Method and apparatus for material analysis

Abstract

A device for detecting the presence of a target substance within a material a body configured be held by a user, the body having a first housing for housing a controller and a second housing for housing at least a signal generator, a signal transmitter and a signal receiver; wherein the second housing is configured to abut a surface of the material to be tested such that the signal transmitter is positioned to transmit a signal onto a surface of the material and the signal receiver receives a reflected signal from the surface of the material, the reflected signal being resultant from the transmitted signal; and wherein the controller analyses the received reflected signal to determine the presence of the target substance within the material.

Description

[0001] METHOD AND APPARATUS FOR MATERIAL ANALYSIS RELATED APPLICATIONS

[0002] The present application claims priority from Australian Provisional Patent Application No. 2025900121 filed 15 January 2025, the entire contents of which are incorporated herein by reference.

[0003] FIELD OF INVENTION

[0004] The present invention relates generally to a method and apparatus for performing analysis of a material for the presence of a substance, and in particular, to a method and device for analysing a material for the presence of a hazardous substance, such as asbestos.

[0005] BACKGROUND OF THE INVENTION

[0006] With an increased knowledge of the health implications that may be associated with human contact with some hazardous materials, there is a need to be more vigilant in the handling of such materials such that appropriate safety measures can be taken if exposure cannot be avoided.

[0007] An example of such a material that was once used extensively in building construction and which is now considered as a severe health hazard to individuals, is asbestos. Asbestos was historically highly valued for its properties, including durability and heat resistance, and was extensively used in building materials to take advantage of these properties. Over time, it has become recognised that when materials containing asbestos are disturbed, the asbestos is released as airborne fibres that can be inhaled by individuals, leading to serious diseases like lung cancer, asbestosis, and mesothelioma. Through this understanding of the harmful implications such a material can have on humans who come into contact with it, the use of the material in new building applications has widely ceased. However, in many existing structures asbestos still remains in the material and this poses a significant health risk to workers and the general public if such structures are renovated or demolished.

[0008] To address this concern, a number of methods for detecting and testing for the presence of asbestos testing in a material have been proposed. Such methods typically involve obtaining a physical sample of the material for testing in a laboratory setting which, whilst reliable, is invasive, time-intensive, requires long delays and raises the risk of fibre release during the sampling procedure ,endangering workers and nearby individuals.

[0009] There have been atempts to provide a testing device in the form of a portable detection device that can be used on-site. However, such devices typically require invasive analysis techniques that require direct contact with a material sample, or they lack the accuracy and adaptability needed for on-site testing conditions.

[0010] Thus, there is a need to provide an apparatus and method for detecting the presence of a target substance in a material that is simple and easy to employ and which is reliable and does not require invasive techniques that destroy the structural integrity of the material being tested.

[0011] The above references to and descriptions of prior proposals or products are not intended to be, and are not to be construed as, statements or admissions of common general knowledge in the art. In particular, the above prior art discussion does not relate to what is commonly or well known by the person skilled in the art, but assists in the understanding of the inventive step of the present invention of which the identification of pertinent prior art proposals is but one part.

[0012] STATEMENT OF INVENTION

[0013] The invention according to one or more aspects is as defined in the independent claims. Some optional and / or preferred features of the invention are defined in the dependent claims.

[0014] Accordingly, in one aspect of the invention there is provided a device for detecting the presence of a target substance within a material, comprising:

[0015] a body configured be held by a user, the body having a first housing for housing a controller and a second housing for housing at least a signal generator, a signal transmiter and a signal receiver;

[0016] wherein the second housing is configured to abut a surface of the material to be tested such that the signal transmiter is positioned to transmit a signal onto a surface of the material and the signal receiver receives a reflected signal from the surface of the material, the reflected signal being resultant from the transmited signal; and wherein the controller analyses the received reflected signal to determine the presence of the target substance within the material.The first housing may have a display screen for conveying information to the user. The information may be conveyed by the display screen comprises information determining whether the target substance is present in the material. The signal generator may be a microwave signal generator that generates electromagnetic signals to be sent to the signal transmitter. The electromagnetic signals may have a frequency modulation across a range of 10 MHz to 10 GHz, with a power output adjustable between 0 dBm and +20 dBm.

[0017] The signal transmitter may be an Ultra-Wideband (UWB) Microwave antenna. The Ultra- Wideband (UWB) Microwave antenna may operate within a 3.1 GHz to 10.6 GHz frequency range and provides a beamwidth of 30° to 90° and a gain of6-12 dBi.

[0018] The receiver unit may be mounted to the second housing to capture the reflected electromagnetic waves from the target material. The receiver unit may operate with a frequency response of 3.1 GHz to 10.6 GHz and a dynamic range of 100 dB.

[0019] The controller may comprise a computer controller configured with machine learning algorithms to analyse the reflected signal to determine the presence of the target substance in the reflected signal based on the target substance’s dielectric properties. The computer controller may analyse the reflected signal by performing real-time analysis of the reflected signal with a sampling rate of at least 10 gigasamples per second (GS / s).

[0020] The machine learning algorithms used by the computer controller may be pretrained on a dataset containing profiles of target substance-containing and nontarget substance materials, allowing differentiation based on dielectric variations. The dielectric properties may include variations in signal amplitude, phase, and frequency that are characteristic of target substance-containing materials.

[0021] Accordingly, in another aspect of the invention there is provided a method for detecting the presence of a target substance within a material, comprising:

[0022] generating and transmitting electromagnetic microwave signals towards a surface of the material;

[0023] capturing reflected electromagnetic microwave signals from the material; analysing the reflected electromagnetic microwave signals to determine the presence of the target substance within the material based on the material’s dielectric properties; anddisplaying real-time detection results determining the presence or otherwise of the target substance in the material.

[0024] The step of generating and transmitting the electromagnetic microwave signals may comprise transmitting the signals in a frequency range of 3.1 GHz to 10.6 GHz.

[0025] The step of analysing the reflected electromagnetic microwave signals may comprise performing real-time analysis of the reflected signals with a sampling rate of at least 10 gigasamples per second (GS / s).

[0026] The step of analysing the reflected electromagnetic microwave signals may further comprise applying machine learning algorithms to the reflected signals to determine the presence of the target substance based on the material’s dielectric properties.

[0027] The machine learning algorithms may be pre-trained on a dataset containing profiles of target substance-containing and non-target substance containing materials, allowing differentiation based on dielectric variations.

[0028] BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The invention may be better understood from the following non-limiting description of preferred embodiments, in which:

[0030] Fig. 1 is a perspective view depicting the device of the present invention being positioned for use in accordance with an embodiment of the present invention;

[0031] Fig. 2 is a plan view of the device of Fig. 1;

[0032] Fig. 3 is a side view of the device of Fig. 1;

[0033] Fig. 4 is a top view of the device of Fig. 1; and

[0034] Fig. 5 is a view showing the device of the present invention in use.

[0035] DETAILED DESCRIPTION OF THE DRAWINGS

[0036] Preferred features of the present invention will now be described with particular reference to the accompanying drawings. However, it is to be understood that the features illustrated in and described with reference to the drawings are not to be construed as limiting on the scope of the invention.The device of the present invention will be described below in relation to an apparatus and method of detecting the presence of asbestos in material present in a building construction. However, it will be appreciated that the apparatus and method of the present invention could be employed to detect the presence of a variety of substances present in a material, and that material could be part of a building construction or a stand-alone material, as will be appreciated by those skilled in the art.

[0037] Referring to Fig. 1 and Fig. 5, a device 10 of the present invention is depicted in use to detect for the presence of asbestos in a building material 5. In the embodiment as depicted in Fig. 5, the building material 5 is shown as a wall of a building structure, although the building material 5 may be present on any surface of the building structure, including a floor surface or a roof surface.

[0038] The device 10 has a body 12 that is configured to be held in a hand of a user 8, in the maimer as shown in Fig. 5. The body 12 has a handle portion 14 that can be gripped by the user 8 for use. The body 12 comprises a first housing portion 16 for housing a computer controller configured having a CPU and related componentry to operate the device 10 for use. The first housing portion may also house a power source, such as a batter pack for providing operating power for the device. The power source may include a 12V-24V lithium-ion battery, offering a capacity between 2000-5000 mAh and an operational runtime of 4-6 hours, although other types of battery systems with variable operating parameters are also envisaged.

[0039] An interactive screen 15 is mounted within a front surface of the first housing portion to provide control of the device for the user. The interactive screen may be a touch screen that enables the user to control the operation of the device and view the results of the analysis performed on the material in real time. Such a control screen 15 ensures that the testing procedure is being conducted appropriately and will provide feed-back to the user on the operating parameters of the device to ensure that the readings provided by the device have been performed correctly.

[0040] The device also comprises a second housing portion 18 that houses the testing components of the device 10. The second housing portion 18 is mounted at a rear of the device 10, behind the first housing portion 16 and interactive screen 15 and is shaped to have an outer contact surface 19 that is positioned against the building material 5 to be tested, as will be discussed in more detail below.

[0041] The second housing portion 18 contains a Portable Microwave Signal Generator(PMSG) that generates an electromagnetic signal that is applied to the surface of the building material 5 by way of an Ultra-Wideband (UWB) Microwave Antenna positioned adjacent the contact surface 19 thereof The PMSG operates to provide frequency modulation across a range of 10 MHz to 10 GHz, and has a power output adjustable between 0 dBm and +20 dBm. The PMSG is capable of generating electromagnetic signals for the UWB antenna that are compatible with the UWB antenna to ensure optimal signal generation.

[0042] The Ultra-Wideband (UWB) Microwave Antenna operates within the 3.1 GHz to 10.6 GHz frequency range, and is configured to provide a beamwidth of 30° to 90° and a gain of 6-12 dBi. Such a wide frequency range facilitates effective penetration of the signal into the building material 5 and sufficient reflection of the signal from a variety of different types of material substrates, which provides for asbestos detection across a variety of different applications.

[0043] To facilitate the receival of the reflected signal, the second housing portion 18 of the device is also fitted with receiver unit incorporated into the outer contact surface 19 thereof. The receiver unit is configured to a capture the reflected electromagnetic waves from the building material and in a preferred form operates with a frequency response of between 3.1 GHz to 10.6 GHz with a dynamic range of 100 dB. Such a receiver unit ensures that the device accurately detects reflections that indicate the presence of asbestos in the building material. It will be appreciated that the operating configuration of the receiver unit may vary depending on the application upon which it is to be used.

[0044] The computer controller housed within the first housing portion 14 may also be configured to process the reflected signals received from the subject building material 5. In this regard, the computer controller has a memory equipped to access advanced machine learning algorithms to enable the processor to perform real-time analysis of the reflected microwave data present in the reflected signals to detect the presence of asbestos. The controller may have high sampling rate of 10 GS / s and sufficient processing power to provide rapid and accurate assessment of the composition of the building material being tested.

[0045] To use the device 10, the device 10 is brought into contact with the surface of the building material 8 to be analysed, such that the outer contact surface 19 of the second housing portion 18 of the device is in contact with the surface of the building material 8, in the maimer as depicted in Fig. 5.

[0046] The device is then activated by the user pressing a button located on the handle of the device or by selecting an icon present on the interactive screen 15. Uponactivation of the device, the PMSG generates an electromagnetic microwave signal which is transmitted to the UWB Antenna and is then transmitted into the building material 8. The signal passes into the building material where it interacts with the unique dielectric properties of the material 8. To optimise the penetration depth of the signal, the PMSG power output may be adjusted based on the material thickness and type.

[0047] As the electromagnetic microwaves interact with elements present in the building material 8, a reflected microwave signal is generated that exhibits a specific pattern, depending on the material components present in the tested material. This reflected signal is reflected back towards the device 10 and is detected by the receiver unit of the device. If asbestos is present in the material 8, the reflected wave signal will contain characteristics unique to the presence of asbestos. The reflected signal contains a degree of specific data indicative of the presence, or absence, of a variety of elements within the material. These signals are then transmitted to the computer controller which analyses the signals and applies machine learning algorithms to determine whether the signals contain the unique characteristics of signals reflected from a material having asbestos present therein.

[0048] Asbestos is a collective term used to describe a set of silicate minerals (chrysotile, amosite, crocidolite, actinolite, tremolite and anthophyllite) that belong to the serpentine and amphibole groups. Of these, chrysotile is the only serpentine type, and accounts for around 95% of naturally occurring asbestos, Thus, by detecting reflected signals indicative of the presence of amosite, chrysotile, and crocidolite fibres present, the device can assess for the detection of asbestos in the material being sampled.

[0049] The integration of Al-based machine learning algorithms to process and analyse the reflected signal enhances detection precision and ensures reliable identification of asbestos across a diverse range of materials and context. These reflected waves are processed by Al-driven algorithms capable of recognising patterns within the signal indicative of asbestos. The algorithms continuously update as data is collected and analysed to improve accuracy and efficiency over and are pre-trained on a dataset containing profiles of asbestos-containing and non-asbestos containing materials, allowing differentiation based on dielectric variations.

[0050] The computer controller performs real-time analysis of the reflected signals with a sampling rate of at least 10 giga samples per second (GS / s). The dielectricproperties analysed by the machine learning algorithms may include variations in signal amplitude, phase, and frequency that are characteristic of asbestoscontaining materials. The computer controller may process the data by filtering out noise and enhancing signal clarity to improve detection accuracy, particularly in environments with interference from other materials. To optimise signal penetration depth based on the material thickness and type, comprising a step of adjusting the electromagnetic signal generator’s power output to optimise signal penetration depth based on the material thickness and type.

[0051] The analysis can then be displayed on the interactive screen 15 to provide the user 8 with an indication as to whether the test material 8 contains asbestos. The interactive screen may be configured to presents asbestos detection results using colour-coded or symbol-based indicators, allowing rapid identification by the user. The user can then repeat this process numerous times on other sites of the building material to conform that the results indicate an absence of asbestos. It will be appreciated that by providing a simple handheld device capable of employing a non-invasive process for detecting asbestos in building materials, offers considerable advantages over existing technology. The device of the present invention employs a method for detecting the presence of a component within a material that directs electromagnetic waves onto the surface of the material to allow the incident waves to interact with the material’s structure, generating reflected signals as part of the process. The reflected signals are then able to be detected by the device in position whereby they can then be interpreted by advanced Al algorithms present within the controller of the device to identify the presence of specific components within the material, based on the presence of unique signal characteristics representative of that component.

[0052] When applied at a building site, the method of the present invention provides real-time, on-site asbestos detection without the need for physical sampling or disruption of the material, greatly improving both accuracy and safety. By offering a practical and efficient alternative to traditional testing methods, the present invention has a variety of applications across construction, demolition, mining, and environmental safety, delivering rapid and reliable asbestos assessments.

[0053] Throughout the specification and claims the word “comprise” and its derivatives are intended to have an inclusive rather than exclusive meaning unless the contrary is expressly stated or the context requires otherwise. That is, the word “comprise” and its derivatives will be taken to indicate the inclusion of not onlythe listed components, steps or features that it directly references, but also other components, steps or features not specifically listed, unless the contrary is expressly stated or the context requires otherwise.

[0054] It will be appreciated by those skilled in the art that many modifications and variations may be made to the methods of the invention described herein without departing from the spirit and scope of the invention.

Claims

The claims defining the invention are as follows:

1. A device for detecting the presence of a target substance within a material:a body configured be held by a user, the body having a first housing for housing a controller and a second housing for housing at least a signal generator, a signal transmitter and a signal receiver;wherein the second housing is configured to abut a surface of the material to be tested such that the signal transmitter is positioned to transmit a signal onto a surface of the material and the signal receiver receives a reflected signal from the surface of the material, the reflected signal being resultant from the transmitted signal; and wherein the controller analyses the received reflected signal to determine the presence of the target substance within the material.

2. A device according to claim 1, wherein the first housing has a display screen for conveying information to the user.

3. A device according to claim 2, wherein the information conveyed by the display screen comprises information determining whether the target substance is present in the material.

4. A device according to claim 1, wherein the signal generator is a microwave signal generator that generates electromagnetic signals to be sent to the signal transmitter.

5. A device according to claim 4, wherein the electromagnetic signals have a frequency modulation across a range of 10 MHz to 10 GHz, with a power output adjustable between 0 dBm and +20 dBm.

6. A device according to claim 4, wherein the signal transmitter is an Ultra- Wideband (UWB) Microwave antenna.

7. A device according to claim 6, wherein the Ultra-Wideband (UWB) Microwave antenna operates within a 3.1 GHz to 10.6 GHz frequency range and provides a beamwidth of 30° to 90° and a gain of 6-12 dBi.

8. A device according to claim 1, wherein the receiver unit is mounted to the second housing to capture the reflected electromagnetic waves from the target material.

9. A device according to claim 8, wherein the receiver unit operates with a frequency response of 3.1 GHz to 10.6 GHz and a dynamic range of 100 dB.

10. A device according to claim 1, wherein the controller comprises a computer controller configured with machine learning algorithms to analyse the reflected signal to determine the presence of the target substance in the reflected signal based on the target substance’s dielectric properties.

11. A device according to claim 10, wherein the computer controller analyses the reflected signal by performing real-time analysis of the reflected signal with a sampling rate of at least 10 gigasamples per second (GS / s).

12. A device according to claim 10, wherein the machine learning algorithms used by the computer controller are pre-trained on a dataset containing profiles of target substance-containing and non-target substance materials, allowing differentiation based on dielectric variations.

13. A device according to claim 10, wherein the dielectric properties analysed by the machine learning algorithms include variations in signal amplitude, phase, and frequency that are characteristic of target substance-containing materials.

14. A method for detecting the presence of a target substance within a material, comprising:generating and transmitting electromagnetic microwave signals towards a surface of the material;capturing reflected electromagnetic microwave signals from the material;analysing the reflected electromagnetic microwave signals to determine the presence of the target substance within the material based on the material’s dielectric properties; anddisplaying real-time detection results determining the presence or otherwise of the target substance in the material.

15. A method according to claim 14, wherein the step of generating and transmitting the electromagnetic micro wave signals comprises transmitting the signals in a frequency range of 3.1 GHz to 10.6 GHz.

16. A method according to claim 14, wherein the step of analysing the reflected electromagnetic microwave signals comprises performing realtime analysis of the reflected signals with a sampling rate of at least 10 gigasamples per second (GS / s).

17. A method according to claim 16, wherein the step of analysing the reflected electromagnetic microwave signals further comprises applying machine learning algorithms to the reflected signals to determine the presence of the target substance based on the material’s dielectric properties.

18. A method according to claim 17, wherein the machine learning algorithms are pre-trained on a dataset containing profiles of target substancecontaining and non-target substance containing materials, allowing differentiation based on dielectric variations.

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