Jeyeon Lee

Synthesis and magnetic properties of nanostructured γ-Ni-Fe alloys

Abstract Nanostructured γ-Ni-xFe alloys with x ≈32, 46, 55, and 64wt% and with an average grain size of 30–40nm were successfully synthesized by a mechano-chemical process. Magnetic measurements indicated that these nano-alloys were ferromagnetic; no superparamagnetic phenomenon was observed at all the temperatures investigated (from ∼300K to 8.5K). The coercivity of these nano-alloys were much higher than that of their conventional coarser-grained counterparts, implying that random anisotropy did not dominate their magnetic behavior although their grain sizes were substantially smaller than the evaluated magnitudes of the ferromagnetic exchange lengths. The increase of coercivity and retentivity with iron content, as x 55wt%, however, the decrease of coercivity with iron content was far slower than one would expect based on the crystalline anisotropy. Microstructural analysis indicated that the high coercivity for the nano γ-Ni-Fe with higher iron content (x = 64wt%) can be explained by its larger shape anisotropy. The variation of the saturation magnetization of the nano γ-Ni-xFe alloys with iron content are basically similar to that of its polycrystalline counterpart. But, the absolute magnitudes were about 11–13% lower than those of the corresponding polycrystalline counterparts. This slight decrease in magnitude can be ascribed to oxidation of the nano γ-Ni-xFe powder after reduction.

Magnetic properties of nanostructured γ-Ni–46Fe alloy synthesized by a mechanochemical process

Magnetic properties and their relations to microstructures of nanostructured γ-Ni–46Fe alloy (nano γ-Ni–46Fe), synthesized by a mechanochemical process, were investigated at temperatures from ∼300 down to 8.5 K. The results indicated that the nano γ-Ni–46Fe with average grain sizes of 20–220 nm displayed ferromagnetism, with no superparamagnetic phenomenon being observed in the temperature range investigated. Coercivity increased with decreasing grain size obeying the law Hc∝1/d well in the grain size regime d>∼100 nm. Contrary to prediction from the random anisotropy model, however, the coercivity at both 300 and 8.5 K was not found to decrease with further decreasing grain size in the size range d=20–100 nm, although the grain sizes were then obviously smaller than the evaluated value (102 nm) of ferromagnetic exchange length, suggesting random anisotropy did not dominate its magnetic behavior. Instead, it was demonstrated that the further increase of coercivity with decreasing grain size as d<100 nm ca...

Synthesis and microstructureal characterization of nanostructured γ-Ni-Fe powder

Abstract The microstructures and their relation to synthesis conditions of nanostructured γ-Ni-Fe alloys, synthesized by a mechano-chemical process, were investigated by using various techniques which include EDS, infrared and optical emission spectroscopy, XRD, TEM, BET and Laser particle analyzer as well as field emission SEM. The results indicate that nanostructured γ-Ni-xFe alloys with x ≈ 32, 46, 55, and 64wt% were successfully synthesized. The impurity contents in these γ-Ni-Fe alloys were very low. Typically, the microstructures of nano γ-Ni-46Fe and their variations with synthesis conditions were focused on. It was documented that γ-Ni-46Fe alloy with a minimum average grain size of 20nm and minimum average particle size ∼70nm can be obtained under specific synthesis conditions. Micro-pore analysis combined with BET and XRD experimental results indicated that the particles usually consisting of less than one hundred grains were actually small (sub-micrometer) polycrystals with no micro-pores within them. Experiments also showed that there were large hard-agglomerates which had an average size of ∼100nm in the nano γ-Ni-46Fe powders obtained at temperatures below 800°C. Above this temperature both the grain growth and the sintering process were significant. Moreover, it was revealed that upon annealing at temperatures below 800°C, the grains of nano γ-Ni-46Fe have an elongated shape with their long axis being in direction and with an aspect ratio of 1.37–1.25. Annealing at temperatures above 800°C caused their grains to change shape gradually to a disk-like form. In addition, the lattice of the nano γ-Ni-46Fe was found to be in a shrunken state with an average grain size of d (14) .

In-situ observation of formation of nanosized TiO2 powder in chemical vapor condensation

Abstract The present study has attempted to in-situ observe the formation ofnanosized TiO2 powder during chemical vapor condensation process. For this purpose, the powders were sampled at various positions in the furnace using a quartz collecting rod of which surface acts as the powder condensation site. It was discussed in terms of size effect on the phase stability that the fine anatase particle transformed to the coarse rutile phase by growing in the high temperature region. Also the parabolic particle growth with the distance of reaction tube was explicated by computated residence time with solving temperature and gas velocity distribution numerically for different oxygen flow rate conditions.

The characteristics of nanosized TiO2 powders synthesized by chemical vapor condensation

Abstract The effect of the ratio of O 2 /He flow rate in the reactor on the characteristics of nanosized TiO 2 powder synthesized by chemical vapor condensation process was investigated under fixed conditions of supersaturation ratio, collision rate and residence time. As the ratio of O 2 /He flow rate increased, the particle size of TiO 2 powder almost remained unchanged but the agglomeration of nanoparticles enlarged in which the degree of agglomeration was defined as the ratio of particle size to crystallite size. Based upon the reaction of Ti(OR) 4 + 18O 2 →TiO 2 + 14H 2 O + 12CO 2 , it is presumed that the water vapor released from the reaction of precursor with oxygen gas is responsible for the particle agglomeration.

A thin film polyimide mesh microelectrode for chronic epidural electrocorticography recording with enhanced contactability.

OBJECTIVE Epidural electrocorticography (ECoG) activity may be more reliable and stable than single-unit-activity or local field potential. Invasive brain computer interface (BCI) devices are limited by mechanical mismatching and cellular reactive responses due to differences in the elastic modulus and the motion of stiff electrodes. We propose a mesh-shaped electrode to enhance the contactability between surface of dura and electrode. APPROACH We designed a polyimide (PI) electrode with a mesh pattern for more conformal contact with a curved surface. We compared the contact capability of mesh PI electrodes with conventionally used sheet PI electrode. The electrical properties of the mesh PI electrode were evaluated for four weeks. We recorded the epidural ECoG (eECoG) activity on the surface of rhesus monkey brains while they performed a saccadic task for four months. MAIN RESULTS The mesh PI electrode showed good contact with the agarose brain surface, as evaluated by visual inspection and signal measurement. It was about 87% accurate in predicting the direction of saccade eye movement. SIGNIFICANCE Our results indicate that the mesh PI electrode was flexible and good contact on the curved surface and can record eECoG activity maintaining close contact to dura, which was proved by in vivo and in vitro test.

Mechano-chemical synthesis of nanosized stainless steel powder

Abstract The present investigation has attempted to fabricate nanosized stainless steel powder of Fe-18Cr-8Ni(wt%) by mechano-chemical synthesis. The synthesis process was conducted by hydrogen reduction of a high energy ball milled mixture of Fe 2 O 3 -NiO-Cr(NO 3 ) 3 ·9H 2 O. In-situ alloying of Ni-Fe-Cr nanophase simultaneously occurring during reduction process is presumably responsible for the formation of nanophase stainless steel powders. The powder characteristics were examined by X-ray diffractometry and SEM observation. The kinetic phenomena in association with the oxide reduction and the alloying process were investigated by thermogravimetry, hygrometry and X-ray diffractometry, and discussed in relation to powder characteristics.

Improved prediction of bimanual movements by a two-staged (effector-then-trajectory) decoder with epidural ECoG in nonhuman primates

OBJECTIVE In arm movement BCIs (brain-computer interfaces), unimanual research has been much more extensively studied than its bimanual counterpart. However, it is well known that the bimanual brain state is different from the unimanual one. Conventional methodology used in unimanual studies does not take the brain stage into consideration, and therefore appears to be insufficient for decoding bimanual movements. In this paper, we propose the use of a two-staged (effector-then-trajectory) decoder, which combines the classification of movement conditions and uses a hand trajectory predicting algorithm for unimanual and bimanual movements, for application in real-world BCIs. APPROACH Two micro-electrode patches (32 channels) were inserted over the dura mater of the left and right hemispheres of two rhesus monkeys, covering the motor related cortex for epidural electrocorticograph (ECoG). Six motion sensors (inertial measurement unit) were used to record the movement signals. The monkeys performed three types of arm movement tasks: left unimanual, right unimanual, bimanual. To decode these movements, we used a two-staged decoder, which combines the effector classifier for four states (left unimanual, right unimanual, bimanual movements, and stationary state) and movement predictor using regression. MAIN RESULTS Using this approach, we successfully decoded both arm positions using the proposed decoder. The results showed that decoding performance for bimanual movements were improved compared to the conventional method, which does not consider the effector, and the decoding performance was significant and stable over a period of four months. In addition, we also demonstrated the feasibility of epidural ECoG signals, which provided an adequate level of decoding accuracy. SIGNIFICANCE These results provide evidence that brain signals are different depending on the movement conditions or effectors. Thus, the two-staged method could be useful if BCIs are used to generalize for both unimanual and bimanual operations in human applications and in various neuro-prosthetics fields.

Decoding Saccadic Directions Using Epidural ECoG in Non-Human Primates

A brain-computer interface (BCI) can be used to restore some communication as an alternative interface for patients suffering from locked-in syndrome. However, most BCI systems are based on SSVEP, P300, or motor imagery, and a diversity of BCI protocols would be needed for various types of patients. In this paper, we trained the choice saccade (CS) task in 2 non-human primate monkeys and recorded the brain signal using an epidural electrocorticogram (eECoG) to predict eye movement direction. We successfully predicted the direction of the upcoming eye movement using a support vector machine (SVM) with the brain signals after the directional cue onset and before the saccade execution. The mean accuracies were 80% for 2 directions and 43% for 4 directions. We also quantified the spatial-spectro-temporal contribution ratio using SVM recursive feature elimination (RFE). The channels over the frontal eye field (FEF), supplementary eye field (SEF), and superior parietal lobule (SPL) area were dominantly used for classification. The α-band in the spectral domain and the time bins just after the directional cue onset and just before the saccadic execution were mainly useful for prediction. A saccade based BCI paradigm can be projected in the 2D space, and will hopefully provide an intuitive and convenient communication platform for users.

Meaning based covert speech classification for brain-computer interface based on electroencephalography

In this study, we investigated that whether covertly spoken words with different meaning are discriminable during electroencephalography (EEG) recording. Neural activities were recorded from 30 channel 10-20 system electrodes. By employing a paired T-test, we briefly identify the difference in spatio-spectro-temporal characteristics between two categories of meaning (number and face). EEG features were then classified by support vector machine. On average, 71.69% of the trials were correctly classified. After extract optimized features using support vector machine based recursive feature elimination, the accuracy was improved up to 92.46%. Our preliminary results shed light on the construction of meaning based speech brain-computer interface.