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Ferrite

Ferrite is a class of magnetic ceramic materials composed of iron oxides and other metallic elements.
These materials exhibit unique magnetic properties, making them essential for a wide range of applications, including electronic devices, power transformers, and data storage.
Ferrites are known for their high electrical resistivity, low eddy current losses, and ability to be engineered with diverse magnetic characteristics.
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Most cited protocols related to «Ferrite»

Five healthy males (aged 27 to 36, right handed) with no history of neurological or psychiatric illness participated. All were screened for MRI and TMS compatibility and gave written informed consent in accord with local ethics. The study was approved by the joint ethics committee of the National Hospital for Neurology and Neurosurgery (University College London Hospitals National Heath Service Trust) and Institute of Neurology (University College London). Our stimulation protocol conformed to published TMS guidelines (Wassermann, 1998 (link)). The scalp position for placing the TMS coil over right parietal cortex was first determined outside the scanner, using the same approach as by Seyal and colleagues (Seyal et al., 1995 (link)). For this purpose, we identified the motor hotspot for the left thenar muscles (motor threshold: mean 63.6 % ± 4.2 S.E.M. of TMS stimulator output) and then moved the TMS coil backwards from the motor hotspot by 2-4 cm to the first point at which there was no longer any hand contractions induced when stimulating at the high TMS intensity of 110% of the resting motor threshold. We used an intensity of 50% of the motor threshold for our low intensity TMS during the main combined TMS-fMRI experiment (see below). This motor threshold was determined inside the scanner room with the same equipment as used during scanning. While some studies suggest that cross-modal interaction can have an impact on TMS thresholds (Ramos-Estebanez et al., 2007 (link)), our main focus for the concurrent TMS-fMRI experiment here was on any interaction between the effects of high versus low TMS, during the presence or absence of concurrent tactile input to the other hemisphere (see Introduction).As described below, we found robust effects of TMS during scanning on somatosensory responses in the other hemisphere, thus confirming that our TMS protocol was effective.
A pair of surface-adhesive electrodes was positioned on the right wrist of the subject for median-nerve stimulation. Constant current pulses (square wave, 200 μs duration) were applied to this site using a neurostimulator (DS7A, Digitimer, Hertfordshire, UK) located within a shielded box (to preclude MR artefacts) inside the scanner room. Stimulation of the right-median nerve in particular was confirmed by subjects’ verbal report of sensation in the first three fingers. We ensured that the median-nerve stimulation intensities used did not induce any twitching. For each subject, sensory threshold (mean 2.4 ± 0.16 mA) was determined by the method of limits (single pulses lasting 200 μs), and stimulation intensity for the experiment was then set to three times that sensory threshold (7.2 ± 0.5 mA), which is clearly detectable but does not induce any muscle effects. We used such suprathreshold somatosensory stimulation to ensure that increased activation of contralateral left SI by right median-nerve stimulation could be reliably detected in fMRI (Arthurs et al., 2000 (link);Blankenburg et al., 2003 (link)).
Our scanning experiment had a fully randomized 2 × 2 factorial design with the orthogonal factors of right parietal TMS (high versus low intensity) and right-wrist median-nerve stimulation (present versus absent). Each trial consisted of three successive ‘mini blocks’ of stimulation, each lasting 500 ms. On a random half of such trials, we applied right-wrist median-nerve stimulation in three trains of 5 pulses (each at 10 Hz). The other half of trials had no somatosensory stimulation. Orthogonally to this, TMS bursts (also 5 pulses at 10 Hz, and thus similar to the combined TMS-fMRI protocols in prior studies from our lab, see Bestmann et al., 2008b (link);Ruff et al., 2006 (link);Ruff et al., 2008 (link)) were applied on each trial with either high or low TMS intensity (random half of trials each). On those 50% of trials in the 2 × 2 factorial design that had both TMS application and median-nerve stimulation on the same trial, the trains of TMS or median-nerve stimulation (both 5 pulses at 10Hz) were temporally interleaved with a 180-degree difference in phase. Thus, each such trial started with a TMS pulse, followed 50 ms later by the first somatosensory stimulation, followed 50 ms later by the next TMS pulse and so on. After each trial, a rest period without any stimulation (neither median nerve nor TMS) was included, lasting four image volumes.
Functional data were acquired on a 1.5T whole-body scanner (Magnetom Sonata, Siemens Medical System, Erlangen, Germany), operating with the standard CP receive head and body transmit coil. We used a multi-slice gradient echo EPI sequence (39 slices, 64 × 96 matrix (readout × phase-encoding), in-plane resolution: 3 × 3 mm, 2 mm slice thickness, 50% spatial gap between adjacent slices, TE=50ms, TR=2880 ms, 2298 Hz/pixel bandwidth, echo spacing 500μs). In addition, oversampling (50%) was used in the phase-encoding direction to shift any possible ghost artifact induced by mere presence of the TMS coil outside of the volume of interest. The last seven slices (33-39, lasting 630 ms) were recorded without an MR excitation high frequency pulse. This enabled us to apply the TMS-pulses and the somatosensory stimulation always within this period, hence without any potential corruption of functional image volumes. In addition, this ensured a constant auditory input from the scanner, as gradients during these slices remained turned on. The acquisition time for one volume was 3.51 sec. Two sessions were acquired for each subject (each session comprised 320 volumes, including 5 dummy scans to allow T1 saturation). We chose to compare high-intensity (effective) TMS versus low-intensity (less effective or ineffective) TMS at a single right-parietal site, similar to the site used by Seyal et al. (1995) (link), rather than comparing different TMS sites, due to the technical problems that relocating the TMS probe within an fMRI session would inevitably cause. Moreover, the appropriateness of high versus low TMS comparisons at a given site during concurrent fMRI has recently been established by other studies from our group using similar concurrent TMS-fMRI protocols (Bestmann et al., 2008b (link);Ruff et al., 2006 (link)).
Each session included 44 randomly intermingled trials, 11 per condition in the 2 × 2 design. To preclude visual changes (e.g., from blinks), subject kept their eyes closed throughout scanning. In addition, to ensure that TMS could not lead to any changes in performance that might otherwise have complicated interpretation of the physiological fMRI data, subjects had no behavioural task during scanning (see also Ruff et al., 2006 (link)). Thus, as per the Introduction, our a-priori aim was to test for a physiological interaction of right parietal TMS with right-wrist somatosensory input, in terms of the BOLD response of left SI (and possibly the thalamus as well: see below). Nevertheless, we also ran a behavioral follow-up experiment outside the scanner (see below), which confirmed that our particular right-parietal TMS protocol, using bursts at 10Hz, could indeed produce the same behavioral effect originally documented by Seyal et al, (1995) (link), namely enhancement of somatosensory detection on the ipsilateral right hand.
TMS during scanning was applied using a Magstim Super Rapid stimulator and MR-compatible non-ferrous figure-of-eight coil with a small-diameter (30mm inner diameter, 70mm outer diameter, 15 turns each winding, wire size 5×1.5mm, 22.9μH inductance, 4.7kVA predicted maximal current at 100%) from the MAGSTIM Company, Dyfed, UK. The coil was positioned over the stimulation site tangentially to the scalp, at approximately 45° from the midline, inducing a biphasic current with an initial anteroposterior direction. We ensured that TMS did not induce any muscle twitches in the experiment (see above for TMS-site selection). The coil was held fixed by a non-ferromagnetic custom-built coil holder, and the participant’s head was fixed with vacuum cushions. To avoid any radio-frequency interference of TMS with image acquisition, the TMS-stimulator was placed in a shielded metal cabinet in the scanner room, and the TMS cable was passed through a custom filter box (the Magstim Company, Dyfed, UK) and further ferrite sleeves (Wuerth Elektronik, Waldenburg, Germany); see also (Bestmann et al., 2008b (link);Ruff et al., 2006 (link);Ruff et al., 2008 (link)). Furthermore, the TMS coil was connected to the stimulator in parallel to a high voltage relay (Magstim ES9486, The Magstim Company). During volume acquisition, this relay was closed, shorting any potential leaking-current. Thus, any current flow through the stimulation coil originating from the stimulator was eliminated while it waited to release a pulse. The relay was opened 50 ms prior to a TMS train, and closed 11 ms after the last TMS pulse of a train.
All stimuli were controlled using the MATLAB (The Mathworks, Natick, Massachusetts, USA) toolbox Cogent 2000 (http://www.vislab.ucl.ac.uk/Cogent/), running on a conventional PC. Image processing and analysis was performed with SPM2 (htt://www.fil.ion.ucl.ac.uk/spm). Functional images were reconstructed offline, and the first five images of each run discarded to avoid T1 equilibration effects. In accord with the standard SPM approach, the remaining functional images were realigned to the first of the series, corrected for movement-induced image distortions, normalized to the MNI anatomical standard space and spatially smoothed with a 9 mm FWHM Gaussian kernel in accord with the standard SPM approach. In addition, the fMRI data were temporally band-pass filtered (lower/upper cutoff-frequency at 7 and 128 sec, respectively).
Statistical parametric maps were calculated by multiple regressions of the data onto a model of the hemodynamic response (Friston et al., 1995 (link)). This model contained regressors for the onsets of every ‘mini-block’ for each of the four conditions in the 2×2 design, convolved with the canonical hemodynamic response function in SPM2. An autocorrelation model was used in order to account for scan-to-scan dependencies in the error term. Statistical inference used a fixed effect model, in accord with the limited number of subjects (n=5) available for this demanding combined TMS-fMRI protocol. However, we also inspected individual data to ensure that the critical fMRI pattern was observed for all subjects (see below). For unrestricted whole-brain analyses we used a threshold of p<0.05, family-wise error (FWE) corrected for the entire image volume. For analyses of activity in brain areas for which we had clear a priori hypotheses (e.g., left somatosensory cortex and thalamus, see Introduction), critical effects were inspected in volumes-of-interest (VOIs) derived either by anatomical criteria (e.g., the thalamus was defined by means of a computerized cytoarchitectonic atlas, see http://www.loni.ucla.edu/ICBM/Downloads/Downloads_Atlases.shtml), or functionally by inclusive masking with an orthogonal contrast used to define specific brain areas of interest (e.g., activation for presence minus absence of right-hand median-nerve stimulation was used to confirm functional localization of contralateral left somatosensory cortex). The results of these hypothesis-driven analyses are all reported at a threshold of p<0.05, FWE-corrected for the VOI (Worsley et al., 1996 (link)).
Here we used bursts of TMS at 10Hz during scanning in order to drive reliable BOLD responses by the TMS (Bestmann et al., 2008b (link);Ruff et al., 2006 (link);Ruff et al., 2008 (link)). Our particular TMS protocol thus differed from the original Seyal et al. (1995) (link) study, which had used single-pulse TMS. Accordingly, we also conducted a new psychophysical study outside the scanner that sought to replicate the classical behavioral findings of Seyal et al (1995) (link), but now using the identical 10Hz-burst TMS protocol as in our concurrent TMS-fMRI study. This follow-up behavioral experiment was conducted in 4 additional subjects, who were again screened for TMS compatibility and gave written informed consent in accord with local ethics. Briefly, we applied 5 pulses of 10 Hz rTMS at the outset of each trial, either at 110% or 50% of motor threshold, as during scanning. The TMS coil was again localized over right parietal lobe, using the identical procedure as for the main fMRI experiment. On a random half of these trials, peri-threshold right median nerve stimulation (of the same duration and timing relative to TMS burst as for the fMRI experiment) was applied during TMS. On the other half of trials, TMS was applied in the absence of median nerve stimulation. After each trial, subjects were asked to respond by button-press whether right-hand tactile stimulation was present or not. Subjects each completed 4 blocks of 60 trials. Tactile stimulation intensity was determined separately for each block, with the aim of keeping the intensities peri-threshold. Some blocks were removed from analysis because of greater than 90% accuracy (3 blocks), a bias towards responding ‘absent’ on more than 90% of trials (2 blocks), or a technical malfunction (2 blocks). Nine blocks remained, yielding a total of 540 trials. Sensitivity (d’) and response bias (criterion) were calculated for each retained block, and paired t-tests were performed to determine the effect of TMS intensity on d’ across blocks.
Publication 2008
Two samples were used in this study. One was a commercial magnetite-based starch-coated nanoparticle suspension (BNF-starch S31009, micromod Partikeltechnologie, GmbH, Rostock, Germany) synthesized by high-pressure homogenization according to the core-shell method35 . The other colloid was synthesized by the co-precipitation procedure previously described and consisted of manganese ferrite-based (MNF-citrate) nanoparticles surface-coated with citrate54 . Both colloids are dispersed in water at physiological conditions.
Publication 2013
Citrate Colloids Magnetite Nanoparticles manganese ferrite physiology Pressure Starch
TMS was implemented using a MagStim Rapid system (The Magstim Company, Dyfed, UK) with a custom-built MR-compatible figure-of-eight stimulation coil (two windings of ten turns each; inner wing diameter 53 mm, distance between outer coil surface and windings of 2-3 mm (variation due to manufacturing tolerance); coil inductance, including cable, of 20 μH; maximal current at 100% stimulator output of ∼5kA). The stimulation unit was housed inside the scanner room in a shielded cabinet, from which the stimulation coil cable was fed through a custom filter box (The Magstim Company). Residual RF transmission along the coil cable was further suppressed using ferrite sleeves. The TMS coil was connected to the stimulator in parallel to a high voltage relay (Magstim ES9486, The Magstim Company). During EPI acquisition, the relay was in closed mode, thereby effectively preventing any residual leakage in current flow from the stimulator. The relay was opened 50 ms prior to TMS pulse discharge, and closed again 8 ms after termination of the last TMS pulse of a trial. The relay and TMS were controlled with a unit developed in-house based on a BASIC Stamp 2 micro-controller (Parallax Inc., Rocklin, California, USA). TMS pulses were applied during the dead time between the EPI navigator echoes and the EPI data readout, and separated from RF slice excitation pulses (Gaussian-like symmetric sync, 2560 ms duration, Bestmann and others 2003a (link)). Throughout scanning, each slice coincided equally often with TMS pulses to avoid any systematic influences on slice-by-slice variance.
The stimulation site over the left dorsal PMd was determined as the scalp point 2 cm anterior and 1 cm medial to the so-called motor ‘hot-spot’ for evoking single muscle twitches in the contralateral first dorsal interosseous (Schluter and others 1999; Johansen-Berg and others 2002 (link); O’Shea and others, 2007 (link)). Inside the scanner, the stimulation coil was placed over the marked location using an MR-compatible custom-built coil holder, allowing stable positioning of the TMS coil with several degrees of freedom. The coil was oriented tangential to the scalp, at approximately 45 degrees from the midline, inducing a biphasic current with an initial antero-posterior induced direction. Foam-padded cushions were used to restrict head-movements.
All visual stimulation, grip-force data acquisition, TMS triggering and intensity regulation, and relay settings were controlled using the toolbox Cogent 2000 (Wellcome Department of Imaging Neuroscience, London, UK; http://www.fil.ion.ucl.ac.uk/cogent) running under Matlab (The Mathworks, Natick, Massachusetts, USA). Participants wore earplugs (SNR = 36dB) to reduce acoustic noise from the scanner and the TMS discharge sound.
Publication 2007
Acoustics Earplugs ECHO protocol ferrite Flatulence Grasp Head Movements Immune Tolerance Muscular Fasciculation Patient Discharge Photic Stimulation Pulses Scalp Sound Transmission, Communicable Disease trimethylsilylmethanesulfonate
The wearable sensing system consisted of a low-profile sensor, a flexible iron-seeded elastomeric target, and a portable data acquisition unit (Figure 1) [16 ,17 (link),18 (link)]. Sensors were embedded into prosthetic sockets and flexible targets were created by incorporating iron particles into elastomeric prosthetic liners so that the sensors measured the distance between the liner and socket wall.
The low-profile sensor was a custom-designed flexible coil antenna (diameter 32.0 mm, thickness 0.15 mm) and a surface-mounted capacitor (220 pF). A 10 kΩ surface mount thermistor was soldered to the antenna to monitor the temperature of the sensing environment and compensate for thermal-induced drift.
A custom-designed portable data acquisition unit containing an inductive sensing chip (LDC1614, Texas Instruments, Dallas, TX, USA) was used to power the sensors and collect proximity data. When powered, the inductor and capacitor operated as an inductor–capacitor (LC) tank oscillator. The presence of the magnetically permeable target within the sensor’s field reinforced the inductor and lowered the sensor’s oscillation frequency in a distance-dependent manner. Therefore, the changes in sensor frequency measured by the inductive sensing chip were a sensitive measure of proximity between the target and sensor antenna. The sensor output (proximity counts) is a ratio of the sensor’s oscillation frequency to an external reference clock frequency.
The wearable sensor target was a ferrous elastomeric liner worn over the residual limb. The liner was constructed so that the iron-doped polymer (thickness 1 mm, iron content 80 percent by weight) was embedded between the liner fabric and the normal (unfilled) elastomer.
Instrumented, adjustable cable-paneled sockets were fabricated for participants with transtibial amputation. Each participant’s regularly-used socket was digitized using a mechanical coordinate measurement machine (FaroArm Platinum, FARO Technologies, Lake Mary, FL, USA) so that the instrumented socket duplicated the shape of the current socket. Sensors were embedded between an inner layup consisting of four layers of Nyglass stockinet (Paceline, Matthews, NC, USA) and epoxy acrylic resin (Paceline, Matthews, NC, USA) and a secondary layup consisting of a single layer of carbon fiber. This was followed by a final four-layer outer layup consisting of two layers of carbon fiber separated by two layers of Nyglass. Tubing and cabling for the panels was placed between the secondary and outer layups. Ferrite shielding (thickness 0.3 mm, Wurth Electronics, Waldenburg, Germany) was attached to the outer-facing side of the inductive sensors to block electromagnetic interference from the carbon fiber and external environment. Sensors were placed in the anterior proximal (AP), anterior midlimb (AM), anterior midlimb distal (AMD), anterior inferior (AI), posterior inferior (PI), posterior midlimb medial (PM), and posterior midlimb lateral (PL) aspects of the socket (Figure 2). The anterior midlimb distal sensor was omitted for participants with short residual limbs. A cable connected the panels of the socket such that by extending or retracting the cable, the panel distances relative to the socket could be adjusted.
Calibration of the embedded sensors was conducted in two stages; a detailed benchtop calibration followed by a reduced-point in-socket calibration. Preliminary sensor repeatability tests revealed that the sensor configuration (i.e., depth in layup, sensor curvature) shifted the sensor sensitivity curve; therefore, each sensor should be calibrated in its final embedded in-socket configuration (Appendix A). Variation in the thickness of the embedded iron layer in the ferrous liners caused a similar shift in sensor response, indicating that the in-socket calibration needed to be performed with the ferrous liner matching the socket of interest (Appendix A). The dual-stage calibration procedure was conducted to determine the offset between a single, detailed calibration and the unique in-socket response due to variation in sensor configuration and target variability. This dual-stage calibration also minimizes any error associated with inconsistency in the target liner during in-plane movements of the limb, which were not assessed in this study but have been addressed previously [18 (link)].
In the first phase of the calibration procedure, a detailed calibration was obtained for a single location on a ferrous liner using a benchtop setup (Figure 3). The liner was placed flat on the bench with a Delrin® block separating the two sides of the liner and isolating the region of interest. A sensor was fastened to the arm of a digital height gauge (Mitutoyo 570-312, Aurora, IL, USA) so that the height gauge measured the distance between the sensor and the target. Data were collected while the sensor was raised 20 mm away from the liner in steps of 0.25 mm from 0 to 5 mm, 0.5 mm from 5 to 15 mm, and 1 mm from 15 to 20 mm. The sensor was then lowered back into contact with the liner with the same step pattern. The benchtop calibration took approximately 10 min to complete.
An in-socket calibration procedure was then performed to measure the sensitivity of the embedded sensors in their final configuration. In-socket sensor response was assessed at four known distances (0.00 mm, 1.09 mm, 2.19 mm, and 3.29 mm). A custom silicone bladder with a proximal tubing port was placed inside the ferrous liner and was inflated to 27.6 kPa to expand the liner to conform to the socket’s contours (Figure 3). Polymeric offset pieces were fabricated from a Shore 60A platinum cure silicone (PlatSil 73-60, Polytek Development Corp., Easton, PA, USA) to restrict liner expansion and obtain measurements at non-zero distances. Measurements were taken by placing the desired number of offset pieces on the inner socket wall and then inflating the bladder and liner to conform to the socket contours. The in-socket calibration procedure took approximately 5 min to complete for a single socket.
In-socket calibration data were offset along the x-axis from the benchtop calibration data. Offsets were calculated by converting the proximity counts measured during the in-socket calibration into distances using the unadjusted benchtop calibration data. Calculated distances were subtracted from the actual socket–liner distances (thickness of the offset pieces), and the median of these differences was taken to obtain a single distance offset for each sensor. Offsets were applied to the benchtop calibration to create individual calibrations for each embedded sensor location that reflected the sensor’s in-socket response.
Participants were included in this study if they had a transtibial amputation at least 18 months prior and regularly used a definitive prosthesis at least four hours per day without assistive aides. Candidate participants were excluded if they presented with skin breakdown or soft tissue injury at the time of study. Study procedures were conducted in accordance with approval #49624 from the University of Washington Institutional Review Board. All participants provided written informed consent prior to any study procedures being performed.
Participants conducted two in-lab procedures to assess the ability of the sensors to measure limb–socket distances and displacements and evaluate socket fit. In the first portion of the test session, participants were asked to don a variety of sock combinations and stand with equal weight bearing for 15 s for each combination. Thickness of each sock combination under incremental loading up to 101.2 kPa was tested after the session using a tabletop test system [19 (link),20 (link)]. The researcher then asked participants to identify a minimum and maximum sock thickness in which they could safely walk, and selected an intermediate sock thickness between the self-selected minimum and maximum. In the second portion of the test session, participants conducted a series of activities while wearing each sock combination. The activities were as follows: stand (15 s), walk (1 min), stand (15 s), sit (1 min), stand (15 s), walk (1 min), stand (15 s), and sit and change sock.
The distance between the limb and socket during the first portion of the test was obtained for each of the three sock combinations as the average of the distance measurements over the 15-s standing period. Peak-to-peak displacements were calculated for the walking portion of the test session as the difference between the maximum and minimum distance for a step. The minimum distance during a walking cycle represented the limb–socket distance during the stance phase of gait, whereas the maximum distance represented swing phase. Displacements were calculated for each step, and averaged over each walking cycle to obtain one average peak-to-peak displacement per sock combination.
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Publication 2018
Before the experiments, the magnetic flux density through the center of the Wiegand wire was simulated by the finite element analysis using ANSYS Maxwell® (ANSYS, Inc., USA). In the simulation, the 3D models of the magnet, the Wiegand wire, the ferrite beads and the air field were defined. The distance from the surface of the magnet to the center of the Wiegand wire was 5 mm. The dimensions of the magnet, the Wiegand wire and the ferrite beads were designed according to the samples used in the experiments, and the air field was designed as a cylinder with a radius of 15 mm and a height of 20 mm. The maximum length of elements of the Wiegand wire, the ferrite beads, the magnet and the air field were set to 0.3, 0.3, 0.5 and 1 mm in the mesh operations, respectively. The corresponding materials were determined according to the samples, as follows: “NdFe35” was chosen for the magnet from the material database of the software, and default parameters were used. A material named FeCoV was defined for the Wiegand wire. Its relative permeability and saturation magnetic flux density were 20 and 1.8 T, respectively, which were provided by the supplier (SWFE, Co. Ltd., Meishan, China). The “ferrite” material in the material database was selected for the ferrite beads, and the parameters were modified according to the sample datasheet of the supplier (TDK, Co. Ltd., Japan). The simulation results in Figure 7a show the magnetic field distribution at a distance of 5 mm from the surface of the magnet that was used in this study. The simulation results in Figure 7b show the magnetic flux density through the center of the Wiegand wire in the “With beads” model and “Without beads” model. The results of Figure 7b verified the judgment that the magnetic flux density through the center of the Wiegand wire was increased by setting a ferrite bead at both ends of the Wiegand wire. This was achieved without changing the intensity of the external magnetic field. This demonstrates the feasibility and effectiveness of the experimental method used in this study.
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Publication 2020
ferrite Magnetic Fields Permeability Radius Surgical Mesh

Most recents protocols related to «Ferrite»

The preparation of zinc ferrite nanoparticles was carried out according to the protocol that was published in our previous article [31 (link)]. The details of the preparation and characterization of the zinc ferrite nanoparticles can be found in the article [31 (link)]. The chemical compounds ZnCl2 (p.a.), FeCl3·6H2O (p.a.), and KOH (99%) were used for the synthesis and were purchased from PENTA, s.r.o. (Prague, Czech Republic). Briefly, the nanoparticles were synthetized by co-precipitation of Zn2+ and Fe3+ with a solution of KOH. After 60 min of vigorous stirring, the reaction was stopped and the supernatant liquid removed by vacuum filtration. The prepared ZnFe2O4 nanoparticles were washed several times with deionized water and left to dry in ambient atmosphere. The zinc ferrite sample, denoted as RT-60 (according to the synthetic parameters; 60 min, room temperature), was used in all following experiments. The preparation scheme is depicted in Figure 1.
The XRD analysis determined a mean coherent length of 2.9 nm by XRD and excluded other crystalline phases. Using TEM, the size of the prepared nanoparticles was found to be under 5 nm. The zinc ferrite nanoparticles exhibited a large BET area (220 m2 g−1) and pore volume. The sample contains highly aggregated nanoparticles with a mean size of aggregates of 85 nm, visually proved by scanning electron microscopy in transmission mode. The phase and elemental purity were checked by XRD, Mössbauer spectroscopy, and energy dispersive spectroscopy. The zinc ferrite is normal cubic spinel with ratio of 1 zinc atom to 2 iron atoms. The nanoparticles were found to be superparamagnetic, as determined by the room-temperature and the low-temperature Mössbauer spectroscopy and low-temperature magnetometric measurements. A representative sample was checked by XRD and Mössbauer spectroscopy; see Figure 2. The broadened diffraction peaks and the Fd-3m structure confirmed the presence of the nanoscaled zinc ferrite. The doublet is a characteristic spectral component of non-magnetic iron compounds, including superparamagnetic nanoparticles [31 (link)].
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Publication 2024
Not available on PMC !

Example 1

A metal precursor solution was prepared by dissolving 288.456 g of zinc chloride (ZnCl2), 1132.219 g of ferric chloride (FeCl3), and 23.678 g of nitric acid (HNO3, 60% purity) in 2,000.00 g of pure water (DI water). In this case, the molar ratio of the metal components comprised in the metal precursor solution was Zn:Fe=1:2, and the amount of HNO3 added was 1 wt % based on the combined weight of the zinc precursor and the iron precursor. An aqueous ammonia solution was added dropwise to the prepared aqueous metal precursor solution such that the pH was 7, and the resulting solution was stirred for 1 hour and co-precipitated. Thereafter, the co-precipitation solution was washed with distilled water, and dried at 90° C. for 24 hours, and then after the resulting product was warmed from 80° C. to 650° C. at a warming rate of 1° C./min under air atmosphere, a zinc-iron oxide (ZnFe2O4) powder having a spinel structure was produced by maintaining the temperature for 6 hours.

A process was performed in the same manner as in Example 1 without adding nitric acid (HNO3).

A scanning electron microscope (SEM, S4800/HITACHI) analysis was performed on the zinc ferrite-based catalysts prepared in the Examples and the Comparative Examples (measured at a magnification of 180,000 times), and the results thereof are shown in the following FIGS. 3 to 6, respectively. More specifically, an SEM photograph of the zinc ferrite-based catalyst according to Example 1 is illustrated in FIG. 3, and an SEM photograph of the zinc ferrite-based catalyst according to Example 2 is illustrated in FIG. 4. Further, an SEM photograph of the zinc ferrite-based catalyst according to Example 3 is illustrated in FIG. 5, and an SEM photograph of the zinc ferrite-based catalyst according to Comparative Example 1 is illustrated in FIG. 6.

The size of the crystal structure was measured by performing the SEM analysis, and the results thereof are shown in the following Table 1. More specifically, after the surface of the sample was measured by using the SEM analysis to enlarge the surface of the sample 180,000 times, the major axis length of the crystal structures confirmed within an arbitrarily sampled range (10 μm or more in width and 15 μm or more in length) in the measured photo was measured as the size of the crystal structure. The number of measurements was at least 10 or more, and an average value of the sizes of the crystal structure obtained after the measurement was calculated.

TABLE 1
Size (nm) of crystal structure
Example 1750
Example 2900
Example 3750
Comparative Example 1550

From the results, it can be confirmed that when an acid solution comprising one or more of nitric acid (HNO3) and hydrocarbon acid is added in the co-precipitation step during the process of preparing the zinc ferrite-based catalyst, the size of the final crystal structure of the catalyst after firing is increased. Further, it can be confirmed that when 5 wt % of nitric acid (HNO3) is added in the co-precipitation step during the process of preparing the zinc ferrite-based catalyst, the final size of the crystal structure after firing is the largest.

In addition, for the zinc ferrite-based catalysts prepared in the Examples and the Comparative Examples, the content of Clions in the dry powdery catalyst before the final firing was analyzed, and the results are shown in the following Table 2. Furthermore, the contents of Fe, Zn and Clions in the powdery catalyst after the final firing were analyzed by inductively coupled plasma (ICP) and are shown in the following Table 3. The ICP analysis was measured by using an inductively coupled plasma-optical emission (ICP-OES) apparatus. More specifically, an ICP-OES (Optima 7300 DV) device was used, and the procedure is as follows.

    • 1) About 0.1 g of a sample was accurately measured in a vial.
    • 2) About 1 mL of concentrated sulfuric acid was put into the vial containing the sample.
    • 3) The sample was carbonized by heating the sample on a hot plate.
    • 4) The sample was allowed to react while adding a small amount of nitric acid thereto in order to promote an oxidation reaction.
    • 5) The color of the solution was changed from dark black to light yellow by repeating the foregoing process.
    • 6) When the sample was completely dissolved to be clear, the sample was diluted with ultrapure water so as to have a volume of 10 mL.
    • 7) The solution was filtered and analyzed by ICP-OES.
    • 8) ICP-OES analysis conditions
    • RF power (W): 1300
    • Torch Height (mm): 15.0
    • Plasma Gas Flow (L/min): 15.00
    • Sample Gas Flow (L/min): 0.8
    • Aux. Gas flow (L/min): 0.20
    • Pump Speed (mL/min): 1.5
    • Internal Standard: Y or Sc

TABLE 2
Cl content (wt %)
Example 13.7
Example 23.0
Example 33.0
Comparative Example 13.7

TABLE 3
FeZnClOther
(wt %)(wt %)(ppm)impurities
Example 126.512.41004Not detected
Example 226.612.51019Not detected
Example 326.512.5950Not detected
Comparative Example 126.712.51130Not detected

From the results, it can be confirmed that when a solution comprising one or more of nitric acid (HNO3) and hydrocarbon acid is added in the co-precipitation step during the process of preparing the zinc ferrite-based catalyst, Clions in the dried powdery catalyst before the final firing are reduced. Further, it can be confirmed that when an acid solution comprising one or more of nitric acid (HNO3) and hydrocarbon acid is added in the co-precipitation step during the process of preparing the zinc ferrite-based catalyst, Clions in the powdery catalyst after the final firing are reduced.

In addition, it can be confirmed that even when a solution comprising one or more of nitric acid (HNO3) and hydrocarbon acid is added in the co-precipitation step during the process of preparing the zinc ferrite-based catalyst, other impurities are not produced.

Example 2

A process was performed in the same manner as in Example 1, except that 5 wt % of nitric acid (HNO3) was added based on the combined weight of the zinc precursor and the iron precursor.

A process was performed in the same manner as in Example 1, except that 10 wt % of sulfuric acid (H2SO4) was added based on the combined weight of the zinc precursor and the iron precursor.

An X-ray diffraction (XRD) analysis was performed on the zinc ferrite-based catalysts prepared in the Examples and the Comparative Examples, and the results thereof are shown in the following FIG. 8 and Table 4. In the following Table 4, the size of the ZnFe2O4 crystal structure represents the average value of the entire crystal structure.

The XRD analysis was measured by using an X-ray diffraction analyzer (Bruker AXS D4-Endeavor XRD), and performed under the analysis conditions of an applied voltage (40 kV) and an applied current (40 mA), and the range of 2 theta measured using a Cu target was 20° to 80°, and measured by performing scanning at an interval of 0.05°.

From the results of the following FIG. 8 and Table 4, according to an exemplary embodiment of the present application, the size of the fired crystal structure of the zinc ferrite-based catalyst can be increased, and accordingly, the activity of the catalyst can be increased. Further, it can be confirmed that when sulfuric acid is added as in Comparative Example 2, the formation of the ZnFe2O4 crystal is hindered.

TABLE 4
Size (nm) of ZnFe2O4
crystal structure
Example 3120.8
Example 484.5
Comparative Example 224.1

Example 3

A process was performed in the same manner as in Example 1, except that 10 wt % of nitric acid (HNO3) was added based on the combined weight of the zinc precursor and the iron precursor.

Under the conditions of 420° C., GHSV=250 h−1, OBR=1, SBR=5, and NBR=4, 1,3-butadiene was produced from the oxidative dehydrogenation reaction by using the zinc ferrite-based catalysts prepared in the Examples and the Comparative Examples,

    • OBR=Oxygen/total 2-butene ratio
    • SBR=Steam/total 2-butene ratio
    • NBR=Nitrogen/total 2-butene ratio)

In addition, in the oxidative dehydrogenation reaction of butene, butene conversion, butadiene selectivity, and the like were measured, and are shown in the following Table 5.

TABLE 5
ButeneButadieneCOx
ConversionSelectivityYieldSelectivity
Catalyst(%)(%)(%)(%)
Example 267.888.459.911.6
Example 365.687.757.512.2
Comparative Example 165.985.156.112.0

From the results, it can be confirmed that when a solution comprising one or more of nitric acid (HNO3) and hydrocarbon acid is added in the co-precipitation step during the process of preparing the zinc ferrite-based catalyst, the butene conversion and the butadiene selectivity are increased compared to Comparative Example 1.

In addition, even when citric acid is added as in Example 4, the effect of the increase in butene conversion and butadiene selectivity can be obtained as described above because it is possible to increase the size of the crystal structure of the zinc ferrite-based catalyst similarly to the case where nitric acid is introduced as in Examples 2 and 3. Furthermore, when sulfuric acid is added as in Comparative Example 2, the formation of the ZnFe2O4 crystal is hindered, so that the effect of the increase in butene conversion and butadiene selectivity as in an exemplary embodiment of the present application cannot be obtained.

Therefore, in an exemplary embodiment of the present application, the acid solution preferably comprises nitric acid or citric acid, and more preferably nitric acid.

As described above, a zinc ferrite-based catalyst according to an exemplary embodiment of the present application can increase the size of the crystal structure of the zinc ferrite-based catalyst by introducing an acid solution comprising one or more of nitric acid (HNO3) and hydrocarbon acid into a co-precipitation step during the process of preparing the zinc ferrite-based catalyst, and accordingly, can increase the activity of the catalyst.

Further, since the zinc ferrite-based catalyst according to an exemplary embodiment of the present application can reduce impurities Clions which can be present in the catalyst, it is possible to prevent the corrosion of preparation process equipment.

Therefore, the zinc ferrite-based catalyst according to an exemplary embodiment of the present application can obtain a higher yield of 1,3-butadiene than a zinc ferrite-based catalyst in the related art, which is used for oxidative dehydrogenation of butene.

Example 4

A process was performed in the same manner as in Example 1, except that 10 wt % of citric acid (C6H8O7) was added based on the combined weight of the zinc precursor and the iron precursor.

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Patent 2024

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Publication 2024
Magnetic cobalt ferrite sorbents (CoFe2O4) were synthesized following the procedure described by Tavares et al. (2020 (link)) based on the oxidative hydrolysis of iron(II) sulfate followed by co-precipitation of Co(II) and Fe(III) ions in alkaline conditions. Ultra-pure water was first deoxygenated with N2 under vigorous stirring for 2 h. Then, 1.90 g (34 mmol) of KOH and 1.52 g (15 mmol) of KNO3 were added to 25 mL of deoxygenated water using a 250-mL round flask. This mixture was heated at 60 °C, under N2 and mechanically stirred at 500 rpm. After total dissolution, 10 mL of aqueous CoCl2.6H2O (6 mmol) and 15 mL of aqueous FeSO4.7H2O (11 mmol) were both added dropwise, and mechanical stirring was then set at 700 rpm. The resulting reacting mixture presented a dark-green color after complete addition of the Co(II) and Fe(II) aqueous solutions and the reaction proceeded over 30 min, after which the reaction vessel was placed in an oil bath at 90 °C, under N2 but without stirring, for 4 h. The resulting black powder was magnetically collected and thoroughly washed with deoxygenated water and ethanol. Finally, the particles were collected and dried in an oven at 40 °C.
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Publication 2024
Cobalt ferrite nanoparticles
were successfully synthesized by a hydrothermal method. A precursor
solution was prepared by dissolving FeCl3·6H2O (13.51 g, 2 mol) and CoCl2·6H2O (5.94
g, 1 mol) separately in 30 mL of distilled water. The two solutions
were then combined under continuous stirring, and glycerol (0.078
M) was slowly introduced. The pH of the resulting solution was carefully
maintained at ∼12.0 through the controlled addition of dilute
sodium hydroxide. Following the formation of a brown precipitate,
the entire mixture was further stirred for 30 min before being transferred
to a Teflon-lined autoclave. The autoclave was sealed and subjected
to hydrothermal treatment in an oven set at 180 °C for a duration
of 6 h. After the mixture cooled, the black precipitate was separated
through centrifugation, subjected to multiple washes with distilled
water to eliminate chloride ions, and subsequently dried at 80 °C
overnight. The resulting dried materials were securely stored in airtight
containers for future utilization.
Publication 2024

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More about "Ferrite"

Ferromagnetic ceramics, iron oxide compounds, magnetic materials, magnetic domain structures, soft magnetic materials, hard magnetic materials, magnetic permeability, magnetic saturation, magnetic coercivity, electromagnetic induction, electrical transformers, data storage devices, permanent magnets, microwave devices, spintronics, magnetic recording, magnetic shielding, ferrimagnetism, ferromagnetism, superparamagnetism, magnetic anisotropy, crystalline anisotropy, shape anisotropy, magnetostriction, magnetocrystalline anisotropy, Curie temperature, Néel temperature, eddy current losses, hysteresis losses, ferrite cores, ferrite powders, ferrite nanoparticles, sintered ferrites, ferrite composites, ferrite manufacturing, ferrite characterization, ferrite applications, ferrite research, PubCompare.ai, research reproducibility, research accuracy, literature review, preprint analysis, patent search, optimal protocols, sodium hydroxide, oleic acid, D8 Advance, ethanol, FBS, iron(III) nitrate nonahydrate, Zetasizer Nano ZS, PPMS, SmartLab, iron(III) acetylacetonate.