Research Proposal on High performance sensor design: from fundamental science to commercial application
PHD LEVEL
Research Proposal on High performance sensor design: from fundamental science to commercial application
Instructions
1.Check the text for grammatical correctness like run-on sentences, hyphens, etc.
2. Rewrite the texts in introduction for structural flow and clarity according to the research community
3 Please follow US english format.
4 Please do not change meaning of the texts in chapters II, III and IV while rewriting.
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6. Kindly please do a thorough proofreading for the whole document
7. Please rewrite all the parts except the experimental procedures in chapter II,III and IV.
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Abstract
Sensors are vital in today’s world, driving technological advancements, enhancing safety and security, protecting the environment, transforming healthcare, enabling connectivity, improving energy efficiency, and facilitating research and development. Colorimetric chemical sensors play a crucial role due to their specific chemical detection, sensitivity, portability, ease of use, visual read-out signal, and potential for multi-analyte detection. Such sensors find diverse applications in environmental monitoring, food safety, and forensic analysis, and can provide rapid, on-site information. In this proposal, three chemical sensors are discussed.
The first chemical sensor reported herein is an organic dye-based colorimetric fluoride sensor. Three organic dyes: bromocresol green, phenol red and bromocresol purple, were tested as potential fluoride ion indicators. Initial testing procedures showed that bromocresol green was suitable as an indicator because it exhibited a strong color change in the presence of fluoride ions. Bromocresol green also showed selectivity in solid-state detection, where a dye-coated filter paper was used. 1H NMR spectral analysis revealed information about how the dye interacted with the analytes, resulting in complex formation. Additionally, the bromocresol green solution’s limit of detection (LOD) and limit of quantification (LOQ) for fluoride ions were established to be at levels that are relevant for monitoring fluoride in drinking water. Ongoing efforts are focused on using these results to develop a commercially viable fluoride sensor.
The second chemical sensor described herein is a novel boronate ester derivative of syn-bimane that was developed as a fluorescent probe for hydrogen peroxide (H2O2). In this sensor, the fluorescence emission of the bimane was significantly quenched in the presence of H2O2. The photophysical properties of the compound after exposure to H2O2 were studied, and the LOD and LOQ for hydrogen peroxide detection were determined using both UV-visible and fluorescence spectroscopy. Continued investigations are currently underway to further investigate the chemical reaction between hydrogen peroxide and bimane. This research sets the stage for the development of an effective hydrogen peroxide detection tool, with potential applications in a variety of scientific, engineering, and biomedical fields.
The final sensor reported herein is a color-changing gel derived from various types of starches and methylene blue. Three types of starches, namely corn flour, tapioca flour and rice flour, were used to produce dye-encapsulated gels. Through microwave heating and slow cooling, gel formation was successfully accomplished for all mixtures of starch flours and organic dyes. Information on the molecular interactions of the dyes in the gels was obtained by solid-state FTIR and UV-visible spectroscopy. Notably, gels made of rice flour and methylene blue displayed a color change over time, which indicates the degradation of the dye. The stability of these gels under different conditions was also investigated.
Chapter I
1.1 Introduction
In today’s technologically advanced world, sensors have become an integral part of our lives. A device that detects and responds to physical or chemical stimuli like temperature, light, pressure, moisture, sound, and motion, then converts these stimuli into a measurable signal, is called a sensor. Traditionally, analyzing materials for their composition, concentration, and classification required the resources of a laboratory and precise equipment.1 Highly skilled analytical chemists were necessary to analyze and interpret the results obtained from experiments, which consumed a considerable amount of time. Hence, there arose a need for a device that could be easily operated by a layman, which led to the invention of sensors. In the upcoming discussion, we will understand how sensors work and focus specifically on chemical sensors. We will explore their working principles and the characteristics that make them essential tools for chemical analysis.
1.2 Sensors
The term “sensor” was first coined in the early 1940s. Notably, the concept of sensing is deeply ingrained and familiar to us and is not limited to humans alone; all living organisms possess the ability to sense and react to stimuli in their environment. In humans, most sensing is accomplished by the five sense organs: the eyes, nose, ears, tongue, and skin. For example, the nerve cells behind our eyes capture the light reflected from an object and send the signal to the brain, where an image is formed, and our tongue can sense six different tastes with the help of sensory receptors called taste buds. In addition to the five sense organs that process external stimuli, the human body also contains internal sensing systems to detect and respond to internal changes, including changes in body temperature, blood glucose levels, and oxygen saturation.
Like humans and other animals, plants also have built-in sensors, which helps them detect and adapt to environmental changes. For example, sunlight is essential for most plants to grow. Plants have five classes of photoreceptors that can sense different wavelengths of incoming light: phytochromes, cryptochromes, and phototropins sense light between 600 and 700 nm; flavin-binding proteins sense light between 320 and 500 nm; and UVR8 protein senses light between 280 and 320 nm.2
In contrast to naturally occurring sensors, a synthetic sensor is composed of three main parts: (1) a sensing unit, which senses the particular signal input; (2) a signal processing unit, which processes the signal input; and (3) an output-generating unit, which responds to the signal input by generating a measurable output. The sensing unit of a sensor (part 1) contains a probe, which is responsible for identifying changes in the condition being measured. For instance, temperature sensors use a thermistor or a thermocouple to sense changes in temperature. The signal processing unit (part 2) usually contains a transducer, which is defined as something that converts energy from one form to another. Examples of such transducers are photodiodes, piezoelectric crystals, and capacitors, which convert the signal input into a form that can be interpreted as an output. In specific cases where the output is a change in the electrical signal, the transducer itself acts as a receptor by detecting and responding to the stimuli. For example, a photodiode is a transducer that acts as a receptor by detecting light. The output of a sensor plays a crucial role in delivering the measured data in a format that is readable by the system that utilizes the sensor’s measurements. Different applications require different types of outputs to effectively interpret and utilize the sensor’s data. For example, colorimetric sensors can provide output in the form of color measurements, such as RGB (red, green, and blue) values.
Sensors are broadly classified into two main categories: active sensors, which require an external power source, and passive sensors, which do not need any external source.3 Examples of active sensors include: radar, global positioning systems (GPS), seismic sensors, and infrared sensors, whereas passive sensors include ultrasonic sensors, magnetic sensors, and light sensors. Furthermore, sensors are also categorised based on the type of analyte they are designed to detect. For example, thermal sensors are designed to detect fluctuations in temperature, enabling them to monitor changes in heat levels. Magnetic sensors, on the other hand, are finely tuned to measure the strength and direction of magnetism. Chemical sensors are developed to identify specific chemicals including gases, and are used in tasks such as gas detection and chemical analysis. Meanwhile, pressure sensors are specialized to detect variations in pressure levels, making them invaluable for applications where pressure changes need to be closely monitored and controlled. A particular focus on chemical sensors will be discussed in the following sections.
Chemical Sensor
A chemical sensor is defined as a device that converts the chemical information of an analyte, such as its composition, concentration, or chemical reactivity, into a measurable signal.3 Chemical sensors can detect various types of chemicals, including gases, volatile organic compounds (VOC), heavy metals and biological (macro)molecules.
The first known chemical sensor is the glass pH electrode reported by Haber and Klemensiewicz in 1909.4 This electrode determines a solution’s acidity or alkalinity by detecting changes in the concentration of hydronium ions in the solution. Today, we are familiar with a range of chemical sensors that have become integral to our everyday routines, including breathalysers5, blood pressure detectors, smoke detectors, and glucose level monitors.
1.2.1.1 Principles of Chemical Sensors
Chemical sensors operate based on specific principles that allow them to selectively recognize and interact with an analyte.6 There are two main types of associations that can occur between the analyte and the chemical sensor: covalent and non-covalent interactions.6
Figure 1: Types of chemical interactions in chemical sensors. Reproduced from
ref [8]and [9].
Dynamic Covalent Interactions
Covalent associations involve the formation of strong chemical bonds between the analyte and the chemical sensor, which are often irreversible, making the sensor that relies on such covalent interactions single use in most cases. To address the issue of lack of reversibility in covalent bond formation, some researchers have focused on dynamic covalent chemistry, which describes thermodynamically-controlled, reversible, covalent bond formation. .7 Some examples of bonds formed via dynamic covalent chemistry include: acyl hydrazone and imine creation, disulfide exchange, olefin metathesis, Diels-Alder reactions, and acetal/hemiacetal formation.8
Non-Covalent Interactions
Non-covalent interactions refer to a class of intermolecular forces or interactions that occur between molecules, and which are generally weaker than covalent bonds. Some examples of non-covalent interactions include hydrophobic association, electrostatic interactions, intermolecular hydrogen bonding, cation-π interactions, anion-π interactions, π-π stacking, and edge-face interactions.6 Supramolecular complexes, formed as a result of these non-covalent interactions, rarely rely on just one type of non-covalent interaction. Rather, they often involve a combination of several interactions to create an effective sensor.9 Sensors often use such non-covalent interactions as the basis for favourable interactions between the sensor and the analyte.
Hydrophobic Interactions
The favourable interactions between two nonpolar molecules in an aqueous environment, when the molecules associate in a way that excludes water, are referred to as hydrophobic association.6 Such association is favorable because it minimizes the unfavourable interactions between nonpolar molecules and water. As a result, hydrophobic interactions become energetically stable with increase in entropy of the water molecules.10 Hydrophobic interactions involve a balance between the favorable enthalpy- driven attraction between hydrophobic species and the favorable entropy-driven ordering of water molecules around them. In specific cases, like bile salts and β-cyclodextrin inclusion complex the hydrophobic effects are driven by enthalpy.11
The hydrophobic effect was first investigated in biochemistry in the 1960s, when Bernard Randall Baker noted its importance in enzyme-substrate interactions and catalysis.6 In 1970, Ronald Breslow and co-workers presented a synthetic use of the hydrophobic effect in the design and operation of an artificial enzyme based on cyclodextrin.12,13 Cyclodextrin has a hydrophobic cavity, and it can bind to hydrophobic guest molecules by displacing the water molecules that were previously occupying the cavity. In some cases, this hydrophobic association of a photophysically active guest with the cyclodextrin molecule induces measurable changes in the UV-visible absorption and/or in the fluorescence emission of the guest molecule. Another example of hydrophobic association can be seen in some polymeric chemical sensors, wherein hydrophobic nanostructures composed of block copolymers are used to detect hydrophobic, small-molecule nitroaromatics.14
Of note, hydrophobic interactions are not limited to the liquid phase; they also occur at solid-liquid and gas-liquid interfaces. For example, hydrophobic interactions between nonpolar molecules have been reported at the air-water interface (an example of a gas-liquid interface).15 Such interactions control the self-assembly of ionic liquids, the interactions of amino acids with lipid monolayers, and the three-dimensional conformation of enzymes, among other processes. The only phase which has no hydrophobic interactions is the gas phase, due to the absence of water molecules, which results in the lack of an energetic driving force that would cause two nonpolar molecules to be in close proximity.16
Electrostatic Interactions
Electrostatic interactions, which involve attractive forces between oppositely charged species, are stronger than intermolecular hydrophobic association, but are still generally weaker than covalent bonds.6 However, the strength of these interactions strongly depends on the phase of the system: in gas-phase environments, the strength of electrostatic interactions approaches the strength of the covalent bond, whereas the electrostatic binding strength in water is weakened via ion solvation.17 Electrostatic interactions can also occur in solid-state systems, where they are often stronger due to the absence of solvent interference.6 Despite the significant advantage that electrostatic interactions confer in terms of high binding strength, they have a notable weakness: low selectivity for target analytes. In order to overcome this weakness in chemical sensor design, researchers often use multiple favourable electrostatic interactions, working in concert.18
Electrostatic interactions have been employed in a variety of chemical sensors, including in luminescent polymer sensors. In one example, cationic polyelectrolytes were used to detect polyanionic DNA, based on electrostatic binding inducing photophysical changes in the polyelectrolytes that result in either turn-on or turn-off fluorescence.6 Nonclassical electrostatic interactions (i.e., favorable electrostatic interactions that do not follow the traditional Coulomb’s law) have also been reported, particularly in situation in which anions stabilize each other by forming anti-electrostatic hydrogen bonds.19
Intermolecular Hydrogen Bonding
Traditionally, hydrogen bonds describe favourable interactions that occurs between a hydrogen bond donor, defined as a hydrogen atom connected to an electronegative atom like oxygen, nitrogen or fluoride, and a hydrogen bond acceptor, defined as an atom with a lone pair of electrons.6 These interactions are weaker than electrostatic interactions but stronger than hydrophobic interactions.6 The definition of hydrogen bonding has been broadened from its original definition to include a wide range of hydrogen bond donors, provided that the atom attached to the hydrogen has significant electronegative character. Intermolecular hydrogen bonding is commonly observed in various structural motifs, such as squaramides, ureas, and thioureas. These interactions typically rely on enthalpy as the primary energetic driving force, although enthalpy/ entropy compensation can also occur, where changes in either enthalpy or entropy are often balanced by changes in the other.6
In addition to experimental investigations of the energetics of intermolecular hydrogen bonding, researchers have also used various computational tools to investigate hydrogen bonding. In one example, researchers used molecular dynamic simulations to investigate the thermodynamics of hydrogen bonding in alcohol/water mixtures, because of the facile ability to form hydrogen bonds in this solvent.20
Cation-π Interactions
Cation-π interactions are defined as the electrostatic association between a positively charged cation and the delocalized π electrons of an aromatic or conjugated system.21 The cation-π interaction was first observed in 1981, when it was discovered the interaction between a gas-phase potassium cation and benzene exhibited greater strength than the interaction between the potassium cation and water.22 Similarly, stronger interactions were observed in various cations, including sodium and lithium cations, with benzene. An example of how cation-π interactions can be used in supramolecular complexation was reported, in which positively charged guests were bound in aromatic catenane hosts.14 Cation-π interactions have played a significant role in chemical sensor development, particularly in the construction of fluorescent chemical sensors for potassium, mercury, and cesium cations.
Cation-π interactions are found in all three phases: solution-phase, solid-phase and gas-phase systems. Cation- π interactions are stronger in gas-phase systems compared to those in solution phase, where the interaction energy decreases with increase in solvent polarity. However, solution-phase cation-π interactions are strong in some solvents, including in nonpolar solvents and in polar aprotic solvents.6 Additionally, cation-π interactions have also been reported in solid state systems. Examples of solid-state cation-π interactions include solid-state interactions between aromatic molecules and potassium cations, interactions between cationic surfactants and aromatic polycyclic aromatic hydrocarbons (PAHs) in a clay matrix, and solid-state interactions between alkali metal cations and an aromatic macrocyclic host.6,23
Anion- π Interactions
Anion- π interactions were largely overlooked until the early 20th century due to their counterintuitive nature. These interactions are thought to be counterintuitive because of the fact that anions with their negative charge and π systems with their electron-rich nature might be anticipated to repel each other, in contrast to cation-π interactions where attraction is expected. However, in aromatic rings with highly electronegative substituents, such as hexafluorobenzene, there is a significant region of positive electron density in the benzene ring, instead of the typical negative electron density found in an unsubstituted benzene ring. This positive region of electron density interacts with a wide variety of anions.
The history of anion-π interactions includes early investigations by Alkorta in 1997, in which he studied interactions between hexafluorobenzene and electron-donating atoms. This was followed by additional work two years later, in which Besnard investigated the interaction between water and hexafluorobenzene. These preliminary studies were the first to identify the existence of these non-covalent forces. In 2002, computational investigations provided additional support for the existence of anion-π interactions, followed by a crystal structure published in 2004 that unambiguously showed close-range contacts (3.4 Å-3.6 Å) between a chloride anion and a pyridine ring in the solid state, confirming the existence of anion-π interactions in the solid-state.24 After the publication of that report, NMR experiments revealed evidence of anion-π interactions in solution, and computational methods CASSCF/CASPT2 calculations provided a much deeper understanding of the energetics of this non-covalent association.6
π- π Interactions
π- π interactions occur between the negative electron density of one aromatic ring and the positive density of another aromatic ring. This interaction can give rise to three possible structures: a sandwich structure, a T- shaped structure, and a parallel displaced structure.25 A T-shaped geometry is formed when two benzene molecules interact; the proton of one molecule interacts with the negative electron cloud of the second molecule.25 A sandwich structure can occur when differently substituted aromatic rings interact, especially those with varying charge densities.26 A parallel displaced structure can be formed when medium and large-sized polycyclic aromatic compounds interact.27
π-π interactions are employed in aromatic analyte detection. This involves supramolecular hosts with an affinity towards pollutants like polycyclic aromatic hydrocarbons, polychlorinated biphenyls, bisphenol analogues, and aromatic insecticides.28,29
Halogen Bonding
Halogen bonding is a non-covalent interaction that occurs between a Lewis acidic halogen and a Lewis base. This type of interaction has recently emerged as a prominent subject within the field of supramolecular chemistry and has received a great deal of interest in supramolecular sensing, templated self-assembly, and catalysis.6 When a halogen atom is covalently bonded to another atom, the electron density around the halogen becomes anisotropically distributed (i.e. the distribution of electrons is unevenly spread around the halogen atom). In the anisotropic electron density distribution, a band of increased electronegativity is formed around the centre of the halogen atom perpendicular to the covalent bond. At the same time, the end of the halogen atom, away from the covalent bond, becomes electropositive and is known as “sigma-hole”. This sigma-hole can engage in non-covalent interactions with other hydrogen or halogen atom.30 Halogen bonding has been investigated in supramolecular sensing applications, such as luminescent sensing, and high-selectivity anion detection.6
1.2.1.2 Characteristics of an Ideal Chemical Sensor
In the past, standards were developed to evaluate the accuracy and reliability of analytical techniques and results, but they weren’t explicitly devised for characterising sensor devices. An ideal chemical sensor possesses a set of essential characteristics that make it highly sought after for numerous fields. This discussion explores the key attributes that define an ideal chemical sensor. Understanding the qualities of an ideal chemical sensor lays the groundwork for advancing sensor technologies and their applications in diverse industries. Below listed are the important parameters developed for assessing the performance and capabilities of the device.
A. Selectivity.
B. Sensitivity.
C. Limit of Detection.
D. Precision.
E. Response time.
F. Saturation.
G. Repeatability.
Selectivity
Selectivity is defined as the sensor’s capacity to respond predominantly to the target analyte while other species are present in the system.31 It is one of the several factors that determine the sensor’s suitability for a specific application, as different applications require sensors that accurately distinguish and quantify specific analytes amidst complex mixtures. The appropriate recognition mechanism and robust contact between the analyte and active element have an impact on a sensor’s selectivity.
Sensitivity
The ability of the sensor to detect and respond to changes in the analyte concentration is known as sensitivity. In a sensitive system, even small changes in the analyte concentration result in significant changes in the output signal and will have a greater ratio of signal change to concentration change. However, a less sensitive system will require a large change in the analyte concentration in order to produce noticeable changes in the output signal, leading to a relatively low signal-to-concentration ratio. The ability of the sensor to detect the analyte at the lowest concentration is one of the key factors in determining its sensitivity.
Limit of Detection
The limit of detection (LOD) and limit of quantification (LOQ) are two important parameters in analytical chemistry that indicates the lowest concentration of an analyte that a measurement system can reliably detect and the lowest concentration that can be reliably quantified, respectively. In theory, the analyte should exist in sufficient quantity to generate a signal that is distinguishable from the background noise. Calculations of the limit of detection and limit of quantification involve utilizing the standard deviation calculated from the output signal. The initial step involves calculating the limit of detection for the blank sample without the analyte in the system (LOD blank). LOD blank is determined by adding the mean of the blank to three times the standard deviation of the blank (SD blank).
LOD blank=mean blank + 3(SD blank)
The mean of the blank and standard deviation of the blank is obtained from multiple measurements taken by the sensor without introducing an analyte.33 Similarly, the LOQ blank is determined by adding the mean blank to ten times the standard deviation of the bank.
LOQ blank=mean blank + 10(SD blank)
After calculating the LOD blank and LOQ blank, a new set of measurements is conducted with increasing concentrations of the analyte. A graph is then plotted between the sensor’s output signal and the concentration of the analyte. LOD blank is entered as the Y-value in the best-fit linear equation for the data, and the corresponding X-value is determined. This X-value is the limit of detection, i.e., the lowest concentration a sensor can detect. Similarly, the LOQ blank is entered into the same best-fit linear equation as the Y-value, and the corresponding X-value represents the limit of quantification. The LOD focuses on detecting the presence of analyte amidst the background noise in the system. The LOQ focuses on quantifying the analyte with accuracy and that’s the reason the multiplier of 10 is used in LOQ calculation.
Precision
Precision quantifies how closely the sensor can reproduce the same results under similar conditions of a device with respect to the true value.3 The standard deviation is commonly used method to measure the precision factor (i.e. the numerical value to quantify precision using the values obtained from standard deviation). Precision can also be determined by calculating the difference between a measured value and a true value.1,3 True value refers to the accurate value of the quantity being measured by the sensor. It’s the value obtained if we could measure the quantity with absolute accuracy without any errors.
Response time
The response time of a sensor is a measure of how rapidly the sensor detects and reacts to the analyte. The desired response time for a sensor depends on the specific application requirements. Some applications, such as high-speed control systems or rapid event detection, may demand a fast response time that is less than a minute. In applications where slow changes occur or where real-time monitoring is not critical, a slower response time is acceptable.
Saturation
The saturation point of a sensor refers to the point at which the sensor’s response to a particular chemical stimulus reaches its maximum value and does not increase further, even if the concentration of the analyte continues to rise. Saturation occurs because sensors are designed with certain limitations in terms of the range of concentrations that they can accurately measure.
Repeatability
Repeatability is a subset of precision which assesses how consistent the results are when the same measurement is repeated under the same conditions without considering true value.3 The variations between readings can be found using the standard deviation of output data, which can also be used to calculate the repeatability rate, defined as the consistency of measurements conducted by different individuals. A lower standard deviation value indicates a higher repeatability rate, and a higher standard deviation indicates a low repeatability rate. An ideal sensor should possess a high repeatability rate.
1.2.1.3 Paper-Based Chemical Sensors
This section focuses on paper-based chemical sensing technologies, which have seen substantial growth in development in recent years. In this section, paper-based chemical sensors that rely on chemical reactions to detect and quantify specific analytes will be discussed. These sensors utilize selective interactions between the target analyte and a sensing element incorporated into the paper substrate. Paper possesses various advantageous properties, including capillary action for liquid flow, flexibility, affordability, widespread availability, and lightweight nature, making it highly suitable for specific sensing purposes.34,35
Cellulose paper, known for its hydrophilic nature, plays a pivotal role as a substrate for various sensors. Its inherent hydrophilicity facilitates the capillary action, allowing aqueous liquids to flow through. In many paper-based sensors, this capillary action serves as a fundamental mechanism to transport the sample to the detection zone, where analyte detection occurs through mechanisms like colorimetric changes or other chemical reactions. 34,37Apart from cellulose paper, a range of other paper types find application in sensor development. For instance, Whatman filter papers, categorized by factors such as pore size, thickness, and weight, serve specific purposes. Notably, Whatman no.1 is popular choice due to its exceptional wicking ability, making it suitable for certain sensor applications.36
The quality and suitability of paper substrates depend on factors like the selection of paper type, which can influence properties such as porosity and surface area. These paper properties can be tailored to meet specific requirements through techniques like functionalization and various physical treatments, including coatings. Researchers have employed a range of methods such as dip coating, drop casting, photolithography, screen printing, plasma treatment, wax treatment, and inkjet printing to modify and enhance paper substrates for diverse sensor applications.34,36
Optical detection is one of the most commonly used detection method of paper-based chemical sensors. However, it’s important to note that colorimetric analysis, a subset of optical detection, can sometimes lead to potential inaccuracies due to the perception of relative color changes for small variations in analyte properties. To this concern, modern smartphones are being utilized to minimize the issue by providing more objective and precise measurements of color changes in the detection process.35
One notable example is a smartphone-paper-based sensor impregnated with CTAB-modified silver nanoparticles, which has been described by Shrivas et al.38 for the detection of ferric ions in blood and water samples. The sensor is made of AgNPs/CTAB (10–50 nm) coated on Whatman filter paper # 1. A noticeable color change from yellow to colorless occurs after exposing the paper to the analyte. The aggregation of nanoparticles following contact with Fe3+ ions is what causes the color change. The aggregation of nanoparticles was confirmed through UV-visible spectroscopy, transmission electron microscopy and FTIR.38 The aggregation of AgNPs is a result of an electron transfer reaction occurring on their surface. The introduction of Fe3+ ions to the nanoparticles leads to the catalytic etching of CTAB that disturbs the stability of the nanoparticles and causing their agglomeration. They have also reported the limit of detection and limit of quantification of the system to be 20 µg L-1 and 65 µg L-1 respectively.
Figure 2: Whatman filter paper #1 functionalized with AgNPs/CTAB (1mM) and exposed to different concentrations of Fe3+cations (50, 100, 300, 400, 500, 600, 700, 800, and 900 µg L-1). (Reprinted from reference [38])
Despite the numerous practical advantages, paper-based chemical sensors have inherent limitations, including lower sensitivity and detection limits compared to other sensing techniques like spectroscopy, chromatography, mass spectrometry or electrochemical techniques. Integrating multiple sensing elements or readout mechanisms onto a single paper substrate presents challenges that researchers are actively addressing. Ongoing efforts aim to optimize these sensors, expanding their range of applications and improving their analytical performance.
CHAPTER II
Colorimetric Detection of Fluoride
2.1 Research Objectives
The major objectives of the research proposed herein are listed below:
To identify different substances that interact with fluoride ions in a way that leads to a measurable change in signal. This involves conducting a systematic investigation of a variety of organic and inorganic compounds so that their interactions with fluoride can be evaluated.
To develop a novel solution-state colorimetric sensor for fluoride ions based on the substances identified in Part 1, by designing and optimizing experimental procedures to achieve target levels of selectivity, sensitivity, and response times.
To incorporate the identified substance onto a paper substrate for the selective solid-state detection of fluoride ions. After identifying the optimal materials in Part 1 and using those materials to develop a solution-state sensor in Part 2, we will translate those results into an on-paper system, and optimize sensitivity, selectivity, response time, and practical applicability for this solid-state system.
2.2 Materials and Methods.
All chemicals were purchased from commercial suppliers and used as received. Ultrapure Milli-Q water was obtained from a Direct-Q machine and has a measured resistance of 18.2 MΩ. UV-visible analyses were conducted on a JASCO V-750 spectrophotometer. All solutions and paper samples were photographed under ambient light, and the resulting photographs were subjected to quantitative colorimetric analysis using freely available ImageJ software (ww.imagej.gov). All the graphs were plotted using OriginPro 2020 and were subjected to curve fitting (either linear or non-linear).
2.3 Experimental Procedures
2.3.1 Solution-State Detection
Stock solutions of bromocresol green, phenol red and bromocresol purple were prepared in ethanol ([dye] = 100 μM). 1 mL of each stock solution was pipetted into a small vial. To each 1 mL of dye solution, 20 μL of a 60 μM solution of tetrabutylammonium fluoride (TBAF), tetrabutylammonium chloride (TBACl), tetrabutylammonium bromide (TBABr) or potassium fluoride (KF) were added, and the color change was noted.
2.3.2 Studies of pH Responsiveness
To find the pH responsiveness of bromocresol green solutions, the color of solutions of bromocresol green at was measured at pH values between 7-14. For each solution, 1 mL of an ethanolic solution of bromocresol green and 50 μL of an aqueous sodium hydroxide solution (of varying concentrations, designed to achieve final pH values between 7 and 14) were combined, and the color change was noted.
2.3.3 Paper-Based Detection
Circular papers of 6 mm diameter, made from different types of filter paper (Whatman 1, Whatman 5, and Whatman 597) were coated with a solution of bromocresol green in water (1mg/mL) via drop-casting the solution onto the papers. After thorough drying of the paper in an open-air environment, 20 μL of a 60 μM solution of TBAF, TBACl, TBABr and KF were added to each paper via pipette, and the resulting color changes were noted.
2.3.4 Colorimetric Analysis
All colorimetric analyses were conducted using Microsoft Paint and ImageJ () to quantify the RGB values (Red, Green, Blue). All results represent an average of the values obtained from six trials of experiment.
2.3.5 Solution-State Limits of Detection
A stock solution of 30 μM of bromocresol green in ethanol was prepared. 3 mL of the prepared stock solution was placed in a cuvette and titrated with a 9 mM aqueous KF solution. The titrations were carried out as follows: 10 μL of 9 mM KF solution was added to the cuvette containing 3 mL of the stock solution of bromocresol green and the UV-visible spectrum was obtained (between 200 and 800 nm). The addition of 10 μL of 9 mM KF solution was repeated 10 times. After each addition (and before the first addition), the UV-visible spectrum of the solution was acquired. The aforementioned procedure was repeated in three independent trials. The data was plotted in two ways:
As the integrated absorbance spectra (a new peak emerges after addition of fluoride in the spectral region between 500 and 800 nm) on the Y-axis and the concentration of bromocresol green (measured in mM) on the X-axis, or
As the intensity of the solution absorbance at 624 nm (624 nm is the λmax of the new peak emerged after fluoride addition) on the Y-axis and the concentration of bromocresol green (measured in mM) on the X-axis.
For each data series, we plotted the average value of the three independent trials on the Y-axis, vs. the concentration of bromocresol green on the X-axis. The equations for the best linear fit to the data were determined.
For each data series, we calculated the limit of the detection of the blank (LODblank) and the limit of quantification of the blank (LOQblank) using the following equations:
LODblank = Averageblank + 3 * SDblank
LOQblank = Averageblank + 10 * SDblank
Where Averageblank represents the average measured value of the repeated trials of the sample without fluoride, and SDblank represents the standard deviation of those measurements. We then entered the value of the LODblank as the Y-value in the equations of best linear fit and solved for the X-value to determine the limit of detection (measured in mM). We entered the value of the LOQblank as the Y-value in the equation of best linear fit and solved for the X-value to determine the limit of quantification (measured in mM).
2.4 Preliminary Results
Initially, a range of dyes comprising anionic, cationic and zwitterionic dyes were tested against fluoride ions. Among them bromocresol green, phenol red and bromocresol purple exhibited variations in response to the analyte. These three dyes belong to sulfonphthalein family that are pH sensitive and change color upon deprotonation.39 Each of these dyes contain hydroxyl groups that can possibly be deprotonated by fluoride ions.40 Solutions of each of these dyes in ethanol were investigated to determine the color change upon addition of fluoride ions. Among the three dyes, bromocresol green demonstrated a strong color change from yellow to green in the presence of fluoride ions (Figure 3). Phenol red also showed a slight change in color, although not as prominent as the color change observed in the presence of bromocresol green, while bromocresol purple did not exhibit any noticeable color change.
Following this initial screening, further experiments were conducted using bromocresol green, focused on measuring and optimizing the pH responsiveness, limits of detection and quantification, and other parameters important for sensor development. To determine if bromocresol green could be effective within the typical pH range of normal water (pH 6-7), the color of the dye in different pH solutions was investigated.
To create solutions of varying pH, different volumes of aqueous sodium hydroxide (NaOH) (1M) and concentrated hydrochloric acid (HCl) (12M) were added to Milli-Q water and stored separately in vials according to their pH. Then the pH solutions were added to an ethanolic solution of bromocresol green. The changes in color were then observed and noted. The results indicated that noticeable pH-induced color changes in basic solutions occurred only at pH 12 and above. There was no color change in the acidic solutions of bromocresol green. This exhibit that bromocresol green in ethanol does not change color at pH 6-7 and the color change that we observed in the screening at these pHs is due to the presence of fluoride ions.
Figure 3. Photograph of solutions of bromocresol green (100 µM): (A) before the addition of analytes; and (B) after the addition of various analytes (H2O, TBACl, TBABr, TBAF and KF)
In addition to testing fluoride salts such as TBAF and KF, the experiment also involved testing other salts, including TBACl and TBABr. Among the four analytes, only TBAF and KF in bromocresol green exhibited color change from yellow to green (Figure 3). This indicates that bromocresol green showed high selectivity towards fluoride ions specifically.
To further investigate the interaction between bromocresol green and the analytes, UV-visible absorbance spectra were recorded. Specifically, the full-spectrum UV-visible absorbance spectra (200-800 nm) were measured for solutions of bromocresol green in the presence of various analytes, with a particular focus on measuring the UV-visible absorbance spectral changes observed in the presence of TBAF and KF analytes. Notably, when bromocresol green was combined with TBAF and KF, a new peak emerged in the absorbance spectrum of bromocresol green, with a λmax = 624 nm (Figure 4). This indicates that the interaction between bromocresol green and fluoride notably changes the photophysical properties of the dye.
Figure 4. UV-visible absorbance spectra of bromocresol green before and after exposure to a variety of analytes (H2O, TBACl, TBABr, TBAF and KF)
However, the addition of other analytes such as TBACl and TBABr did not lead to any significant differences in the absorbance spectra of bromocresol green, indicating limited or no interaction between these analytes and the bromocresol green dye. Despite the presence of different counterions (tetrabutylammonium and potassium) in the analytes, fluoride demonstrated the same color shift.
The limit of detection (LOD) and limit of quantification (LOQ) in solution-state colorimetric analysis was carried out by adding small amounts of potassium fluoride to the solution of 30 μM bromocresol green in ethanol. The UV-visible spectrum of the solution of bromocresol green was acquired after each addition (Figure 5), and two distinct sets of data were used to calculate the LOD and LOQ.
Figure 5. UV-visible absorbance spectra of solutions of bromocresol green (30 µM) with increasing concentrations of KF (between 0 µM – 300 µM, added in 30 µM increments)
The first set of data comprised analysing the integrated absorbance spectra between 500 to 800 nm, while the second analysis focused on the intensity of absorption at 624 nm. A calibration curve was plotted between absorbance and concentration of KF in bromocresol green, resulting in a linear relationship (Figure 7).
Figure 7. Illustration of the linear relationship between the concentration of KF (measured in mM) and the absorbance of bromocresol green solution (A) the integrated absorbance spectra (between 500 and 800 nm) and (B) the intensity of absorbance at 624 nm.
The limits of detection (LOD) for the tested samples were determined using the integrated absorbance spectra and absorption intensity at 624 nm, resulting in values 14.8 ± 0.4 µM and 6.9 ± 0.2 µM, respectively. Similarly, the limits of quantification (LOQ) were calculated based on the integrated absorbance spectra and absorption intensity at 624 nm, the values are 40.2 ± 0.3 µM and 12.9 ± 0.2 µM, respectively (as shown in Table 1).
Table 1. Limits of detection and quantification of fluoride anion using changes in the UV-visible absorbance of bromocresol green, measured in two ways: (a) using the integrated absorbance spectra (between 500 and 800 nm), and (b) using the intensity of the absorbance at 624 nm.
To explore the solid-state detection of fluoride ions, different types of Whatman filter papers (Whatman # 1 with pore size 11µm, Whatman # 5 with pore size 2.5µm, and Whatman # 597 with pore size 4-7 µm) were used. The filter papers were subjected to a coating process where, the dye solution is dropped directly onto the surface, allowing it to spread and form a thin layer. Specifically, a volume of 20 µL of dye solution is dispensed on the filter paper using a micropipette. After the papers were dried in open air at room temperature, 20 µL of the analyte solution were dropped onto the coated papers and the papers were observed for color change. The papers exposed to fluoride ions turned dark green, while the papers exposed to other analytes retained their original color (Figure 8). Importantly, all three types of Whatman filter papers exhibited a color change in the presence of fluoride ion. Whatman filter paper # 1 exposed to TBAF shows a green color and KF shows a bluish green color which could be because of different counter ions. Other types of filter papers showed yellow to green color change with both TBAF and KF.
Figure 8. On-paper colorimetric changes of bromocresol green after exposure to H2O, TBACl, TBABr, TBAF and KF.
This observation suggests that the bromocresol green-coated filter papers can effectively detect fluoride ions. The color change from yellow to dark green serves as an indication of the presence of fluoride ions on the paper, providing a simple and visual means of detecting fluoride.
To quantify the colorimetric value of fluoride exposed to Whatman filter papers (#1, #3, #4, #5 and #597) coated with bromocresol green, the photographs of papers exposed to varying concentrations of KF (0-1000 ppm) were analysed using ImageJ software. The quantitative analysis included the calculation of grayscale values (combo1: 0.299R+0.587G+0.114B and combo 2: (R+G+B)/3) in addition to the RGB values (red, blue, green), which was calculated using the formula provided in Table 2. The color change observed in Whatman paper #597 upon exposure to increasing amounts of fluoride was non-linear, making their quantification challenging. In contrast, other filter papers demonstrated a gradual change in color with the increase in fluoride concentration.
Table 2. Quantitative colorimetric values of filter papers functionalized with bromocresol green, after exposure to various concentrations of potassium fluoride (KF)
Figure 9. Quantitative colorimetric values of fluoride anion, determined by analyzing different colorimetric values (red, green, blue, combo-1: 0.299R+0.587G+0.114B; combo-2: (R+G+B)/3) of various filter papers (Whatman #1, Whatman #3, Whatman #4, Whatman #5, Whatman #597) functionalized with bromocresol green
2.5 Future Plan
The next steps in the project involve a comprehensive investigation into the interaction between fluoride and bromocresol green, using a variety of analytical tools including 1H, 13C and 19F NMR, FTIR, and mass spectroscopy.
In the specific case of studying the bromocresol green and fluoride interaction, NMR spectra of bromocresol green alone and bromocresol green in the presence of TBAF/KF will be recorded to understand their interaction. Any shifts or changes in the chemical shifts of specific atoms can indicate the involvement of these moieties in the interaction. In this investigation, the hydroxyl group present in bromocresol green serves as the focal point, and the alterations in its chemical shift resulting from the addition of fluoride will be examined using 1H and 13C NMR spectroscopy. From 19F NMR spectroscopy we can determine the presence and environment of the fluorine atom and can possibly understand their interaction.
FTIR spectra will be obtained for bromocresol green alone and bromocresol green in the presence of fluoride ions. The focus of analysis will be on the differences observed in the spectrum, particularly in the region associated with the hydroxyl group. One possible interaction is between fluoride ions and the hydroxyl group of bromocresol green, where they might form HF, causing changes in the OH peak. These changes could include peak shift to higher wavenumber indicating strong hydrogen bonding, broadening of OH peak suggesting interaction with another chemical environment, or the disappearance of OH peak indicating alterations in the hydroxyl group.41 By examining these differences, we can understand the interaction between bromocresol green and fluoride ions.
Mass spectroscopy analysis can give information on new peaks corresponding to the complex formation between bromocresol green and fluoride based on the conditions used during the analysis. By obtaining the molecular mass of the complex, mass spectroscopy enables the identification and characterization of this interaction.
A comprehensive literature review will be carried out to explore any related studies or similar sensing mechanisms. This will enhance the understanding of the current knowledge about the ion-dye interactions. Furthermore, our research endeavors will encompass a comprehensive investigation involving a diverse array of ions including arsenic, nitrates, nitrites, lead, copper, chromium, and mercury. These ions will be systematically tested with bromocresol green, which will allow us to understand how different ions interact with the dye and also establish a comprehensive dataset of ion-dye interactions.
Once the mechanism of interaction between fluoride and bromocresol green is better understood, the detection system will be further optimized and modified to enhance its selectivity, sensitivity, and ability to quantify fluoride in the environment. One potential approach to enhance the detection system involves combining bromocresol green with a supramolecular compound. Macrocyclic anion receptors have exhibited a promising potential for improving the effectiveness and selectivity of fluoride binding.42,43 Among these receptors, calixarene, a supramolecule has hydrophobic cavities that can hold small molecules or ions. Calixarene-based receptors have been studied for their recognition towards anions. Some calixarene derivatives like calix[4]pyrrole, dihydroquinoline-derivatized calix[4]arene, fluorene-derivatized calix[4]arene have demonstrated enhanced selectivity towards fluoride ions.42 By incorporating a suitable supramolecule, the detection system’s sensitivity and selectivity towards fluoride ions can be enhanced.
Furthermore, an effective paper-based sensor will be fabricated by functionalizing the paper with the suitable supramolecule and dye which will be selective and sensitive to fluoride. The developed detection system can be integrated into such a device, allowing for simple and convenient fluoride detection in various environmental samples.
The project’s overall goals include deepening our understanding of the interaction between fluoride and bromocresol green by thoroughly examining and comprehending the spectroscopic changes that occur when fluoride interacts with bromocresol green. This research intends to unravel the mechanisms and dynamics underlying the interaction between fluoride and bromocresol green. This project lays the groundwork for the design and construction of a highly sensitive and selective sensor. This sensor will serve as a valuable tool for accurately detecting and quantifying fluoride ions in various contexts, such as environmental monitoring, industrial process, and healthcare applications. The outcomes of this research will provide broadly applicable principles of fluoride-dye interactions, anion-bromocresol dye systems, supramolecular effects on these kinds of interactions.
CHAPTER III
Highly Sensitive Hydrogen Peroxide Detection Through Changes in a Syn-Bimane Based Boronic Acid Derivative.
3.1 Research Objectives
The major objectives of the research proposed herein are listed below:
To develop a highly efficient fluorometric sensor for hydrogen peroxide based on compound 3 by optimizing the hydrogen peroxide-induced photophysical response, by optimizing all components of the sensor design, and by optimizing a broad range of experimental parameters.
To gain a comprehensive understanding of the interaction between compound 3 (an ethynyl pinacol boronate ester derivative of syn-bimane) and hydrogen peroxide. This involves studying the mechanism of the reaction between compound 3 and hydrogen peroxide, through monitoring and understanding the observed photophysical changes.
3.2 Materials and Methods
All chemicals, including spectroscopic grade solvents, were purchased from commercial suppliers, and used without further purification. All UV-visible absorption spectra were recorded on a Jasco V-750 UV-visible spectrophotometer. Fluorescence spectra were recorded on a Varian Cary Eclipse fluorescence spectrophotometer with excitation and emission slit width fixed at 2.5nm. The fluorescence spectra were integrated vs wavenumber on the X-axis using OriginPro 2020. All photographs were processed using freely available ImageJ software ().
3.3 Experimental Procedures
3.3.1 Synthesis of Compound 3
Figure 10. Schematic of the synthesis of Compound 3
The synthesis of compound 1 was performed by Prof. Flavio Grynszpan’s group at Ariel University and the structure was fully characterized using spectroscopic methods and the results were in line with the report.44 Compound 3 (Ethynyl pinacol boronate ester derivative of syn-bimane)was synthesized from compounds 1 (syn-(Ph,I)bimane) and 2 (4-ethynylphenylboronic acid pinacol ester). Bis(triphenylphosphine)palladium (II) chloride (20 mg, 0.028 mmol, 0.10 eq.) and cuprous iodide (2.7 mg, 0.014 mmol, 0.05 eq.) were added to a solution of 4-ethynylphenylboronic acid pinacol ester 3 (140 mg, 0.61 mmol, 2.2 eq.), diisopropylethylamine (0.48 mL, 2.8 mmol, 10 eq.), and compound 2 (150 mg, 0.28 mmol, 1.0 eq.) in CH3CN (200 mL). The mixture was stirred at 80 °C for one hour under a nitrogen atmosphere. After one hour, the solvent was evaporated under reduced pressure, and the resulting crude product was purified via flash chromatography over silica gel eluting with 5% ethyl acetate: 95% dichloromethane. The product was isolated as a reddish-yellow colored solid in 67 % isolated yield (138 mg). 1H NMR (400MHz) (CDCl3): 7.72-7.70 (d, J = 8 Hz, 2 H, Ar-H), 7.36-7.34 (d, J = 8 Hz, 2 H, Ar-H), 7.32-7.28 (m, 2 H, Ar-H), 7.24-7.23 (d, J = 4 Hz, 1 H, Ar-H), 7.17-7.13 (m, 2 H, Ar-H), 1.33 (s, 12 H, 2(-Me)2); 13C NMR (100MHz) (CDCl3): 134.64, 131.24, 130.89, 129.31, 128.29, 84.15, 25.01; DEPTQ: 134.96, 131.20, 129.63, 128.61, 126.12, 25.32; HRMS calculated: 741.3322, found: 741.3332.
3.3.2 Solution-State Colorimetric studies.
A stock solution of compound 3 in acetonitrile ([compound 3] = 10 µM) was prepared by dissolving 370.6 µg of compound 3 in 10 mL of acetonitrile. 500 µL of this solution was added to each of the ten vials. 100 µL of an aqueous hydrogen peroxide solution (H2O2 concentrations: 0.08 x 103 M, 0.17 x 103 M, 0.26 x 103 M, 0.35 x 103 M, 0.44 x 103 M, 0.52 x 103 M, 0.61 x 103 M, 0.70 x 103 M, 0.79 x 103 M and 0.88 x 103 M) was added to each vial. The samples were photographed under ambient lighting and under 365 nm excitation (excitation provided by a long-wave, hand-held TLC lamp). The same step was repeated with water as the additive rather than hydrogen peroxide, solutions of water in acetonitrile were prepared, and 100 µL of this solution (0.92 x 103 M, 1.85 x 103 M, 2.77 x 103 M, 3.7 x 103 M, 4.62 x 103 M, 5.55 x 103 M, 6.47 x 103 M, 7.4 x 103 M, 8.32 x 103 M and 9.25 x 103 M) was added to each vial. Then the samples were photographed under ambient lighting and under 365 nm excitation (excitation provided by a long-wave, hand-held TLC lamp). All photographs were processed using freely available ImageJ software (). The results reported herein represent an average of at least two independent trials.
3.3.3 Limit of Detection (LOD) Experiment.
The initial sample was prepared by diluting a solution of Compound 3 ([3initial] = 50 µM; [3final] = 10 µM in acetonitrile. The diluted solution was transferred to a quartz cuvette, and excited at 450 nm, using a Varian Cary Eclipse fluorescence spectrometer. The excitation and emission slit widths were fixed at 2.5 nm. The fluorescence emission spectrum was collected between 460 and 800 nm. Each fluorescence measurement was repeated six times. 10 µL of a 30% aqueous hydrogen peroxide solution (hydrogen peroxide/ water = 30/ 70 by volume) was added to the cuvette and the solution was shaken by hand. The UV-visible absorbance spectrum between 200 to 800nm of the solution was collected. The steady-state fluorescence emission spectrum was collected by exciting the solution at 450 nm and recording the emission between 460 nm and 800 nm. Six repeat measurements were taken. Addition of hydrogen peroxide was repeated five times for total addition volumes of 10 µL, 20 µL, 40 µL, 60 µL, 80 µL and 100 µL of the aqueous hydrogen peroxide solution, corresponding to hydrogen peroxide concentrations of 0.029 mM, 0.058 mM, 0.12 mM, 0.17 mM, 0.23 mM, and 0.28 mM. In all cases, the solution was excited at 450 nm and the fluorescence emission spectra were recorded between 460 and 800 nm. We plotted the concentration of hydrogen peroxide (in mM) on the X-axis, and a variety of measured output data on the Y-axis (integrated UV-visible absorbance spectra; intensity of absorbance at 457 nm, integrated fluorescence emission spectra, and intensity of fluorescence emission at 534 nm). The equations for the best linear fit for each data set were determined. The limit of detection of the blank (LODblank) and limit of quantification of the blank (LOQblank) were calculated using the following equations:
LODblank = Averageblank + 3 * SDblank
LOQblank = Averageblank + 10 * SDblank
Where Averageblank represents the average measured value of the repeated trials of the sample without hydrogen peroxide, and SDblank represents the standard deviation of those measurements. For each category of output data, we plugged the LODblank value into the best linear fit equation as the Y-value and solved for the X-value. This value represents the limit of detection, measured as the concentration of hydrogen peroxide in mM. For each category of output data, we plugged the LOQblank value into the best linear fit equation as the Y-value and solved for the X-value. This value represents the limit of quantification, measured as the concentration of hydrogen peroxide in mM.
3.4 Preliminary Results
Fluorescent probes have been utilized for the detection of hydrogen peroxide (H2O2) in various cellular environments, including intracellular H2O2 in mice peritoneal macrophages, H2O2 levels in human embryotic kidney cells, and Mito- H2O2 a probe that specifically detects mitochondrial-associated hydrogen peroxide.45 A new group of fluorescent probes has been developed with the specific purpose of detecting H2O2. These probes uses boronate deprotection mechanism, enabling them to selectively detect hydrogen peroxide in aqueous solutions while effectively differentiating against other reactive oxygen species (ROS) such as superoxide, nitric oxide and others.46
One of the key reactions involved in hydrogen peroxide detection is the reaction between H2O2 and boronic acid or boronic esters, resulting in the formation of an alcohol derivative.44 In the context of this study, compound 3 was utilized as a sensor for detecting hydrogen peroxide (H2O2), based on its sensitive and rapid reaction with hydrogen peroxide that led to noticeable color changes. In the preliminary experiments, the changes in the UV-visible and emission spectrum of Compound 3 (10 µM in acetonitrile) upon the addition of varying concentration of H2O2 were recorded. Compound 3 exhibited a maximum absorption wavelength (λmax) of 450 nm and decrease in the peak intensity was observed upon addition of H2O2 (Figure 11 (A)). However, the fluorescence emission of Compound 3, which was initially strong, underwent a substantial reduction of 37% and experienced a slight blue shift upon the introduction of hydrogen peroxide (Figure 11 (B)). The fluorescence quenching of compound 3, resulting from the addition of H2O2, is presumably attributed to the deprotection of the arylboronates on compound 3 induced by H2O2.46
Figure 11. (A) UV-visible spectra and (B) fluorescence emission spectra of solutions of compound 3 ([3] = 0.01 mM) in the presence of hydrogen peroxide.
To find the minimum detection limit and the lowest quantifiable concentration, absorbance and emission spectra were collected for compound 3 with varying concentrations of hydrogen peroxide (Figure 12). Concentrations ranging from 0 to 0.28 mM of H2O2 were added to a stock solution of compound 3 (10 µM), and the fluorescence emission spectra were recorded after excitation at 450nm.
Figure 12. (A) UV-visible spectra and (B) fluorescence emission spectra of solutions of compound 3 ([3] = 0.01 mM) in the presence of increasing concentrations of hydrogen peroxide
Figure 13. Graphical representation of the effect of increasing hydrogen peroxide concentration on the (A) integrated fluorescence emission spectra of solutions of compound 3 and (B) the intensity of fluorescence emission at 534 nm of solutions of compound 3
The results showed a gradual decrease in the intensity of the fluorescence emission of compound 3 with an increase in the concentration of hydrogen peroxide. The calculated LOD and LOQ for hydrogen peroxide, based on the integrated fluorescence emission spectra between 460-800 nm were determined to be 0.038 ± 0.004 mM and 0.079 ± 0.005 mM, respectively. It is noteworthy that while some studies have reported nanomolar level detection limits, our research achieved detection in the millimolar range.45
We measured solution-state changes in the fluorescence of compound 3 with the addition of a hydrogen peroxide solution (concentrations ranging from 0 mM, 0.08 x 103 M, 0.17 x 103 M, 0.26 x 103 M, 0.35 x 103 M, 0.44 x 103 M, 0.52 x 103 M, 0.61 x 103 M, 0.70 x 103 M, 0.79 x 103 M and 0.88 x 103 M ), and compared those changes observed when analogous concentrations of water were added. Hydrogen peroxide-induced changes in the visible color of the solution, as well as changes in the color of solution under 365 nm excitation, are shown in Figure 14.
(A)
(B)
Figure 14. Photographs of solutions of compound 3 upon exposure to increasing concentrations of hydrogen peroxide (0 x 103 M, 0.08 x 103 M, 0.17 x 103 M, 0.26 x 103 M, 0.35 x 103 M, 0.44 x 103 M, 0.52 x 103 M, 0.61 x 103 M, 0.70 x 103 M, 0.79 x 103 M and 0.88 x 103 M) , photographed (A) under excitation with 365 nm light and (B) under ambient light.
The results revealed that compound 3 went through a color transformation when 0.17 x 103 M hydrogen peroxide was added. Furthermore, compound 3’s distinctive green color vanished entirely when hydrogen peroxide concentration was raised to 0.88 x 103 M. Additionally, there was a slight change observed in the compound’s appearance in ambient light conditions upon the addition of H2O2. Comparatively, when the same experiment was repeated using varying concentrations of water, a gradual color change from green to pale yellow was observed without significant changes in the ambient light (Figure 15).
(A)
(B)
Figure 15. Photographs of solutions of compound 3 upon exposure to increasing concentrations of water (0 x 103 M, 0.92 x 103 M, 1.85 x 103 M, 2.77 x 103 M, 3.7 x 103 M, 4.62 x 103 M, 5.55 x 103 M, 6.47 x 103 M, 7.4 x 103 M, 8.32 x 103 M and 9.25 x 103 M) photographed (A) under excitation with 365 nm light and (B) under ambient light.
3.5 Future Plan
At its core, this research delves into the chemical properties of boronates, their interactions with oxidants. Furthermore, it seeks to investigate the potential of linking boronates to fluorophores to enable fluorescence-based sensing of a wide range of oxidants. The research endeavors to explore the effects of structural modifications in the linkage of boronates to fluorophores, as well as the impact of the chosen fluorophore itself. Through systematic investigation, the study aims to enhance our knowledge of boronate chemistry and develop optimized strategies for effectively utilizing fluorescence-based sensing in the detection of diverse oxidants.44 The reaction of hydrogen peroxide with aryl boronic acids and boronate esters leads to the production of phenols,46 which has been utilized in the development of fluorescent probes for hydrogen peroxide detection. Therefore, this reaction holds promise for the detection of hydrogen peroxide.
A variety of analytical methods will be used to understand the interaction of Compound 3 with hydrogen peroxide. One method will be through NMR titration experiments, in which solutions of compound 3 will be exposed to increasing concentrations of H2O2. By analyzing the changes in the NMR spectra that occur with increasing concentrations of hydrogen peroxide, the interaction between the boronate ester group of Compound 3 and hydrogen peroxide can be observed. Specifically, formation of new -OH signal is expected in the NMR spectra as result of the interaction.
In the second method, we plan to use high-resolution mass spectrometry analysis (HRMS) where, compound 3 will be exposed to hydrogen peroxide and then analyzed. The obtained results will enable us to discern the alterations in the molecular weight of our compound after hydrogen peroxide treatment. Additionally, these results will confirm the cleavage of the boronate ester group in our compound and the formation of hydroxyl group. This comprehensive analysis will enhance our understanding of the intricate interaction between compound 3 with H2O2.
FTIR investigations will also be conducted to examine the infrared absorption spectrum of our compound before and after exposure to hydrogen peroxide. The FTIR analysis, will confirm the cleavage of the boronate ester group and the subsequent formation of a hydroxyl group. We can expect the appearance of new O-H band, which would serve as evidence for the formation of phenol.
Additionally, the solid-state photophysical properties of Compound 3 will be investigated by coating it on different types of papers. These coated papers will be tested against hydrogen peroxide to observe any changes in the fluorescence properties. Experiments with H2O will also be conducted to establish a comparative result.
The study aims to investigate the complexation of compound 3 with a supramolecule and analyze any changes in solution and solid-state photophysical properties following exposure to H2O2 . Supramolecules, such as cyclodextrins, are widely used in host-guest complexations. Cyclodextrins are cyclic oligosaccharides composed of α-(1,4) linked glucose units and are categorized based on the number of glucose units they contain. Specifically, α-cyclodextrins have six glucose units with a cavity size 5.7Å, β-cyclodextrins have seven glucose units with cavity size of 7.8Å and γ-cyclodextrins have eight glucose units with a diameter 9.5Å.47 These cyclodextrins are known to form inclusion complexes with bimanes, enhancing the sensing properties of bimanes.48,49 The inclusion complex is formed through various non-covalent interactions such as hydrophobic interaction, hydrogen bonding, and van der waals force.47
In this experiment, α, β, and γ-cyclodextrins will be used as hosts and compound 3 will be complexed in both solution and solid state to determine the stable complexation. By considering the different cavity sizes of the cyclodextrins, the most suitable one for compound 3 will be identified. Previous reports have indicated that β-cyclodextrins have been a good fit for triazole bimanes resulting in a complex with decreased fluorescence compared to the standalone bimane.49 After the complexation is achieved, the resulting complexes will be exposed to hydrogen peroxide. The presence of cyclodextrin has been found to lower the limit of detection in some cases, but in another study, the complexation did not show any advantage.48,49
By accomplishing these planned experiments and studies, the project aims to contribute to the understanding of the fundamental reactivity between boronate esters and oxidants, and how the attachment of a fluorophore to the boronate can enable the detection of hydrogen peroxide through measurable signals. Simultaneously, we are developing a sensor specifically designed for hydrogen peroxide detection. To assess its potential utility, it is necessary to consider existing hydrogen peroxide sensors reported in the literature, as well as the concentrations and limit of detection values of hydrogen peroxide relevant for detection by the general population.
CHAPTER IV
A Unique Color-Changing Gel Derived from Rice Flour and Methylene Blue
4.1 Research Objectives
The major objectives of the research proposed herein are listed below:
To understand the process of gel formation in starch-based hydrogels involving commercially available flours (rice flour, tapioca flour, and corn flour) under microwave heating conditions;
To investigate the ability of starch-based hydrogels to encapsulate seven different organic dyes.
To identify and characterize the unique properties of the gel formed from the combination of rice flour and methylene blue.
To propose a mechanism for the highly specific color change observed in the rice flour-methylene blue gel.
To generalize the obtained results and leverage them to gain a broader fundamental understanding of the supramolecular interactions in gels that bind small guest molecules.
4.2 Materials and Methods
All starch sources (rice flour, tapioca flour, and corn flour) were purchased from a local supermarket and used as received. All microwave heating was done using a Hyundai Ham-M21M 1200W brand microwave. All dyes were purchased from chemical suppliers and used as received. All UV-visible and fluorescence spectra were obtained using a hand-held Indigo spectrometer (purchased from Goya Labs; ). The UV-visible absorbance spectra were taken using an external telephone flashlight to provide consistent illumination. All FTIR spectra were measured using a JASCO FT/IR-4700 instrument between 500 cm-1- 4000 cm-1. The measurements were carried out using a MIRacleTM Single Reflection ATR Accessory from PIKE Technologies, without any sample processing. All colorimetric data was obtained from photographs taken using a telephone camera Samsung Galaxy A50s, with the data processed using ImageJ software () and/or with Microsoft Paint. All plotted data, integration of spectra, and other spectral analyses were conducted using OriginPro 2022.
4.3 Experimental Procedures
4.3.1 Gel Preparation Procedures:
All gels were prepared by heating an aqueous suspension of the commercially available starch flour (1gm/5mL dye) and a dye (1-7, Figure 12) in a microwave for 30 seconds at medium heat (with thorough mixing both before heating and after 15 seconds of heating), followed by allowing the mixture to cool to room temperature. All analyses were conducted on samples that were allowed to warm to room temperature prior to analysis.
4.3.2 Harsh Conditions Stability Analysis:
The stability of gels derived from rice flour and methylene blue was measured under a variety of conditions, by fabricating the gel using the procedures detailed above, followed by exposing the gels to 1 mL of the following solutions: 1 M HCl, 1 M NaOH, 30% H2O2 and distilled H2O (as a control). The changes that occurred in the gel after the addition of each of these solutions were noted and photographed using Samsung Galaxy A50s, with colorimetric analysis of the photographs occurring via Microsoft Paint.
4.3.3 Solid-State Colorimetric Analysis:
Photographs of the gels were subjected to colorimetric analysis using Microsoft Paint, via the following procedure: Each photograph was cropped so that the image of the gel was visible, and 10 random points on the cropped photograph were selected. Each of these points were analysed using Microsoft Paint and ImageJ to determine the quantitative Red, Green, Blue, Hue, Saturation, and Luminescence (RGB-HSL) values, and the average value of the 10 data points was reported for each photograph.
4.3.4 Solid-State UV-Visible Absorbance Analysis:
Solid-state UV-visible absorbance spectra of the prepared gels were measured using IndiGo, a portable UV-visible spectrometer obtained from Goya Labs (). The distance between the IndiGo spectrometer and the gel sample being measured was set at 2 cm. The UV-visible absorbance spectrum (200 to 800nm) of each sample was measured three times, and the resulting data was processed using OriginPro 2022.
4.3.5 Solid-State Fluorescence Emission Analysis:
The solid-state fluorescence emission spectra of the prepared gels were measured using IndiGo, a portable UV-visible spectrometer obtained from Goya Labs. The distance between the IndiGo spectrometer and the gel sample being measured was set at 3 cm. The fluorescence of each sample was measured three times and the data was recorded. Excitation of the sample was accomplished via UVA LEDs, with excitation at 375 nm, and the emission spectra were plotted between 420-790 nm.
4.3.6 Solid-State FTIR Analysis:
All FTIR spectra were measured using a JASCO FT/IR-4700 instrument. The measurements were carried out using a MIRacleTM Single Reflection ATR Accessory from PIKE Technologies, without any sample processing.
4.3.7 Gel pH Analysis:
The pH measurements of each gel were carried out using an OHAUS STARTER 3100 pH meter, which was calibrated before usage. The pH of the starch powders prior to gelation was determined by dipping the sensing probe into the vial containing sample powders and the water mixture. After gelation, the gels were divided into three vials, and the pH probe was immersed in each gel. The displayed pH value indicated the pH of the newly formed gels.
4.4 Results and Discussion
The selection of three starches (rice flour, tapioca flour, and corn flour) for their potential ability to form dye-containing starch gels was guided by the literature-reported success in using all three of these flours to form gels,50 combined with the fact that these flours are readily available from a variety of commercial sources. Moreover, these starches have significant differences in their chemical composition, and these differences are expected to affect the properties of dye-containing gels derived from these starches. For example, rice flour contains substantially higher protein51 and lipid concentrations52 compared to the other two starches, and tapioca flour is markedly more hygroscopic than the other two starches investigated.53
Figure 16. Chemical structures of the organic dyes (4-10) used in the experiment.
The seven dyes selected for analysis (dyes 4-10, Figure 16) are all well-known in colorimetric materials applications,54 with several of them previously reported as components of dye-containing gels. Moreover, the seven dyes have been rationally selected to include cationic, anionic, and zwitterionic dyes, so that the effect of these differences in dye charges on the properties of dye-encapsulated starch-containing gels can be explicitly investigated.
Gel formation was rapid for all combinations of starch flours and organic dyes investigated, with 30 seconds of microwave heating, followed by gradual cooling for 20mins to room temperature, resulting in the formation of gels. Notably, such microwave heating has been shown to effectively form a variety of starch-containing gels,55 with effects on the swelling power and other properties of the resulting gels noted.56 Effective gel formation was confirmed in all cases via the inverted-vial test (Figure 17).
Figure 17. Gel formation from a variety of starch flours and organic dyes 4-10: (A) rice flour gels; (B) tapioca flour gels; and (C) corn flour gels, all shown as inverted vials to confirm gel formation. (FB = fuschin basic (dye 4); RB = rhodamine b (dye 5); MO = methyl orange (dye 6); R6G = rhodamine 6g (dye 7); BB = brilliant blue (dye 8); MB = methylene blue (dye 9); MV = methyl violet (dye 10))
Such gelation was confirmed spectroscopically via solid-state FTIR analysis (Figure 17). As expected, the FTIR spectra of the gels showed a strong peak between 3500-3000 cm-1, corresponding to the O-H stretching that occurs due to the presence of water in the gels. Moreover, the peaks between 1200 and 1000 cm-1 provide additional confirmation that gelation has occurred, with the peaks at 1085 cm-1 (C-O-H bending) and 1159 cm-1 (C-O and C-C stretching) consistent with a gelatinized starch structure.57 Finally, a comparison of the FTIR spectra of the dyes themselves to the FTIR spectra of the dyes in the gels reveals significant differences in the positions of the spectral peaks between 1800-1200 cm-1, indicating a difference in the energy required for the requisite molecular vibrations. Differences in the FTIR spectra of a free molecule and the FTIR spectra of that same molecule encapsulated in a supramolecular structure have been previously reported58, including in situations of dye encapsulation in starch-based gels.59
Figure 18. FTIR spectra of starch gels containing: (A) methyl orange (dye 6); (B) brilliant blue (dye 8); and (C) methylene blue (dye 9). (Grey line = compound alone; red line = compound in corn flour gel; blue line = compound in tapioca flour gel; green line = compound in rice flour gel; insets for each panel show the region of the FTIR spectra between 1800 and 1200 cm-1).
Analysis of the dye-encapsulated gels via solid-state UV-visible spectroscopy revealed additional information about the molecular environment of the dyes and their intermolecular interactions (Figure 19). For most of the dyes investigated, the wavelength of maximum absorption shifted upon inclusion in the starch-based gel, indicating a change in their immediate environment, including a significant red-shift for cationic dyes Fuschin Basic (4), Rhodamine B (5), and Rhodamine 6G (7), (Table 4, entries 1-3). These results are in line with literature reports that cationic dyes interact favourably with starch-based gels,60 which are generally anionic in un-buffered aqueous solution due to hydroxyl group deprotonation.61 For anionic dye Methyl Orange (3), by contrast, virtually no shift in the maximum absorption wavelength was observed, which indicates less effective interactions between the anionic dye the anionic starch (Table 4, entry 4), and is in line with literature reports that Methyl Orange is more readily removed by metal-containing cationic absorbents rather than anionic starch adsorbents.62 In all cases, a substantial increase in the intensity of the absorbance was also observed upon dye encapsulation (Figure 19), this could be because of concentrated dye molecules within the gel matrix enhances the ability to absorb light than in non-encapsulated state.
For the other dyes, which include both zwitterionic Brilliant Blue (8) and cationic Methylene Blue (9) and Methyl Violet (10), the position of the maximum absorption wavelength varied depending on the identity of the starch (Table 4, entries 5-7). Methyl Violet in particular demonstrated drastic changes in the wavelength of maximum absorption, as well as the formation of multiple emission peaks, corresponding to different types of electronic transitions (Figure 19C).
Table 4. Maximum absorption wavelengths of dyes 4-10, in the absence of a gel and in the presence of various starch-based gels
Figure 19. Changes in the UV-visible absorbance spectra of organic dyes in the absence of the gel, and when encapsulated in corn flour gel, rice flour gel, and tapioca flour gel, for three representative dyes: (A) rhodamine 6g (7); (B) methyl orange (6); and (C) methyl violet (10)
Interestingly, gels formed from rice flour and methylene blue (dye 9) underwent a gradual color change when left at room temperature, turning from bright blue to white over 48 hours (Figure 20). This color change was highly specific to this dye-starch combination, with other starch gels that contained dye 9 retaining their blue color under identical experimental conditions.
Figure 20. Methylene blue-encapsulated hydrogels (rice flour, tapioca flour and corn flour) observed in room temperature.
The color change that occurs in methylene blue-rice flour gels is not surprising, as methylene blue has been reported to undergo facile photo-degradation under a variety of conditions,63 with a concomitant color change from the blue color of the dye and dye-containing products before degradation, to a white solid that forms a clear solution after degradation.64 What is surprising, by contrast, is the fact that methylene blue does not undergo a similar color change in gels formed from tapioca starch or corn starch, a fact that indicates the existence of a protective effect of these starches against methylene blue degradation.
Further attempts to explain the anomalous behaviour of methylene blue-containing rice flour gels focused on the stability of these gels following exposure to a variety of aqueous solutions, including acidic (1 M HCl), basic (1 M NaOH), and oxidizing (30% hydrogen peroxide) solutions (Figure 21). While exposure of the gel to the acidic and oxidizing conditions had no immediate effect on the gel, a significant effect was observed upon exposure to basic conditions. Notably, a color change in the gel started after four hours at room temperature and continued until the gel turned completely white after 48 hours (Figure 21C). This color change was accompanied by a degradation of the gel structure (Figure 21D), resulting in the formation of a dark black, free-flowing liquid from the initial bright blue gel. The base sensitivity of methylene blue has been noted previously65, and used to achieve more effective photo-degradation, with particular relevance of such enhanced photo-degradation for environmental remediation efforts.66
Figure 21. Methylene blue-encapsulated gels exposed to different conditions (H2O, NaOH, HCl, and H2O2): (A) before exposure; (B) immediately after exposure; (C) after four hrs of exposure; and (D) after 48 hrs of exposure
Currently, the distinct behaviour of gels created from rice flour and methylene blue, as opposed to gels formed from tapioca flour and corn flour using the same dye, lacks a definitive mechanistic explanation. However, we plan to investigate this phenomenon in upcoming studies.
4.5 Future Plan
The observation of a visible color change in methylene blue-containing rice flour gels is likely due to the known propensity of methylene blue to undergo photodegradation under various experimental conditions, with a concomitant blue-to-white color change. Surprisingly, however, methylene blue does not undergo a similar color change in gels formed with tapioca starch or corn starch, presumably due to enhanced stability/ protection from photodegradation. Our future research will focus on a detailed investigation of this anomalous behavior, and will include analyses of rice gels’ chemical, physical, and mechanical properties.
Additionally, the formation of rice-containing gels via other methods, followed by analysis of their ability to induce similar color changes in methylene blue contained therein, would provide significant information about the robustness and general applicability of the observed results. Such alternative methods will include the use of variable pH conditions, variable rice concentrations, and a variety of changes in other experimental parameters, all of which are expected to change the structure of the rice gel and its concomitant effect on the color-changing properties of the methylene blue dye guest.
By conducting tests on these systems, we aim to examine the potential formation of host-guest complexes between small molecule dye, such as methylene blue and the starch polymer. Further to investigate how the dye molecules are incorporated or bound within the constrained system i.e., gels. This knowledge has the potential to go beyond theoretical understanding and contribute to practical applications, particularly in the development of real-time sensors.
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