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Article

Magnetic and Impedance Analysis of Fe2O3 Nanoparticles for Chemical Warfare Agent Sensing Applications

by
Jennifer R. Soliz
1,
Smriti Ranjit
2,
Joshua J. Phillips
2,
Richard A. Rosenberg
3 and
Adam J. Hauser
2,*
1
US Army DEVCOM Chemical Biological Center, 5183 Blackhawk Road, Aberdeen Proving Ground, Edgewood, MD 21010-5424, USA
2
Department of Physics and Astronomy, University of Alabama, Tuscaloosa, AL 35487, USA
3
Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439, USA
*
Author to whom correspondence should be addressed.
Magnetochemistry 2023, 9(9), 206; https://doi.org/10.3390/magnetochemistry9090206
Submission received: 18 July 2023 / Revised: 13 August 2023 / Accepted: 23 August 2023 / Published: 25 August 2023
(This article belongs to the Section Applications of Magnetism and Magnetic Materials)
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Abstract

:
A dire need for real-time detection of toxic chemical compounds exists in both civilian and military spheres. In this paper, we demonstrate that inexpensive, commercially available Fe2O3 nanoparticles are capable of selective sensing of chemical warfare agents (CWAs) using frequency-dependent impedance spectroscopy, with additional potential as an orthogonal magnetic sensor. X-ray magnetic circular dichroism analysis shows that Fe2O3 nanoparticles possess moderately lowered moment upon exposure to 2-chloroethyl ethyl sulfide (2-CEES) and diisopropyl methylphosphonate (DIMP) and significantly lowered moment upon exposure to dimethyl methylphosphonate (DMMP) and dimethyl chlorophosphate (DMCP). Associated X-ray absorption spectra confirm a redox reaction in the Fe2O3 nanoparticles due to CWA structural analog exposure, with differentiable energy-dependent features that suggest selective sensing is possible, given the correct method. Impedance spectroscopy performed on samples dosed with DMMP, DMCP, and tabun (GA, chemical warfare nerve agent) showed strong, differentiable, frequency-dependent responses. The frequency profiles provide unique “shift fingerprints” with which high specificity can be determined, even amongst similar analytes. The results suggest that frequency-dependent impedance fingerprinting using commercially available Fe2O3 nanoparticles as a sensor material is a feasible route to selective detection.

1. Introduction

Chemical warfare agents (CWA) are highly toxic chemical compounds that have lethal effects on humans and are dispersed as liquid, gas, or aerosol threats [1,2]. Real-time detection of toxic chemical compounds, more specifically CWA structural analogs, is a great need in both civilian (pesticide/herbicide buildup) and military (chemical weapons) spheres [3,4]. In civilian life, similar compounds are widely used in agriculture as herbicides/pesticides, despite environmental evidence that these chemicals have been transported through our water systems and into the food chain [5,6], with adverse health effects most keenly felt by our minor and prenatal subpopulations [7,8,9,10,11,12]. Efforts to detect and remove these chemicals continue, but current detection methods that are both sensitive and selective remain elusive [13,14,15,16,17,18,19,20,21,22,23,24].
The use of metal oxide nanoparticles in CWA detection and decontamination has been widely researched [1,25,26]. Metal oxyhydroxide nanoparticles are excellent candidates for the detection and removal of hazardous compounds owing to their large surface area-to-volume ratios and tailored surface functionality [27,28,29,30,31]. Metal oxyhydroxides include structures that are stable in various environmental conditions (temperature, humidity) but have strong, specific reaction mechanisms (hydrolysis, oxidation) with toxic compounds. These properties suggest high chemical sensitivity and selectivity and, thus, are attractive for chemical sensing applications. Additionally, the dielectric properties and large surface areas of these materials are conducive to rapid responses toward real-time detection. Previous studies show the potential of metal oxide for developing electrochemical sensors, gas sensors, and biosensors to detect harmful gases and toxic pollutants [2,32,33,34,35,36]. A variety of metal oxides have been studied, including Al2O3 [37], TiO2/WO3 [38], CeO2 [39,40], SnO2 [41,42,43], Fe2O3 [35,44,45,46], and ZnO [47,48]. For instance, Štengl et al. studied the hydrolysis of CWA using TiO2. They compared their result with the commercial product developed by NanoScale Corporation, FAST-ACT®, which is made up of TiO2 and MgO for the decomposition of CWA, such as soman (GD), sulphur mustard, and VX agent [25,33].
Recently, metal oxide composites such as Fe2O3/graphene nanocomposite and transition metal doped metal oxide nanoparticles have shown promising results and received a great deal of attention due to their improvement in sensing performance and electrochemical properties [36,49,50,51]. Furthermore, due to their physiochemical properties, metal oxide nanoparticles are widely used in a range of applications. They are extensively used in (a) biomedical and bioengineering applications for tissue repair and regeneration [52], (b) the pharmaceutical industry for drug delivery and disease treatment [53], and (c) cosmetology and dermatology such as treatment creams, sunscreen, and deodorants [54].
In this work, we demonstrate that inexpensive, commercially available Fe2O3 nanoparticles may be of use in sensing and distinguishing between CWAs if we utilize frequency-dependent impedance spectroscopy (FDIS) to create frequency “fingerprints” for each analyte [55,56]. This technique takes advantage of changes in a dielectric sensor material due to adsorption or reaction of an airborne analyte upon contact. These changes can manifest as shifts in existing conduction frequencies or as new conduction pathways but with a common result of frequency-dependent shifts in the impedance spectra. This work shows that significant differences in the frequency dependence and direction of impedance shift can be found even between two very chemically similar analytes. From the perspective of sensor development, we demonstrate FDIS as a feasible route to improving selectivity without affecting the sensitivity or chemistry of the sensor dielectric. We perform element-specific X-ray absorption spectroscopy (XAS) and X-ray magnetic circular dichroism (XMCD) measurements to confirm reaction with tested analytes, characterize changes in the magnetic properties to determine the potential for orthogonal sensing, and show that the reactions are consistent with the previously reported redox (Fe2O3 to FeO) reaction [57,58,59].

2. Materials and Methods

Fe2O3 nanoparticles with a 30–60 m2/g surface area were purchased from Nanostructured and Amorphous Materials, Inc., and were not altered or further purified. Our X-ray diffraction (XRD) results (Figure S1) confirmed two polymorphs of Fe2O3 are present, and α–Fe2O3 and γ–Fe2O3 adopt the R-3c and P4332 space group symmetries, respectively. The refined lattice parameters and atomic positions are reported in Table S1, and our results are similar to Zhang et al. [30,60] and Sahoo et al. [42]. The occupancies and thermal isotropic positions were held constant within the refinement. Using the Scherrer Equation, a crystallite size of 23 nm was calculated for unexposed particles and 21 and 19 nm for particles after DMMP and DMCP exposures, respectively, evidence of chemical reactivity. The nanoparticles were exposed to 10 wt. % liquid dosage of four CWA structural analogs (DMMP, DMCP, DIMP, 2-CEES) and held within a sealed vial for 1 day. Nanoparticles were similarly exposed to (GA, G-type chemical warfare nerve agent) as above, for 1 h.
Element-specific X-ray absorption spectroscopy (XAS) and X-ray magnetic circular dichroism (XMCD) measurements of the Fe L and O K edges were taken at the soft (4-ID-C) X-ray beamline at the Advanced Photon Source at Argonne National Laboratory. The Fe L edge was collected in total electron yield mode, while the O K edge was taken in fluorescence mode to optimize signal strength. Both the applied magnetic field and incident X-rays were aligned 20° from the plane of the film surface. Sample powder was mounted on conductive carbon tape to avoid possible charging, and an external magnetic field of 1T was used for XMCD measurements. Systematic errors introduced by (1) the placement of the absorption edge step and (2) the normalization of experimental spectra to the absorption edge step intensity are the dominant sources of error in our XMCD measurements. The case of (1) is further complicated as the Fe L edge requires a split edge step due to multiple oxidation values. As such, we have analyzed the data for the range of all reasonable step edge centroid energies, edge step splitting, and normalization values and report the average moment and error values consummate with the range of possible moments. The choice of placing the step edge at the white line maximum intensity will generally correspond with the lower magnitude moment values. Placing the step edge at the maximum derivative of the XAS (center of rising edge) with respect to energy will yield magnitude moment values in the upper end of the error range. Finally, normalization of the spectra to sit higher (lower) with respect to the intensity step will result in lower (higher) moment values.
Another route to sensor feasibility with Fe2O3 nanoparticles is that of frequency-dependent impedance spectroscopy (FDIS), in which even slight variations in analyte chemistry will change the chemical structure just enough to result in changes in the characteristic transport frequencies and/or the magnitude of the changed impedance. To test the feasibility of such an approach, nanoparticle powders were exposed as described previously, pressed into 25 mm diameter pellets for DMMP and DMCP and 13 mm diameter pellets for GA exposure, and then analyzed via impedance spectroscopy using a Solartron Analytical 1260 impedance analyzer equipped with a 1296 dielectric interface and 12962A sample holder with brass plates. AC impedance measurements were collected on bulk pellets over a frequency range of 10−2 to 106 Hz with an applied voltage of 100 mV. The dwell time was 1 s with 5 points measured per frequency decade. Measurements were repeated three times, checked for consistency and drift, and then averaged.

3. Results

The hole number values used in this work result from estimates from X-ray photoelectron spectroscopy (XPS). Analysis of spectra taken on the DMCP-exposed sample yields a Fe3+/Fe ratio in the sample of 0.26, an indicator of a reduction of Fe2O3 to FeO. All other samples were nearly identical and yielded a Fe3+/Fe ratio of 0.65, reflective of mixed valency that is expected and previously seen in Fe2O3 nanoparticles [61,62]. This allows us to adjust the holes/Fe site value used in XMCD calculations for the DMCP exposed sample to 4.26 and find (based on oxygen loss) a 4.5% increase in Fe sites/g (and consequent 4.5% decrease in moment/Fe) for the purposes of our SQUID calculation and comparison.
The XAS and corresponding XMCD spectra at the Fe L edge are shown after exposure to each analyte (as well as the unexposed material for reference) in Figure 1. The most obvious difference exists in powder exposed to DMCP, which shows a much larger white line intensity. This increase is indicative of the significant Fe reduction seen in the XPS results and further evidence for a reduced hole/Fe site value. Applying the aforementioned hole estimates to the L edge sum rules, we arrive at the values and errors for the total, spin, and orbital moment values as written in Table 1 and displayed in graph form in Figure 2 [63].
For both the unexposed and analyte-exposed Fe2O3 samples, spin moment ( m s ) is larger than the orbital moment ( m l ). We found that the m l / m s ratio for the unexposed sample is larger compared to the exposed Fe2O3 samples to analytes, suggesting the reduction in the orbital moment. This decrease in orbital moment is observed for all four exposed samples, and in the case of DMCP, the reduction in orbital moment is very large, approximately 96%. The spectroscopic splitting factor, g-factor g = 2   ( 1 + m l m s ) is calculated using the orbital-moment-to-spin moment ratio [64]. The values are compared to the magnetic moment values per Fe site, as measured by SQUID magnetometry (Quantum Design Magnetic Property Measurement System (MPMS)) at the same temperature. Our hole estimates result in total moment values by XMCD analysis that fit well with our magnetometry values. Note that the relative XA intensity for the Fe L2 edge is plotted in Figure 3a.
As expected, the DMCP-exposed Fe2O3 nanoparticles show an approximately 40% decrease in magnetization as compared to unexposed material. The orbital moment drops to a statistically insignificant value, but a massive loss in the spin moment also occurs. This is likely the result of the reduction of Fe2O3 to FeO and a loss of orbital ordering as the unchanged magnetic regions become smaller and more isolated, inhibiting long-range ordering. Interestingly, different CWA structural analogs show different drop-offs in magnetization: DMMP-exposed samples show a smaller but still robust 20% decrease in total moment and a loss of nearly all orbital moment, while DIMP shows only a very slight decrease that is well within the bounds of our estimated experimental uncertainty and can be considered statistically insignificant. Meanwhile, the same exposure dosage to 2-CEES results in a moderate (~10%) decrease in total and orbital moments that is statistically significant by our estimates.
The magnetic characterization results are consistent with the previously reported reaction pathway in Fe2O3 to organophosphate compounds [57,58]. We more directly observe the chemical changes in Figure 3a,b, which show normalized and overlaid XAS spectra for the Fe L2 and O K edges, respectively. In addition to the larger white line intensity already discussed, the Fe L2 spectra (resulting from the excitation of 2p1/2 orbitals to unoccupied d or s states) show a higher t2g/e*g intensity ratio for DMCP exposure as compared to other exposures, which show little change compared to unexposed material. Generally speaking, the probability of absorption is proportional to the density of unoccupied states, so a relative increase in the t2g orbital results from the reduction of Fe3+ to Fe2+. Likewise, the O K edge shows similar results for all exposures except DMCP, in which the spectra (while still clearly in a mixed state) begin to more strongly resemble standards previously reported for FeO than that of Fe2O3 [65,66]. The O K edge XAS spectra reflect the electron transition from the O 1s core level to the O 2p unoccupied state and its hybridization with the orbitals of neighboring Fe ions.
The lower energy region between 528 and 534 eV indicates O 2p hybridization with Fe 3d states, while the higher energy region between 534 and 545 eV illustrates hybridization between O 2p and Fe 4sp orbitals [67]. The lower energy region of Fe2O3 spectra displays the two peak structure of Fe3+ high spin d5 complex that can be attributed to t2g-eg split for octahedral sites or e-t2 split for tetrahedral sites [67]. The intensity of analyte-exposed samples is close to the unexposed Fe2O3 sample, other than of the DMCP-exposed Fe2O3 sample, which decreases in the lower energy part of the spectra, and shows a change in positive to negative intensity at higher energy regions. These spectral changes are visible in Figure 4a,b which show the relative intensity and calculated difference spectra between X-ray absorption measured for unexposed and exposed Fe2O3 samples, respectively.
Our spectroscopic results show that Fe2O3 nanoparticles exhibit the expected chemical reaction and show magnetic sensitivity to CWA structural analogs. The changes in the magnetic moment to our dosages are significant, but the data has a scalar value that decreases with all our explored analytes. Although this makes magnetic sensing less useful as a stand-alone technique for differentiating CWAs, it does have promise as an orthogonal technique in a composite sensing system. While detection by synchrotron X-ray absorption or magnetometry techniques is unfeasible for field or rapid detection, small electronic devices incorporating Fe2O3 nanoparticles may be a viable sensing option.
Samples dosed with DMMP and DMCP, simulants for G-type nerve agents, were chosen as they are (1) the most reactive analytes, as evidenced by Figure 2, and (2) the most similar compounds chemically, providing a test of frequency fingerprinting as a selective method. Samples were also exposed to tabun (GA, G-type chemical warfare nerve agent), and impedance measurements were performed to understand the changes from GA exposure and to distinguish from simulants of nerve agents using the shift in impedance.
The impedance magnitude |Z| and phase angle θ of unexposed (black), DMMP-exposed (blue), and DMCP-exposed (red) Fe2O3 pressed pellets as a function of frequency are plotted in log-scale in Figure 5a,b, respectively. A normalization approximation for pellet thickness variance (less than 5% sample-to-sample) was applied to |Z| values. The small variation did not qualitatively change the results of measurements. Each sample shows typical capacitive behavior at higher frequencies and resistive behavior at low frequencies. This is corroborated by the phase angle data, which trends from nearly 0° (resistive) to nearly 90° (capacitive) as the frequency increases. The most striking result of this work lies in the opposite impedance responses to DMCP and DMMP exposures in Figure 5a: Despite the similar chemical structure of the two analytes, Fe2O3 nanoparticles exposed to DMMP exhibit significantly increased impedance across the entire frequency range measured, while exposure to DMCP resulted in a strong decrease in impedance at all but the very highest frequencies.
Figure 6a,b shows the impedance magnitude and phase angle of unexposed (gray) and GA-exposed (green) Fe2O3 pressed pellets as a function of frequency. This sample shows a similar behavior as previous DMMP- and DMCP-exposed samples, suggesting a trend from capacitive to resistive when the frequency decreases from 1 MHz to 1 Hz, as shown in the phase angle plot. The impedance magnitude data in Figure 6a shows the decrease in magnitude at all frequencies measured, showing the bigger difference as frequency decreases from 1 MHz to 1 Hz.
Figure 7a replots the data in Figure 5a and Figure 6a to show the percent change in the impedance magnitude due to DMMP, DMCP, and GA exposures. This plot accentuates the frequency-dependent features in Figure 5a: DMMP exposure creates large increases in |Z| at the low and high ends of the frequency regime and nearly no change from 10–100 Hz, while DMCP shows a crossover from negative to positive impedance change just below 1 MHz, an almost completely steady 50% decrease in |Z| from 1–100 Hz, and a maximum impedance drop of nearly 80% at approximately 2.5 kHz. Compared to DMMP and DMCP, GA exposure shows a decrease in impedance magnitude across the entire frequency range measured; there is a gradual decrease in |Z| from 1 MHz-1 kHz and then a steady decrease of 100% from 1 kHz–1 Hz. In Figure 7b, we extract the real component of the impedance (ZRe) to derive a value for the electrical resistance R = ZRe = |Z|cos (θ), where θ is the phase angle measured and shown in Figure 5b and Figure 6b. Below the kHz range, the system is dominated by resistive behavior, and so R and |Z| are closely related. At higher frequencies, we see large increases in resistance for both DMMP (460% @ 200 kHz) and DMCP (480% @ 550 kHz). The data in Figure 5b and Figure 6b are replotted to show the change in phase angle due to DMMP, DMCP, and GA exposure in Figure 7c. The change in phase angle shows strikingly different frequency-dependence when exposed to DMMP, DMCP, and GA exposures. The impedance magnitude and resistance drop by more than 99% upon GA exposure, while the phase shift is positive, that is, trending more capacitive. This seemingly incongruous group of results suggests sufficient decomposition to highly short any resistive region of the material left, increasing the overall weighting of capacitive regions (which are measured by the AC probe regardless of resistive shorting) compared to resistive regions (which are not fully probed when resistive shorts occur). We can observe initial signs of the resistive shorting in the negative phase shifts centered at 30 Hz in DMMP-exposed material and 300 Hz in DMCP-exposed material, leading to positive shifts (peaks centered at 100 kHz in DMCP-exposed material and 800 Hz in GA-exposed material, with a low, broad increase centered about 10 kHz for DMMP-exposed material) at the higher frequencies that more selectively probe capacitance. It is worth noting here both the size of the impedance increase as well as the large change in the frequency at which maximum phase shift occurs is useful in finding a combination of sensitivity and chemical differentiation/selectivity required for a potential sensing application.

4. Discussion

The sensitivity and selectivity of an impedance-based sensor are determined not only by the dielectric material being probed but also by the technique used and the method of analysis [68,69,70,71]. We note an array of other sensor methodologies to probe dielectrics, including pressure/strain-induced capacitive changes [72,73], optical detection [74,75,76], and chemiresistance/conductometrics [77,78,79,80]. The general challenge of choosing a dielectric material for airborne sensing lies in the trade-off between sensitivity and selectivity: If a material is sensitive to one analyte, it is often sensitive to many analytes, reducing its selectivity considerably. Modulation of chemistry has made strides in increasing selectivity, but the selectivity often comes at the price of sensitivity losses. Put glibly: If a material is sensitive, it is sensitive to everything. If it is selective, it is not very sensitive to anything.
Device technologies have advanced through this limitation via the use and cross-reference of many dielectric materials at once, creating something akin to an artificial nose with many types of point receptors to reasonable success. However, currently produced devices primarily use direct current or single-frequency AC modulation [78]. The results herein (combined with previous results [55,56,81,82]) suggest another potential route: Probing the same dielectric at a series of frequencies to create a frequency-dependent “fingerprint” of the system, reducing the need for as many elements or allowing a sensor the luxury of redundancy without increasing size, weight, power, and cost (SWaP-C) requirements. The FDIS technique can be employed to improve the selectivity of a device without adversely affecting the sensitivity of the sensor material’s dielectric properties to the analyte of choice.
In this paper’s case of Fe2O3, the stability born of a stable metal-oxide material combined with a specific (hydrolysis) reaction pathway does not, on its own, create sufficient selectivity for an organophosphate sensor: Just within this paper, we see a reaction from all organophosphates tested, as well as the non-organophosphate analog 2-CEES. Even limiting our FDIS scope to DMMP, DMCP, and GA, it would be very hard to identify (using the data in Figure 7) which analyte was present but for a few specific frequencies, and even in such a case, single-frequency detection of a combination of analytes is impossible. However, this changes considerably if one can use a range of frequencies—the shapes of the curves, in addition to the magnitudes, would help analysis considerably. It is important to note that a multi-analyte environment would almost certainly not yield a simple linear superposition of the single-analyte FDIS curves we show in this paper. However, it is nearly certain that the FDIS measurement approach would be of tremendous use in boosting the selectivity of any impedance-based sensor as described above.

5. Conclusions

XAS and XMCD results have provided evidence for varying levels of the expected Fe2O3 → FeO reaction due to contact with four CWA structural analogs. We find a range of significant decreases in the magnetic moment due to exposure to the analytes. The impedance results show that Fe2O3 nanoparticles have promise in the detection and differentiation of simulants DMMP and DMCP and between simulant and GA exposure if a device can be made to examine the frequency profile of the impedance and/or resistance. While the parallel-plate geometry utilized here with a pressed pellet is not conducive to sensitive airborne chemical detection, devices incorporating an electrically contiguous film or a porous structure with a high surface area could result in a detection device with excellent sensitivity and selectivity.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/magnetochemistry9090206/s1, Figure S1: X-ray powder diffraction pattern of Fe2O3 nanoparticles in 2θ range of 20 to 80 degrees. The black filled circles, red line, gray line, blue crosses, and pink crosses are the observed pattern, calculated pattern, difference curve between the observed and calculated curves, allowed Miller indices for space group P4332, and allowed Miller indices for space group R-3c, respectively; Table S1: Lattice parameters, atomic positions, weight fractions, crystallite size, and RF2 factor from X-ray powder diffraction Rietveld refinement.

Author Contributions

Conceptualization, J.R.S., S.R. and A.J.H.; methodology, J.R.S., R.A.R., S.R. and A.J.H.; validation, A.J.H.; formal analysis, J.J.P., S.R. and A.J.H.; investigation, J.J.P., S.R. and R.A.R.; data curation, S.R. and A.J.H.; writing—original draft preparation, J.R.S. and S.R.; writing—review and editing, A.J.H.; supervision, J.R.S. and A.J.H.; J.R.S. and A.J.H. All authors have read and agreed to the published version of the manuscript.

Funding

Use of the Advanced Photon Source was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357. A.J.H. acknowledges funding under U.S. Army Research Office STIR award #W911F-15-1-0104. This research was performed while J.R.S. held a National Research Council Research Associate Award at the U.S. Army DEVCOM Chemical Biological Center and was under the mentorship of Dr. Christopher Karwacki. The authors would also like to gratefully acknowledge the Defense Threat Reduction Agency for funding this work under CB10599.

Data Availability Statement

Data will be made available upon request.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Magnetochemistry 09 00206 g001
Figure 1. Normalized XANES and XMCD spectra at the Fe L edge for Fe2O3 nanoparticles exposed to various analytes, as well as unexposed material. XANES (XMCD) spectra are on top (bottom) within each pair, with analytes labeled below each pair.
Figure 1. Normalized XANES and XMCD spectra at the Fe L edge for Fe2O3 nanoparticles exposed to various analytes, as well as unexposed material. XANES (XMCD) spectra are on top (bottom) within each pair, with analytes labeled below each pair.
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Figure 2. Spin (solid red diamond), orbital (open green square), and total (solid navy circle) magnetic moment for Fe2O3 nanoparticles as received (“unexposed”) and after exposure to analytes as labeled, as determined by XMCD. Corroborating measurements of the total magnetic moment by SQUID magnetometry are overlaid as hollow orange triangles.
Figure 2. Spin (solid red diamond), orbital (open green square), and total (solid navy circle) magnetic moment for Fe2O3 nanoparticles as received (“unexposed”) and after exposure to analytes as labeled, as determined by XMCD. Corroborating measurements of the total magnetic moment by SQUID magnetometry are overlaid as hollow orange triangles.
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Figure 3. Normalized X-ray absorption spectra for different exposed samples at the (a) Fe L2 and (b) O K edge.
Figure 3. Normalized X-ray absorption spectra for different exposed samples at the (a) Fe L2 and (b) O K edge.
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Figure 4. (a) O K-edge normalized XAS spectra of Fe2O3 samples, unexposed and exposed, and (b) Difference spectra of the exposed Fe2O3 samples, where the spectrum of the unexposed sample was subtracted from the different analyte-exposed samples.
Figure 4. (a) O K-edge normalized XAS spectra of Fe2O3 samples, unexposed and exposed, and (b) Difference spectra of the exposed Fe2O3 samples, where the spectrum of the unexposed sample was subtracted from the different analyte-exposed samples.
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Figure 5. (a) Impedance magnitude |Z| and (b) phase angle as a function of the frequency of Fe2O3 nanoparticles before (black) and after exposure to DMCP (red) and DMMP (blue), as labeled.
Figure 5. (a) Impedance magnitude |Z| and (b) phase angle as a function of the frequency of Fe2O3 nanoparticles before (black) and after exposure to DMCP (red) and DMMP (blue), as labeled.
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Figure 6. (a) Impedance magnitude |Z| and (b) phase angle as a function of the frequency of Fe2O3 nanoparticles before (gray) and after exposure to GA (green) as labeled.
Figure 6. (a) Impedance magnitude |Z| and (b) phase angle as a function of the frequency of Fe2O3 nanoparticles before (gray) and after exposure to GA (green) as labeled.
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Figure 7. Percent changes in (a) impedance magnitude |Z| and (b) resistivity (R, the real component of the impedance), and (c) change in phase angle as a function of frequency for Fe2O3 nanoparticles after exposure to DMCP (red), DMMP (blue), and GA (green), as labeled above each curve.
Figure 7. Percent changes in (a) impedance magnitude |Z| and (b) resistivity (R, the real component of the impedance), and (c) change in phase angle as a function of frequency for Fe2O3 nanoparticles after exposure to DMCP (red), DMMP (blue), and GA (green), as labeled above each curve.
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Table 1. Magnetization results with an applied 1 T magnetic field by XMCD and comparison to SQUID Magnetometry results published previously on the powder samples.
Table 1. Magnetization results with an applied 1 T magnetic field by XMCD and comparison to SQUID Magnetometry results published previously on the powder samples.
CompoundFe L3/L2 Branching RatioSpin
Moment mS per Fe by XMCD (μB/Fe)		Orbital
Moment mL per Fe by XMCD (μB/Fe)		mL/mSg-FactorTotal
Moment per Fe Site by XMCD (μB/Fe)		Magnetization per Fe Site by SQUID (μB/Fe)
Fe2O33.0 (2)0.91 (4)0.12 (2)0.13 (2)2.26 (2)1.03 (5)1.03
Fe2O3, DMCP3.0 (1)0.60 (3)0.004 (4)0.007 (6)2.013 (6)0.60 (3)0.64
Fe2O3, DMMP3.4 (2)0.78 (4)0.03 (1)0.04 (1)2.09 (1)0.81 (4)0.79
Fe2O3, DIMP3.3 (2)0.89 (4)0.11 (2)0.12 (2)2.25 (2)1.00 (5)
Fe2O3, 2-CEES3.0 (2)0.84 (4)0.09 (1)0.10 (1)2.21 (1)0.92 (4)
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Soliz, J.R.; Ranjit, S.; Phillips, J.J.; Rosenberg, R.A.; Hauser, A.J. Magnetic and Impedance Analysis of Fe2O3 Nanoparticles for Chemical Warfare Agent Sensing Applications. Magnetochemistry 2023, 9, 206. https://doi.org/10.3390/magnetochemistry9090206

AMA Style

Soliz JR, Ranjit S, Phillips JJ, Rosenberg RA, Hauser AJ. Magnetic and Impedance Analysis of Fe2O3 Nanoparticles for Chemical Warfare Agent Sensing Applications. Magnetochemistry. 2023; 9(9):206. https://doi.org/10.3390/magnetochemistry9090206

Chicago/Turabian Style

Soliz, Jennifer R., Smriti Ranjit, Joshua J. Phillips, Richard A. Rosenberg, and Adam J. Hauser. 2023. "Magnetic and Impedance Analysis of Fe2O3 Nanoparticles for Chemical Warfare Agent Sensing Applications" Magnetochemistry 9, no. 9: 206. https://doi.org/10.3390/magnetochemistry9090206

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