France



Transcranial Magnetic Stimulation (TMS) is a non-invasive technique of modulating the neurons of the brain. TMS is a tool in cognitive neuroscience for studying neuronal connectivity and brain mapping. It is also approved for the treatment of neuropsychiatric disorders [1]. It functions on the principle of electromagnetic induction in inducing electric field in the neurons of the brain. With TMS studies, accurate estimation of the induced E-Field is usually necessary. This, however, requires a lot of processes, which include the three-dimensional head model generation from magnetic resonance imaging (MRI) scans using the SimNIBS software [2] and finite element analysis to calculate the induced E-Field (Fig. 1). These processes are time-consuming and computationally expensive. Additionally, with each head model&#39;s uniqueness, outcomes cannot be generalized across a particular population as the intensity of E-Field is subject-specific [3], [4]. A few studies have explored the use of artificial neural networks to estimate the electric field from T1 MR images [5], [6], [7]; however, none has considered the effect of different coil geometry in their estimation. In this research, the authors develop a deep neural network (DNN) model to determine the induced E-Field directly from the patient’s MRI scan and across different coil types. We trained neural network models from realistically anatomic head models’ and across 16 different coil type to predict the induced electric field in the brain and scalp (E-Max brain and scalp) and the volume of stimulation of the brain and scalp (V-half brain and scalp) from T1 MR images and specific coil type. Using Deep Neural Network (DNN), the processing and estimation time for the induced E-Field is measurably reduced, which is helpful both to clinicians and researchers as the need to create subject-specific anatomical head structures is eliminated. This will also be beneficial to the TMS patients as there will be no need for additional stimulation sessions with the different coil types as the DNN network is able to predict the outcome from each coil type. The other advantage of the DNN model is that the E-Field from the different coil types can be compared simultaneously. Preliminary results for the E-Max brain report a correlation coefficient of 0.93 for the training dataset and 0.88 on the test dataset, as shown in the parity plot in Fig. 2.

**References:**

[1] L. J. Gomez, S. M. Goetz, and A. V. Peterchev, “Design of transcranial magnetic stimulation coils with optimal trade-off between depth, focality, and energy,” &lt;i&gt;J. Neural Eng.&lt;/i&gt;, vol. 15, no. 4, p. 046033, Aug. 2018, doi: 10.1088/1741-2552/aac967.&lt;br/&gt;[2] M. Windhoff, A. Opitz, and A. Thielscher, “Electric field calculations in brain stimulation based on finite elements: An optimized processing pipeline for the generation and usage of accurate individual head models,” &lt;i&gt;Human Brain Mapping&lt;/i&gt;, vol. 34, no. 4, pp. 923–935, 2013, doi: 10.1002/hbm.21479.&lt;br/&gt;[3] F. Syeda, H. Magsood, E. G. Lee, A. A. El-Gendy, D. C. Jiles, and R. L. Hadimani, “Effect of anatomical variability in brain on transcranial magnetic stimulation treatment,” &lt;i&gt;AIP Advances&lt;/i&gt;, vol. 7, no. 5, p. 056711, Jan. 2017, doi: 10.1063/1.4974981.&lt;br/&gt;[4] P. C. Miranda, L. Correia, R. Salvador, and P. J. Basser, “The Role of Tissue Heterogeneity in Neural Stimulation by Applied Electric Fields,” in &lt;i&gt;2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society&lt;/i&gt;, Aug. 2007, pp. 1715–1718, doi: 10.1109/IEMBS.2007.4352640.&lt;br/&gt;[5] J. A. Livezey, K. E. Bouchard, and E. F. Chang, “Deep learning as a tool for neural data analysis: Speech classification and cross-frequency coupling in human sensorimotor cortex,” &lt;i&gt;PLoS Comput Biol&lt;/i&gt;, vol. 15, no. 9, p. e1007091, Sep. 2019, doi: 10.1371/journal.pcbi.1007091.&lt;br/&gt;[6] Tatsuya Yokota, Toyohiro Maki, Tatsuya Nagata, Takenobu Murakami, Yoshikazu Ugawa, Ilkka Laakso, Akimasa Hirata, Hidekata Hontani, Real-time estimation of electric fields induced by transcranial magnetic stimulation with deep neural networks, Brain Stimulation, Volume 12, Issue 6, 2019, Pages 1500-1507, ISSN 1935-861X, doi: ttps://doi.org/10.1016/j.brs.2019.06.015.&lt;br/&gt;[7] A. S. Lundervold and A. Lundervold, “An overview of deep learning in medical imaging focusing on MRI,” &lt;i&gt;Zeitschrift für Medizinische Physik&lt;/i&gt;, vol. 29, no. 2, pp. 102–127, May 2019, doi: 10.1016/j.zemedi.2018.11.002.

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/10a9e1ff81dca3187fa7c5110e4343b1.jpg)
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![](https://assets.underline.io/uploads/markdown_image/1/image/f95e4f0342ee55c312e1ab3dc38bfa6d.jpg)
Fig. 2. Parity Plots of the E-Max Brain for the Test and Training dataset.

INTERMAG 2021

 Neural Network Model for Estimation of the Induced Electric Field during Transcranial Magnetic Stimulation


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The energy cost of storing and moving data is enormous, and is growing at such a rapid pace that within a few years the power demand may overwhelm some developed nations. Non-volatile memory (NVM) and neuromorphic computing are enabling critical advances in this area, but there are many scientific and technological challenges. This conference will cover the materials challenges of different memory technologies, from the fundamental processes and mechanisms, to industry perspectives on materials specifications and manufacturing.

Materials Challenges for Memory

Transcranial Magnetic Stimulation (TMS) is a non-invasive technique of modulating the neurons of the brain. TMS is a tool in cognitive neuroscience for studying neuronal connectivity and brain mapping. It is also approved for the treatment of neuropsychiatric disorders [1]. It functions on the principle of electromagnetic induction in inducing electric field in the neurons of the brain. With TMS studies, accurate estimation of the induced E-Field is usually necessary. This, however, requires a lot of processes, which include the three-dimensional head model generation from magnetic resonance imaging (MRI) scans using the SimNIBS software [2] and finite element analysis to calculate the induced E-Field (Fig. 1). These processes are time-consuming and computationally expensive. Additionally, with each head model's uniqueness, outcomes cannot be generalized across a particular population as the intensity of E-Field is subject-specific [3], [4]. A few studies have explored the use of artificial neural networks to estimate the electric field from T1 MR images [5], [6], [7]; however, none has considered the effect of different coil geometry in their estimation. In this research, the authors develop a deep neural network (DNN) model to determine the induced E-Field directly from the patient’s MRI scan and across different coil types. We trained neural network models from realistically anatomic head models’ and across 16 different coil type to predict the induced electric field in the brain and scalp (E-Max brain and scalp) and the volume of stimulation of the brain and scalp (V-half brain and scalp) from T1 MR images and specific coil type. Using Deep Neural Network (DNN), the processing and estimation time for the induced E-Field is measurably reduced, which is helpful both to clinicians and researchers as the need to create subject-specific anatomical head structures is eliminated. This will also be beneficial to the TMS patients as there will be no need for additional stimulation sessions with the different coil types as the DNN network is able to predict the outcome from each coil type. The other advantage of the DNN model is that the E-Field from the different coil types can be compared simultaneously. Preliminary results for the E-Max brain report a correlation coefficient of 0.93 for the training dataset and 0.88 on the test dataset, as shown in the parity plot in Fig. 2.

**References:**

[1] L. J. Gomez, S. M. Goetz, and A. V. Peterchev, “Design of transcranial magnetic stimulation coils with optimal trade-off between depth, focality, and energy,” J. Neural Eng., vol. 15, no. 4, p. 046033, Aug. 2018, doi: 10.1088/1741-2552/aac967. [2] M. Windhoff, A. Opitz, and A. Thielscher, “Electric field calculations in brain stimulation based on finite elements: An optimized processing pipeline for the generation and usage of accurate individual head models,” Human Brain Mapping, vol. 34, no. 4, pp. 923–935, 2013, doi: 10.1002/hbm.21479. [3] F. Syeda, H. Magsood, E. G. Lee, A. A. El-Gendy, D. C. Jiles, and R. L. Hadimani, “Effect of anatomical variability in brain on transcranial magnetic stimulation treatment,” AIP Advances, vol. 7, no. 5, p. 056711, Jan. 2017, doi: 10.1063/1.4974981. [4] P. C. Miranda, L. Correia, R. Salvador, and P. J. Basser, “The Role of Tissue Heterogeneity in Neural Stimulation by Applied Electric Fields,” in 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Aug. 2007, pp. 1715–1718, doi: 10.1109/IEMBS.2007.4352640. [5] J. A. Livezey, K. E. Bouchard, and E. F. Chang, “Deep learning as a tool for neural data analysis: Speech classification and cross-frequency coupling in human sensorimotor cortex,” PLoS Comput Biol, vol. 15, no. 9, p. e1007091, Sep. 2019, doi: 10.1371/journal.pcbi.1007091. [6] Tatsuya Yokota, Toyohiro Maki, Tatsuya Nagata, Takenobu Murakami, Yoshikazu Ugawa, Ilkka Laakso, Akimasa Hirata, Hidekata Hontani, Real-time estimation of electric fields induced by transcranial magnetic stimulation with deep neural networks, Brain Stimulation, Volume 12, Issue 6, 2019, Pages 1500-1507, ISSN 1935-861X, doi: ttps://doi.org/10.1016/j.brs.2019.06.015. [7] A. S. Lundervold and A. Lundervold, “An overview of deep learning in medical imaging focusing on MRI,” Zeitschrift für Medizinische Physik, vol. 29, no. 2, pp. 102–127, May 2019, doi: 10.1016/j.zemedi.2018.11.002.

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/10a9e1ff81dca3187fa7c5110e4343b1.jpg)
Fig. 1. Conventional Process for Induced Electric Field Estimation during TMS

![](https://assets.underline.io/uploads/markdown_image/1/image/f95e4f0342ee55c312e1ab3dc38bfa6d.jpg)
Fig. 2. Parity Plots of the E-Max Brain for the Test and Training dataset.

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Three-dimensional (3D) nanomagnets provide a great potential to be implemented in future spintronic devices for high-density magnetic memories, nano-sensing or logic. Among these appealing architectures, ferromagnetic nanotubes (NTs) are currently being investigated for their fast and low-power domain wall conduit properties [1]. The development of novel 3D nanostructures typically requires complex synthetic methods. Moving forward in this challenging task, Focused Electron Beam Induced Deposition (FEBID) has been explored for the growth of 3D ferromagnetic NTs. This unique one-step nanolithography technique, based on the decomposition of organometallic precursor gas molecules in the vicinity of a substrate [2,3], has demonstrated great versatility in terms of shape, composition and magnetic properties [4]. Following the strategy developed for the fabrication of core-shell nanowires [5], the growth of 3D Co NTs by FEBID has been performed for the first time [6]. The heterostructured design is composed by a vertical Pt-C nanowire (≤100 nm in diameter) acting as a core, and a Co coating forming the shell with a thickness down to ~11 nm. These are exotic magnetic nanostructures in which unconventional domain walls are formed, presenting high domain wall velocities without Walker breakdown phenomenon and where great stability during propagation has been predicted. Furthermore, Transmission Electron Microscopy (TEM) experiments reveal that these NTs present a nanocrystalline structure and a metallic content of ~70 at. %. In addition, a coercivity of ~16 mT has been obtained by Magneto-Optical Kerr Effect magnetometry, and magnetic characterization performed by Electron Holography evidences the ferromagnetic behaviour, estimating a remanent magnetic induction up to 1.3 T and detecting complex head-to-head magnetic domain walls. The application of magnetic fields inside the TEM has allowed the pinning and imaging of domain walls in these nanostructures. Finally, micromagnetic simulations have been performed for a deeper understanding of the magnetization dynamics [6].

**References:**

[1] R. Hertel, J. Phys.: Condens. Matter, 28, 483002 (2016). [2] J. Pablo-Navarro, S. Sangiao, C. Magén, et. al., Nanofabrication: Nanolithography techniques and their applications. Edited by J. M. De Teresa, IOP Publishing (2020). [3] A. Fernández-Pacheco, L. Skoric, J. M. De Teresa, et. al., Materials, 13, 3774 (2020). [4] J. Pablo-Navarro, D. Sanz-Hernández, C. Magén, et al., J. Phys. D. Appl. Phys., 50, 18LT01 (2017). [5] J. Pablo-Navarro, C. Magén and J. M. De Teresa, Nanotechnology, 27, 285302 (2016). [6] J. Pablo-Navarro, et al., manuscript in preparation.

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/fee42b13b0eb696628dd941a85be6a72.jpg)
Fig. 1. (a) HAADF-STEM image of a Co-FEBID NT cross-section. (b,c) Experimental and simulated magnetic phase shift around a domain wall obtained by Electron Holography and micromagnetic simulations, respectively. (d) Spin configuration obtained from micromagnetic simulations.

 Ferromagnetism of Single Vertical Cobalt Nanotubes Grown by Focused Electron Beam Induced Deposition

Magnetocuring of carbon and glass fibers was performed using Curie temperature tuned magnetic nanoparticles (CNP) embedded in adhesives, aka magnetoadhesive. Interestingly, these fibers were cured under alternating magnetic field (AMF) within a few minutes. In-situ heating of the magnetoadhesive were controlled by CNP loading and AMF strength. The heating of the CNP switches off above its Curie temperature, which prevents overheating. For the first time, custom made AC-magnetorheometry was employed to understand the kinetics of curing of the magnetoadhesive under AMF. The storage modulus of the magnetocured adhesive exceeded 10 MPa at shear strain of &lt; 10%. Introduction: Magnetic field activated adhesive curing, aka magnetocuring have recently been reported[1]. Magnetocuring overcomes the limitations of available curing techniques e.g. photocuring[2], snap-curing[3], and electrocuring[4], etc, it allows the adhesive to be activated on-demand when an alternating magnetic field is applied. Carbon fiber is increasingly being used to make lighter and stronger parts in many industries including aviation, automobile, marine, sports, defence, IT, etc.[5] However, in most of the applications, these fibers need to bond with suitable thermosets/thermoplastics in order to achieve high stiffness, for which thermal curing is required. Magnetocuring of carbon and glass fibers can be applied in a range of commercial applications. The present work addresses the design and application of one-pot epoxy adhesives by the ‘modifier’ methodology for on-demand contactless curing under alternating magnetic field (AMF). Material and Methods: Curie nanoparticles (CNP) were synthesized by the hydrothermal method and functionalized using oleic acid and epoxy [1]. Functionalized CNP were mixed into commercial 1C epoxy adhesive and cured under an alternating magnetic field (AMF, 400 kHz, 140 Oe). The kinetic parameters, real-time properties of the adhesive and composite were evaluated using a custom made AC magneto-rheometry setup. Results and Conclusions: Magnetoadhesive applied carbon and glass fibers were passed through the magnetic field generated solenoid coil and cured within 5 min (Fig. 1A). This fast curing is important for in-line manufacturing of fiber based components. The magnetoadhesive property under AMF and at room temperature were studied using AC-magnetorheometry. The gelation point of the magnetoadhesive with 30wt.% CNP was achieved within 70 s of AMF exposure (Fig. 1B and 1C). On applying the magnetic field, a decrease in the loss modulus (G’) and increase in the storage modulus (G’’) was observed due to the initiation of crosslinking and composite hardening. The storage modulus of the magnetocured adhesive was observed to be &gt;10 MPa at shear strain of &lt; 10% (Fig. 1C). These results suggest that magnetocuring is an attractive technology for in the sports goods, automotive, aerospace and medical industries. It offers an energy efficient, remote, and wireless method of on-demand adhesion by exploiting self-regulating Curie nanoparticles (CNP). Real time magnetorheology measurements were used to determine the properties, fast gelation within 70 s was observed.. Acknowledgement: Authors acknowledge the financial support from the Agency for Science, Technology and Research (A*Star) for the grant IRG17283008 “Microprocessor- based methods of composite curing.

**References:**

[1] R. Chaudhary, V. Chaudhary, R.V. Ramanujan, T.W.J. Steele, Applied Materials Today 21, 100824 (2020). [2] S.C. Ligon-Auer, M. Schwentenwein, C. Gorsche, J. Stampfl, R. Liska, Polymer Chemistry 7, 257-286 (2016). [3] X.K. Hillewaere, R.F. Teixeira, L.T.T. Nguyen, J.A. Ramos, H. Rahier, F.E. Du Prez, Advanced Functional Materials 24, 5575-5583 (2014). [4] J. Ping, F. Gao, J.L. Chen, R.D. Webster, T.W. Steele, Nature communications 6, 1-9 (2015). [5] C. Soutis, Materials Science and Engineering: A 412, 171-176 (2005).

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/d53ed9987bc3b153986b15a11f93643b.jpg)
Fig.1 Magnetocuring and AC-magnetorheology of composites. (A) Magnetocuring of carbon and glass fibers for in-linemanufacturing, (B-C) Oscillation and amplitude sweep of 30wt.% CNP within epoxy adhesive.

 Magnetocuring and Magnetorheology of Composites

Investigation of magnetic properties of nanostructured ferromagnets, such as oriented arrays of carbon nanotubes (CNTs) containing ferromagnetic nanoparticles (FNPs), is still relevant. In addition to attractive applications in magnetoelectronics, CNTs with embedded FNPs are also a very useful model object for studying magnetic interaction of the latter through a conducting medium. For this, it is important to establish a relation between macroscopic and microscopic parameters of the system. In nanostructured ferromagnets, this dependence is described within the random magnetic anisotropy model (RAM), in which the spin system and, consequently, the main macroscopic characteristics (coercivity, susceptibility, saturation magnetization) are determined by such microscopic parameters as the exchange interaction constant, FNP magnetization, local magnetic anisotropy constant and grain size 2Rc [1]. Recently, magnetic parameters like the exchange and anisotropy fields, effective magnetic anisotropy constant, Bloch and exchange constants in aligned CNT arrays containing FNPs were determined within the RAM by analyzing law of the approach to saturation magnetization and corresponding modeling of the correlation functions of the magnetic anisotropy axes of the FNPs [2,3]. Presence of the interplay between the exchange interaction and magnetic anisotropy, as well as the contribution of not only random, but also coherent anisotropy, was established [3]. In addition, the important role of magnetoelastic anisotropy in the case when a single FNP is localized inside a CNT has been revealed. [4]. It was shown also that aligned CNT arrays with a low concentration of FNPs embedded only inside CNT have relatively high values of exchange fields, random and coherent anisotropy. They are manifested in CNT arrays, in which the average distance between the FNPs significantly exceeds the size of the latter, reaching hundreds of nanometers. These effects, which do not currently have a convincing explanation, are mainly associated with the presence of the exchange interaction between FNPs through the CNT matrix. However, there is still no reasonable mechanism of the long-range exchange interaction in CNT arrays. In our work [5], it was assumed that these effects are related to the indirect exchange coupling of the RKKY type via the conducting electrons of CNTs. The obtained preliminary estimations have shown that the RKKY exchange interaction is enhanced by the presence of spin-orbit coupling (SOC) and could propagate up to a micrometer scale [5]. In this contribution a multiwall CNT (MWCNT) with embedded FNPs is considered. We present the results of modeling of the RKKY interaction in MWCNT depending on its diameter, chirality, chemical potential and SOC constant within the Klinovaya-Loss model [6]. The influence of the external longitudinal magnetic field is also studied. It is assumed that the main contribution to the RKKY interaction is caused by the conduction electrons of the inner wall of CNTs which is in contact with the FNP. In addition, spin-orbit interaction (SOI) in CNT is also considered. The SOI in a nanotube can occur due to the curvature effect, which significantly increases its contribution in comparison with a flat graphene. It can also be enhanced by FNP, CNT defects or impurity states. The spin susceptibility of CNT conduction electrons χ/χ0 is evaluated (χ0=a2kG/hυF, where υF is the Fermi velocity, kG is the circumferential direction, a is the lattice constant). Fast oscillations are excluded and only slowly changing envelopes of spin susceptibility are considered. The chemical potential is tuned inside the gap opened by SOI. It is shown that the decay of the amplitude of the χ/χ0 oscillations depends strongly on the chemical potential: the higher the Fermi energy of the CNT (εF), the more significant is the decay. The frequency of spin susceptibility oscillations increases with the increase of the εF (Fig. 1). This is due to the fact that with the increase of the Fermi energy of CNTs, the discrepancy between the gap opened by SOI and the value of chemical potential increases. Finally, the proposed approach allows evaluating the energy of the exchange interaction between FNPs belonging to the same CNT. The work is supported by the COST Action CA19118.

**References:**

[1] E.M. Chudnovsky, J. Appl. Phys., Vol. 64, p. 5770 (1988). [2] A.L. Danilyuk, A.L. Prudnikava, I.V. Komissarov, et al. Carbon, Vol. 68, p. 337 (2014). [3] A.L. Danilyuk, I.V. Komissarov, V.A. Labunov, et al. New J. Phys. Vol. 17, p. 023073 (2015). [4] S.L. Prischepa, A.L. Danilyuk, A.V. Kukharev, et al. IEEE Trans. Magn. Vol. 55, p. 2300304 (2019). [5] A.L. Danilyuk, I.V. Komissarov, A.V. Kukharev, et al. Europhys. Lett. Vol. 117, p. 27007 (2017). [6] J. Klinovaja and D. Loss D, Phys. Rev. B, Vol. 87, p. 045422 (2013).

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/29f3738a3c00cb89dafac5b18dcdb897.jpg)
Fig. 1. Decay of the oscillation amplitude of the spin susceptibility χ/χ0 along the axis of the semiconductor CNT of diameter of 25 nm with chiral indices (235,129) and εF=18 (blue) and 30 (red) meV. The chirality angle is 39.54° and kG =0.053 nm-1.

 Long-Range Exchange Interaction between Ferromagnetic Nanoparticles Embedded in Carbon Nanotubes

Superparamagnetic nanoparticles are drawing increasing interests because of their special physical properties and potential applications in magnetic-supporting catalysts, water purification, ferrofluids, and medical diagnostics [1,2]. For most applications, an accurate determination of the particle size distribution is required since the magnetic properties of the particles directly depend on the particle size. Transmission electron microscopy (TEM) and X-ray diffraction (XRD) are widely used to determine the particle size. However, both methods have limitations. For TEM, normally only a limited amount of particles contribute to the result, and the precise determination of extremely small particles is difficult due to the limit of the TEM image contrast and resolution. For XRD, the evaluation is based on the Scherrer equation, the result of which is the average size of the particles, more detailed information of the size distribution is not available. Furthermore, this method assumes that the peak broadening is solely due to the crystallite size, other effects such as microstrain are neglected. Besides, neither TEM nor XRD are suitable for embedded nanoparticles. Above the blocking temperature TB, the magnetic moment versus external magnetic field curve (m-H curve) of superparamagnetic particles is described by m=mSL(x), with x=μ0mp(d)H/kBT, where m is the magnetic moment, mS is the saturation magnetic moment, μ0 is the vacuum permeability, mp is the magnetic moment of one particle of size d, H is the external field, kB is the Boltzmann constant, T is the temperature in Kelvin, L(x) is the Langevin function: L(x)=coth(x)-1/x. Since the m-H curve is dependent on the size d, the particle size could be determined by fitting the measured m-H curve with the Langevin function. Note that the realistic particle size normally includes a distribution. In 1978, Chantrell et al. [3] presented a method to determine the mean value and standard deviation of the particle size distribution. This method fits the measured m-H curve to a weighted sum of Langevin functions using a pre-assumed lognormal size distribution. Since then, this method has been used in many works for decades [4-6]. However, the assumption that the size distribution is always lognormal might not be accurate for particles synthesized by all the techniques. In this work, we present a fitting method without a pre-assumed distribution. The measured m-H curve is fitted to a superposition of Langevin functions with corresponding fractions, and the combination of these fractions is the size distribution. In the fitting procedure, three constraints are considered: 1) The method of least squares is used to obtain a well fitted m-H curve; 2) The smoothness of the distribution is a criterion to judge whether the fit is valid, since a realistic distribution should be relatively smooth; 3) Saturation magnetic moment should be fitted since the small particles cannot be saturation magnetized. To obtain a fitted distribution under all these three constraints with optimized weights, we apply the method of Lagrange multipliers. This fitting method is used to determine the size distribution of five different superparamagnetic γ-Fe2O3/SiO2 core-shell nanoparticle samples. The m-H curves were measured by VSM and then fitted with a supperposition of Langevin functions. The fitting results of selected samples are shown in Fig. 1 and Fig. 2. The fitted mean values and standard deviations of the particle size distributions agree well with the results of TEM and XRD [1].

**References:**

[1] C. Zambrzycki, R. Shao, A. Misra, C. Streb, U. Herr, R. Güttel, Catalysts. 2021; 11(1):72. [2] V. Wu, E. Huynh, S. Tang, V Uskoković, Acta biomaterialia 88 (2019): 422-447. [3] R. Chantrell, J. Popplewell, S. Charles, IEEE Transactions on Magnetics 14.5 (1978): 975-977. [4] E. Ferrari, F. Da Silva, M. Knobel, Physical Review B 56.10 (1997): 6086. [5] F. C. Fonseca, et al., Physical review B 66.10 (2002): 104406. [6] D. Henrard, et al., Journal of Nanomaterials 2019 (2019).

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/6279613868c29c4338baf60488d7fb64.jpg)
Fig. 1: Core size distributions of two selected as-made γ-Fe2O3/SiO2 core-shell particle samples fitted from corresponding m-H curves with a superposition of Langevin functions.

![](https://assets.underline.io/uploads/markdown_image/1/image/826ddadf83bfd7ab6d66c2fb0a51f531.jpg)
Fig. 2: m-H curve of as-made 5.4nm γ-Fe2O3/SiO2 core-shell particles measured by VSM together with fitted curve with a superposition of Langevin functions.

 Advanced Size Distribution Determination of Superparamagnetic Nanoparticles

Magnetic nanoparticles reveal unique magnetic properties making them relevant for data storage, electronic and mechanical engineering, and biomedical applications. Despite the considerable technological relevance and fundamental importance, a key challenge in magnetic nanoparticle research remains in the quantitative interpretation of the three-dimensional magnetic configuration and the nanoscale distribution of magnetization and spin disorder. Disorder effects - being intrinsic to nanomaterials - crucially determine the magnetization properties such as the heating performance of magnetic nanoparticles [1-4]. In this regard, the main aspects of fundamental interest are the effective magnetic anisotropy and the magnetization distribution within individual nanoparticles. Polarized, magnetic contrast-enhanced small-angle neutron scattering (SANS) is a versatile technique to investigate the chemical morphology and magnetization with nanoscale spatial resolution [5]. Addressing structural and magnetic inhomogeneities on the nanoscopic length scales of 1-300 nm, magnetic SANS is highly relevant for a variety of materials classes ranging from permanent magnets, magnetic steels and alloys to vortex lattices, skyrmions, and ferrofluids. For monodisperse nanoparticles, magnetic SANS has the unique potential to isolate core and surface-related effects of magnetization and spin disorder. In this contribution, we will present our recent results on the spin disorder in ferrite nanoparticles induced during synthesis. For the synthesis of iron oxide nanoparticles via thermal decomposition of iron oleates [7], the initial formation of FeO nanoparticles followed by topotaxial oxidation to magnetite and maghemite is well established [8,9]. Polarized SANS offers a great potential for the understanding of the microstructure in such heterostructured nanoparticles and allows to disentangle the magnetic field response of core and shell magnetization. For cobalt ferrite nanoparticles, our polarized SANS study reveals even in the wuestite-like nanoparticle core a significant magnetization contribution at ambient temperature (Fig. 1), in contrast to the conventionally assumed paramagnetic or AFM behavior. This FiM@FiM core-shell morphology explains the origin of the low-temperature exchange-bias phenomenon. Despite the overall homogeneous Co:Fe distribution within the nanoparticles, the sensitivity of SANS to nanoscale density variations enables us to verify the chemical core-shell morphology for as-prepared cobalt ferrite nanoparticles. We analyze the progressive oxidation of the core and the aging process into single-phase ferrite nanoparticles with nearly homogeneous magnetization distribution. This process occurs within years and does not compromise the size distribution or stability of the dispersion. In an idealized picture, one would consider such single-crystalline magnetic nanoparticles as defect-free nanocrystals with a collinearly magnetized core. In real systems, however, the internal spin structure exhibits a greater complexity, e.g., at the surface where compositional and structural defects are ubiquitous. As a result, the surface region is described as a shell of magnetically disordered surface spins. Polarized SANS has the necessary sensitivity to resolve the spatial distribution of spin disorder in fine detail. We have recently shown that the magnetization configuration inside magnetic nanoparticles may not be static – as often presumed – but rather susceptible to magnetic fields with the occurrence of field-dependent magnetization processes [6]. We have established a significant field-induced growth of the total particle moment by a magnetic ordering transition at the structurally disordered surface (Fig. 2). In our study, polarized SANS extends the traditional macroscopic magnetic characterization and reveals the local magnetization response. This approach allows us to quantitatively separate surface-spin disorder from intra-particle disorder contributions. Finally, we have elucidated the intra-particle spin-disorder energy, giving indirect insight into the structural defect profile in magnetic nanoparticles.

**References:**

[1] P. Bender et al., J. Phys. Chem. C, 122, 3068 (2018). [2] A. Lak et al., Nano Lett., 18, 6856 (2018). [3] A. Lappas et al., Phys. Rev. X, 9, 041044. [4] A. Lak, S. Disch, P. Bender, Adv. Science, accepted (2020). [5] S. Mühlbauer, D. Honecker, S. Disch, A. Michels et al., Rev. Mod. Phys., 91, 015004 (2019). [6] D. Zákutná, D. Honecker, S. Disch et al., Phys. Rev. X, 10, 031019 (2020). [7] T. Hyeon, Chem. Comm., 927 (2003). [8] E. Wetterskog et al., ACS Nano, 7, 7132 (2013). [9] R. Ichikawa et al., Small, 14, 1800804 (2018).

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/512f07f4eb799d5c5563531026bd63e4.jpg)
Fig. 1: Polarized SANS reveals distinct magnetization in the wuestite-like core (green) and spinel-type shell (red) of as-synthesized ferrite nanoparticles. The volume average magnetization (orange) is in excellent agreement with macroscopic magnetization (blue). Ageing (right panel) leads to the single-phase behavior of homogeneous ferrite nanoparticles.

![](https://assets.underline.io/uploads/markdown_image/1/image/2df2e75052850c79d8f659ba5207bc1c.jpg)
Fig. 2: In a strong magnetic field, the collinearly magnetized cores of ferrite nanoparticles grow due to the gradual alignment of initially disordered surrounding spins [6].

 Revealing Magnetic Morphologies and Spin Disorder in Ferrite Nanoparticles using polarized SANS

I. INTRODUCTION Although ferromagnetic particles can nowadays be grown in various size and shapes, controlling the crystallinity and microstructure parameters have remained challenging.[1,2] Nanomagnets with single-crystalline structures are particularly important because they typically exhibit reduced crystalline defects. Motivated by these prospects, our group has been established efforts in producing single-crystal and single-domain nanowires (NWs) through chemical process.[3-5] The single-domain structure led to changes in the magnetization reversal mechanism from the complicated domain-wall-motion-related process to a magnetic moment rotation mechanism that found to results in record high coercivity. Having a technique at hand that could extend growth to oriented magnetic NW array films would be highly desirable. Thin films of magnetic materials with high perpendicular anisotropy and coercivity have great application potential in ultrahigh-density perpendicular magnetic recording, permanent magnets, and spintronic and magnetoresistance sensors.[6] However, to synthesize and organize magnetic NWs to a vertically oriented structure is still a challenging task because of its dipolar interaction between neighboring NWs. In this work, we demonstrate bottom-up epitaxial solution growth of ferromagnetic Co NW arrays on crystalline metal surfaces. It is found that the coercivity of the NW arrays is strongly related to the wire diameter, film thickness, and orientation. This study provides a simple procedure to achieve recording media with ultra-high-recording densities. II. EXPERIMENTAL SECTION Single step chemical approach was used to fabricate the vertically aligned Co NW array films. In a typical synthesis, silicon substrates sputtered with Pt (111) film were kept in the solution of 1,2-butanediol, cobalt (II) laurate, and hexadecylamine (HDA). The growth was allowed for 12 h at 180 C in a Teflon-lined stainless-steel hydrothermal reactors. The diameter of Co NWs was controlled by varying the Pt film thickness from sub-10 nm to 100 nm. III. RESULTS AND DISCUSSION Figures 1a-c show top view scanning electron microscopy image of Co NW arrays having wire diameter of 12-20 nm. The NW array film is highly dense, and nanowires are uniformly distributed over large wafer-scale area (25 x 25 mm) with an areal density up to 5.6 × 1012 wires/in2. The cross-sectional view (inset of Figures 1a-c) of the as-grown film demonstrates that the NWs are vertically aligned. High-resolution TEM imaging and X-ray diffraction (XRD) measurements demonstrate the epitaxial growth of cobalt on platinum, with c-axis along the long-axis of NWs (i.e perpendicular to the film). As one would expect, the 6-fold symmetry of the Pt(111) surface favors Co crystal growth along the hexagonal (0001) wire basal planes and hence with a single in-plane orientation. Figures 1d-e shows the room temperature magnetic hysteresis loops of the Co NWs arrays of wire diameters 12-20 nm. It is found that the coercivity for the NW arrays along the wires axis () increases from 3.4 kOe to 7 kOe with the decrease of diameter. In addition, it can also be seen from Figures 1d-e that the squareness in hysteresis loop and coercivity in the parallel direction of NWs are larger than those in the perpendicular direction, which reveals that the easy axis of the Co NWs is parallel to the wire axis. Although the NW array film shows a typical perpendicular anisotropic behavior with high coercivity along NW axis, the squareness ratio of MR/MS &lt; 0.9. The low value of squareness could be due to the closely packed NW array, which induces a demagnetizing field along the plane of the film. The effective magnetic anisotropy of the NWs in an array film can results from the interplay of three major effects:1) the macroscopic demagnetization field, Hd = -6πMsp, where p is the porosity of the film and Ms is bulk value of the saturation magnetization for Co; 2) the shape anisotropy of individual nanowire Hsh= 2πMs; and 3) magnetocrystalline anisotropy field, Hm = -2K1/Ms, where K1 is the magnetocrystalline anisotropy constant for Co.[7] Thus, the theoretical effective coercive field of the nanowires is given by = Hd + Hsh + Hm. The calculated effective anisotropy field for Co NWs is ~12.5 kOe, which is much higher than the experimental coercivity values. The observed low coercivity in the prepared Co NW arrays is due to the strong magnetostatic coupling among the NWs, which is in agreement with those reported in the literature.[8] IV. CONCLUSIONS In conclusion, highly ordered Co NW arrays with coercivity comparable to magnetocrystalline anisotropy have been successfully fabricated via a simple chemical synthesis process. Magnetic measurements show that the as-prepared Co NWs with different diameters display magnetic anisotropy with the easy magnetization direction parallel to the axis of Co NW. The coercivity measured along the wire axis increases with decreasing nanowires diameter, which reveals the role of shape anisotropy in enhancing coercivity. In addition, this study opens an interesting option in tuning the magnetic properties of NW arrays and their applications in perpendicular recording media.

**References:**

[1] Y. Soumare, C. Garcia, T. Maurer, G. Chaboussant, F. Ott, F. Fiévet, J. Y. Piquemal, G. Viau, Adv. Funct. Mater. 2009, 19, 1971. [2] A. Ramazani, M. Almasi Kashi, A. H. Montazer, J. Appl. Phys. 2014, 115, 113902. [3] K. Gandha, K. Elkins, N. Poudyal, X. Liu, J. P. Liu, Sci. Rep. 2014, 4. [4] K. Gandha, J. Mohapatra, J. P. Liu, J. Magn. Magn. Mater. 2017, 438, 41. [5] J. Mohapatra, M.Y. Xing, J. Elkins, J. Beatty, J. P. Liu, Adv. Funct. Mater. 2021, doi:10.1002/adfm.202010157. [6] N. Liakakos, T. Blon, C. Achkar, V. Vilar, B. Cormary, R. P. Tan, O. Benamara, G. Chaboussant, F. Ott, B. Warot-Fonrose, E. Snoeck, B. Chaudret, K. Soulantica, M. Respaud, Nano Letters 2014, 14, 3481. [7] J. Mohapatra, J. P. Liu, in Handbook of Magnetic Materials, Vol. 27 (Ed: E. Brück), Elsevier, 2018, 1. [8] A. Encinas-Oropesa, M. Demand, L. Piraux, I. Huynen, U. Ebels, Phys. Rev. B 2001, 63, 104415.

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/1c5af62198df1058b18cbbe023632c49.jpg)
Figure 1 (a,b.c) SEM images of Co/CoO NWs array film with different wire diameters prepared via hydrothermal decomposition of cobalt carboxylate and inset shows the cross-section view of the nanowire array film. (d,e,f) The respective room temperature hysteresis loops by applying the magnetic field parallel (‖) and perpendicular (⊥) to the NW axis.

 Epitaxially Grown Single-Crystalline Cobalt Nanowires with High Coercivity

The FeRh alloy in CsCl-type (B2) phase is well-known for its metamagnetic transition from antiferromagnetic (AFM) to ferromagnetic (FM) state when the temperature increases over 370 °C, which comes with a volume expansion of 1% [1,2]. Nowadays, magneto-structural transition near room temperature are of great interest for potential applications to the control of nanostructures, magnetocaloric refrigeration and antiferromagnetic spintronics [3,4]. However, there are still open questions at nanoscale, including the finite size effects on magnetic FeRh phase transition, which require to continue efforts. The aim of this work is to explore the magnetic behavior of a 150 nm-thick FeRh nano-structured film prepared from Low Energy Cluster Beam Deposition (LECBD). Using a laser vaporization source, the initial diameter of the deposited FeRh nanoparticles is ranging from 2 nm to 10 nm in a log-normal distribution according to previous transmission electron microscopy observations [5]. The sample was annealed at 700 °C for 3 hours under vacuum in order to achieve the chemically ordered B2 phase. Then X-ray Diffraction spectrum reveals all α’-bcc peaks with a lattice parameter of 2.98 Å as expected in the B2-FeRh phase for a concentration of 48%-56% of Rh. The full width at half maximum (FWHM) of the α’(001) peak gives us an estimated crystallite size of about 30 nm in agreement with a limited coalescence upon annealing, leading to a surface granular thick-sample as observed from Atomic Force Microscope observations (Fig. 1). To study the magnetic behavior, various SQUID magnetometry measurements have been performed, including thermal dependence magnetization m(T) in the heating and cooling mode, for different values of applied magnetic field. As reported in Fig. 2, one can observe a broad and asymmetric metamagnetic transition with a significant fraction of residual magnetization at low temperature. This persistent FM state is attributed to the presence of non-switchable FM Grains. By increasing the external field, the metamagnetic transition is pushed to lower temperature following a linear correlation which is observed from the temperatures of maximum and minimum magnetization evolution as a function of field (insert Fig. 2). From Hysteresis magnetization m(H) loops a decreasing coercive field as the temperature increases has also been observed sustaining the evidence of FM regions down to low temperature. This behavior supports the coexistence of these two magnetic orders in thick nanogranular B2-like FeRh film. Moreover, field cooling - zero field cooling (FC-ZFC) susceptibility measurements have revealed the presence of single FM domains with a superparamagnetic (SPM) like-transition at temperatures higher than 130K. But because there is no exchange bias measured in m(H) loops at low temperature, there is no strong FM-AFM exchange coupling as could be expected for FM core@AFM shell morphology. In this work, the magnetic behavior of such thick nanogranular B2-like FeRh film will be described as a competition between different magnetic orders as a function of nanoparticle size, temperature and applied magnetic field. Several thermal regimes in the heating and cooling modes will be proposed as 1) Low temperature regime for T&lt;50K where small persistent FM nanoparticles co-exist with large AFM nanoparticles, 2) Intermediate regime with AFM-FM switching events and 3) at T&gt;300K the whole film is in the FM state. In the cooling mode, the FM dragging effect will be related to the applied magnetic field. A simple model could be considered with two distinct nanoparticles populations: one with smaller nanoparticles (FM/SPM) where relaxation effects avoid reaching AFM states at low temperature, and one with larger nanoparticles with AFM-compressed core@FM-relaxed shell morphology up to room temperature. To go a step further, X-ray magnetic circular dichroism results on mass-selected FeRh nanoparticles embedded in matrix recently measured at DEIMOS-SOLEIL will be discussed to illustrate finite size and surface/interface effects on metamagnetic FeRh transition.

**References:**

[1] V. L. Moruzzi and P. M. Marcus. Antiferromagnetic-ferromagnetic transition in FeRh. Phys. Rev. B 46, 2864 (1992) [2] M. R. Ibarra and P. A. Algarabel. Giant volume magnetostriction in the FeRh alloy. Phys. Rev. B 50, 4196 (1994) [3] Y. Liu, L. Phillips, R. Mattana et al. Large reversible caloric effect in FeRh thin films via a dual-stimulus multicaloric cycle. Nat. Commun. 7, 11614 (2016). [4] Y. Wang, M. M. Decker, T. N. G. Meier et al. Spin pumping during the antiferromagnetic–ferromagnetic phase transition of iron–rhodium. Nat. Commun. 11, 275 (2020). [5] V. Dupuis, A. Hillion, G. Khadra., N. Blanc, D. Le Roy, F. Tournus, C. Albin, O. boisron and A. Tamion. Cubic chemically ordered FeRh and FeCo nanomagnets prepared by mass-selected low-energy cluster-beam deposition: a comparative study. Beilstein J. Nanotechnol. 7, 1850 (2016).

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/d409a03b7297f8100cc511db7bddf17b.jpg)
Fig 1: Atomic Force Microscope image of the FeRh 150nm sample taken on tapping mode.

![](https://assets.underline.io/uploads/markdown_image/1/image/47e000de87e62064d603a08ea4fcdd8f.jpg)
Fig 2: Magnetization as function of the temperature for FeRh 150 nm at different external field. Inserted the temperature of maximum magnetization which follow T=338.1 K-9.11*Hμ0 K/T (blue), and temperature of minimum magnetization which follow T=321.6 K-9.11*Hμ0 K/T (yellow).

 On metamagnetic phase transition in 150 nm B2-like FeRh film of nanoclusters assembled.

Hybrid nanomaterials, combining ferromagnetic and plasmonic metals, are potential model systems for the study of emergent magnetoplasmonic phenomena, which motivate the development of new nanoarchitectures through top-down and bottom-up approaches. Vertically aligned nanocomposites (VANs) consist of two functional phases embedded in a matrix as illustrated on Fig. 1. Such epitaxial systems can be grown by pulsed laser deposition (PLD); the embedded nanowires (NWs) are formed by self-assembly during the growth. In this contribution, we report on the growth and study of the structural and physical properties of new VANs consisting of hybrid NWs with ferromagnetic and plasmonic parts embedded in an epitaxial oxide thin film. Cobalt and gold, compounds that show low bulk miscibility, are a typical material choice for the growth of hybrid NWs [1]. However, at room temperature pure Co has a hexagonal close-packed structure, which is detrimental to the uniaxial anisotropy of the NWs, and thus would not permit to obtain a bistable magnetic system. In order to get hard uniaxial magnetic properties, the ferromagnetic NWs were made of a CoNi alloy. A compromise of 80% of cobalt and 20% of nickel was chosen to retain an optimal anisotropy with the face-centered cubic phase (fcc) [2]. The plasmonic NWs were made of Au, known to be able to sustain localised plasmon resonances, collective oscillations of the free electrons, in the visible-near infrared range. Au/Co0.8Ni0.2 nanowires embedded in a SrTiO3 matrix were grown on a SrTiO3(100) substrate at a substrate temperature of 600 °C and high vacuum (&lt; 10-5 mbar) by sequential pulsed laser deposition, leveraging self-assembly mechanisms driven by metal-oxide phase segregation. Sequential PLD, based on the repetition of a basic sequence, is well suited to get a precise nanoarchitecture; as it offers control over the structure (morphology, epitaxy) and the composition. Such control is crucial for future magnetoplasmonic studies, given that both magnetic and plasmonic properties are significantly affected by the structural properties. The structural properties of the NWs were investigated by transmission electron microscopy (TEM) and x-ray diffraction (XRD) using synchrotron radiation. TEM images (Fig. 1) in plan view show circular sections with a diameter of ≈ 4 nm, whereas scanning transmission microscopy images in high-angle annular dark field mode in cross-sectional view reveal elongated segregated ribbons, thus proving the growth of VANs displaying phase segregation in oxide matrix. Further TEM investigations show the proneness of gold NWs to grow onto cobalt-nickel NWs with a proportion of ≈ 80%. XRD indicates a fcc structure with a predominant cube-on-cube epitaxy of Au, Co0.8Ni0.2 and SrTiO3 lattices; in addition, an axial strain up to 5% is measured, pointing a stretch of the Co0.8Ni0.2 NWs along the growth axis. The magnetic hysteresis of hybrid NWs were recorded by vibrating sample magnetometry. As shown on Fig. 2, the response is anisotropic and the easy axis of magnetisation is along the long axis of the NW, with a coercive field Hc ≈ 1 T at 10 K; such significant anisotropy stems from the elongated shape of the NWs (shape anisotropy) and the axial strain induced by the epitaxy with the STO matrix (magnetoelastic anisotropy). Furthermore, spectroscopic ellipsometry measurements display two absorption bands at visible and near-infrared wavelengths, attributed to the transverse and longitudinal localised plasmon resonances (LPRs). Such LPRs can be shifted to a suitable wavelength by control of the morphology of the Au NWs. Due to the Joule effect, the plasmon excitation would provoke the heating of the Au NWs, which would act as nanosources of heat for future heat-assisted magnetisation reversal experiments. The separate control of the structure of Au and CoNi NWs enables the tuning of highly anisotropic magnetic and optical properties. This establishes hybrid metallic NWs embedded in an oxide matrix grown by PLD as promising systems for future magnetoplasmonic studies.

**References:**

[1] M. Hennes, X. Weng, E. Fonda, B. Gallas, G. Patriarche, D. Demaille, Y. Zheng and F. Vidal. "Phase separation and surface segregation in Co-Au-SrTiO3 thin films: Self-assembly of bilayered epitaxial nanocolumnar composites". Phys. Rev. Materials 3 (3), p. 035002 (2019). doi: 10.1103/PhysRevMaterials.3.035002 [2] X. Weng, M. Hennes, T. Tran, N. Casaretto, D. Demaille, F. Vidal and Y. Zheng. "Orientation and lattice matching of CoNi nanowires embedded in SrTiO3: unveiling novel strain relaxation mechanisms in vertically aligned nanocomposites". CrystEngComm 22 (28), pp. 4730-4739 (2020). doi: 10.1039/d0ce00574f

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/bd8df4d101955929e83bd911d3519259.png)
Fig. 1 : (top left) High-resolution transmission electron microscopy image of Au/CoNi nanowires (NWs) embedded in a SrTiO3 matrix in plan view and (top right) scanning transmission electron microscopy image in high-angle annular dark field mode in cross-sectional view show the ability to grow segregated hybrid NWs. (bottom) Illustration of the metallic hybrid NWs in an oxide matrix; the gold phase sustains localised plasmon resonances, whereas the alloy phase is ferromagnetic.

![](https://assets.underline.io/uploads/markdown_image/1/image/a64a524617b0b6d7f7af3183f0c21c11.jpg)
Fig. 2 : Magnetic hysteresis cycles at low temperature (T = 10 K), recorded along the easy axis (black curve) and the hard axis (grey curve) of magnetisation.

 Hybrid Metallic Nanowires: Tailoring Magnetic and Plasmonic Properties by Pulsed Laser Deposition

Magnetic nanowires (MNWs) have been proposed for nanobarcoding and biolabeling for decades, but surprisingly, they have never been successfully realized for these applications due to the lack of reliable decoding strategies. Here, several types of MNWs were electrodeposited into track-etched polycarbonate templates for application as nanobarcodes. The coercivity (Hc) and interaction fields (Hu) of the MNWs were engineered using the composition, dimensions, and spatial distributions to tune the remanence spectra1 of each barcode. This remanence spectrum is the irreversible switching field distribution measured at zero applied field after applying a pre-defined field sequence. It is therefore a complex function of both coercivity and interaction field. An automated algorithm was also introduced to reliably decode unknown remanence spectra measured in unknown combinations of the magnetic nanobarcodes2,3. Up-to-date, Hc and Hu distributions themselves have been the most popular magnetic signatures using in decoding, usually measured using the first-order reversal curve (FORC) method1,4. Despite numerous works on tuning Hc and Hu, these parameters can only be independently decoded for a limited number of unique signatures. For instance, Hu for arrays of non-interacting MNWs is always zero, which limits signatures to simply Hc and its distribution. Typical decoding is even harder when both non-interacting and highly interacting magnetic nanobarcodes are at the readout simultaneously3. Although the Hc distribution may be inherently distinct for each MNW; fully decoupling the interaction field effects from the coercivity distribution is a very tedious procedure5. Our recently proposed approach of decoding via the remanence spectra6 has overcome these limitations while also using orders of magnitude fewer measurement points compared to FORC leading to a significantly faster decoding procedure. Figure 1 shows the remanence spectra of a combined measurement with two barcodes: 30nm-diameter Ni MNWs and 100nm-diameter Ni MNWs. The automated algorithm makes a best fit assuming the number of barcodes presence are 1, then 2, then 3. The root-mean-square (RMS) error between the fit and the measurement is calculated at each step. At “n” steps (here 3), the RMS error is not significantly improved, so the number of barcodes is determined to be “n-1” (here 2). This presentation will demonstrate how to expand the number of magnetic nanobarcodes remanence spectra using coercivity and interaction field as independent encoding parameters tuned through various strategies. We will also describe our automated algorithm to reliably and rapidly decode unknown remanence spectra, which is also capable to decode other nanobarcodes including magneto-optic nanobarcodes. In summary, remanence spectra are promising for decoding nanobarcodes for three reasons. First, the coercivity and interaction field can be tailored individually to expand the number of remanence spectra encoded magnetic nanobarcodes. Second, the decoding can reliably be done even when one of these parameters is zero or negligible and also when the number of nanobarcodes presence is unknown. Third, remanence spectra are much faster to measure and less tedious to decouple than standard signatures.

**References:**

1 Y. G. Velázquez, A. L. Guerrero, J. M. Martínez, E. Araujo, M. R. Tabasum, B. Nysten, L. Piraux and A. Encinas, Sci. Rep., 2020, 10, 21396. 2 M. R. Zamani Kouhpanji and B. Stadler, Part. Part. Syst. Charact., 2020, 2000227. 3 M. R. Zamani Kouhpanji and B. J. H. Stadler, Nanoscale Adv., 2021, DOI:10.1039/D0NA00924E. 4 D. A. Gilbert, G. T. Zimanyi, R. K. Dumas, M. Winklhofer, A. Gomez, N. Eibagi, J. L. Vicent and K. Liu, Sci. Rep., 2014, 4, 1–5. 5 C.-I. Dobrotă and A. Stancu, J. Appl. Phys., 2013, 113, 043928. 6 M. R. Zamani Kouhpanji, A. Ghoreyshi, P. B. Visscher and B. J. H. Stadler, Sci. Rep., 2020, 10, 15482.

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/fae2d498dd387a1e4ac20feae07613b1.jpg)
Figure 1: shows encoding and decoding remanence spectra of magnetic nanowires (MNWs) for nanobarcoding, (a) depicts the normalized backward remanence (BRM) spectra of the individual MNWs “encoded” using their dimensions, and (b-d) illustrate the decoding of an unknown BRM spectrum composed of Ni MNWs with diameters of 30nm and 100nm. The reduction percentage of the root-mean-square (RMS) error is the convergence criteria for identifying the number and type of magnetic nanobarcodes in the unknown BRM spectra.

 A roadmap toward expanding magnetic nanobarcodes signatures

In the medical world, today’s challenge is to kill and control tumors at a cellular level, allowing a more localized (confined) treatment for the eradication of malign cells. Due to this localized treatment, secondary harmful effects induced on healthy cells would be significantly reduced. In fact, different approaches have been used to apply hyperthermia in tumor regions, but with the harmful side effects it defeats the purpose. Hence, the author selected to engineer biocompatible magnetic nanoparticles, which are much smaller than the pore size of the cells, for hyperthermia and drug delivery applications. In order to achieve this goal, one has to produce biocompatible magnetic nanoparticles that are controlled regarding size and shape for hyperthermia treatment. Furthermore, FeCo nanoparticles have superior magnetic properties (230-240 emu/g) than iron oxide nanoparticles (60-80 emu/g). However, this idea is not yet fully explored because of the commercial unavailability of nanoparticles that are size and shape controlled; toxicity also plays a key role in regards to the commercial unavailability. Here, the author presents a cheap, economical, and environmentally friendly approach to prepare the surfactant coated FeCo nanoparticles in the size range of 15-25 nm with a decent amount of dispersity. The FeCo nanoparticles are prepared by using the "mixed glycol process,” with which Fe and Co metal salts are used as precursors. Mixed polyols can increase the viscosity of the solution and can reduce the migration rate of metal ions in the solution. First, the ethylene glycol, trimethylene glycol, and polyrhrtlene glycol were used in different ratios, and then the metal precursors were added as well. To increase the reaction rate, sodium hydroxide(NaOH) was used. Further, the surfactants (i) polyvinylpyrrolidone (PVP) and (ii) a mixture of oleylamine and oleic acid were used to control the particle size, shape and dispersity. The experiment was carried out at different temperatures between 70 and 110° C. Once the temperature was raised to 60° C, the solution started to change from a pink to green color. At 110° C, the solution also became a dark green color as well. After an hour, the reaction was completed, the mantle heater was switched off, and left for cooling. After cooling, the reaction flask was taken out and the viscous solution was poured into four centrifuge tubes; this is to centrifuge the samples at 2500 rotation per minute using a small lab scale centrifuge set up for 10 minutes. The following are the observations and data seen in the experiment. The particles that were prepared without surfactants had agglomerated (clustered) particles with an irregular shape. However, the PVP and oleylamine/oleic acid coated samples had a cubic shape for the particles, with average particle sizes in the range of 15 and 20-25 nm respectively. The X-ray diffraction analysis characterized that the structure was the single phase body centered cubic structure. The magnetization value is about 210 emu/g, which is higher than the iron oxide nanoparticles. The nanoparticles with surfactants show well dispersion in ethanol. Since these nanoparticle sizes are smaller than the size of the cell, it is possible to use these particles for hyperthermia and drug delivery applications.

**References:**



**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/f04444c872f5213610d3ffe6258fb41c.jpg)
Fig. 1. SEM image of the low temperature synthesized FeCo nanoparticles through mixed glycol process

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