The authors would like to acknowledge the contributions made by Michael Behrens and Damith Abeywardana in designing and implementing several sensing hardware components. Research supervision roles played by Alen Alempijevic, Teresa Vidal-Calleja, Gamini Dissanayake, and Sarath Kodagoda, as well as contributions made by all persons and organizations who funded and partnered with the Critical Pipes Project, are also acknowledged.

1. Extracting the decay rate &#946; from an available detector coil-based PEC signal
<ol>
	<li>Express an available experimentally captured PEC signal (i.e., a time domain detector coil voltage (denoted as V(t))) in the logarithmic form of ln[V(t)]. A typical PEC signal expressed in the form of ln[V(t)] is shown in Figure 1.</li>
	<li>Find a linear region in the form of <img align="center" alt="Equation 2" src="/files/ftp_upload/59618/59618eq2g.jpg" /> such that the signal satisfies the condition <img align="center" alt="Equation 3" src="/files/ftp_upload/59618/59618eq3g.jpg" /> where <img align="center" alt="Equation 4" src="/files/ftp_upload/59618/59618eq4g.jpg" />. As per the signal in Figure 1, <img align="center" alt="Equation 5" src="/files/ftp_upload/59618/59618eq5g.jpg" /> happens to be a satisfactory and practicable linear region.</li>
	<li>As illustrated in Figure 2, fit the straight-line model <img align="center" alt="Equation 6" src="/files/ftp_upload/59618/59618eq6g.jpg" /> to the experimental signal data within the identified linear region and estimate the value of &#946;.</li>
</ol>
2. Quantification of relative thickness
<ol>
	<li>Suppose there are multiple signals (Figure 3) acquired from an NDE task performed on a conductive ferromagnetic test piece having varying thickness. First, identify a linear region common to all signals and extract &#946; values. As per the signals in Figure 3, <img align="center" alt="Equation 8" src="/files/ftp_upload/59618/59618eq8g.jpg" /> seems to be an adequate and practicable linear region.</li>
	<li>Select the maximum &#946; value and label it as &#946;ref since the maximum &#946; value should in principle correspond to the maximum thickness according to the &#946; <img align="center" alt="Equation 7" src="/files/ftp_upload/59618/59618eq7a.jpg" /> &#956;&#963;d2 proportionality6,7,8.</li>
	<li>Express relative thickness percentage in the form <img align="center" alt="Equation 9" src="/files/ftp_upload/59618/59618eq9g.jpg" />, where the index <img align="center" alt="Equation 10" src="/files/ftp_upload/59618/59618eq10g.jpg" /> corresponds to the <img align="center" src="/files/ftp_upload/59618/59618eq11d.jpg" />th measurement.</li>
</ol>
3. PEC_Signal_Processor installation
<ol>
	<li>Locate the file PEC_Signal_Processor.exe. Double click the file and allow to execute.</li>
	<li>When the interface below appears, click Next. When the interface pops up, specify the file location for installation, and tick the checkbox Add a shortcut to the desktop to add the software icon to the desktop. Then click Next.</li>
	<li>Specify the installation location for the required Runtime Environment, then click Next. If the required Runtime Environment is already installed, just click Next.</li>
	<li>Read, and agree to prompted license terms and conditions. Then click Install.</li>
	<li>Click Finish when installation is complete. The desktop icon will appear.</li>
</ol>
4. Preparation of signals
<ol>
	<li>Ensure the PEC sensor outputs [raw signals, i.e., V(t)] are arranged as a table.</li>
	<li>Copy the table containing signals to the desktop (or to a folder contained within the parent directory where the application is installed). For convenience, the desktop is recommended.</li>
</ol>
5. Executing the application
<ol>
	<li>Double click the desktop icon to run the application. The interface will open.</li>
	<li>Load signals by clicking on the Load Signals tab and select the file containing the signals in order to import the signals to the software interface.</li>
	<li>Wait until the number of signals which is contained within the table containing raw signals appear in front of Number of Signals =.</li>
	<li>Click on Plot Signals and observe the signals plotted in logarithmic scale.</li>
	<li>Click on the Zoom tab and adjust the plot window for the linear region to be clearly visible.</li>
	<li>After observation, decide on reasonable lower and upper margins for the linear region and enter the values in the editable text spaces.</li>
	<li>Click on Plot Margins and wait for the margins to be plotted in green.</li>
	<li>Click on Extract Features and observe how straight-line segments are plotted in red.</li>
	<li>Click on Calculate Relative Thickness and observe how a histogram of calculated relative thickness values is plotted.</li>
	<li>Click on Save Relative Thickness to save the calculated relative thickness values. Provide a filename and click OK.</li>
	<li>Confirm filename by clicking OK again to confirm the filename. The relative thickness values will be saved as a table on the desktop.</li>
</ol>

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Authors have no conflicts of interest to disclose. Authors would like to recommend works2,6,7,8,9,10,11 as additional reading material.

A protocol to quantify the relative thickness (i.e., thickness as a percentage with respect to a reference) of conductive ferromagnetic materials using detector coil-based PEC sensors was presented. The main advantage of this method is the ability to overcome the calibration requirement (i.e., overcome the need to measure or estimate the magnetic permeability and electrical conductivity of the material being inspected to enable thickness quantification). The protocol involves logarithmic representation of the time domain...

The pulsed eddy current (PEC) sensing technique is perhaps the most versatile member of the family of eddy current (EC) non-destructive evaluation (NDE) techniques and has many applications in detection and quantification of defects, and the geometry of metals and metallic structures1. Thickness quantification of conductive ferromagnetic wall-like structures, having wall thicknesses of no more than a few millimeters to a few tens of millimeters, is a high demand engineering service in the field of structural health monitoring of infrastructure. Critical infrastructure made of ferromagnetic alloys that require this service are commonly available in the energy, water, oil, and gas industries. While PEC sensors can be designed following several architectures, the detector coil-based architecture was determined to be the most effective and commonly used in condition assessment of ferromagnetic materials2,3,4,5. Therefore, it is the detector coil-based PEC sensor architecture that sets the foundation to the problem of thickness quantification of conductive ferromagnetic materials.
The detector coil-based PEC sensor architecture is typically comprised of two concentrically wound, air cored, conductive coils2,3,4,5,6 (typically copper coils). It is quite common to wind these coils to be circular in shape2,3,4,5,6, but occasionally, rectangular shaped coils6 have been used. From the two coils in the sensor, one behaves as an exciter coil while the other acts as the detector coil. In a PEC sensor, the exciter coil is excited by a voltage pulse - something that can be characterized as a Heaviside step function in principle. This pulsed excitation generates a transient magnetic field (called the primary field) around the sensor. When the sensor is placed adjacent to a conductive test piece (e.g., a conductive ferromagnetic wall-like structure), this transient magnetic field induces time varying eddy currents in the test piece. These eddy currents generate a secondary magnetic field (called the secondary field) that opposes the primary field. In response to the resultant effect of the primary and secondary fields, a transient voltage is induced in the detector coil - which becomes the time domain PEC signal of interest for this work.
The PEC sensor detector coil voltage decay rate (denoted as &#946;) has been reported6,7,8 to show the proportionality &#946; <img align="center" alt="Equation 7" src="/files/ftp_upload/59618/59618eq7a.jpg" /> &#956;&#963;d2, when a signal is acquired placing a PEC sensor above a conductive ferromagnetic layer of magnetic permeability &#956;, electrical conductivity &#963;, and thickness d. Although this decay rate signal feature has considerable immunity to parameters such as sensor size, sensor shape, and lift-off6,7,8, which makes the decay rate highly desirable for challenging NDE scenarios such as in situ pipe condition assessment9,10,11, this feature must be calibrated (i.e., &#956;, &#963; of the material being inspected be estimated) to enable thickness (i.e., d) quantification. To enable conventional methods of decay rate-based thickness quantification6,8, this calibration must be done by extracting calibration samples6,8 or by involving eddy current-based material property characterization methods12,13. Alternatively, the complexity of calibration can be avoided by representing thickness in the form of relative thickness. Suppose an NDE exercise is carried out and &#946; values are extracted from signals, then, the &#946; value qualitatively representative of the maximum thickness point in the test piece is considered as a reference (i.e., &#946;ref <img align="center" alt="Equation 7" src="/files/ftp_upload/59618/59618eq7a.jpg" /> &#956;&#963;dmax2); then, the thickness of any other location can be represented as a percentage of the maximum thickness in the form <img align="center" alt="Equation 1" src="/files/ftp_upload/59618/59618eq1g.jpg" />, presenting a relative thickness as the output, which is still useful qualitative information as an NDE output that also carries the simplicity of not having to calibrate for &#956;, &#963;. The protocol presented herein describes the steps to be followed to accomplish this.
Since the decay rate &#946; shows generality to the detector coil-based PEC sensor architecture while showing immunity to parameters of the sensor design as well as lift-off6,7,8,14, practitioners may use any detector coil-based PEC sensing system of their choice on a suitable conductive ferromagnetic material to perform relative thickness quantification following the protocol here. A PEC sensor design example for a conductive ferromagnetic material is available for interested readers15. The signals and results presented in this work were acquired using the PEC system developed by University of Technology Sydney6,8. The conductive ferromagnetic material used for representative results acquired by the PEC system is grey cast iron extracted from a pipe test-bed9,10,11 in Sydney Australia.
It should be noted that the methods, results, and discussions presented in this publication explicitly focus on the use of the detector coil-based PEC sensor architecture&#39;s time domain signal&#39;s decay rate for thickness quantification of conductive ferromagnetic materials. The publication does not include a broader discussion on general conventions of PEC sensing principles and sensor configurations. Other published work16,17,18 can be useful for readers to gain more insight about PEC sensor configurations other than the detector coil-based sensor architecture.

<table><tbody><tr><td>A Detector Coil-based PEC Sensing System.</td><td>N/A</td><td></td><td>The representative results in this work were generated using the PEC system developed by University of Technology Sydney (UTS), Australia and published in works 6,8. This system may be accessible to readers via collaborating with UTS.</td></tr><tr><td>A suitable conductive ferromagnetic material of varying thickness.</td><td>N/A</td><td></td><td>The representative results in this work were generated by acquiring PEC measurements on grey cast iron test pieces extracted from a pipe test-bed located in Sydney Australia, whose location and vintage details are available in references 9-11. The pipe test-bed as well as the extracted calibration samples may be accessible to readers via collaborating with UTS.</td></tr><tr><td>A computation platform for PEC signal processing</td><td>MathWorks, Natick, MA, USA.</td><td></td><td>A computation platform in which the PEC signal processing algorithm can be coded and executed is required. In this publication, PEC signal processing was done using a software executable named &quot;PEC_Signal_Processor&quot;, produced using MATLAB R2017b, Publisher: MathWorks, Natick, MA, USA.</td></tr><tr><td>An application that can produce a table containing raw PEC signals (e.g., Microsoft Office Excel).</td><td>Microsoft Corporation, One Microsoft Way, Redmond, Washington, USA.</td><td></td><td>Microsoft Office Excel (Office 16) was used for the work of this publication.</td></tr></tbody></table>

quantifying the relative thickness of conductive ferromagnetic materials using detector coil-based pulsed eddy current sensors

Thickness quantification of conductive ferromagnetic materials by means of non-destructive evaluation (NDE) is a crucial component of structural health monitoring of infrastructure, especially for assessing the condition of large diameter conductive ferromagnetic pipes found in the energy, water, oil, and gas sectors. Pulsed eddy current (PEC) sensing, especially detector coil-based PEC sensor architecture, has established itself over the years as an effective means for serving this purpose. Approaches for designing PEC sensors as well as processing signals have been presented in previous works. In recent years, the use of the decay rate of the detector coil-based time domain PEC signal for the purpose of thickness quantification has been studied. Such works have established that the decay rate-based method holds generality to the detector coil-based sensor architecture, with a degree of immunity to factors such as sensor shape and size, number of coil turns, and excitation current. Moreover, this method has shown its effectiveness in NDE of large pipes made of grey cast iron. Following such literature, the focus of this work is explicitly PEC sensor detector coil voltage decay rate-based conductive ferromagnetic material thickness quantification. However, the challenge faced by this method is the difficulty of calibration, especially when it comes to applications such as in situ pipe condition assessment since measuring electrical and magnetic properties of certain pipe materials or obtaining calibration samples is difficult in practice. Motivated by that challenge, in contrast to estimating actual thickness as done by some previous works, this work presents a protocol for using the decay rate-based method to quantify relative thickness (i.e., thickness of a particular location with respect to a maximum thickness), without the requirement for calibration.

Representative results within this section have been generated using the PEC signals provided as supplementary material with reference8; as mentioned above, the signals have been captured on grey cast iron samples extracted from the pipe test bed in Sydney Australia, whose location and vintage details are provided in references9,10,11.
Figure 1 sho...

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Quantifying the Relative Thickness of Conductive Ferromagnetic Materials Using Detector Coil-Based Pulsed Eddy Current Sensors

Here, we present a protocol to quantify the relative thickness (i.e., thickness as a percentage with respect to a reference) of conductive ferromagnetic materials using detector coil-based pulsed eddy current sensors, while overcoming the calibration requirement.

Thickness quantification of conductive ferromagnetic materials by means of non-destructive evaluation (NDE) is a crucial component of structural health ...

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5.6K Views.  University of Melbourne. Here, we present a protocol to quantify the relative thickness (i.e., thickness as a percentage with respect to a reference) of conductive ferromagnetic materials using detector coil-based pulsed eddy current sensors, while overcoming the calibration requirement.

Video: Quantifying the Relative Thickness of Conductive Ferromagnetic Materials Using Detector Coil-Based Pulsed Eddy Current Sensors

Ultrasound Velocity Measurement in a Liquid Metal Electrode

A growing number of electrochemical technologies depend on fluid flow, and often that fluid is opaque. Measuring the flow of an opaque fluid is inherently more difficult than measuring the flow of a transparent fluid, since optical methods are not applicable. Ultrasound can be used to measure the velocity of an opaque fluid, not only at isolated points, but at hundreds or thousands of points arrayed along lines, with good temporal resolution. When applied to a liquid metal electrode, ultrasound velocimetry involves additional challenges: high temperature, chemical activity, and electrical conductivity. Here we describe the experimental apparatus and methods that overcome these challenges and allow the measurement of flow in a liquid metal electrode, as it conducts current, at operating temperature. Temperature is regulated within &#177;2 &#176;C using a Proportional-Integral-Derivative (PID) controller that powers a custom-built furnace. Chemical activity is managed by choosing vessel materials carefully and enclosing the experimental setup in an argon-filled glovebox. Finally, unintended electrical paths are carefully prevented. An automated system logs control settings and experimental measurements, using hardware trigger signals to synchronize devices. This apparatus and these methods can produce measurements that are impossible with other techniques, and allow optimization and control of electrochemical technologies like liquid metal batteries.

Ultrasound velocimetry is used to study mixing by fluid flow in liquid metal electrodes. The focus of this manuscript is to illustrate the methods used for making precise, spatially-resolved ultrasound measurements while limiting oxidation and controlling and monitoring temperature, applied current, and the heater power being supplied.

A growing number of electrochemical technologies depend on fluid flow, and often that fluid is opaque. Measuring the flow of an opaque fluid is inherently ...

Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples

The setup of a planar Frequency Mixing Magnetic Detection (p-FMMD) scanner for performing Magnetic Particles Imaging (MPI) of flat samples is presented. It consists of two magnetic measurement heads on both sides of the sample mounted on the legs of a u-shaped support. The sample is locally exposed to a magnetic excitation field consisting of two distinct frequencies, a stronger component at about 77 kHz and a weaker field at 61 Hz. The nonlinear magnetization characteristics of superparamagnetic particles give rise to the generation of intermodulation products. A selected sum-frequency component of the high and low frequency magnetic field incident on the magnetically nonlinear particles is recorded by a demodulation electronics. In contrast to a conventional MPI scanner, p-FMMD does not require the application of a strong magnetic field to the whole sample because mixing of the two frequencies occurs locally. Thus, the lateral dimensions of the sample are just limited by the scanning range and the supports. However, the sample height determines the spatial resolution. In the current setup it is limited to 2 mm. As examples, we present two 20 mm &#215; 25 mm p-FMMD images acquired from samples with 1 &#181;m diameter maghemite particles in silanol matrix and with 50 nm magnetite particles in aminosilane matrix. The results show that the novel MPI scanner can be applied for analysis of thin biological samples and for medical diagnostic purposes.

A scanner for imaging magnetic particles in planar samples was developed using the planar frequency mixing magnetic detection technique. The magnetic intermodulation product response from the nonlinear nonhysteretic magnetization of the particles is recorded upon a two-frequency excitation. It can be used to take 2D images of thin biological samples.

The setup of a planar Frequency Mixing Magnetic Detection (p-FMMD) scanner for performing Magnetic Particles Imaging (MPI) of flat samples is presented. ...

Design, Instrumentation and Usage Protocols for Distributed In Situ Thermal Hot Spots Monitoring in Electric Coils using FBG Sensor Multiplexing

Random wound coils are a key operational element of most electric apparatus in modern industrial systems, including low voltage electric machines. One of the major current bottlenecks in improved exploitation of electrical devices is the high sensitivity of their wound components to in-service thermal stress. The application of conventional thermal sensing methods (e.g., thermocouples, resistance temperature detectors) for thermal condition monitoring of current carrying random wound coils can impose considerable operational limitations due to sensor size, EMI sensitivity and the existence of electrically conductive material in their construction. Another substantial limitation exists in distributed sensing applications and is caused by what is often a considerable length and volume of conventional sensor wiring leads.
This paper reports the design of a fiber optic FBG&#160;sensing system&#160;intended for enabling real-time distributed internal thermal&#160;condition monitoring within random wound coils. The procedure of random wound coil instrumentation with the FBG sensing system is reported in a case study on an IEEE standard wound coil representative of those utilized in electrical machines. The reported work also presents and discusses important practical and technical aspects of FBG sensing system implementation and application, including the FBG array geometry design, sensing head and fiber packaging, the sensor array installation and calibration procedure and the use of a commercial interrogation system for obtaining thermal measurements. Finally, the in situ multiplexed FBG sensing system thermal monitoring performance is demonstrated in representative static and dynamic thermal conditions.&#160;

This paper presents a protocol that enables instrumentation of random wound electric coils with fiber Bragg grating (FBG) thermal sensors for the purpose of distributed condition monitoring of internal thermal hot spots.

Random wound coils are a key operational element of most electric apparatus in modern industrial systems, including low voltage electric machines. One of ...

Fabrication and Characterization of Thickness Mode Piezoelectric Devices for Atomization and Acoustofluidics

We present a technique to fabricate simple thickness mode piezoelectric devices using lithium niobate (LN). Such devices have been shown to atomize liquid more efficiently, in terms of &#64258;ow rate per power input, than those that rely on Rayleigh waves and other modes of vibration in LN or lead zirconate titanate (PZT). The complete device is composed of a transducer, a transducer holder, and a &#64258;uid supply system. The fundamentals of acoustic liquid atomization are not well known, so techniques to characterize the devices and to study the phenomena are also described. Laser Doppler vibrometry (LDV) provides vibration information essential in comparing acoustic transducers and, in this case, indicates whether a device will perform well in thickness vibration. It can also be used to &#64257;nd the resonance frequency of the device, though this information is obtained more quickly via impedance analysis. Continuous &#64258;uid atomization, as an example application, requires careful &#64258;uid flow control, and we present such a method with high-speed imaging and droplet size distribution measurements via laser scattering.

Fabrication of piezoelectric thickness mode transducers via direct current sputtering of plate electrodes on lithium niobate is described. Additionally, reliable operation is achieved with a transducer holder and &#64258;uid supply system and characterization is demonstrated via impedance analysis, laser doppler vibrometry, high-speed imaging, and droplet size distribution using laser scattering.

We present a technique to fabricate simple thickness mode piezoelectric devices using lithium niobate (LN). Such devices have been shown to atomize liquid ...

Optimized Setup and Protocol for Magnetic Domain Imaging with In Situ Hysteresis Measurement

This paper elaborates the sample preparation protocols required to obtain optimal domain patterns using the Bitter method, focusing on the extra steps compared to standard metallographic sample preparation procedures. The paper proposes a novel bespoke rig for dynamic domain imaging with in situ BH (magnetic hysteresis) measurements and elaborates the protocols for the sensor preparation and the use of the rig to ensure accurate BH measurement. The protocols for static and ordinary dynamic domain imaging (without in situ BH measurements) are also presented. The reported method takes advantage of the convenience and high sensitivity of the traditional Bitter method and enables in situ BH measurement without interrupting or interfering with the domain wall movement processes. This facilitates establishing a direct and quantitative link between the domain wall movement processes&#8211;microstructural feature interactions in ferritic steels with their BH loops. This method is anticipated to become a useful tool for the fundamental study of microstructure&#8211;magnetic property relationships in steels and to help interpret the electromagnetic sensor signals for non-destructive evaluation of steel microstructures.

Optimized Setup and Protocol for Magnetic Domain Imaging with In Situ Hysteresis Measurement

This paper elaborates the sample and sensor preparation procedures and the protocols for using the test rig particularly for dynamic domain imaging with in situ BH measurements in order to achieve optimal domain pattern quality and accurate BH measurements.

This paper elaborates the sample preparation protocols required to obtain optimal domain patterns using the Bitter method, focusing on the extra steps ...

Bioengineering

Fabrication of Magnetic Platforms for Micron-Scale Organization of Interconnected Neurons

The ability to direct neurons into organized neural networks has great implications for regenerative medicine, tissue engineering, and bio-interfacing. Many studies have aimed at directing neurons using chemical and topographical cues. However, reports of organizational control on a micron-scale over large areas are scarce. Here, an effective method has been described for placing neurons in preset sites and guiding neuronal outgrowth with micron-scale resolution, using magnetic platforms embedded with micro-patterned, magnetic elements. It has been demonstrated that loading neurons with magnetic nanoparticles (MNPs) converts them into sensitive magnetic units that can be influenced by magnetic gradients. Following this approach, a unique magnetic platform has been fabricated on which PC12 cells, a common neuron-like model, were plated and loaded with superparamagnetic nanoparticles. Thin films of ferromagnetic (FM) multilayers with stable perpendicular magnetization were deposited to provide effective attraction forces toward the magnetic patterns. These MNP-loaded PC12 cells, plated and differentiated atop the magnetic platforms, were preferentially attached to the magnetic patterns, and the neurite outgrowth was well aligned with the pattern shape, forming oriented networks. Quantitative characterization methods of the magnetic properties, cellular MNP uptake, cell viability, and statistical analysis of the results are presented. This approach enables the control of neural network formation and improves neuron-to-electrode interface through the manipulation of magnetic forces, which can be an effective tool for in vitro studies of networks and may offer novel therapeutic biointerfacing directions.

This work presents a bottom-up approach to the engineering of local magnetic forces for control of neuronal organization. Neuron-like cells loaded with magnetic nanoparticles (MNPs) are plated atop and controlled by a micro-patterned platform with perpendicular magnetization. Also described are magnetic characterization, MNP cellular uptake, cell viability, and statistical analysis.

The ability to direct neurons into organized neural networks has great implications for regenerative medicine, tissue engineering, and bio-interfacing. ...

Experimental Multiscale Methodology for Predicting Material Fouling Resistance

The buildup of fouling deposits in energy systems degrades their ability to transfer heat, reducing efficiency and causing operational issues such as localized corrosion. Each application presents its own challenges. In nuclear power generation, the buildup of CRUD (Chalk River Unidentified Deposits) induces unique, negative effects ranging from reduced heat transfer, to axial power shifting, to CRUD-induced localized corrosion (CILC), the last of which can cause sudden fuel failure. Knowledge of why CRUD forms and how to prevent its adhesion to nuclear fuel rods represents a major step towards eliminating it. This paper demonstrates a methodology to ascertain which materials may resist CRUD adhesion, thereby preventing its initiation and growth. It presents experiments targeted at multiple length scales: pool boiling (macroscale), which simulates CRUD initiation processes in a nuclear reactor, and atomic force microscope (AFM) force spectroscopy (microscale), which yields quantitative measurements of the adhesion between CRUD constituents and candidate CRUD-resistant materials. Early data from both sets of experiments show some correlation, suggesting that AFM force spectroscopy (AFM-FS) may be used to pre-screen materials for fouling resistance. So far, it appears that CRUD constituents adhere well to oxides, while carbides show both greatly reduced adhesion and no adherent CRUD in pool boiling experiments. The relationship between the AFM-FS measurements and the growth of CRUD in the pool boiling experiments may provide insight into the possible factors affecting fouling in nuclear reactors, eventually leading to its elimination.

Fouling, or corrosion product deposition, plagues numerous fields of energy production. At its core is adhesion between dissimilar materials. Understanding and controlling adhesion could reduce or eliminate fouling. This paper demonstrates pool boiling experiments (macroscale) and atomic force microscope force-spectroscopy measurements (microscale), which when in agreement, indicate fouling-resistance.

The buildup of fouling deposits in energy systems degrades their ability to transfer heat, reducing efficiency and causing operational issues such as ...

Biology

Stimulating Mouse Brain with a Low-Cost EEG and Millimeter-Sized Coil

Low-Cost Electroencephalographic Recording System Combined with a Millimeter-Sized Coil to Transcranially Stimulate the Mouse Brain In Vivo

A low-cost electroencephalographic (EEG) recording system is proposed here to drive transcranial magnetic stimulation (TMS) of the mouse brain in vivo, utilizing a millimeter-sized coil. Using conventional screw electrodes combined with a custom-made, flexible, multielectrode array substrate, multi-site recording can be carried out from the mouse brain. In addition, we explain how a millimeter-sized coil is produced using low-cost equipment usually found in laboratories. Practical procedures for fabricating the flexible multielectrode array substrate and the surgical implantation technique for screw electrodes are also presented, which are necessary to produce low-noise EEG signals. Although the methodology is useful for recording from the brain of any small animal, the present report focuses on electrode implementation in an anesthetized mouse skull. Furthermore, this method can be easily extended to an awake small animal that is connected with tethered cables via a common adapter and fixed with a TMS device to the head during recording.The present version of the EEG-TMS system, which can include a maximum of 32 EEG channels (a device with 16 channels is presented as an example with fewer channels) and one TMS channel device, is described. Additionally, typical results obtained by the application of the EEG-TMS system to anesthetized mice are briefly reported.

Author Spotlight: Low-Cost Electroencephalographic Recording System Combined with a Millimeter-Sized Coil to Transcranially Stimulate the Mouse Brain In Vivo  

A low-cost electroencephalographic recording system combined with a millimeter-sized coil is proposed to drive transcranial magnetic stimulation of the mouse brain in vivo. Using conventional screw electrodes with a custom-made, flexible, multielectrode array substrate, multi-site recording can be carried out from the mouse brain in response to transcranial magnetic stimulation.

A low-cost electroencephalographic (EEG) recording system is proposed here to drive transcranial magnetic stimulation (TMS) of the mouse brain in vivo, ...

Neuroscience

MRM Microcoil Performance Calibration and Usage Demonstrated on Medicago truncatula Roots at 22 T

This protocol describes a signal-to-noise ratio (SNR) calibration and sample preparation method for solenoidal microcoils combined with biological samples, designed for high-resolution magnetic resonance imaging (MRI), also referred to as MR microscopy (MRM). It may be used at pre-clinical MRI spectrometers, demonstrated on Medicago truncatula root samples. Microcoils increase sensitivity by matching the size of the RF resonator to the size of the sample of interest, thereby enabling higher image resolutions in a given data acquisition time. Due to the relatively simple design, solenoidal microcoils are straightforward and cheap to construct and can be easily adapted to the sample requirements. Systematically, we explain how to calibrate new or home-built microcoils, using a reference solution. The calibration steps include: pulse power determination using a nutation curve; estimation of RF-field homogeneity; and calculating a volume-normalized signal-to-noise ratio (SNR) using standard pulse sequences. Important steps in sample preparation for small biological samples are discussed, as well as possible mitigating factors such as magnetic susceptibility differences. The applications of an optimized solenoid coil are demonstrated by high-resolution (13 x 13 x 13 &#956;m3, 2.2 pL) 3D imaging of a root sample.

MRM Microcoil Performance Calibration and Usage Demonstrated on Medicago truncatula Roots at 22 T

A protocol to study biological tissue at high spatial resolution using ultra-high field magnetic resonance microscopy (MRM) using microcoils is presented. Step-by-step instructions are provided for characterizing the microcoils. Finally, optimization of imaging is demonstrated on plant roots.

This protocol describes a signal-to-noise ratio (SNR) calibration and sample preparation method for solenoidal microcoils combined with biological ...

Characterization of Magnetic Components

Source: Ali Bazzi, Department of Electrical Engineering, University of Connecticut, Storrs, CT.
The objective of this experiment is to achieve hands-on experience with different magnetic components from design and material perspectives. This experiment covers B-H curves of magnetic material and inductor design through identifying unknown design factors. The B-H curve of a magnetic element, such as an inductor or transformer, is a characteristic of the magnetic material forming the core around which windings are wrapped. This characteristic provides information about the magnetic flux density that the core can handle with respect to the current flowing in the windings. It also provides information about limits before the core is magnetically saturated, i.e. when pushing more current through the coil leads to no further magnetic flux flow.

Magnetic Components; Relative Permeability and BH Curve

Source: Ali Bazzi, Department of Electrical Engineering, University of Connecticut, Storrs, CT.
The objective of this experiment is to achieve hands-on ...

Quantifying the Relative Thickness of Conductive Ferromagnetic Materials Using Detector Coil-Based Pulsed Eddy Current Sensors

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Rozdziały w tym wideo

Ultrasound Velocity Measurement in a Liquid Metal Electrode

Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples

Design, Instrumentation and Usage Protocols for Distributed In Situ Thermal Hot Spots Monitoring in Electric Coils using FBG Sensor Multiplexing

Fabrication and Characterization of Thickness Mode Piezoelectric Devices for Atomization and Acoustofluidics

Optimized Setup and Protocol for Magnetic Domain Imaging with In Situ Hysteresis Measurement

Fabrication of Magnetic Platforms for Micron-Scale Organization of Interconnected Neurons

Experimental Multiscale Methodology for Predicting Material Fouling Resistance

Low-Cost Electroencephalographic Recording System Combined with a Millimeter-Sized Coil to Transcranially Stimulate the Mouse Brain In Vivo

MRM Microcoil Performance Calibration and Usage Demonstrated on Medicago truncatula Roots at 22 T

Characterization of Magnetic Components