United States

Magnetic nanomaterials such as magnetite (Fe3O4) are sought after for their promising applications in sensing, drug delivery, tissue engineering and are also used as
contrast agents in magnetic resonance imaging [1 ].Synthesis of these nanomaterials with good uniformity in size and shape is imperative for their use in different applications.
Microfluidic-based synthesis in designed reactors is a new technology which controls the mixing of fluids within a fixed channel dimension (ranging from 10-100 μm), offers efficient mass
transport in channels and also reduces reaction time.[2,3] Herein, we present a new synthesis strategy which utilizes 3D-printing technique to develop a microfluidic device for synthesis of
Fe3O4 nanoparticles by droplet-based method.[4] The droplet-based method uses oil as a continuous medium which confines the reactants in a droplet, and each droplet functions as an
individual reactor thereby providing high level of control on synthesis conditions. This strategy overcomes the limitations of conventional glassware-based synthesis such as polydispersity
and batch-to-batch variability in nanoparticles. Enhanced magnetism and monodispersity in Fe3O4 nanoparticles were controlled by application of AC voltage across the electrodes placed
beneath the reaction chamber of the device. The present device can be used for synthesing metal nanoparticles.&lt;br&gt;

**References References:**&lt;br&gt;

1. A. H. Lu, E. Salabas, F. Schüth (2007) Magnetic nanoparticles: synthesis, protection, functionalization, and application. Angew. Chem. 119:1242-1266; Angew. Chem. Int. Ed. 46:
1222-1244&lt;br&gt;
2. G. M. Whitesides (2006) The origins and the future of microfluidics. Nature 442:368–373&lt;br&gt;
3. K. Ren, J. Zhou, H. Wu (2013) Materials for Microfluidic Chip Fabrication. Acc. Chem. Res. 46:2396–2406&lt;br&gt;
4. V. Singh, R. Singh (2022) Voltage-Driven Microfluidic Synthesis of Magnetite and Gold Nanomaterials. J Flow Chem. DOI: 10.1007/s41981-022-00231-3.&lt;br&gt;

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MMM 2022

Simple Synthesis of Magnetic Nanomaterials by Microfluidics Process

Magnetic nanomaterials such as magnetite (Fe3O4) are sought after for their promising applications in sensing, drug delivery, tissue engineering and are also used as
contrast agents in magnetic resonance imaging [1 ].Synthesis of these nanomaterials with good uniformity in size and shape is imperative for their use in different applications.
Microfluidic-based synthesis in designed reactors is a new technology which controls the mixing of fluids within a fixed channel dimension (ranging from 10-100 μm), offers efficient mass
transport in channels and also reduces reaction time.[2,3] Herein, we present a new synthesis strategy which utilizes 3D-printing technique to develop a microfluidic device for synthesis of
Fe3O4 nanoparticles by droplet-based method.[4] The droplet-based method uses oil as a continuous medium which confines the reactants in a droplet, and each droplet functions as an
individual reactor thereby providing high level of control on synthesis conditions. This strategy overcomes the limitations of conventional glassware-based synthesis such as polydispersity
and batch-to-batch variability in nanoparticles. Enhanced magnetism and monodispersity in Fe3O4 nanoparticles were controlled by application of AC voltage across the electrodes placed
beneath the reaction chamber of the device. The present device can be used for synthesing metal nanoparticles. 

**References References:** 

1. A. H. Lu, E. Salabas, F. Schüth (2007) Magnetic nanoparticles: synthesis, protection, functionalization, and application. Angew. Chem. 119:1242-1266; Angew. Chem. Int. Ed. 46:
1222-1244 
2. G. M. Whitesides (2006) The origins and the future of microfluidics. Nature 442:368–373 
3. K. Ren, J. Zhou, H. Wu (2013) Materials for Microfluidic Chip Fabrication. Acc. Chem. Res. 46:2396–2406 
4. V. Singh, R. Singh (2022) Voltage-Driven Microfluidic Synthesis of Magnetite and Gold Nanomaterials. J Flow Chem. DOI: 10.1007/s41981-022-00231-3. 

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poster

#### Welcome to the 67th Annual Conference on Magnetism and Magnetic Materials, MMM 2022!

The Conference was held from October 31 to November 4 and offers both in-person and on-demand content.

MMM 2022 is sponsored jointly by AIP Publishing and the IEEE Magnetics Society. Members of the international scientific and engineering communities interested in recent developments in fundamental and applied magnetism are invited to attend and contribute to the technical sessions. The technical program will include invited and contributed papers in oral and poster sessions, invited symposia, a tutorial, and several special sessions. This Conference provides an outstanding opportunity for worldwide participants to meet their colleagues and collaborators and discuss developments in all areas of magnetism research.

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For tickets to the event, please register at the following link: https://magnetism.org/registration

Please register for the MMM 2022 conference to join the event.

This Conference provides an outstanding opportunity for worldwide participants to meet their colleagues and collaborators and discuss developments in all areas of magnetism research.

Magnetic fluids have a unique combination of magnetic properties, fluidity and colloidal stability, which has allowed them to find applications in a variety of technical
devices, such as shock absorbers and sensors. One of the features of this smart material is the ability to control its physical properties using various combinations of magnetic influences. In
this work, the dynamics of a magnetic fluid volume levitating in an uniform magnetic field of an electromagnet undergoing oscillatory shear is investigated. The samples with different
physical parameters were considered, the dependence of magnetoviscous effect was investigated. It is shown that the microstructure of the sample and the presence of large magnetic
particles have the greatest influence on the dynamics of the magnetic fluid undergoing oscillatory shear and magnetoviscous effect. The results of the work can be used to develop a method
for express testing of magnetic fluid samples, as well as to develop acceleration and vibration sensors based on magnetic fluids [1].
The dependences of the viscosity for samples MF-1 – MF-4 are plotted, shown in Figure 1. The obtained dependences of the viscosity show an increase in its value by a factor of 5 for the
MF-1 sample, with an increase in the field to 1000 kA/m, which can be explained by interparticle interactions and the formation of weakly bound aggregates in the near-wall layer in a
more concentrated initial MF-1 sample. In a more diluted sample MF-2, such an increase in viscosity is not observed. The images MF-3, MF-4 are characterized by the presence of an
excess free surfactant, which negatively affects the magnetoviscous effect.
The work was carried out the part of the implementation of the program of strategic academic leadership "Priority-2030" (Agreements No. 075-15-2021-1155 and No. 075 -15-2021-1213)
and as part of the implementation of the state task (No 0851-2020-0035). 


**References:** 1. Ryapolov P. A., Polunin V. M., Shel'deshova E. V, JMMM 496, 165924 (2020). 

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Dynamics of magnetic fluids and bidisperse magnetic systems undergoing oscillatory shear

Various biomedical magnetic robots have been widely investigated as a possible means to replace conventional invasive regimens [1-3]. In our previous research, we
proposed a self-assembled magnetic millirobot with a multi-modular ring-like chain structure (SAMR) that can change its equilibrium formations under different conditions [4]. The SAMR
is composed of several arc-shaped modules serially connected by a pin joint, and a magnet is inserted into each module (Fig. 1). Due to the interactive magnetic forces and torques within
the modules, the SAMR can be self-assembled into a ring formation without a magnetic field or disassembled into a straight formation under an applied magnetic field. In this research, we
further investigated the wireless manipulation abilities of the SAMR’s mechanical motions. Based on the mechanical conditions of the SAMR shown in Fig. 1, we established a method that
can generate and maneuver the SAMR’s various mechanical movements, including self-assembly, disassembly, tumbling, crawling, and cargo trapping motions simply via the control of an
external magnetic field. Since such motions allow the low-invasive manipulation of the SAMR during the access to a target area and the performance of functions in the area, as shown in
Fig. 1, these motion abilities can significantly improve the SAMR’s usefulness as a minimally invasive device. In an experiment to verify the effectiveness of the proposed motion control
method, the SAMR initially disassembled and loaded inside the medical syringe successfully self-assembled into a ring formation. The SAMR then sequentially showed tumbling,
crawling, and cargo trapping and delivery motions actuated by different magnetic fields. This research can contribute to developing minimally invasive biomedical magnetic robots. 

**References** 

[1] M. Sitti, H. Ceylan, W. Hu, J. Giltinan, M. Turan, S. Yim, and E. Diller, “Biomedical Applications of Untethered Mobile Milli/Microrobots,” Proc. IEEE, vol. 103, no. 2,
pp. 205-224, 2015. 
[2] S. Yim and M. Sitti, “SoftCubes: Stretchable and self-assembling three-dimensional soft modular matter,” Int. J. Rob. Res., vol. 33, no. 8, pp. 1083–1097, 2014. 
[3] H. Lee and S. Jeon, “A Study on the Self-Assembly and Disassembly Abilities of a Multi- Modular Magnetic Millirobot,” J. Inst. Cont., Rob. Sys.,” vol. 25, no. 3, pp. 235-240, 2019. 
[4] H. Lee and S. Jeon, “An intravascular helical magnetic millirobot with a gripper mechanism performing object delivery and collecting motions actuated by precession rotating magnetic
fields,” AIP Adv., vol. 11, pp. 025236, 2021. 

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Wireless Manipulation of a Self Assembled Magnetic Millirobot with a Multi

An effective vibration isolator (VI) strategy isolates payloads from vibration sources by introducing compliance. Passive isolators are low cost and easy to install but are
tuned to a specific vibration spectrum, and rapidly lose effectiveness if the design point shifts. In contrast, adaptive vibration isolators using magnetorheological fluids (MRFs) continuously
adapt to changing vibration spectra by adjusting a magnetic field using an appropriate control algorithm. Traditional fabrication methods using injection molding are economical only when
large numbers are produced, which precludes customizing VIs for a specific available jitter space, natural frequency, or static displacement. 3D printing is advantageous for a
magnetorheological vibration isolator (MRVI) because designs can be customized: the shape of the hydraulic rubber reservoir can be economically customized to meet the requirements of
a specific application by tuning such properties as axial stiffness, bulge stiffness, diameter, height, and Shore hardness of the membrane wall. The MRVI, which is a squeeze-mode device,
consists of a reservoir or rubber bellow with customizable stiffnesses, a plastic lid which houses an electromagnetic coil, and an MRF (Figure 1). The rubber bellow and plastic lid were
fabricated using a Masked Stereolithography (MSLA) 3D printer. The electromagnet was mounted onto the lid, the reservoir was filled with an MRF, and the lid was twisted onto the
reservoir using a large thread. Using a testing machine, the damper forces of the 3D-printed MRVI were measured under constant velocity conditions for different magnetic fields. From
these tests, the magnetic field-controllable performances such as the dissipated energy and the dynamic force range of the MRVI were obtained (Figure 2). The feasibility of the 3D-printed
MRVI was experimentally confirmed. In addition, using 3D printing, and by selecting Shore hardness of the material, wall thickness, bellow shape, and MRF Fe particle volume fraction, a
broad range of VI applications can be addressed with this design methodology. 

**References:** 

1. S. Kaul, “Modeling and Analysis of Passive Vibration Isolation Systems,” Elsevier, 1st Ed., 2021, DOI: 10.1016/C2019-0-00013-1. 
2. M. Brigley, Y. T. Choi and N. M. Wereley, “Experimental and Theoretical Development of Multiple Fluid Mode Magnetorheological Isolators,” Journal of Guidance, Control, and
Dynamics, Vol. 31, No. 3, pp. 449-459, 2008, DOI: 10.2514/1.32969. 

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Design and Performance of A 3D Printed Magnetorheological Fluid

Magnetic hydrogels, or ferrogels, are smart materials that can mechanically deform in response to an applied magnetic field. Ferrogels possess potential applications in soft robotics, tissue engineering, artificial muscles, and controlled drug release due to their soft tissue-like state and biocompatibility potential. Hydrogels are a 3D hydrophilic polymer network linked by physical or chemical crosslinking, whereas ferrogels are a composite material that possess magnetic responsiveness due to the incorporation of a magnetic additive. Polyvinyl Alcohol (PVA) is unique among hydrogels due to the formation of physical crosslinks when subjected to cycles of freezing and thawing, enhancing mechanical properties without the use of a crosslinker that could compromise biocompatibility. The first aim of this work is to understand the differing effects of magnetically soft and hard additives on the magnetic, mechanical, and magnetoactive properties of PVA ferrogels by investigating PVA hydrogels impregnated with soft and hard magnetic particulate (magnetite (Fe3O4) and strontium ferrite (SrFe12O19), respectively). While magnetically hard polymer composites allow for more complex motion and actuation, soft magnetic additives often allow for greater response due to particle realignment when under a magnetic field. The next aim of this work studies the effects of magnetic annealing these ferrogels during crosslinking by comparing their resulting magnetic, mechanical, and magnetoactive properties with analogous control samples. Magnetoactive properties are determined by quantifying the angle of sample deflection in an applied transverse magnetic field, via a custom digital image overlay program. Scanning Electron Microscopy (SEM) is used to analyze the structural features of the hydrogel matrix and magnetic particulate. Results yield highly magnetoactive hydrogel samples with tunable responsiveness based upon magnetic particulate type, content, and presence of magnetic annealing. Figure 1 shows the magnetic responsiveness of ferrogels at varying magnetite contents.

Magnetoactive Properties of Biocompatible Magnetic Hydrogel Composites: Effects of Magnetic Particulate Type and Magnetic Annealing

Herewith, magnetic MOF composite material was utilized as an efficient adsorbent for direct red 81 (DR81) dye from wastewater. The synthesized magnetic MOF was
characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), field emission transmission electron microscopy (FETEM), Fourier-transform infrared spectroscopy
(FTIR), thermal gravimetrical analysis (TGA), and Brunauer-Emmett-Teller (BET) surface area analysis. The adsorption profile of DR81 anionic dye onto the prepared magnetic MOF was
investigated with various processing parameters such as pH, contact time, and dosage. The optimum dosage from the fabricated magnetic MOF for decontamination of DR81 was 2.0 g L-1
at pH=7 after 30 min. The maximum Langmuir monolayer adsorption capacity of the DR81 decontamination via the synthesized magnetic MOF was recorded 67.35 mg/g. These promising
data confirmed the availability of the synthesized magnetic MOF composite as an excellent adsorbent material for the adsorption of DR81 from aqueous media.
Acknowledgment: This paper is based upon work supported by Science, Technology & Innovation Funding Authority (STDF) under grant (43565). 

**References:** 

Mona Ossman,(1),* Eslam Salama,(1) Ali Hamdy,(1) Hassan Shokry Hassan,(2) Marwa F. Elkady(2)
(1)City of Scientific Research and Technological Applications (SRTA-City), New Borg El-Arab City, Alexandria 21934, Egypt
(2)Egypt-Japan University of Science and Technology, New Borg El-Arab City, Alexandria 21934, Egypt
Correspondence should be addressed to Mona Ossman; mhr1410@hotmail.com

Magnetic MOF composite material for decontamination of direct red 81 from polluted water

High-precision 3D reconstruction of multiple magnetic targets aims to reconstruct the three-dimensional shape, position and magnetic susceptibility of magnetic targets
based on discrete and noisy magnetic anomaly measurement data. It is widely used in anti-submarine, unexploded ordnance detection, subway magnetic pipeline detection and so on [1].
Currently, the research on three-dimensional reconstruction of multiple magnetic targets is still in the primary stage. Similar magnetic inversion technology is mainly studied in geological
exploration, including morphological inversion and physical inversion. For morphological inversion, it needs to know the magnetic susceptibility in advance, which leads to some
limitations in the reconstruction of unknown magnetic targets. For the physical inversion, because of the error of magnetic data and the lack of prior information, the effect of traditional
physical inversion method is not satisfactory [2]. In order to solve this problem, this paper proposes a center weighting 3D imaging method based on normalized cross-correlation
coefficient. The method selects magnetic gradient tensor data as observation data, calculates its cross-correlation coefficient with each cuboid cells. This can reduce the interference caused
by magnetic measurement noise and errors of magnetic measurement parameters [3]. And visualize the results to obtain the preliminary distribution of magnetic target (Figure 1). Then, the
spatial distribution and center position of the magnetic target are used as prior information to constrain the inversion equation. This can reduce the degree of freedom of the inversion
equation and get the optimal solution of the reconstruction result. Our simulation and experimental results show that the proposed method can realize 3D reconstruction of several simple
magnetic targets with a higher degree of fidelity (Figure 2). 

**References:** 

[1] Y. Wang, I. I. Kolotov, D. V. Lukyanenko et al, Computational Mathematics and Mathematical Physics, Vol. 60, p. 1000–1007 (2020) 
[2] G. Yin, L. Zhang, Journal of Magnetism and Magnetic Materials, Vol. 482, p. 229–238 (2019) 
[3] Y. Li, J. Song, H. Xia et al, Journal of Applied Physics, Vol. 127,104701 (2020) 

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High precision 3D Reconstruction of Multiple Magnetic Targets Based on Center Weighting Method

Ultra-thin silicon steel with thickness less than 0.1 mm can be used to make the iron core of high-frequency transformers and high-speed motor. The design of these electrical equipment often relies on the data of alternating magnetic characteristics measured under standard excitation, ignoring the influence of working conditions such as stress and temperature, etc.. Under stress conditions, the magnetic properties of the magnetic material are quite different from those under standard sinusoidal excitation. Therefore, it is very important to accurately measure the magnetic properties of ultra-thin silicon steel under stress conditions.
Previously, many scholars have studied the influence of mechanical stress on the magnetic properties of conventional electrical steels [1-5]. However, there are few reports on the properties of high frequency materials under stress. When testing the properties of high frequency materials under stress, we can refer to the traditional testing methods of silicon steel. Compared with the other methods of stress application, such as motor or oil pressure, piezoelectric material has good flexibility and control accuracy, and it will not produce stray magnetic field to interference the measurement. The most important thing is that piezoelectric materials have good applicability to the magnetizer. In other words, it is not necessary to reconstruct the magnetic circuit structure and stress actuator, and the relevant experiments can be carried out directly by pasting piezoelectric materials in the sample.
This paper uses the magnetizer proposed in paper [6], which has good test accuracy. The sample is ultra-thin silicon steel with 0.1 mm thickness (GT100). The size of the sample is 50 mm×50 mm. Two Macro Fiber Composite (MFC) [7] are symmetrically pasted on the upper and lower surfaces of the sample center. A maximum tensile or compressive stress of about 150 N or 107 MPa can be obtained in the sample along one direction. The distribution of stress is calculated by the FEM and shown in Fig. 1. It can be seen that the stress is uniform in the measuring region. Fig. 2 gives the hysteresis loops under different stress along rolling direction. 

**References:** 

[1] Permiakov, V. , et al. "Loss separation and parameters for hysteresis modelling under compressive and tensile stresses." Journal of Magnetism & Magnetic Materials 272.supp-S(2004):E553-E554. 
[2] Singh, D. , et al. "Effect of stress on excess loss of electrical steel sheets." IEEE (2015):1-1. 
[3]Hameyer, et al. "Effect of mechanical stress on different iron loss components up to high frequencies and magnetic flux densities." COMPEL: The international journal for computation and mathematics in electrical and electronic engineering 36.3(2017):580-592. 
[4]Ali, K. , Atallah, K. and Howe, D. . "Prediction of mechanical stress effects on the iron loss in electrical machines." Journal of Applied Physics 81.8(1997). 
[5]Naumoski, H. , Maucher , A. and Herr, U. . "Investigation of the influence of global stresses and strains on the magnetic properties of electrical steels with varying alloying content and grain size." Electric Drives Production Conference IEEE, 2015. 
[6]Yue, S. , et al. "Comprehensive Investigation of Magnetic Properties for Fe–Si Steel Under Alternating and Rotational Magnetizations Up to Kilohertz Range." IEEE Transactions on Magnetics 55.7(2019):1-5. 
[7]Wilkie, W. K. , et al. "Low-cost piezocomposite actuator for structural control applications." Proceedings of Spie the International Society for Optical Engineering (2000). 

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Influence of stress on the magnetic properties of ultra thin silicon steel in high frequency range

Abstract—The Jiles-Atherton (J-A) model [1] predicts that an inductor has different inductances at the current ramp-up time (ton) and ramp-down time (toff). For the first time, this phenomenon is observed at the boost converter (Fig. 1) during the voltage converter (Fig. 2) and a simple equation to depict it is derived. Moreover, the methodology to calculate the inductor power loss from the voltage conversion data directly for a boost converter is presented.
I. Model and Experiment Result
From Fraday law, the inductance is
L=NdBA/(dt(dI/dt))=uNA d(H+M)/dI= Lo(1+dM/dH) (1)
From the J-A model, the differential of total magnetization M is
dM/dH=(Man-M)/((dk/m)-a(Man-M)) (2)
where d=1 if dH/dt>0 and d=-1 if dH/dt < 0.
From the experiment result in Figs. 3-5, Man-M can be approximated to a constant MD since the voltage almost keeps constant at each ton or toff based on L=V/(dI/dt). So, the inductance at ton is given by
L= Lo(1+mMD/(k-aMD)=L1 (3)
The inductance at toff is given by
L= Lo(1-mMD/(k+aMD)=L2 (4)
Figs. 3-5 show the voltage and current waveforms of the boost converter during the voltage conversion for the output currents (IOUT in Fig. 1) 0A, 0.2A and 0.4A. However, the three different IOUT waveforms have different ton and toff due to different slops by aligning their base current level with the current waveform for IOUT=0A as shown in Fig. 6. It proves that an inductor has the different inductances at ton and toff and L1 at ton is larger than Lo and L2 at toff is smaller than Lo. This is caused by the hysteresis effect as shown in Fig. 7 since it follows the initial hysteresis loop, minor hysteresis curves i and ii for Lo, L1 and L2. Integrating the powers at ton and toff and divided by the period T, the inductor power loss for boost converter during the voltage conversion is Ploss=(L1-L2)dIL(dIL+2IMIN)/2T (5)
where dIL is the peak-to-peak inductor current, IMIN is the minimum inductor current in Fig. 2.
Table-I shows the input power, measured inductor power loss based on Figs. 3-5 and calculated inductor power loss based on Eq. (5). The calculated power loss is very close to the measured power loss, verifying Eq. (5) valid to calculate the inductor power loss from the electrical voltage conversion data directly. 


**References:** 

[1] D. C. Jiles and D. L. Atheron, “Theory of Ferromagnetic Hysteresis,” J. of Magnetism and Magnetic Materials, pp. 48-60, 1986. 

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A New Inductor Power Loss Model for Boost Converter during the Voltage Conversion Based on the Jiles Atherton Model

Different contributions and mechanisms of the unidirectional magnetoresistance (UMR) have become a recent research hotspot in spintronics. Field sweeping DC measurements are used to measure the value of UMR instead of second harmonic measurements. [1,2] In this paper, potential measurement errors in conventional DC measurements are studied. Oersted field and field-like torque usually do not influence the measurement, but a large field-like torque can result in anisotropic magnetoresistance (AMR) difference when the sample is not perfectly aligned. The existence of ordinary magnetoresistance (OMR) can also contribute to a large background. An alternative measurement method is demonstrated to address this issue. This work broadens the understanding of the error sources and provides a standard measurement method for UMR DC measurements. 

**References:** 

[1] Duy Khang, Nguyen Huynh, and Pham Nam Hai. "Giant unidirectional spin Hall magnetoresistance in topological insulator–ferromagnetic semiconductor heterostructures." Journal of Applied Physics 126.23 (2019): 233903. 
[2] Chang, Ting-Yu, et al. "Large unidirectional magnetoresistance in metallic heterostructures in the spin transfer torque regime." Physical Review B 104.2 (2021): 024432.

Origins of potential observational errors in field sweeping DC measurements for unidirectional magnetoresistance

Amniotic fluid is a clear and pale yellowish liquid that surrounds and protects the fetus (unborn baby) throughout pregnancy [1]. The amniotic fluid analysis can diagnose certain health disorders in the unborn baby such as Down syndrome, Cystic fibrosis, Sickle cell disease, Tay-Sachs disease, and Neural tube defects [2]. Fig. 1 shows a 10-week-old human fetus surrounded by amniotic fluid within the amniotic sac [3]. 

This abstract presents a deep learning/machine learning pipeline that can be used for the prediction of health problems in an unborn baby by using deep learning or machine learning algorithm. The work is divided into two parts; (1) data collection, and (2) data processing and analysis. Fig. 2(a) shows data collection workflow. In the data collection, 20 samples of amniotic fluid are collected from a healthy and an unhealthy woman during 15~20 weeks of pregnancy [4]. After collecting the samples, the nuclear magnetic resonance (NMR) is performed for data acquisition [5]. Fig. 2(b) depicts workflow of data processing and analysis. In this part, metabolites data is normalized and a data reduction method like PCA is applied. After getting the preprocessed data, feature selection algorithms are performed to remove the irrelevant data. Three variable selection algorithms such as (i) Correlation-based feature selection (CFS), (ii) Partial least squares regression (PLS), and (iii) Learning Vector Quantization (LVQ) [4] are used. For each selected set of metabolites, support vector machine (SVM) models are generated using 10-fold cross-validation. To evaluate the performance and significance of the optimized model, we perform a permutation test. In the 10-fold cross-validation, the permutation test shuffles the class labels while using the same set of metabolites. For data analysis, we calculate true/false positives and true/false negatives for each round of our 10-fold cross-validation to report average sensitivity (TP/P) and specificity (TN/N) values along with the average of the area under the curve (AUC) [4]. 

**References:** 

[1]. C. M. Broek, J. Bots, I. V. Lasheras, M. Bugiani, F. Galis, S. V. Dongen, “Amniotic fluid deficiency and congenital abnormalities both influence fluctuating asymmetry in developing limbs of human deceased fetuses,” PLoS One, vol. 8, no. 11, 2013. 
[2]. MedlinePlus [Internet], National Library of Medicine (US), “Amniocentesis (amniotic fluid test),” Available from: https://medlineplus.gov/lab-tests/amniocentesis-amniotic-fluid-test/. 
[3]. https://en.wikipedia.org/wiki/Amniotic_fluid. 
[4]. R. O. B. Singh, A. Yilmaz, H. Bisgin, O. Turkoglu, P. Kumar, E. Sherman, A. Mrazik, A. Odibo, and S. F. Graham, “Artificial intelligence and the analysis of multi-platform metabolomics data for the detection of intrauterine growth restriction,” PLoS One vol. 14, no. 4, 2019. 
[5]. https://www.jeol.co.jp/en/products/nmr/basics.html. 

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