Optical sensor for detecting metal ions

Abstract

An optical sensor (100) for detecting metal ions comprises a resonant structure (102) coated with an L-glutathione-based chelating agent for binding to metal ions in a sample. The metal ions bind to the L-glutathione-based chelating agent upon contact therewith and change its material index, resulting in a change in resonance wavelength over time depending on the unique absorption kinetics of each metal ion. The change in resonance wavelength over time is analyzed using a trained neural network to determine the concentration of each metal ion within the sample.

Description

Technical Field

[0001] The utility model generally relates to the detection of metal ions, and particularly to a general sensor for detecting the concentrations of multiple metal ions in a sample. Background Art

[0002] Heavy metal ions are ubiquitous in the environment, including various foods and water sources. High concentrations of heavy metal ions exceeding the permitted safety limits can cause temporary health problems such as nausea, diarrhea, respiratory problems, etc. However, long-term exposure to heavy metal ions can have a permanent impact on human health, manifested as chronic kidney, nervous system, and DNA damage, etc. Therefore, it is necessary to detect and remove heavy metal ions in the environment such as drinking water sources.

[0003] There are various methods and devices for detecting various heavy metal ions in water on the market. U.S. Patent US9261474B2 discloses a method for analyzing a fluid sample using a resonance sensor. The method includes the following steps: providing a sensor assembly having a sensing region with a plurality of resonance circuits and a plurality of tuning elements. The method also includes exposing the sensor assembly to an environment containing the sample and probing the sample with one or more frequencies generated by the sensor assembly. In addition, the method includes determining the impedance of the sensor response within the measurement spectral frequency range of the sensor assembly and correlating the measured value of the impedance of the sensor assembly with at least one environmental property of the sample. The method performs a multivariate statistical analysis on the resonance impedance spectrum of the sensor response to detect the components in the test environment (such as water).

[0004] Another sensing system and related method for detecting metal ions in water are disclosed in the publication entitled "On-Site / In Situ Continuous Detecting ppb-Level Metal Ions in Drinking Water Using Block Loop-Gap Resonators (BLGR) and Machine Learning". With this sensor, there is no physical contact between the water sample and the sensing element, thus extending the service life of the sensor and maintaining the consistency of its function. The proposed BLGR is small in size, highly adjustable in resonance frequency, and moderate in Q value, and can be flexibly integrated into the existing water supply system. The proposed system uses machine learning algorithms (SVM and SVR) to replace the existing mathematical models that are limited to fixed ions in solid matrices. The system can estimate the concentration of metal ions in water only using the raw data. Due to the increase in resonance frequency, the measurement error decreases with the increase in the number of gaps in the BLGR.

[0005] Another method for detecting heavy metal ions in water uses a chelation layer coated on a metal ion optical sensor. However, this type of sensor is static and can only detect one or a few metal ions by analyzing the change in the optical index of the chelation layer material when a metal ion sample is added to the chelation layer of the metal ion optical sensor. This method can also be used to detect the concentration of relevant metal ions by analyzing the change in the resonance condition of the metal ion sensor. However, this method also has some disadvantages. For example, it requires the chelation layer to have high selectivity, and other forms of ions cannot be absorbed by the chelation layer. Therefore, one type of chelation layer can only be used to sense one type of metal ion. In addition, integrating different chelation layers for sensing different metal ions will lead to complex packaging, thus affecting the large-scale manufacturing ability of the optical sensor.

[0006] Currently, commercial methods for detecting metal ion concentration include inductively coupled mass spectrometry / optical emission spectrometry (ICP-MS / OES) and the use of test strip sensors. However, these methods require expensive and bulky equipment and are time-consuming and laborious. ICP-MS / OES can be quantitative, but it requires expensive and bulky test equipment, sample pretreatment, sample collectors, and trained laboratory analysts. On the other hand, although test strip sensors are convenient for on-site measurement, they can only be colorimetric and cannot provide quantitative values of metal ion concentration. In addition, different people may have different perceptions of color, which may lead to inaccurate results.

[0007] Therefore, there is a need for a device to quantitatively determine various metal ions and their concentrations in samples such as water on-site. The required sensor should be compact in structure and easy to use, and can be widely applied to monitoring systems to determine the concentrations of various heavy metal ions. Summary of the Utility Model

[0008] On the one hand, the present utility model provides an optical sensor for detecting multiple metal ions in a sample. The optical sensor includes an optical structure having an L-glutathione-based chelation layer formed by coating an L-glutathione-based chelating agent on the top surface of a resonance structure.

[0009] In one embodiment, the L-glutathione-based chelation layer is configured to bond with metal ions after contact therewith, thereby changing the material index of the L-glutathione-based chelation layer.

[0010] In one embodiment, a trained neural network is provided to determine the concentration of each metal ion in the sample based on the shift of the resonance wavelength over time.

[0011] In one embodiment, when in contact with metal ions in a sample, the L - glutathione - based chelating layer on the resonance structure causes the resonance wavelength to vary over time according to the unique absorption kinetics of each metal ion in the sample. Advantageously, the variation of the resonance wavelength over time is unique for each metal ion in the sample and can be analyzed using a trained neural network to determine the type and concentration of each metal ion in the sample.

[0012] In one embodiment, the optical sensor is formed on a silicon - on - insulator (SOI) platform functionalized with a chelating agent. Advantageously, this helps in fabricating the optical sensor in a compact footprint.

[0013] In one embodiment, a single chelating layer is formed on the top surface of the resonance structure by coating the resonance structure with an L - glutathione - based chelating agent configured to bond with metal ions in the sample.

[0014] The resonance structure is generally selected from compact optical structures, including Mach - Zehnder interferometers, microring resonators, etc.

[0015] In one embodiment, the optical sensor is configured to operate in a wavelength range of 1955 - 1992 nanometers, which helps the optical sensor to avoid the absorption peak of H2O.

[0016] In one embodiment, the chelating layer formed on the resonance structure of the optical sensor forms a waveguide with a width of 500 - 600 nanometers and a height of 80 - 500 nanometers.

[0017] In one embodiment, the optical structure coated with a chelating agent forms a waveguide of sufficient length and width to propagate light. The waveguide formed on the resonance structure generally enables at least 33% of the propagated light to interact with the chelating layer.

[0018] In one embodiment, an optical sensor including a resonance structure with an L - glutathione - based chelating layer measures the various metal ions and their respective concentrations in a detection sample by analyzing the variation of the resonance wavelength of the optical sensor over time using a trained neural network and dynamically.

[0019] In one embodiment, the optical sensor is compact and can be used for on - site measurement of the concentration of various metal ions.

[0020] In one embodiment, the chelating layer formed on the resonance structure is configured to bond with metal ions in the sample including lead (Pb), zinc (Zn), mercury (Hg), nickel (Ni), cadmium (Cd), copper (Cu), chromium (Cr), and arsenic (As) upon contact. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] A deeper understanding of the present utility model will be obtained by referring to the following description in conjunction with the accompanying drawings:

[0022] Figure 1 FIG. is a block diagram showing an optical sensor according to an embodiment of the present utility model;

[0023] Figure 2 FIG. is a diagram showing the change in resonance wavelength of a chelating layer when metal ions are added thereto according to an embodiment of the present utility model;

[0024] Figure 3 FIG. is a diagram showing the change in resonance wavelength of an optical sensor when a specific concentration of metal ions is added according to an embodiment of the present utility model;

[0025] Figure 4 FIG. is a diagram showing the dynamic change in resonance wavelength of a chelating layer over time when metal ions are added to a sample according to an embodiment of the present utility model; and

[0026] Figure 5 FIG. is a block diagram showing the analysis of the dynamic change in resonance wavelength in an optical sensor using a neural network according to an embodiment of the present utility model. Detailed Embodiments

[0027] Based on the above overview, some specific and alternative embodiments will be described below to understand the creative features of the present utility model. However, it is obvious to those skilled in the art that the present utility model can be implemented without these specific details. To avoid obscuring the present utility model, certain details may not be described in detail. For ease of reference, when referring to the same or similar common features in the drawings, the same reference numerals will be used throughout the drawings.

[0028] The embodiments of the present utility model are described by way of illustration. As will be realized, the present utility model may also have other different embodiments, and several details thereof may also be modified in different aspects, all of which will not depart from the scope of the present utility model. It should be noted that standard devices or components may not be shown as they are known in the art.

[0029] The present utility model provides an optical sensor for detecting multiple metal ions in samples such as water. As Figure 1The optical sensor 100 shown includes a resonant structure 102 that has a chelating layer on the top surface of the resonant structure. The chelating layer is formed by coating an appropriate chelating agent for bonding with multiple metal ions in the sample on the top surface of the resonant structure 102. In one case, a single chelating layer is formed by coating a chelating agent configured to bond with metal ions in the sample on the top surface of the resonant structure 102. Generally, the chelating layer will bond after contacting the metal ions, thereby changing the refractive index of the material of this layer. Therefore, the change in the refractive index of the material of the chelating layer causes the resonant wavelength to change over time according to the unique absorption kinetics of each metal ion in the chelating layer. Since the absorption kinetics of each metal ion in the sample are unique, the change in the resonant wavelength over time is also unique and can be used to train a neural network. The trained neural network can be used to determine the type and concentration of each metal ion in the sample.

[0030] In one embodiment, the optical sensor 100 is formed by heterogeneous integration between an L - glutathione - based chelating layer and the resonant structure 102. Generally, the L - glutathione - based chelating layer is fabricated on the top surface of the resonant structure 102. As Figure 2 shown, after adding metal ions and subsequent absorption of the metal ions into the chelating layer, the refractive index of the material of the L - glutathione - based chelating layer changes. With the addition of metal ions, this change in the refractive index of the material effectively changes the resonant wavelength of the optical sensor 100.

[0031] Figure 3 is a schematic diagram showing the change in the resonant wavelength of the optical sensor 100 with the concentration of metal ions in the chelating layer after adding a specific concentration of metal ions to the chelating layer. After adding a specific concentration of metal ions, the resonant wavelength changes from λ1 to λ2. However, the rate at which the chelating layer absorbs metal ions in the sample is different, and the resonant wavelength of the optical sensor will also change over time based on the type and amount of metal ions absorbed into the chelating layer. Since the absorption kinetics of each metal ion in the sample are unique, the change in the resonant wavelength over time caused by each metal ion is also unique.

[0032] In one embodiment, as Figure 4 shown, when a mixture of metal ions with different concentrations is added, the change in the resonant wavelength obtained from the optical sensor 100 over time is a superposition of the unique absorption kinetics of each metal ion in the sample. As Figure 5As shown, the curves of the resonance wavelengths of different concentrations of metal ions changing with time can be used to train a neural network. The training set and validation set of the neural network are labeled according to the metal ion type and the concentration level of each metal ion. Since the dynamic measurement of the wavelength change of the optical sensor 100 is the superposition of the absorption kinetics of different metal ions at different concentrations, the trained neural network can determine the metal ion type and their respective concentrations in the sample.

[0033] The proposed optical sensor 100 is compatible with all optical platforms. In one embodiment, the resonance structure 102 is selected from compact optical structures including Mach-Zehnder interferometers, microring resonators, etc. In one embodiment, the optical sensor 100 can be formed on a silicon-on-insulator (SOI) platform functionalized with a chelating agent. Advantageously, this helps to fabricate the optical sensor 100 in a compact footprint of approximately ~20×20μm 2 of the compact footprint.

[0034] In one embodiment, a single chelation layer is formed on the top surface of the resonance structure 102 by coating an L-glutathione-based chelating agent on the resonance structure. Generally, the L-glutathione-based chelating agent will bond with the metal ions in the sample, causing the resonance wavelength of the optical sensor to change with time according to the unique absorption kinetics of each metal ion in the sample. A trained neural network can be used to obtain the type and their respective concentrations of the metal ions in the sample.

[0035] In one embodiment, the optical sensor 100 is configured to operate in the wavelength range of 1955 - 1992 nanometers to avoid the absorption peak of H2O. In another case, the free spectral range of the optical sensor 100 is 4 - 10 nanometers.

[0036] The optical structure 102 coated with the chelating agent produces a waveguide with sufficient length and width to propagate light. In one embodiment, the waveguide formed on the resonance structure 102 can make at least 33% of the propagated light interact with the chelation layer. In another embodiment, the waveguide width formed on the resonance structure of the optical sensor is 500 - 600 nanometers, and the height is 220 nanometers. In another case, the waveguide width formed on the resonance structure of the optical sensor is 500 - 600 nanometers, and the height is approximately between 80 - 500 nanometers. The optical sensor 100 with an L-glutathione-based chelation layer detects various metal ions and their respective concentrations in the sample by using a trained neural network to analyze the change of the resonance wavelength of the optical sensor 100 with time through dynamic measurement.

[0037] According to an alternative embodiment, the present utility model provides an optical sensor for detecting multiple metal ions in samples such as water. The optical sensor 100 includes a resonant structure 102 having an L - glutathione - based chelating layer on its top surface. The L - glutathione - based chelating layer is formed by coating an appropriate L - glutathione - based chelating agent for bonding with multiple metal ions in the sample on the top surface of the resonant structure 102. In one case, a single chelating layer is formed by coating a chelating agent configured to bond with metal ions in the sample on the top surface of the resonant structure 102. Generally, after the L - glutathione - based chelating layer comes into contact with metal ions, bonding occurs, thereby changing the refractive index of the layer material. Therefore, the change in the refractive index of the material of the L - glutathione - based chelating layer causes the resonant wavelength to change over time according to the unique absorption kinetics of each metal ion in the chelating layer. Since the absorption kinetics of each metal ion in the sample are unique, the change in the resonant wavelength over time is also unique and can be used to train a neural network. The trained neural network can be used to determine the type and concentration of each metal ion in the sample.

[0038] In one embodiment, the chelating layer on the resonant structure 102 bonds with metal ions including lead (Pb), zinc (Zn), mercury (Hg), nickel (Ni), cadmium (Cd), copper (Cu), chromium (Cr), arsenic (As), silver (Ag), iron (Fe), manganese (Mn), molybdenum (Mo), boron (B), calcium (Ca), antimony (Sb), cobalt (Co), etc.

[0039] In one embodiment, the optical sensor 100 can be obtained by functionalizing the surface of the resonant structure 102 on an integrated photonics platform (i.e., III - V, silicon photonics) or the resonant surface of an optical sensor (such as a Mach - Zehnder interferometer, micro - ring resonator, etc.).

[0040] Compared with traditional methods or devices (such as ICP - MS / OES) that require complex sample pretreatment and operation by highly skilled personnel, the integration of the chelating layer with the resonant surface of the optical sensor 100 enables a compact sensor in a portable form. In addition, the present optical sensor 100 can also be mass - produced by functionalizing the chip surface of a silicon photonics platform (such as a Mach - Zehnder interferometer, micro - ring resonator, etc.).

[0041] Although the present utility model has been described according to the requirements of the preferred embodiment and specific operating ranges and conditions, those skilled in the art will understand that the present utility model can be subject to other changes and modifications in addition to the specifically described content.

Claims

1. An optical sensor (100) for detecting metal ions, characterized in that, Comprising: A resonance structure (102) having a top surface; And A chelation layer formed on the top surface of the resonance structure (102) by coating an L-glutathione-based chelating agent on the resonance structure; Wherein the chelation layer has a plurality of waveguides with a width of 500-600 nanometers and a height of 80-500 nanometers.

2. The optical sensor (100) for detecting metal ions according to claim 1, characterized in that, The resonance structure (102) is configured to operate in a wavelength range of 1955-1992 nanometers.

3. The optical sensor (100) for detecting metal ions according to claim 1, characterized in that, The resonance structure (102) is selected from compact optical structures including Mach-Zehnder interferometers and microwave resonators.

4. The optical sensor (100) for detecting metal ions according to claim 1, characterized in that, The plurality of waveguides are capable of enabling at least 33% of the propagating light to interact with the chelation layer.

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