

[0029] Building on a long history of innovation in microfluidic mixing techniques, the system now integrates a cutting-edge, contactless stirring mechanism that markedly accelerates reaction kinetics within the droplet. Traditional methods, such as mechanical stirrers adapted from bulk chemistry, often suffer from direct contact issues that can lead to contamination, while ultrasonic stirring — though effective in some scenarios — tends to be energy-intensive and may even disrupt sensitive microfluidic components. In contrast, this novel approach leverages a pair of strategically positioned magnets beneath the DMF substrate; when rotated by an external motor, they generate a precise circular motion within the droplet. This motion increases the effective surface area of iron oxide nanoparticles (ionps) and facilitates rapid and efficient mixing by promoting frequent collisions between reactants and ionps, thereby expediting the colorimetric reaction without any physical contact. Historically, early microfluidic devices struggled with achieving uniform mixing due to scale limitations and contamination risks. This invention represents a significant evolution, drawing on earlier research in magnetic actuation and droplet dynamics to offer a non-invasive, energy-efficient, and highly controllable solution. The ability to fine-tune stirring intensity via motor speed adjustments further enhances its suitability for high-throughput diagnostic applications, setting it apart from conventional methods by combining precision, scalability, and reliability.Advantageous Effects of Invention

[0030] The invention integrates iron oxide nanozymes with digital microfluidic technology, offering a highly sensitive, portable, and efficient system for detecting hydrogen peroxide. By leveraging the superior catalytic activity of nanozymes, the system achieves enhanced sensitivity and comparable detection limits to conventional methods, while also ensuring high selectivity to minimize interference from other substances in complex samples. Additionally, the nanozymes exhibit exceptional stability across various environmental conditions, including temperature and pH variations, enabling reliable and rapid detection within the same concentration range as existing colorimetric methods but with significantly faster response times.

[0031] The integration with digital microfluidics provides a compact and cost-effective platform that reduces reliance on bulky equipment and skilled operators, making it ideal for on-site or point-of-care applications. The precise control over microliterscale fluid volumes significantly reduces sample and reagent requirements, leading to greater resource efficiency and lower operational costs. Furthermore, the magnetic properties of the nanozymes enable precise manipulation and separation within the system, enhancing the reliability of the detection process by reducing cross-contamination and ensuring optimal reaction conditions.

[0032] This innovative system also supports modem data integration through smartphone-based machine learning, allowing users to analyze data in real-time, remotely monitor results, and share findings effortlessly. Such features make the system accessible and versatile for diverse applications, including clinical diagnostics, environmental monitoring, and industrial quality control. The flexibility of the digital microfluidic platform, combined with the tunable nature of nanozymes, enables the adaptation of this invention for detecting other analytes, further expanding its applicability in various scientific and industrial fields.Brief Description of Drawings

[0033] The novel features which are believed to be characteristic of the present disclosure, as to its structure, organization, use, and method of operation, together with further objectives and advantages thereof, will be better understood from the following drawings in which a presently preferred embodiment of the present disclosure will now be illustrated by way of example. It is expressly understood, however, that the drawings are for illustration and description only and are not intended as a definition of the limits of the present disclosure. Embodiments of the present disclosure will now be described by way of example in association with the accompanying drawings in which:

[0034] [FIG. 1] illustrates an example schematic representation of an integrated microfluidic system, consistent with one or more exemplary embodiments of the present disclosure.

[0035] [FIG. 2] illustrates a cross-section view of the integrated microfluidic system consistent with one or more exemplary embodiments of the present disclosure.

[0036] [FIG. 3] illustrates an exemplary process for fabricating a digital microfluidic substrate, involving the formation of patterned electrodes, dielectric insulation, and the application of a hydrophobic coating, consistent with one or more embodiments of the present disclosure.

[0037] [FIG. 4] illustrates a hydrogen peroxide detection method utilizing iron oxide nanoparticles and an ABTS buffer on a digital microfluidic (DMF) platform, consistent with one or more exemplary embodiments of the present disclosure.

[0038] [FIG. 5A] illustrates the prediction results from the RF model, showcasing the actual vs predicted plot, consistent with one or more exemplary embodiments of the present disclosure.

[0039] [FIG. 5B] presents the correlation heatmap of features employed in the RF model, highlighting the relationships between the extracted features, consistent with one or more exemplary embodiments of the present disclosure.

[0040] [FIG. 6] illustrates color changes over 60 seconds for different concentrations of hydrogen peroxide (H2O2), consistent with one or more exemplary embodiments of the present disclosure.Description of Embodiments

[0041] The novel features that are believed to be characteristic of the present disclosure, as to its structure, organization, use, and method of operation, together with further objectives and advantages thereof, will be better understood from the following discussion.

[0042] Aspects of the invention are illustrated by way of example and not by way of limitation in the Figures of the accompanying drawings in which references indicate similar elements. It should be noted that references to “an” and “one” embodiment in this disclosure are not necessarily to the same embodiment, and such references mean at least one. In the following description, numerous specific details are set forth to provide a thorough description of the invention. However, it will be apparent to one skilled in the art that the invention may be practiced without these specific details. In other instances, well-known features have not been described in detail so as not to obscure the invention.

[0043] The following description provides a detailed overview of various embodiments of a hydrogen peroxide detection system 10, designed for the rapid and accurate quantification of hydrogen peroxide (H2O2) concentrations in a solution. The hydrogen peroxide detection system 10 of the present disclosure, can detect a color change resulting from the interaction of hydrogen peroxide with a specific buffer solution containing ABTS (2,2 ' -azino-bis(3-ethylbenzothiazoline-6- sulfonate)). ABTS is well-known for its ability to undergo a distinct colorimetric shift in the presence of hydrogen peroxide. The degree of this color change is directly proportional to the concentration of hydrogen peroxide in the solution, making it a reliable method for analytical measurements across diverse applications.

[0044] In one embodiment of the present disclosure, the hydrogen peroxide detection system 10 may incorporate an integrated microfluidic system 100 that facilitates the controlled reaction between the hydrogen peroxide-containing solution and the ABTS buffer. The microfluidic system 100 acts as the central component of the detection system, enabling efficient and automated colorimetric analysis.

[0045] FIG. 1 provides a detailed depiction of one embodiment of the microfluidic system 100. The microfluidic system 100 may comprise a working surface 104 and a lid 102, with a working gap 106 between them. This configuration creates a controlled environment in which chemical reactions can be observed and quantified accurately. The microfluidic system 100 operates by using precise electrical actuation to move and manipulate fluid droplets on the working surface 104.

[0046] In one embodiment, the lid 102, as illustrated in FIG. 1, may house a color sensor 400 designed to detect the color changes that occur during the reaction. The sensor 400 is strategically positioned within the lid 102, aligned to capture the color change occurring within the droplets 202 and 203 as they pass through the digital microfluidic platform 200. The design of the working surface 104 and the lid 102 can be critical to the system’s overall functionality. A key feature of this design is a recess 103 integrated into the lid 102, which allows for the precise placement and installation of the sensor 400. The sensor 400, hereinafter referred to as “RGB sensor 400,” may be an RGB sensor securely positioned within the recess 103, ensuring that it is well-aligned with the digital microfluidic platform 200 and capable of detecting subtle color changes that correspond to variations in hydrogen peroxide concentration.

[0047] When the lid 102 is closed, the outer edges of the lid 102 may align flush with the surface of the working surface 104, securing the digital microfluidic platform 200 and the sensor 400 within the recess 103. This alignment not only enhances the mechanical stability of the system but also ensures that the reaction between the buffer and hydrogen peroxide occurs under controlled conditions, with consistent monitoring throughout the process. By minimizing the risk of misalignment or sensor displacement, this feature enables the system to perform reliably and accurately, making it suitable for a wide range of analytical applications.

[0048] In one embodiment, as illustrated in FIG. 1, the integrated microfluidic system 100 may include a digital microfluidic platform 200, which serves as the foundation for the reaction. The digital microfluidic platform 200 enables precise control over the movement, placement, and merging of droplet 202 containing the ABTS buffer and droplet 203 containing the hydrogen peroxide solution. This platform may feature functionalities such as droplet manipulation, automated mixing, and thermal regulation to optimize reaction conditions, thereby ensuring maximum speed, sensitivity, and accuracy in the detection process.

[0049] In one embodiment, the digital microfluidic platform 200, as shown in the FIG.2, may be equipped with a series of patterned electrodes 206 that form the primary interface for manipulating fluid droplets 202 and 203. These electrodes 206 are configured to apply controlled electrical signals to the droplets, enabling precise positioning and merging of the droplets 202 and 203 that contain the ABTS buffer and hydrogen peroxide.

[0050] In one embodiment, the reaction rate between hydrogen peroxide and the buffer solution can be enhanced to achieve rapid detection and synthesis of hydrogen peroxide. Magnetic nanoparticles 204 may be integrated into the system to facilitate efficient mixing of the two solutions. While various types of nanoparticles can be utilized, the invention specifically focuses on nanoparticles with catalytic properties that accelerate the reaction between the ABTS buffer and hydrogen peroxide. By leveraging these catalytic properties, the reaction is improved in both speed and efficiency, enabling faster and more accurate detection of hydrogen peroxide concentration.

[0051] In some embodiments, iron oxide nanoparticles 204 can be selected for their unique catalytic properties and magnetic responsiveness. These nanoparticles are added to the buffer droplet 202 and, upon merging with a droplet 203 containing hydrogen peroxide, they participate in the catalytic reaction.

[0052] Additionally, the magnetic properties of the iron oxide nanoparticles 204 offer a distinct operational advantage. Integrated magnets positioned beneath the DMF substrate enable efficient separation and recovery of the iron oxide nanoparticles 204 after each reaction. Although the reagents (e.g., ABTS and H2O2) are replaced for subsequent tests, the iron oxide nanoparticles 204 referred to nanozyme ionps, remain intact and free from waste. This reusability not only enhances the cost efficiency of the system by reducing the need for fresh reagents but also supports a sustainable and low-cost detection method.

[0053] Referring to FIGs. 1 and 2, in an exemplary embodiment, to facilitate the optimal movement and distribution of the iron oxide nanoparticles 204 within the droplet 202 and merge with droplet 203, a magnetic actuator 300 is employed beneath the digital microfluidic platform 200. The magnetic actuator 300 generates a rotating magnetic field that causes the magnetic nanoparticles 204 to move within the droplets 202 and merge with droplet 203, ensuring thorough mixing and promoting the catalytic reaction between the hydrogen peroxide and the buffer solution. By accelerating the movement and interaction of the reactive components, this arrangement significantly enhances the overall reaction rate, enabling a quicker synthesis of hydrogen peroxide and, therefore, a faster and more efficient detection process.

[0054] In one embodiment, as depicted in FIG. 2, at least two activated magnets 302 can be placed beneath the digital microfluidic platform 200, with the magnets being preferably supported by a support structure 304. These magnets 302 generate the magnetic field necessary to induce movement in the iron oxide nanoparticles 204 within the droplets.

[0055] In another embodiment, the activated magnets 302 may rotate in a circular motion around each other on the support structure 304, inducing complete rotational movement of the magnetic nanoparticles 204 within the droplets without direct physical contact. This motion enhances mixing and accelerates the reactionrate between the two droplets 202 and 203 due to the circular movement of inducted iron oxide nanoparticles.

[0056] Referring to FIG. 1, in an exemplary embodiment, the microfluidic system 100 may further comprise a combination of an ESP32 microcontroller 600 and a TIP122 transistor 500 in an integrated circuit configuration. The microfluidic system 100 can be designed to achieve precise detection and efficient data processing while ensuring operational stability and reliability.

[0057] In one embodiment, the system may be powered by a 14 V direct current (DC) power source, which is regulated to 3.3 V using a buck converter. The regulated voltage ensures a stable power supply, necessary for the consistent operation of the ESP32 microcontroller 600 and associated components.

[0058] The ESP32 microcontroller 600 serves as the central processing unit of the system, controlling various subsystems and processing data received from an RGB sensor 400. In this embodiment, the ESP32 manages the activation of a high- voltage converter and a motor. The high-voltage converter is configured to supply power to digital microfluidic electrodes 206, which facilitate the merging of droplets containing hydrogen peroxide and ABTS reagents. The microcontroller 500 achieves this through a TIP122 transistor 600, employed as a switching element. A 100-ohm resistor is connected in series with the base of the TIP122 transistor 600 to limit the current, thereby protecting the transistor from potential damage.

[0059] In addition, in some embodiments, the hydrogen peroxide detection system 10 may further comprise a connected device, such as a smartphone, for further processing and analysis of the obtained results from the sensor 400. This feature ensures that the data is sent in real-time, enabling immediate feedback to the user. The transmission can occur via Bluetooth, Wi-Fi, or other wireless communication protocols, and may be supported by a dedicated application installed on the smartphone. In one embodiment, the smartphone may take data’s from the ESP32 microcontroller via Bluetooth connection between themselves. The mobile application receives the sensor 400 data, analyses the intensity of the detected color change, and correlates it with the concentration of hydrogen peroxide in the solution. The application may include a pre-programmed calibration curve ormachine learning algorithm that may enable it to interpret the sensor data and display the corresponding hydrogen peroxide concentration.

[0060] Additionally, the application may provide visual indicators, such as numerical values or graphs, to assist the user in interpreting the results.

[0061] In one exemplary embodiment, upon merging droplet 203, containing hydrogen peroxide (H2O2), with droplet 202, containing ABTS and dispersed magnetic nanoparticles 204, the resulting merged droplet 205 undergoes a colorimetric transition from colorless to a blue-like hue due to the oxidation of ABTS. However, the intrinsic absorbance of the magnetic nanoparticles 204, which appear black, introduces significant noise in the RGB sensor 400 output — particularly affecting the red channel — complicating accurate analyte detection. To mitigate this interference, raw red-channel intensity values may be continuously acquired by an ESP32 microcontroller 600 and wirelessly transmitted to a smartphone application via Bluetooth. Within the application, the raw sensor data undergoes preprocessing using a Gaussian filter with an experimentally optimized sigma (o) value of 8, which was determined to achieve an optimal balance between noise suppression and signal fidelity. After preprocessing, the filtered red-channel data and extracted features are analyzed using a pre-trained Random Forest machine learning model for quantitative H2O2prediction. The combined benefits of effective noise filtering via the smartphone application and the reusability of the nanozyme ionps underscore the potential of this approach for reliable, high-throughput diagnostic applications in resource-limited settings.

[0062] In an exemplary embodiment, the microcontroller 600 may collect raw red-value data from the sensor 400 every second over a one-minute interval and may transmit these measurements to a smartphone. On the smartphone, the Gaussian filter is applied to the received signal to reduce noise, followed by feature extraction on the filtered data. The resulting red-value measurements and the extracted features are then used as input to a machine learning model, which enhances the concentration prediction of hydrogen peroxide. This integrated approach leverages both the temporal resolution of the sensor data and its statistical characteristics to improve the accuracy of hydrogen peroxide concentration estimations.

[0063] The following description provides a detailed overview of a user-friendly, efficient, and rapid method for detecting hydrogen peroxide concentration. Generally, the method may comprise the steps of: providing a digital microfluidic platform 200; dispensing a buffer solution and a hydrogen peroxide solution as separate droplets on a digital microfluidic platform 200; merging the droplets to initiate a colorimetric reaction; detecting the resulting color change using a sensor 400; and analyzing the detected color change to determine the concentration of hydrogen peroxide.

[0064] As depicted in FIG. 3, the fabrication process of the digital microfluidic platform 200 may involve multiple sequential steps, numbered 310 to 370, ensuring precise electrode formation, electrical insulation, and surface hydrophobicity for optimal droplet manipulation.

[0065] The fabrication process may begin with step 310, where a printed circuit board PCB 208 is prepared as the substrate for the microfluidic platform. In one exemplary embodiments of the present disclosure, in step 320, a series of electrodes 206 are patterned onto the PCB surface using a standard photolithographic technique to define electrode locations with high precision. Step 330 may follow with a copper-sulfuric acid solution cleaning step to remove any excess material or contaminants, improving adhesion and surface integrity. An acid etching process may be conducted in step 340, selectively removing unwanted conductive material, leaving behind well-defined electrodes that form the core components responsible for the electrowetting-based movement and manipulation of fluid droplets within the microfluidic platform.

[0066] In one exemplary embodiment of the present disclosure, to prevent electrical short-circuiting and unwanted leakage, a dielectric insulation layer composed of silicone rubber 210 may be applied over the structured electrodes in step 350. This silicone rubber layer 210 may provide effective electrical insulation, ensuring that the signals applied to the electrodes 206 are precisely controlled, thereby enabling reliable and uniform droplet actuation across the platform.

[0067] To further enhance the microfluidic platform’s functionality, a hydrophobic polyethylene terephthalate (PET) thin film 212 may be placed over the dielectric silicone rubber layer 210 in step 360. The PET thin film 212 may impart ahydrophobic surface property to the digital microfluidic platform 200, which is essential for efficient droplet manipulation. The hydrophobic nature of the PET thin film 212 may minimize the surface tension between the droplets and the digital microfluidic platform 200, allowing fluid droplets 202 - 203 to move smoothly across the platform while preventing unintended spreading or merging. This hydrophobic treatment may ensure that the droplets remain confined to designated electrode- controlled regions where the chemical reactions occur, significantly improving reaction efficiency and reproducibility.

[0068] In an exemplary embodiment of the present disclosure, the final fabricated digital microfluidic platform 200 may be obtained in step 370, integrating all these components into a single, functional device that enables precise and contamination-free manipulation of droplets using electrowetting techniques. This configuration not only improves control over droplet positioning but also enhances the reliability and reproducibility of microfluidic chemical reactions.

[0069] The combination of these carefully engineered layers- the conductive electrodes 206, the dielectric silicone rubber layer 210, and the hydrophobic (PET) thin film 212 - works synergistically to create an optimized environment for the digital microfluidics platform 200. This multilayered platform's design ensures that reaction droplets, such as one containing the ABTS substrate, remain stable, precisely positioned, and responsive to electrical actuation. The precise control over droplet movement and isolation is essential for ensuring that the reaction occurs under controlled conditions and that the detection of any colorimetric changes in the droplet can be accurately measured.

[0070] Further details on the method for detecting hydrogen peroxide concentration, according to one or more embodiments of the present disclosure, are illustrated in FIG. 4, specifically in steps 410 to 450. Upon preparation of the digital microfluidic platform 200, a first droplet 202 containing ABTS and a second droplet 203 containing a hydrogen peroxide solution may be placed onto the hydrophobic surface of the platform. In one specific embodiment of the present disclosure, to enhance the reaction efficiency, iron oxide nanoparticles 204, with a diameter of approximately 100 nm and a concentration of 100 pg, are introduced into the ABTS droplet 202 (FIG. 4 (410)).

[0071] As shown in FIG. 4 (420), the two droplets 202 and 203 are merged using electrowetting actuation from the patterned electrodes 206, forming a reaction droplet 205. This merging initiates the catalytic oxidation reaction between ABTS and hydrogen peroxide. To ensure effective mixing and maximize reaction efficiency, step 430 of FIG. 4 introduces a rotating magnetic field generated by a motor-driven magnetic actuator 302, positioned beneath the digital microfluidic platform 200. The motor is controlled by the ESP32 microcontroller 600, which regulates power delivery via a TIP122 transistor 600. To prevent voltage spikes caused by the motor’s inductive load, a 1 N4007 diode is placed across the motor terminals, protecting the circuit and enhancing component longevity.

[0072] In some embodiment of the present disclosure, the magnets 302 are oriented with opposite poles facing upwards, generating a rotational magnetic field that facilitates uniform nanoparticle dispersion in the reaction droplet 205. As shown in FIG. 4 (440), the rotating field induces dynamic motion within the droplet, ensuring thorough mixing of iron oxide nanoparticles 204 and reactants.

[0073] The controlled stirring of nanoparticles 204 is essential for promoting efficient reactant interaction, accelerating the reaction kinetics, increasing the effective surface area of nanoparticles, and ensuring that color change occurs rapidly and uniformly. The optimal stirring speed may be 1000 rpm, as this level of agitation provides sufficient kinetic energy to ensure an even distribution of nanoparticles within the droplet, minimizing aggregation while maintaining high catalytic activity.

[0074] As the reaction progresses, the resulting color change in the reaction droplet 205 is continuously monitored by an RGB color sensor 400, positioned above the droplet. As shown in FIG. 4 (450), the sensor 400 may capture real-time spectral variations in the droplet for a period of one minute, generating data on the reaction’s progression. This data is transmitted to a processing unit, which applies a machine-learning-based algorithm to interpret the hydrogen peroxide concentration with high accuracy.

[0075] The RGB sensor 400 is connected to the ESP32 microcontroller and is positioned to monitor the color changes in the droplet. In this embodiment, the system measures the red channel values, which directly correlate with the concentration of hydrogen peroxide. Upon user initiation through a smartphoneapplication, the ESP32 first activates the high-voltage converter to merge the droplets and then sequentially controls the motor and the RGB sensor 400. Within a predefined period (e.g., 60 seconds), the RGB sensor 400 records the red channel values and transmits the data to the ESP32.

[0076] The data collected by the RGB sensor 400, especially the red channel values, may be transmitted to an ESP32 microcontroller 600. The microcontroller 600 may receive the sensor data and send it to a connected device for further analysis.

[0077] The spectral features extracted from the sensor data may include, but are not limited to, the initial color intensity; the rate of color transition; the color stabilization time; and temporal color shifts. These spectral features will be further elaborated in reference to FIG. 5B.

[0078] On the smartphone, the proper smoothing method filters noisy red values, and advanced data processing algorithms extract key features from the sensor data, such as the normalized initial red value, the minimum red value observed, and the slopes of the color change over time. These features are important indicators of the reaction’s progress and can be used to accurately calculate the concentration of hydrogen peroxide in the sample.

[0079] To predict the hydrogen peroxide concentration, the extracted features with filtered red values may be input into machine learning models, such as the Random Forest (RF) model and the Linear Regression (LR) model. These models are employed to establish a correlation between the features extracted from the sensor data and the hydrogen peroxide concentration. The Random Forest model, due to its capability to handle complex and nonlinear relationships within the data, has shown superior performance compared to the Linear Regression model. The RF model has demonstrated an impressive prediction accuracy of up to 99%, making it highly reliable for estimating hydrogen peroxide concentrations in real-world samples.

[0080] Once the hydrogen peroxide concentration is predicted using the machine learning models, the result is displayed on the smartphone app, providing users with a fast and accurate readout of the concentration level. FIG. 5A illustrates the prediction results from the RF model, showcasing true values of hydrogen peroxide concentration alongside the model's predicted values. Extracted features arerepresented as a correlation heat map, which visually depicts the relationship between the input features of the trained RF model. This visual representation aids the user in understanding how the various input features contribute to each other to form the final prediction, enhancing the accuracy of the results. Regarding to FIG. 5B, correlation heatmap helps visualizing the relationship between all extracted features with respect to hydrogen peroxide concentrations. In the heatmap, (-1) represents very high negative correlation. On the other side, highly positive correlation is represented by (1). This plot provides insight into how each feature correlates to hydrogen peroxide concentration and shows interdependencies between variables which can be seen in FIG. 5B.

[0081] Referring back to FIG. 5B, initial color intensity extracted from the sensor data may be captured through the normalized red value at time zero (Rz0), reflecting the baseline color level of the system. In this figure, the rate of color transition, also extracted from the sensor data, may be determined by measuring the change in red channel intensity (m_R) between the initial time (t0) and key time points, such as when the minimum normalized red value (Rzmin) occurs and up to the plateau time (tp). Additionally, color stabilization time extracted from the sensor data may be defined as the plateau time (tp), indicating when the red channel color levels off and the reaction reaches a steady state, and temporal color shifts extracted from the sensor data may be evaluated through features such as the Area Under the Curve (AUC), individual normalized red values at specific time intervals (Rz0, Rz15, R 30, R 45, Rz60), and overall statistical measures (mean p and standard deviation o) of the red channel intensity.

[0082] FIG. 6 illustrates the color changes over 60 seconds for different concentrations of hydrogen peroxide (H2O2). These color changes serve as crucial indicators of the progress of the chemical reaction, enabling real-time and precise measurement of the hydrogen peroxide concentration. The data obtained are essential for validating the performance of the nanozyme catalysts and assessing the overall design of the system.EXAMPLES

[0083] In one or more exemplary embodiments of the present disclosure, a series of experiments were conducted to optimize key reaction parameters, including thestirring speed of the magnetic actuator 300, the size and concentration of iron oxide nanoparticles 204, and the concentration of the ABTS reagent. The goal of these optimizations was to enhance catalytic efficiency and improve the sensitivity of colorimetric detection in the digital microfluidic platform 200. The following examples illustrate the impact of these variables on the system's performance.

[0084] Example 1: Optimization of Stirring Speed

[0085] In one exemplary embodiment of the present disclosure, to ensure efficient mixing and maximize reaction rates, the rotational speed of the magnetic actuator 300 was optimized. The stirring speed influences the dispersion of iron oxide nanoparticles 204 within the ABTS-containing droplet 202 and their subsequent interaction with hydrogen peroxide.

[0086] Initial experiments may be conducted at 600 rpm and 800 rpm, which resulted in slow discoloration, indicating inefficient mixing and reduced catalytic efficiency. It may be observed that at these speeds, nanoparticle aggregation occurred, leading to non-uniform distribution within the droplet and limiting its catalytic activity.

[0087] In one embodiment, upon increasing the stirring speed to 1000-1200 rpm, the reaction kinetics can be significantly improved. This speed was found to supply sufficient kinetic energy to break up nanoparticle aggregates, ensuring homogeneous dispersion and maximized effective surface area within the droplet. The enhanced interaction between iron oxide nanoparticles and ABTS at this speed may led to an accelerated oxidation reaction, resulting in a clear and rapid colorimetric response detectable by the RGB sensor 400.

[0088] In a specific embodiment, the stirring speed of exactly 1200 rpm was found to be optimal for achieving maximum reaction efficiency. At 1200 rpm, the dispersion of iron oxide nanoparticles 204 was sufficiently controlled, ensuring a balance between enhanced mixing and reduced aggregation. The increased movement of nanoparticles at 1200 rpm resulted in minimal optical interference, avoiding noise in the RGB sensor 400 and providing clear detection of the reaction progress. This stirring speed ensures both efficient catalytic activity and accurate detection by the sensor, making it the preferred operational speed for the reaction.

[0089] At stirring speeds exceeding 1200 rpm according to one exemplary embodiment of the present disclosure, excessive dispersion of iron oxidenanoparticles 204 was observed. The increased movement of black-colored nanoparticles caused unwanted optical interference, reducing the region of interest (ROI) in the RGB sensor 400 and introducing noise into the detection process.

[0090] Example 2: Determination of Optimal Nanoparticle Size and Concentration

[0091] To determine the ideal size and concentration of iron oxide nanoparticles 204, experiments were conducted to evaluate their catalytic activity, dispersibility, and effect on optical detection.Nanoparticle Size

[0092] In one or more exemplary embodiments of the present disclosure, it was observed that smaller nanoparticles (20-30 nm) had a higher tendency to aggregate under stirring conditions. This aggregation significantly reduced the available surface area for catalytic reactions, lowering reaction efficiency and causing delayed and weak colorimetric responses.

[0093] Conversely, larger nanoparticles (greater than 150 nm) exhibited reduced catalytic activity due to a lower surface-area-to-mass ratio and slower Brownian motion, which limited their interaction with hydrogen peroxide.

[0094] Through systematic testing, it was determined that an optimal nanoparticle size range of 80-120 nm provided the best balance between stability, dispersion, and catalytic efficiency. At this size range, the iron oxide nanoparticles 204 remained well-dispersed within the merged droplet 205, ensuring efficient catalysis of the hydrogen peroxide-ABTS reaction. In one specific embodiment, nanoparticles of 100 nm yielded the most consistent results in terms of reaction efficiency and signal clarity.Nanoparticle Concentration

[0095] The concentration of iron oxide nanoparticles 204 was also found to directly affect the colorimetric response. At lower concentrations (50 pg and 70 pg), the catalytic activity was insufficient, resulting in slower reaction rates and delayed color changes.

[0096] At a concentration of 120 g, excessive nanoparticle accumulation was observed, causing the droplet to turn opaque black, which interfered with optical detection by the RGB sensor 400.

[0097] Based on these experimental findings, the optimal concentration range for iron oxide nanoparticles 204 was determined to be 80-125 pg, ensuring a balance between enhanced catalytic activity and minimal interference with sensor readings.

[0098] In a specific embodiment, a concentration of 100 pg yielded the most optimal performance, providing a high reaction rate, strong signal clarity, and minimal optical interference, making it the preferred concentration for precise hydrogen peroxide detection

[0099] Example 3: Optimization of ABTS Concentration

[0100] In one exemplary embodiment, to determine the optimal concentration of ABTS in the buffer solution, experiments were conducted to evaluate its effect on reaction rate and colorimetric response.

[0101] At lower ABTS concentrations, the reaction exhibited delayed and weak color changes, indicating a limited availability of substrate molecules. This resulted in prolonged reaction times and reduced detection sensitivity.

[0102] In contrast, excessively high ABTS concentrations led to overly intense color changes, which caused potential saturation of the RGB sensor 400, reducing the accuracy of colorimetric detection.

[0103] Experimental trials demonstrated that an ABTS concentration of 1.82 mM provided an optimal balance between reaction speed and signal clarity. At this concentration, the reaction between ABTS and hydrogen peroxide reached completion within 60 seconds, generating a distinct, reproducible color change that allowed for precise quantification of hydrogen peroxide concentration, j

Claims

1. Claims

1. i A hydrogen peroxide detection system (10) comprising:- a digital microfluidic platform (200) configured to manipulate fluid droplets, comprising:a first droplet (202) containing a buffer solution comprising ABTS (2,2' -azino-bis(3-ethylbenzothiazoline-6-sulfonate));a second droplet (203) containing a hydrogen peroxide solution; - iron oxide nanoparticles (204) integrated into the first droplet (202), wherein the iron oxide nanoparticles (204) are magnetically responsive to facilitate dynamic mixing during the reaction and exhibit peroxidase- like catalytic activity to accelerate the reaction between hydrogen peroxide and the buffer solution;- at least one magnetic actuator (300) positioned beneath the digital microfluidic platform (200), comprising at least two oppositely oriented rotating magnets (302) configured to generate a rotating magnetic field, ensuring enhanced dispersion and interaction of iron oxide nanoparticles within a merged droplet (205) formed by the first and second droplets;- a color sensor (400), positioned to detect color changes resulting from the reaction in the merged droplet (205);- a data processing module, comprising:a wireless communication unit for transmitting detected data from the color sensor (400);a machine learning-enabled device configured to analyze transmitted data based on filtered color values and features extracted from them over time, wherein the machine learning algorithm predicts hydrogen peroxide concentration with an accuracy of at least 99%, based on a pre-calibrated dataset.

2. The system of claim 1 , wherein the rotating magnetic actuator (300) is configured to generate a rotating magnetic field at a frequency of 1000-1200 RPM, ensuring homogeneous dispersion of iron oxide nanoparticles (204) within the merged droplet (205) and enhanced reaction kinetics.

3. The system of claim 1, wherein the iron oxide nanoparticles (204) have an optimized size range of 80-120 nm.

4. The system of claim 1, wherein the digital microfluidic platform (200) and the color sensor (400) are enclosed in a sealed housing to eliminate external light interference and maintain a controlled environment for stable reaction conditions.

5. The system of claim 1, wherein the digital microfluidic platform (200) comprises patterned electrodes, configured to precisely control the movement, merging, and separation of droplets via electrowetting actuation.

6. The system of claim 1 , wherein the color sensor (400) is an RGB sensor configured to measure real-time spectral intensity variations in the merged droplet (205).

7. The system of claim 1 , further comprising a microcontroller (600) configured to process the sensor data and transmit it to a connected device via wireless communication.

8. The system of claim 1, wherein the connected device comprises a smartphone, equipped with- a dedicated application configured to receive data from the wireless communication unit;- a machine learning algorithm processing to analyze sensor data; and - display graphical representations of reaction progress and predicted hydrogen peroxide concentration.

9. The system of claim 1, further comprising a machine learning module integrated into the data processing unit to predict hydrogen peroxide concentration with enhanced accuracy and configured to process the extracted features comprising initial color intensity, rate of color transition, color stabilization time, and temporal color shifts.

10. The system of claim 1 , wherein the iron oxide nanoparticles (204) are used at an optimal concentration of 80-125.

11. The system of claim 1, wherein the ABTS concentration in the buffer solution is optimized to 1.82 mM

12. The system of claim 1, wherein the data processing module applies a Gaussian filter with an optimized sigma (o) value of 8 to the raw RGB sensor (400) data.

13. A method for detecting hydrogen peroxide concentration using a digital microfluidic platform, the method comprising:- providing a digital microfluidic platform (200), wherein the platform comprises patterned electrodes for electrowetting-based droplet manipulation;- dispensing a first droplet (202) containing a buffer solution comprising ABTS onto the digital microfluidic platform (200);- incorporating iron oxide nanoparticles (204) into the first droplet (202); - dispensing a second droplet (203) containing a hydrogen peroxide solution onto the digital microfluidic platform (200);- merging the first droplet (202) and the second droplet (203) via electrode-actuated movement, forming a merged droplet (205) for initiating the reaction;- applying a rotating magnetic field at a frequency of 1000-1200 RPM, inducing homogeneous nanoparticle dispersion and accelerating reaction kinetics, wherein the iron oxide nanoparticles (204) catalyze the hydrogen peroxide reaction via peroxidase-like catalytic activity and are magnetically, enabling controlled dispersion and increasing reaction rate by increasing the effective surface area of iron oxide nanoparticles; - detecting the color change in the merged droplet (205) using an RGB sensor (400) positioned above the digital microfluidic platform (200); - extracting spectral features from sensor data, including:Initial color intensity;- rate of color transition;- color stabilization time;- temporal color shifts,wherein the extracted features are processed by a machine learning model for hydrogen peroxide concentration prediction,- transmitting sensor data to a wireless processing module, wherein noise filtration and feature extraction are performed on color intensity variations, and a machine learning algorithm predicts hydrogen peroxide concentration with at least 99% accuracy, based on a pre-calibrated dataset;- outputting the detected concentration data via a connected device, wherein the device is a smartphone with a dedicated application, configured to process received data locally or via cloud computing, and display graphical representations of reaction progress and predicted hydrogen peroxide concentration.

14. The method of claim 13, wherein the buffer solution comprises ABTS (2,2Z-azino-bis(3-ethylbenzothiazoline-6-sulfonate)).

15. The method of claim 13, wherein the connected device employs the machine learning algorithm to predict the concentration of hydrogen peroxide from the sensor data.

16. The method of claim 13, further comprises fabricating the digital microfluidic platform with a (PET) thin film for precise droplet manipulation.

17. The method of claim 13, further comprises collecting raw red-value data from the sensor (400) every second over a one-minute interval by the microcontroller (600).

18. The method of claim 13, further comprises preprocessing the raw sensor data from the RGB sensor (400) using a Gaussian filter with an experimentally optimized sigma (o) value of 8. i

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