WO2022090995A1 - Système et procédé de détection de la présence de microplastiques dans des liquides - Google Patents

Système et procédé de détection de la présence de microplastiques dans des liquides Download PDF

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Publication number
WO2022090995A1
WO2022090995A1 PCT/IB2021/059981 IB2021059981W WO2022090995A1 WO 2022090995 A1 WO2022090995 A1 WO 2022090995A1 IB 2021059981 W IB2021059981 W IB 2021059981W WO 2022090995 A1 WO2022090995 A1 WO 2022090995A1
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WO
WIPO (PCT)
Prior art keywords
liquid
light
container
illumination
microplastics
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Application number
PCT/IB2021/059981
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English (en)
Inventor
Erin Mira KOWALSKY
Timothy David PURVIS
Kaylyn Johanna SINISGALLI
Matthew Victor FALCONE
Original Assignee
PIENAAR, Danie
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Publication date
Application filed by PIENAAR, Danie filed Critical PIENAAR, Danie
Publication of WO2022090995A1 publication Critical patent/WO2022090995A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G01N21/645—Specially adapted constructive features of fluorimeters
    • G01N21/6456—Spatial resolved fluorescence measurements; Imaging
    • G01N15/1433—
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
    • G01N2015/0007—Investigating dispersion of gas
    • G01N2015/0011—Investigating dispersion of gas in liquids, e.g. bubbles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
    • G01N2015/0042—Investigating dispersion of solids
    • G01N2015/0053—Investigating dispersion of solids in liquids, e.g. trouble

Definitions

  • the present application relates to a system and method for detecting the presence of microplastics in liquids.
  • Microplastics are an emerging contaminant and the need to detect their presence, particularly in water, is becoming increasingly more important.
  • the present system seeks to address this need.
  • a system for detecting the presence of microplastics in liquids comprising: a container for holding a liquid sample; a light source for illuminating the liquid and any particles contained in the liquid within the container with a selected illumination wavelength or warmth of light; a light receiver for receiving light reflected and/or emitted from the liquid and particles contained in the liquid at a receiving wavelength greater than or equal to the illumination wavelength or greater than or equal to the illumination warmth; a processor programmed to: access signal data received by the light receiver; and analyze the signal data to determine the presence of microplastics in the liquid sample.
  • the illumination wavelength may be between 370-400 nm.
  • the illumination wavelength may be between 435-460nm.
  • the illumination wavelength may be between 780-880 nm.
  • the illumination wavelength is supplemented by light greater than or equal to a Kelvin temperature of 3500.
  • liquid sample and contents in the container are not altered by the addition of dyes or acids prior to illumination, neither are the liquid sample and contents in the container dried prior to illumination.
  • the light source sequentially illuminates the liquid and contents with a plurality of selected illumination wavelengths and/or warmth of light.
  • the sequential illumination wavelengths may include 370-400nm, 435- 460nm, and/or 780-880 nm.
  • the container, light source, light receiver and processor may be enclosed in a liquid resistant housing with a liquid inlet pipe through which liquid can pass into the container and a liquid outlet pipe through which liquid can pass out of the container and wherein only the liquid inlet pipe and the liquid outlet pipe are open to liquid outside the housing to allow the liquid to pass into and out of the housing.
  • the light source is preferably one or more Light Emitting Diodes (LEDs).
  • LEDs Light Emitting Diodes
  • the light receiver is preferably a camera which takes an image of the illuminated liquid and particles contained in the liquid.
  • the data may be stored locally or exported to an external device.
  • the data may also be analyzed using artificial intelligence (Al), specifically machine learning (ML), as the method of characterization.
  • Al artificial intelligence
  • ML machine learning
  • the innate autofluorescence of the particles is the primary indicator utilized by the system.
  • a method for detecting the presence of microplastics in liquids including: placing a liquid sample in a container; illuminating the liquid and any particles contained in the liquid within the container with a selected illumination wavelength or greater than or equal to the illumination warmth of light; receiving light reflected and/or emitted from the liquid and particles contained in the liquid at a receiving wavelength greater than or equal to the illumination wavelength or warmth; access signal data received by the light receiver; and analyze the signal data to determine the presence of microplastics in the liquid sample.
  • Figure 1 is a block diagram illustrating the various components of a system for detecting the presence of microplastics in liquids
  • Figure 2 shows a first example embodiment of a system incorporating the components of Figure 1 ;
  • Figure 3 shows a second example embodiment of a system incorporating the components of Figure 1 ;
  • Figure 4 shows a third example embodiment of a system incorporating the components of Figure 1 ;
  • Figure 5 shows the third example embodiment of Figure 4 enclosed in a housing.
  • Microplastics are defined as plastic particles less than 5mm along their longest dimension.
  • the system 10 for detecting the presence of microplastics in liquids includes a container 12 for holding a liquid sample.
  • a light source 14 is used for illuminating the liquid and any particles contained in the liquid within the container 12 with a selected illumination wavelength or a selected warmth of light. ln one example, the light source 14 is one or more Light Emitting Diodes (LEDs).
  • LEDs Light Emitting Diodes
  • a light receiver 16 is used for receiving light reflected and/or emitted from the liquid and particles contained in the liquid at a receiving wavelength greater than or equal to the illumination wavelength or greater than or equal to the illumination warmth.
  • the light receiver 16 in one example is a camera which takes an image of the illuminated liquid and particles contained in the liquid.
  • a processor 18 is programmed to control the system.
  • the processor will have software executing thereon to access signal data, typically in the form of images, received by the light receiver and analyze the signal data to determine the presence of microplastics in the liquid sample.
  • the processor may take the form of at least one microprocessor and/or microcontroller.
  • the processor 18 does the image processing but it will be appreciated that this could be done remotely whereby the processor described includes a processor located remotely from the remainder of the components.
  • the light receiver data typically camera images
  • the remote processor is transmitted to the remote processor.
  • a memory 22 is used for storing the data required to be used by the system.
  • the processor 18 is comprised of an chicken Uno microprocessor together with a microcontroller to control the system.
  • Uno is an open-source microcontroller board developed by Engineering Task Force.
  • Microprocessors can be used to control electronic components that include timing and running code in a loop in order to execute a list of tasks repetitively. Additionally, chickens and other microprocessors can be controlled by other microcontrollers with code (e.g. Python) embedded in the microcontroller.
  • code e.g. Python
  • the prototype system used a camera connected via USB as the light receiver 16, which camera could not be controlled by the PC microprocessor directly.
  • microprocessors have memory components attached separately. Microcontrollers also have the ability to store data which is needed to retain testing images and a master script. The Python script on the microcontroller sends a signal to the chicken microprocessor to execute a task. The chicken microprocessor then controls the other electrical components of the device, for example to make the LEDs turn on and off and to make the valves (described below) open and close.
  • the PC microprocessor sends a signal back to the microcontroller indicating it is ready for the next command.
  • the Python script sends a signal to the camera through the microcontroller to capture and save the image in the memory 22.
  • the system will be described with reference to the liquid being water, which is one application of the system described, and the system is used to detect the presence of microplastics in the liquid, in this case water.
  • a first example embodiment is illustrated in Figure 2.
  • the container 12 is a cuvette which can be removed from the housing 28 in order to be filled with water.
  • a cap is placed on the cuvette and it is inserted into the housing 28.
  • this embodiment could be used by citizen scientists as it features a simple manual sample collection via the cuvettes.
  • the cuvette 12 would then be inserted into the housing 28 which consists of a light-tight box where LEDs 14 are used to excite the water sample.
  • the water sample can then be rapidly analyzed, and the results are displayed to the user via the display 26.
  • FIG. 3 A second example embodiment is illustrated in Figure 3 where the container 12 is a glass or see-through tube.
  • This device includes an inlet pipe 30 with an opening 32 via which water can enter into the device and pass through the pipe into the container 12.
  • An outlet pipe 34 allows water to pass from the container 12 and out of an opening 36.
  • the goal of this embodiment is to passively detect microplastics. Chambers could be added to this embodiment to control the flow of water through the device allowing for sedimentation to occur.
  • the device would be anchored in a body of water with continuous flow to drive water into the container 12.
  • Filters will be placed inside the inlet pipe 30 to filter out any coarse sediments. These filters will need to be cleaned periodically.
  • a light source in the form of LEDs 14 are placed next to the container 12 to illuminate the contents thereof.
  • Figure 4 illustrates a further embodiment in which the inlet pipe 30 bifurcates and directs water towards two containers 12a and 12b.
  • Light sources 14 are located alongside each of the containers 12 to illuminate the contents thereof.
  • the containers 12 used in this example are UV-B transparent glass tubes.
  • This embodiment also includes valves 38 which control the flow of water into the containers 12.
  • the valves are typically solenoid valves.
  • the valves 38 are placed in the liquid inlet pipe and the liquid outlet pipe adjacent to the containers, and are opened and closed to allow liquid to pass into the containers and then closed to keep liquid in the containers.
  • the opening and closing of the valves is controlled by the processor 18. In this way the two containers 12 are controlled as isolated units using the valves 38.
  • valves 38 will isolate the container 12a or 12b from the remainder of the system, allowing samples to be taken from the flow of water through the system.
  • valve closest to outlet 36 adjacent one of the containers 12a and 12b is closed, and then after a delay to allow the container 12a or 12b to fill with water, the valve closest to inlet 32 is closed.
  • the other testing chamber when testing is being conducted in one branch, the other testing chamber remains open allowing for continuous flow through the system.
  • testing chambers also allows for testing periods intermixed with continuous flow periods thereby allowing for previous sample remains to be flushed from the testing chamber 12 and the LEDs 14 time to cool after use.
  • Figure 5 illustrates the components from Figure 4 enclosed in a housing 40 which has a base 40a and a lid 40b connected together via hinges 42.
  • the housing 40 is a liquid resistant housing with only the liquid inlet pipe 30 and the liquid outlet pipe 34 open to liquid outside the housing to allow the liquid to pass into and out of the housing.
  • the hinged lid 40b could easily be lifted by maintenance personnel when necessary and then easily resealed using clamps to apply pressure between the device lid and a rubber liner below the lid, and the base.
  • UV-B tubing 12 and the LEDs 14 are thereby encompassed by a light tight box to ensure the highest quality images and mitigate user and environmental safety hazards from exposure to UV light.
  • an attachment point for an anchoring mechanism On the outside of the housing 40, typically beneath the base 40b is an attachment point for an anchoring mechanism.
  • the anchor allows the housing to remain in the center of flow, preventing drift, and making the housing easy to retrieve.
  • the anchor can be detached from the housing for transport purposes. Before deploying the housing, the anchor would need to connected to the base of the housing. A rope drum can also be used for the anchor rope to be tied off. This removeable anchor system increases portability.
  • the inlet pipe 30 typically has a filter, such as a mesh filter, located inside the pipe to prevent debris from entering the system.
  • a filter such as a mesh filter
  • the housing 40 exterior is designed to complement the interior. Preventing filters from clogging is an important design consideration as during deployment filters may become clogged due to debris larger than the mesh size accumulating on the surface. In order to mitigate this, the system 10 was designed such that water could flow though the housing 40 in both directions.
  • rudder flaps (not shown) controlled by processor 18, and modelled after kayak rudders, would be lowered from the base 40b by a solenoid to cause the housing 40 to rotate in the water flow about the fixed anchor point.
  • housing 40 was designed with rotation in mind, the housing 40 was engineered to be symmetrical about all axes.
  • the housing 40 is symmetrical as well to maintain a symmetrical mass distribution for rotation.
  • the housing is programmed to rotate clockwise and then counter-clockwise to prevent from over-rotating the anchor.
  • the liquid sample and contents in the container are not altered by the addition of dyes or acids prior to illumination and certainly do not need to be dried prior to illumination.
  • Prototypes of the system described above used a plurality of LEDs as the light source 14.
  • the plurality of LEDs included one or more of white light LEDs (with a temperature greater than or equal to a Kelvin temperature of 3500), LEDs with an illumination wavelength of between 370-400 nm, LEDs with an illumination wavelength between 435-460nm and LEDs with an illumination wavelength between 780-880 nm.
  • All of the LEDs selected to be used in a particular application are controlled by the processor 18 to illuminate the sample liquid and particles contained therein.
  • the light sources sequentially illuminate the liquid and contents with a plurality of selected illumination wavelengths and/or colour intensities of light.
  • the sequential illumination wavelengths include at least some of 370-400nm, 435-460nm, and 780-880 nm.
  • a water sample is entered into the testing chamber 12.
  • the sample is photobleached with UV light to mitigate biological fluorescence interference in the images [1].
  • the wait time of photobleaching sediments suspended in the water column would be able to settle.
  • Colloidal particulates in the water sample may not settle out but are likely too small to be detected by the camera 14 and are not of concern for interference with microplastic detection.
  • the white light LEDs then 385 nm LEDs, and lastly 448 nm LEDs are activated sequentially. It will be appreciated that the exact sequence of the lights could be varied.
  • the processor 18 is implemented using a combination of a microprocessor connected to a microcontroller executing a Python script, for example.
  • the Python script would place a heavy importance on timing and would vary with each embodiment.
  • the functions of the main loop of code would proceed as outlined for the third embodiment.
  • the Python script carries out the following steps: a. Activate solenoid to drop rudder flap into flow and initiate device rotation. b. Wait for rotation initiation. c. Activate opposite solenoid to drop opposing rudder flap slow device rotation. d. Wait. e. Raise rudder flaps. f. Resume main loop
  • the ML process starts with data collection.
  • images that contain microplastic samples images that do not contain microplastics, and images that contain objects which may appear in microplastic samples that are not microplastics (like fertilizer, paper, soil).
  • Images used to create a data set for training the ML algorithm are produced experimentally, using the same process outlined above. Images used in the training process are categorized as training images, validation images, or test images.
  • Training images are used throughout the algorithm improvement cycle to modify the algorithm while validation images are only to be used intermittently to evaluate the training progress. Test images are only to be used on the final algorithm, so that model changes are made from these images to improve the algorithm’s predictive capabilities.
  • Image processing may involve image scaling, colour alteration, or other tools. Most importantly, the training data set must be labelled. Image analysis software, like the open source tool Imaged, used by the University of Warwick in Coventry, U.K. to identify microplastics in ocean water, is used to aid with this process [2],
  • images are labelled to identify which pixels contain microplastics. Labels allow the algorithm to learn how to distinguish microplastics from other objects in samples. Once labelled, to create a sufficient number of images to train, validate, and test the algorithm, the image set is expanded. Data augmentation is the most common method used to create more test images to improve model performance and reduce the amount of time spent manually collecting images.
  • model outputs are evaluated. Based on labels in the data, models are able to self-evaluate and auto update algorithm parameters to improve performance.
  • Algorithms are written in many coding languages, including Python. As the electronics portion of the system will be controlled with a Python script, the ML algorithm could be integrated into the Python script, allowing for in-situ image processing.
  • initial testing was conducted to determine the validity of fluorescence as a microplastic detection method in water.
  • the first phase employed a spectrofluorometer, which is a machine used to measure the fluorescence signature of a sample at a specified excitation or emission wavelength range.
  • the second phase required testing to occur outside of the tightly controlled environment of a spectrofluorometer to determine if experimental results could be recreated with a preliminary sensor prototype.
  • the spectrofluorometer used in testing provided information regarding the intensity of fluorescence as a function of excitation, which was used to pinpoint optimal excitation wavelengths to use in the sensor. Based on background microplastic research, testing was performed on PE, PS, PP, and PET, the most prevalent microplastics in the environment.
  • Paper fluorescence is visible when excited between 280-450 nm [3].
  • testing was the speed at which non-plastic samples settled. After inducing turbulence, the sample was loaded and pictures were taken, with a maximum of 10 seconds elapsing before the first image was captured to avoid having the material settle. As it was observed that environmental materials settled, in the future the testing rig would be set to the higher mounting locations, so that light could be directed to the top portion of the testing tube to only target microplastics. In doing so, the testing cell could isolate the response of plastics by only allowing plastic to be hit by the excitation light.
  • the system provides the rapid quantification of microplastics in liquid using fluorescence as a microplastic detection method in water, machine learning and image analysis in a portable solution that can be used in-situ. It will also be appreciated that the innate autofluorescence of the particles is the primary indicator utilized by the system.
  • the system can be used by researchers in the field and by water and wastewater facilities. Additionally, the bottling industry interested in water quality will also be able to use the system described.

Abstract

On décrit sur un système et un procédé de détection de la présence de microplastiques dans des liquides. Le système comprend un récipient permettant de contenir un échantillon de liquide. Une source lumineuse sert à éclairer le liquide et toutes les particules contenues dans le liquide à l'intérieur du récipient selon une longueur d'onde sélectionnée d'éclairage ou selon une chaleur sélectionnée de lumière. Un récepteur de lumière sert à recevoir la lumière réfléchie et/ou émise par le liquide et par les particules contenues dans le liquide à une longueur d'onde de réception supérieure ou égale à la longueur d'onde d'éclairage ou supérieure ou égale à la chaleur d'éclairage. Un processeur est programmé pour accéder à des données de signaux reçues par le récepteur de lumière et pour analyser les données de signaux pour déterminer la présence de microplastiques dans l'échantillon de liquide.
PCT/IB2021/059981 2020-10-29 2021-10-28 Système et procédé de détection de la présence de microplastiques dans des liquides WO2022090995A1 (fr)

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US63/107,025 2020-10-29

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115468882A (zh) * 2022-09-15 2022-12-13 中国水利水电科学研究院 一种测定水体中微塑料生物结膜沉降速率的室内模拟装置
CN117554319A (zh) * 2023-10-20 2024-02-13 广东省水利水电科学研究院 一种微塑料丰度的检测方法、系统、装置及存储介质

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US6488892B1 (en) * 1998-04-17 2002-12-03 Ljl Biosystems, Inc. Sample-holding devices and systems
US20190293565A1 (en) * 2015-10-15 2019-09-26 Woods Hole Oceanographic Institution System for rapid assessment of water quality and harmful algal bloom toxins
KR102094373B1 (ko) * 2018-09-05 2020-03-27 주식회사 마하테크 미세플라스틱 검출장치
KR20200097087A (ko) * 2019-02-07 2020-08-18 연세대학교 산학협력단 Uv led를 이용한 미세플라스틱 검출기

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6488892B1 (en) * 1998-04-17 2002-12-03 Ljl Biosystems, Inc. Sample-holding devices and systems
US20190293565A1 (en) * 2015-10-15 2019-09-26 Woods Hole Oceanographic Institution System for rapid assessment of water quality and harmful algal bloom toxins
KR102094373B1 (ko) * 2018-09-05 2020-03-27 주식회사 마하테크 미세플라스틱 검출장치
KR20200097087A (ko) * 2019-02-07 2020-08-18 연세대학교 산학협력단 Uv led를 이용한 미세플라스틱 검출기

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115468882A (zh) * 2022-09-15 2022-12-13 中国水利水电科学研究院 一种测定水体中微塑料生物结膜沉降速率的室内模拟装置
CN117554319A (zh) * 2023-10-20 2024-02-13 广东省水利水电科学研究院 一种微塑料丰度的检测方法、系统、装置及存储介质

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