Detection and Characterisation of Conductive Objects Using Electromagnetic Induction and a Fluxgate Magnetometer

Elson, L.; Meraki, Adil; Rushton, Lucas M; Pyragius, Tadas; Jensen, Kasper

doi:10.3390/s22165934

Cited by 7 publications

(4 citation statements)

References 33 publications

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“…A simple demonstration of the magnetic response of such 2D Fe 2 O 3 /rGO nanocomposite to a moving magnet is shown in Movie S1 in the supporting information. The 440 C stainless steel is high-carbon chromium steel (C ∼ 1 wt.%, Cr ∼ 18 wt%), which also has ferromagnetic behavior with a magnetic permeability of about 50 H m −1 [44]. Thus, the magnetic interaction between the ultrathin 2D hybrid nanosheets and the substrate leads to the hierarchical assembled structure observed here.…”

Section: Tribological Properties Of 2d Fe 2 O 3 /Rgo Nanocompositesmentioning

confidence: 68%

Two-dimensional iron oxide/graphene-based nanocomposites as high-performance solid lubricants

Kasbe,

Yang,

Bosch

et al. 2024

2D Mater.

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Beyond conventional 2D layered materials such as graphene and transition metal dichalcogenides, 2D metal oxides have also received much interest in recent years. They have unique electronic (such as 2D TiO2 and MoO2), catalytic (such as 2D CeO2 and MnO2), and magnetic properties (such as 2D Fe2O3) compared with bulk metal oxides due to their atomically thin structures. Certain types of 2D metal oxides also have the potential to be a new type of high-performance solid lubricants due to the tunable interlayer interaction and possibility for 2D heterostructure formation, but this remains largely unexplored. In this work, we developed a scalable microwave-assisted solid-state synthesis of 2D Fe2O3 and their nanocomposites with reduced graphene oxide (rGO). The 2D Fe2O3/rGO nanocomposites were systematically characterized by electron microscopies and spectroscopies, and their utilization as solid lubricants was studied by pin-on-disk tribometer on both silicon and steel substrates. The results show that due to the easy sliding between 2D Fe2O3 and rGO nanosheets and their unique magnetic-induced assembled morphology, low coefficient of friction (COF) can be achieved for both steel-silicon and steel-steel interfaces. Superlubricity (COF ∼ 0.007) can be achieved for the 2D Fe2O3/rGO nanocomposite with a GO primer layer on a steel substrate. This work provides new insights into the development of functional 2D nanocomposites and expands their applications to solid lubrication and beyond.

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Section: Tribological Properties Of 2d Fe 2 O 3 /Rgo Nanocompositesmentioning

confidence: 68%

Two-dimensional iron oxide/graphene-based nanocomposites as high-performance solid lubricants

Kasbe,

Yang,

Bosch

et al. 2024

2D Mater.

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“…Researchers have also used finite element models to verify analytical models and experimental results. Conducting objects in air were detected and characterized using EMI and a fluxgate magnetometer [68], with experimental results found to be in good agreement with a COMSOL Multiphysics ® [69] software program model. An analytical model of the magnetic flux density of a permeable spherical shell with an internal current band in Earth's magnetic field was found to be in good agreement with a finite element solution utilizing COMSOL Multiphysics ® [70].…”

Section: Statistical Classification Of Metallic Objects In Seawater U...mentioning

confidence: 88%

Machine learning classification of permeable conducting spheres in air and seawater using electromagnetic pulses

Thomas,

Salmon,

Holloway

et al. 2024

Meas. Sci. Technol.

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This paper presents machine learning classification on simulated data of permeable conducting spheres in air and seawaterirradiated by low frequency electromagnetic pulses. Classification accuracy greater than 90% was achieved. The simulated datawere generated using an analytical model of a magnetic dipole in air and seawater placed 1.5 – 3.5 m above the center of the spherein 50 cm increments. The spheres had radii of 40 cm and 50 cm and were of permeable materials, such as steel, and non-permeablematerials, such as aluminum. A series RL circuit was analytically modeled as the transmitter coil, and an RLC circuit as the receivercoil. Additive white Gaussian noise was added to the simulated data to test the robustness of the machine learning algorithms tonoise. Multiple machine learning algorithms were used for classification including a perceptron and multiclass logistic regression,which are linear models, and a neural network, 1D convolutional neural network (CNN), and 2D CNN, which are nonlinear models.Feature maps are plotted for the CNNs and provide explainability of the salient parts of the time signature and spectrogram dataused for classification. The pulses investigated, which expand the literature, include a two-sided decaying exponential, Heavisidestep-off, triangular, Gaussian, rectangular, modulated Gaussian, raised cosine, and rectangular down-chirp. Propagation effects,including dispersion and frequency dependent attenuation, are encapsulated by the analytical model, which was verified usingfinite element modeling. The results in this paper show that machine learning methods are a viable alternative to inversion ofelectromagnetic induction (EMI) data for metallic sphere classification, with the advantage of real-time classification without theuse of a physics-based model. The nonlinear machine learning algorithms used in this work were able to accurately classify metallicspheres in seawater even with significant pulse distortion caused by dispersion and frequency dependent attenuation. This paperpresents the first effort towards the use of machine learning to classify metallic objects in seawater based on EMI sensing.

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“…Designs for fluxgate magnetometers can be generally divided into two categories: with rod cores and utilizing ring cores. Many other designs, mainly based on rod cores, exist, but none have attained the level of development and efficiency of the two abovementioned kinds [ 14 , 22 , 23 ].…”

Section: Methodsmentioning

confidence: 99%

Non-Destructive Investigation of Intrinsic Magnetic Field of Austenitic Biomaterials by Magnetic Field Sensors

Smetana

Gombarska

Psenakova

et al. 2022

Sensors

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Investigation of the intrinsic magnetic field of austenitic biomaterial specimens after various heat-treatment processes and mechanical deformation is a matter in this study. Both heat-treatment and mechanical deformation influences are under investigation. A new approach incorporates innovative solutions with the goal to increase the resolution of gained signals in contrast to conventional methods. The proposed procedure was tested on real material specimens. A magnetic field sensor (fluxgate type) was used for this purpose. The presented results clearly show that gained signals can be increased when the appropriate probe instrumentation is used, and the characteristics are further mathematically processed.

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Detection and Characterisation of Conductive Objects Using Electromagnetic Induction and a Fluxgate Magnetometer

Cited by 7 publications

References 33 publications

Two-dimensional iron oxide/graphene-based nanocomposites as high-performance solid lubricants

Two-dimensional iron oxide/graphene-based nanocomposites as high-performance solid lubricants

Machine learning classification of permeable conducting spheres in air and seawater using electromagnetic pulses

Non-Destructive Investigation of Intrinsic Magnetic Field of Austenitic Biomaterials by Magnetic Field Sensors

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