A Dual Nanostructured Approach to SERS Substrates Amenable to Large-Scale Production 

An Inside Look at Near-Infrared Spectroscopy

Information-Based Classifications to Distinguish Characteristics of Raw Agricultural Materials 

Survey of Key Descriptive References for Chemometric Methods Used for Spectroscopy

Raman Spectroscopy as a Tool for Rapid Feedback of Perovskite Growth Crystallinity and Composition 

The topics discussed include a comparison of total reflection X-ray fluorescence (TXRF) and graphite furnace atomic absorption spectrometry (GFAAS) for the analysis of trace meals in biological materials, the use of grazing incidence X-ray diffraction to study nanostructured layers, multielemental analysis of apatite crystals by TXRF, and tracing the absorption and transport of foliar sprayed nutrients in plants.

Comparison of Total Reflection X-ray Fluorescence (TXRF) and Graphite Furnace Atomic Absorption Spectrometry (GFAAS) for Analysis of Trace Metals in Biological Material

Martina Schmeling and Michelle Gende, Loyola University Chicago, Chicago, Illinois

A sample preparation procedure was developed for analyzing trace metals in biological materials using 

as the model organism. The samples were spiked with specific trace metal concentrations, and the prepared samples were analyzed by both total reflection X-ray fluorescence (TXRF) and graphite furnace atomic absorption spectrometry (GFAAS). Both measurement techniques were similarly effective and produced similar results, but some concentration differences existed for one of the added target elements because of the naturally present concentration of this element. More experiments choosing different standard elements are planned to validate these results.

Trace metals are involved in many biological functions by acting as cofactors in enzymes and stabilizing proteins. The excess of a trace metal and the lack thereof can be detrimental to an organism and result in malfunctions and disorders. Trace metals can also be toxic and disrupt fundamental enzymatic processes as is the case for lead, which interferes with calcium-dependent enzymes and leads to oxidative stress, among other actions (1). Consequently, it is important to monitor levels of trace metals in biological systems carefully. However, this is not an easy task because one has to consider the high organic matrix content, the presence of metabolic elements in high concentrations, and the natural variability of biological materials. The development of a robust and reliable procedure can serve as the basis for future research. Such a procedure should be based on a model system, which is easily available in the laboratory setting and also has sufficient natural variability to be representative. 

 was discovered in 1885 by T. Escherich and is the most studied microorganism. Its genome is completely sequenced and many groundbreaking discoveries in genetics and microbiology have used 

 as a model system (2). It is the basis for biotechnological processes and is often used in therapeutics. Moreover, 

 has the advantage over more complex organisms in that it is easy to grow in the 

laboratory; as a result, it lends itself for research purposes.

We present here the initial results obtained for the development of a sample preparation method to determine trace metals in 

 by total reflection X-ray fluorescence (TXRF) and graphite furnace atomic absorption spectrometry (GFAAS). The samples were artificially spiked with chromium and copper at predetermined concentrations. We selected chromium and copper because both elements are essential for many biological systems and are also easily detectable at those levels by both methods. For TXRF quantification, we used gallium as the internal standard, and it was added to the samples together with chromium and copper, whereas for GFAAS, we used external calibration for quantification.

All the samples were prepared from the same 

BL21 cell line and grown in Luria-Bertani broth (Fisher Bioreagents) for 12–18 h at 37 °C. (3). The cells were harvested after being centrifuged for 5 min at 5000 rpm and washed twice with 5 mL of 50 mM Hepes ((4-(2-hydroxyethyl)-1-pi-perazineethanesulfonic acid; C

S; pH = 7.5; VWR). After the second wash, the cells were resuspended in 500 μL of a balanced salt solution (BSS) and then dispersed in 50 μL aliquots into different 1.5 

mL centrifuge tubes. These 50 μL aliquots of cells were dried overnight at 95 °C, and the dried cell pellets were stored at room temperature until they were ready for final sample preparation.

For analysis by TXRF and GFAAS, 500 μL of a 1:4 H

 (70%) (both Sigma Aldrich) solution was added to the cell pellets along with standards containing chromium, copper, and gallium (EMD) in different concentrations as shown in Table I. Gallium served as the internal standard. Each set of samples was prepared in triplicate along with one control containing 100 ng/mL gallium also as the internal standard. The resuspended samples 

were microwave digested at 1200 watts (MW535OW, Samsung Corporation) for 1 min and then dried again for 48 h at 100 °C before adding 500 μL of the solution of 1:4 H

. The final samples were then divided into equal parts for analysis by TXRF and GFAAS.

For TXRF analysis, 5 μL of each sample was pipetted on a clean and prechecked quartz support and dried in a desiccator overnight. Analysis was carried out with a S2 PicoFox (Bruker) using molybdenum excitation at 50 kV and 600 μA. The counting time was 2000 s. The spectra were evaluated using the factory installed software with gallium as internal standard.

For GFAAS analysis, 20 μL of each sample was pipetted onto the graphite furnace platform and measured using the protocols as shown in Table II (AA-7000 Shimadzu). External calibration in form of calibration curves was used for quantification.

In Figure 1, the TXRF spectrum for one of the samples containing 100 ng/mL chromium, copper, and gallium, with the latter one serving as the internal standard, is displayed.

 sample containing 100 ng/mL of chromium, copper, and gallium. The three elements are shown in red text.

Table III lists the results obtained for each concentration along with its standard deviation. Figure 2 shows the percent deviation from the added target value. For chromium, the measured concentrations were aligned with the target concentrations and only the lowest value was slightly overestimated. Moreover, the agreement between TXRF and GFAAS data was outstanding considering the differences between these two techniques with respect to the quantification method and the type of analysis. For copper, the picture was slightly different: TXRF overestimated the lowest value and GFAAS underestimated it. For the middle value, both TXRF and GFAAS were returning much higher results than expected, and for the upper data point, TXRF was accurate whereas GFAAS underestimated it again.

FIGURE 2: Deviation from target concentrations in percent for chromium and copper for both TXRF (red bars) and GFAAS (blue bars).

There could be several reasons for these discrepancies toward the copper target values: For one, copper was present naturally in the samples in substantial concentrations and natural variability might have played a role even if the cells have grown from the same line and under the same conditions. The average copper concentrations for the nonspiked samples was 75.3 ± 88 ng/mL measured by TXRF and 102 ± 80 ng/mL measured by GFAAS, respectively. On the other hand, the chromium concentration for the nonspiked samples was always below detection limit for both methods. Second, the standard concentrations added to the samples were not exactly on target, which could be the case for the middle value of copper as both methods grossly overestimated it. 

Finally, the copper distribution on the homogeneous and the GFAAS furnace TXRF sample support was not sufficiently program needed to be better optimized. 

Because of the reasons described above, It is very likely a combination of all these factors did impact the copper data. Therefore, additional experiments selecting one or more different standard elements are necessary to confirm the results obtained for chromium.

The initial data presented here show agreement between TXRF and GFAAS for the developed sample preparation method with respect to the element chromium, but not for copper. The reason for the latter is possibly because of the large intrinsic and variable copper concentration. Therefore, more experiments are needed with standard elements having below detection limit intrinsic concentrations for both techniques to solidify the data for chromium.

, e05826; DOI:10.7554/elife.05826 (2015).

(3) A.R. Tuttle, N.D. Trahan, and M.S. Son, 

 are with Loyola University Chicago, in Chicago, Illinois. Direct correspondence to: 

The Use of Grazing Incidence X-ray Diffraction to Uncover the Strain Relaxation Processes in Highly Dense Nanowire Arrays

National Institute for Research and Development in Microtechnology Voluntari, Romania

The grazing incidence X-ray diffraction technique allows nanowire arrays to be scanned along their entire length. The rocking curves, ω-scan, and φ-scan recorded at different incidence angles allow us to obtain the bending and torsion profiles along the 

profiles help us prove the absence or existence of the strain relaxation processes. Furthermore, quantification of the screw and edge threading dislocation density becomes possible in the framework of the bending and torsion energy profiles. The results point to the close relationship between the nanowire morphology and the strain relaxation processes that give rise to the occurrence of threading dislocations or other structural defects.

Because of their unique and customizable optical and physical properties, low-dimensional materials have under

gone a revolution regarding their applications in various fields, including electronics, photonics, and biodevices. A key role at the boundary between the microstructure and the physical properties is represented by the strain and the underlying processes. Strain has always been a double-edged sword for heterostructure design. On the one hand, the constraining of certain materials can be combined to form coherent structures. On the other hand, strain engineering provides a means to modify material properties (1). In this regard, X-ray-based methods provide valuable information for a wide range of materials and push 

knowledge in material science forward. So far, X-ray analysis of low-dimensional layers has been limited, mostly because of the difficulty of data interpretation, attributed to their strong three-dimensional local inhomogeneity of the investigated materials (2–4).

This article presents our findings related to one-dimensional silicon nanowires (SiNWs) obtained by metal-assisted chemical etching. Specifically, we are interested in the extended structural defects, such as edge- and screw-threading dislocations in highly dense nanowire arrays. Because threading dislocation density represents a key aspect in the context of physical properties, particular attention should be focused on their formation.

The Need for a New Formalism in Nanostructured Silicon 

Transmission electron microscopy has been one of the most frequently used 

techniques to investigate the strain and related defects in nanowire systems (5), with the disadvantage that the strain state can be altered by the flux of the accelerated electron beam. On the other side, spectroscopic techniques, such as Raman spectroscopy (6,7), photoluminescence (8), and X-ray diffraction (XRD), employing both single nanowires (1,4,9) and nanowire arrays, have been utilized (10–12). A major problem in characterizing strain in nonplanar structures is to resolve in a standard framework the interplay between the different contributions given by structural defects, bending and torsion effects in Raman modes, photoluminescence bands, and XRD peaks. Moreover, taking into account the strong anisotropy of the strain along the 

-direction, it is necessary to develop suitable spectroscopic methods to discriminate along the 

For instance, in the case of nanowires, the defective region cannot be treated 

individually by the free-defect region, because this protocol usually gives misleading results, whether using Raman spectroscopy, photoluminescence, or X-ray rocking curves. The above aspects urged us to find suitable spectroscopic tools that can characterize one-dimensional systems that feature an inherent anisotropy of the strain-related phenomena along the 

Overcoming the Related Issues to the Nanowires

To gain information at different points along the 

-axis, we explored the ability of the X-rays to encode information at different penetration depths via the grazing incidence X-ray diffraction (GI-XRD) technique. This technique allows the tuning of information depth in the range from tens of micrometers for symmetric skew diffraction down to tens of nanometers for strongly asymmetric skew geometries, where the angle of incidence is below the critical angle of total external reflection (13). In this study, GI-XRD (111) reflection was employed, for which the incidence angle of the source (α

) was appropriately adjusted to get a penetration depth of the X-rays comparable with the length of the array, whereas the detector is kept to 2θ

. The schematic representation of the grazing incidence X-ray diffraction is shown in Figure 1.

FIGURE 1: Grazing incidence X-ray diffraction (GI-XRD) scheme for depth dependent investigations of porous silicon. Reproduced with permission of the International Union of Crystallography (16).

We preferred (111) reflection for these kinds of investigations, because of the high structure factor and high inclination angle, χ = 54.7°, which corresponds to the angle between the (111) and (001) direction. It is worthy to note that in addition to (224) or (115) reflections, for which χ = 35.26 and 15.79°, respectively, a high χ angle allowed us to obtain smaller penetration depths and assess the regions close to the surface. The X-ray penetration depths were calculated based on the Beer-Lambert formula, where the pathlength was assumed as the sum of the incident and the scattered path. Further details in the calculation of the X-ray penetration depth can be found in our previous work (14–16), where grazing incidence X-ray diffraction was applied 

for silicon nanowires, and GaN was applied for the heteroepitaxial layers and porous silicon, respectively.

Furthermore, by recording multiple rocking curves at different incidence angles, whose value should be adjusted properly considering the length of the nanowire array, we are able to have a complete description of the strain-related phenomena. For instance, ω-scanning measures the tilt (φ

 of nanowire array, which can be viewed as the bending profile. At the same time, the φ-scan encodes the twist (φ

), which further express the torsion profile.

Bending and Torsion Profiles in Nanowire Arrays

Figures 2a–c show the bending and torsion profiles for two nanowire arrays with lengths of 1.5 μm and 9.5 μm, denoted further as short and long nanowires.

FIGURE 2: (a–c) Bending and torsion profiles for short and long nanowire arrays, (b) bending and torsion energy profiles and a (d) closer inspection of the energy lost.

One can observe that for short nanowires both φtilt and φtwist increase monotonically with decreasing penetration depth. By contrast, for long nanowires, both φ

 present a dip. In our extensive work (14), we showed this dip is related to the coalescence regions from the upper part of the nanowire arrays and indicates the presence of bending and torsion relaxation mechanisms in the long nanowires. Meanwhile, for the short nanowire array, the relaxation processes are absent.

Quantification of the Threading Dislocations in Nanowire Arrays

To gain a quantitative description of the strain relaxation processes in the long nanowires where they are clearly manifesting, the energy lost through these processes was evaluated. The energy lost was evaluated from the bending and torsion energy profiles as shown in Figure 2b. Whereas the short nanowire array does not present strain relaxation processes, in the case of long nanowires, the whole structure observed in the bending and torsion profiles is now mirrored in the energy distribution along the nanowire length. We evaluated the energy losses from the point where the relaxation starts up to where the relaxation dips, which 

can be seen in Figure 2d. The energy accumulated in a nanowire is usually released by the generation of dislocation networks (17). To calculate the dislocation density in a nanowire, we consider that both the bending (ΔE

) energies in the nanowire are practically consumed through the forma- tion of screw and edge threading dislocations. The full derivation of the screw and edge threading dislocation density was presented in (14) and it is not presented here. Finally, it was found that the screw threading dislocation density (3.64 × 107 cm

) is higher than the edge one (8.48 × 106 cm

). These results indicate that the number of dislocations running across the nanowires, generated by tilt relaxation, is higher than the number running along the nanowires, that it is according to theoretical investigations made in the framework of the elastic theory of cylindrical thin rods (17).

We showed the ability of grazing-incidence X-ray diffraction at different incidence angles to give us reliable information about bending and torsion profiles, as well to help improve our understanding of the strain relaxation processes, in highly dense nanowire arrays. Grazing incidence X-ray diffraction investigations were realized on the asymmetric (111) reflection at different incidence angles, allowing us to vary the X-ray penetration depth from tens of nanometers up to 10 micrometers, corresponding to the array’s length.

(1) R.B. Lewis, P. Corfdir, H. Kupers, T.Flissikowski, O.Brandt, and L. Geelhaar, 

(2) A. Davtyan, D. Kriegner, V. Holy, A. AlHassan, R.Lewis, S. McDermott et al., 

(3) A.A. Hassan, W.A. Salehi, R.B. Lewis, T. Anjum, C. Sternemann, L. Geelhaar, and U. Pietsch, 

(4) J. Wallentin, D. Jacobsson, M. Osterhoff, M.T. Borgstrom, and T. Salditt, 

(5) K.L. Kavanagh, I. Saveliev, M. Blumin, G. Swadener, and H.E. Ruda, 

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S. Rubini, F. Martelli, and A.S. Bhatti, 

(8) B. Jenichen, O. Brandt, C. Pfuller, P. Dogan, M. Knelangen, and A. Trampert, 

(9) T. Stankevic, E. Hilner, F. Seiboth, R. Ciechonski, G. Vescovi, O. Kryliouk, U. Johansson, L. Samuelson, G. Wellenreuther, G. Falkenberg, R. Feidenhans’l, R., and A. Mikkelsen, 

(10) H.V. Stanchu, A.V. Kuchuk, V.P. Kladko, 

M.E. Ware, Y.I. Mazur, Z.R. Zytkiewicz, A.E. Belyaev, and G.J. Salamo, 

(11) D. van Treeck, G. Calabrese, J.J.W. Goertz, V.M. Kaganer, O. Brandt, S. Fernandez-Garrido, S, and L. Geelhaar, 

(12) T. Auzelle, X. Biquard, E. Bellet-Amalric, Z. Fang, H. Roussel, A. Cros, and B. Daudin, 

(13) D. Grigoriev, S. Lazarev, P. Schroth, A.A. Minkevich, M. Kohl, T. Slobodskyy, M. Helfrich, D.M. Schaadt, T. Aschenbrenner, D. Hommel, and T. Baumbach, 

(14) C. Romanitan, M. Kusko, M. Popescu, P. Varasteanu, A. Radoi, and C. Pachiu, 

(15) C. Romanitan, I. Mihalache, O. Tutunaru, and C. Pachiu, 

(16) C. Romanitan, P. Varasteanu, D. Culita, A. Bujor, O. Tutunaru, 

(17) V.M. Kaganer, B. Jenichen, and O. Brandt, 

is with the National Institute for Research and Development in Microtechnology in Voluntari, Romania. Direct correspondence to: 

Total Reflection X-ray Fluorescence Spectrometry (TXRF) as a Prospective Method for the Multielement Analysis of Apatite Crystals

Artem S. Maltsev, Alexei V. Ivanov, and Galina V. Pashkova, 

Institute of the Earth’s Crust, Irkutsk, Russian Federation

The elemental analysis of apatite minerals is an important study that is utilized in various applications in geology, biology, medicine, agriculture, technology, and environmental remediation. Two new methods for fast and cost-efficient multielement analysis of apatite samples by total reflection X-ray fluorescence (TXRF) spectrometry have been developed. The first method was used for the apatite samples to solve the “provenance” geological task. The second was developed for analyzing microcrystals of apatite with a size of 50–60 μm, focusing on uranium. Determining the uranium concentration in apatite microcrystals is the prerequisite of the fission-track dating technique. Sample preparation strategies, quantitative analysis procedures, and an accuracy assessment for both TXRF methods have been considered. The advantages of TXRF spectrometry in comparison to other analytical methods are also discussed.

(F, Cl, OH), is among the most common minerals on Earth and is found in many sedimentary, metamorphic, and igneous rocks (1). Besides possessing a high content of calcium and phosphorous, apatite minerals are also rich in the rare earth elements strontium, uranium, and thorium, the quantification of which can be used for provenance analysis (matching sediments to their source igneous and metamorphic rocks) and petrology (deciphering genesis of these rocks) genesis deciphering in geology (2). Uranium in apatite is important because knowledge of its concentration is a prerequisite for fission-track dating (3). Laser-ablation inductively 

coupled plasma–mass spectrometry (LA-ICP–MS) is the most common technique for the elemental analysis of apatite (4). However, that technique is not cost-efficient. Other analytical techniques, such as electron probe X-ray microanalysis (5) and wavelength dispersive X-ray fluorescence spectrometry (6), are either not sensitive enough or require large-mass samples. Therefore, we utilized TXRF spectrometry because it is rapid and cost-effective, and has the capability to analyze a very small mass of material (tens of micrograms) (7).

The TXRF analysis of apatite samples was performed using a S2 Picofox spectrometer (Bruker Nano) equipped with a Moanode X-ray tube (working conditions are 50 kV and 0.75 mA). Measurements were carried out during a 500 s period. The following reagents and materials were used: Concentrated nitric acid (suprapure grade, Merck) and ultrapure deionized water (18.2 MΩ, Elga Labwater) were used for the sample preparation; quartz carriers served as sample holders; and the Durango reference material was used for a trueness assessment. TXRF was applied to the apatite samples from the Slyudyanka metamorphic complex (Siberia), which is famous for its mineralogical versatility (8) and Tanzibei (9) complex (Altai).

Based on previous papers dedicated to TXRF analysis of geological objects, there are two main sample preparation techniques: suspension and acid digestion. The suspension preparation requires a careful milling procedure with a final particle size of less than <10 μm, which is difficult if only a small sample amount is available. The acid digestion procedure is more suitable for analyzing apatite crystals. The sample preparation technique for the bulk-type apatite was optimized as follows: 5–10 mg of the ample was mixed with 100 μL of HNO

 in a closed PTFE vessel and heated at 160 °C for 30 min; the obtained solution was diluted with 800 μL of ultrapure H

O and 100 μL of the internal standard of gallium.

For a micro-sized sample of apatite (crystals with a mass of approximately a few tens of micrograms), we prepared the specimen straight on the carrier because of the inability to reproduce the afore- mentioned sample preparation technique with such a small sample. A single crystal of apatite was carefully placed onto the center of the presiliconized quartz carrier under the microscope. Then, 500 nL of concentrated nitric acid was deposited over the crystal, and the carrier was heated at a gradually increasing temperature (90 °C) to obtain a well-distributed specimen. Ultrapure water of approximately 1 μL was added next to dissolve the calcium salts, and the specimen was dried again. Using Raman spectroscopy allowed us to study the formations of the minerals and their distribution within the crystal. Because of the homogeneous distribution of phosphorous in the specimen, its stoichiometric value was used as an internal standard (equal to 170 ± 10 g/kg) for the quantitative analysis.

The Durango reference material was analyzed to assess the trueness of two TXRF methods. Because the reference material is not certified, ICP-MS analysis was conducted for validation purposes. Table I presents both the TXRF and ICP-MS results and reference values of Durango apatite (10), expressed by the average concentration values with associated standard deviations. Results of both TXRF methods for most elements align with the ICP-MS and reference values. The detection limits of TXRF for bulk samples were in a range between 0.1 and 0.7 mg/kg. For the microcrystals, the detection limits ranged between 1.0 and 20 mg/kg. Some deviations in the results may have been caused by the inhomogeneity of the apatite sample, especially in the case of the microcrystal analysis.

The TXRF method for bulk samples was applied to the apatite minerals from the Slyudyanka complex and the results were processed by principal component analysis (PCA). Figure 1 shows the three main groups of apatite minerals that differ by their genesis. The results of PCA are 

fully consistent with the geochemical data of the samples.

FIGURE 1: PCA results based on the element composition of apatite samples from Slyudyanka. Three apatite groups were obtained by their genesis: The metamorphic group is highlighted in blue; the hydrothermal-metasomatic group is highlighted in green; and P923 is apatite of carbonate-silicate rocks.

The TXRF method for microcrystals of apatite is currently under consideration to apply to determining uranium for the fission track dating. The 16 microcrystals of apatite from the Tanzibei complex were analyzed for uranium by TXRF and LA-ICP-MS. The concentration values of uranium were found between 3.0 and 155 mg/kg. A good convergence of results was obtained, expressed by the correlation coefficient 

 = 0.9932, and the average recovery value 96 ± 13%. Some results obtained by the methods were not agreed upon, which might be caused by the dissimilarity between the two methods. The LA-ICP-MS is a local method and the measurement takes place at an 

ablation pit of ~20–30 μm of the crystal, unlike TXRF, where the whole crystal is under analysis.

Considering the advantages of TXRF compared to the routine methods of apatite analysis, including rapidity, cost-efficiency, and simplicity of performance, the proposed methods may be an excellent tool for apatite-involving applications.

The study was conducted in the frame of the grant of the Ministry of Science and High Education of the Russian Federation (Grant number: 075-15-2019-1883). The research was performed using the equipment of joint-use center Geodynamics 

and Geochronology at the Institute of the Earth’s Crust SB RAS. The authors are grateful to S. Panteeva for the ICP-MS analysis, A. Marfin for the Raman spectroscopy analysis, and L. Reznitskii and Yu. Bishaev for providing the samples.

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Articles

In our annual presentation of trends in X-ray analysis, experts in the field provide updates on recent developments in X-ray analysis, focusing on the technique and relevant applications. 

Recent Developments in X-ray Analysis

Jerome Workman Jr. serves on the Editorial Advisory Board of Spectroscopy and is currently with Unity Scientific LLC. He is also an adjunct professor at Liberty University and U.S. National University. He can be reached at jworkman04@gsb.columbia.edu.

L. Robert Baker is an associate professor at The Ohio State University in the Department of Chemistry & Biochemistry. His research focuses on X-ray spectroscopy, nonlinear and time-resolved spectroscopy, the chemistry of surfaces and interface science, and energy conversion and catalysis—work that may lead to better solar energy conversion materials. He is the winner of the 2021 Emerging Leader in Atomic Spectroscopy Award, which is presented by 

 magazine. This annual award, begun in 2017, recognizes the achievements and aspirations of a talented young atomic spectroscopist, selected by an independent scientific committee.

You are the lead scientist for the new National Science Foundation (NSF) National eXtreme Ultrafast Science (NeXUS) Facility. Would you explain to our readers the purpose of this exciting new facility and what you might hope to discover using its facilities?

This 5-year, $9.5M project is supported by the National Science Foundation through the Mid-scale Infrastructure program. Mid-scale infrastructure refers to facilities that are small enough to adapt quickly to rapidly evolving research needs but bigger than could be supported in a traditional academic laboratory. The NSF recently identified mid-scale infrastructure as one of the 10 big ideas that will shape the future of multidisciplinary basic science.

As the principal investigator (PI) and co-director of NeXUS, I am working with a talented team to build this national NSF user facility on the Ohio State campus. The combination of attosecond pulses, soft X-ray photon energies, and high repetition rate at NeXUS will enable measurements that currently cannot be made anywhere else in the United States. Accordingly, NeXUS is designed to fill a key strategic gap in the U.S. research infrastructure and will make OSU an international focal point serving a broad user community in ultrafast optical science.

In one of your 2020 publications, you used extreme ultraviolet reflection–absorption (XUV-RA) spectroscopy to study the surface electronic structure and carrier kinetics in photocatalytic transition metal oxides (1). Specifically, the dynamics of small polaron formation at hematite surfaces demonstrates that the rate of carrier self-trapping is slower than in the bulk due to a greater lattice reorganization energy required for surface polaron formation. Why is this research important? What were the major analytical hurdles you encountered when developing XUV-RA?

Polaron formation is one of the major obstacles to efficient solar energy conversion using earth-abundant metal oxide catalysts. A polaron can be thought of as a local defect that forms inside a solid in response to the excited electron. It traps the electron so it can only move by a slow, thermally activated hopping process.

In this work we learned two important things: First, we found that the dynamics of polaron formation and hopping are very different at a hematite surface compared to the bulk of the same material. Second, we showed that these dynamics can be controlled in a rational way by functionalizing the surface with tunable molecules. These findings provide a strategy for controlling electron dynamics at a catalyst surface in order to improve solar energy conversion efficiency by these earth-abundant materials.

The biggest challenge associated with this work was the development of XUV reflection–absorption spectroscopy that enables us to visualize electron motion with femtosecond time resolution and surface sensitivity. This is exciting because these findings are just one example of the discoveries that can be made by directly observing electron dynamics at surfaces.

You have also published recent work using in-situ, plasmon-enhanced vibrational sum frequency generation (SFG) spectroscopy to directly observe carbon dioxide electroreduction on gold surfaces (2). Infrared and near-infrared frequencies were used to observe the gold and electrolyte interface during active electrocatalysis, determining that the rate-determining step for this reaction is CO

 adsorption. This type of spectroscopy provides the capability for direct observation of interfacial processes that govern the surface kinetics of electrocatalysis. Would you explain the significance and meaning of this work for the readers of 

Electrocatalysis is an important process for turning electrical energy into useful chemicals. However, the ability to watch how a molecule reacts on an electrode surface is a real challenge. It requires the ability to measure molecular spectra with surface specificity and to do so with high sensitivity even under in situ reaction conditions. In this paper, we described a new iteration on a well-established technique known as 

sum frequency generation vibrational spectroscopy

. We find that using the surface plasmon resonance of a gold electrode, we can couple light to an active electrode/electrolyte interface and measure vibrational spectra at the interface with detection limits less than 1% of a surface monolayer. This is possible even under conditions of active electrocatalysis. We hope that this method will provide a new window into the details of how reaction selectivity can be controlled in electrocatalytic systems.

In other work you have carried out fundamental studies in applying SFG vibrational spectroscopy to investigate strong metal–support interactions (SMSI) in Pt/TiO

 catalysts (3). You and your colleagues were the first to report the use of SFG to study oxide–metal interfaces during catalytic reactions. The results of this study demonstrated that TiO

catalyst occurs by an acid–base mechanism and controls the distribution of furfuraldehyde hydrogenation. How is this work important for laboratories studying catalysis? How do you envision SFG assisting a broader analytical community?

This result showed how acid-base chemistry on oxide surfaces represents the molecular basis for widely studied strong metal-support interactions in heterogenous catalysis. SFG was key to this discovery because it provided the ability to observe the reaction intermediates involved in this process. For this reason, SFG will continue to play an important role for communities that are interested in understanding molecular structure and chemical reactivity at interfaces.

What issues and problems would you define as previously ignored or neglected your field? What major developments do you see as important in new techniques and in energy conversion and catalysis? What is your vision for improvements that could be made in instrumentation for studying these phenomena and the materials involved?

The question of how electrons move at surfaces and control chemical reaction selectivity and energy conversion efficiency is a very important one. I don’t think this question has really been neglected; the challenge is just that it is still very difficult to make direct observation of these processes. This ability is needed to learn more about the material properties that control interfacial electron dynamics. Toward this goal, my research group at Ohio State has constructed an ultrafast XUV light source for the study of electron dynamics at photochemically relevant surfaces and interfaces. Using this instrument, we measure XUV spectra with femtosecond time resolution and surface sensitivity. Because XUV spectroscopy is element-specific, it is possible to track electron dynamics with site-selective resolution by measuring transient oxidation and spin state changes in real-time. We hope that this technique will contribute to a better understanding of electron dynamics so that eventually energy conversion at surfaces can be tuned with the same level of precision as natural photosynthetic systems.

What would you consider to be the most useful contributions of your work?

Although the scientific discoveries mentioned above are important, the most rewarding part of this work is the education and training of students who represent the next generation of the field. They will be faced with many challenges to solve and providing them with the training and experiences they will need to succeed is without question the most important and rewarding aspect of this work.

What are some key aspects that motivate you? Would you share some of your work and organizational habits that have helped you be productive and successful professionally?

It is always exciting to try to see something that has never been seen before. That is what we’re trying to accomplish when we build a new spectrometer or embark on a new experiment. It is the thrill of discovery and the opportunity to work with great students and colleagues that keeps me motivated. I have also benefited from the guidance of excellent mentors and the support of family. I also enjoy taking time off once in a while for a good run.

What can you share with our readers regarding your next area of interest for your research?

There are many exciting directions to go from here. One area we are enthusiastic about is utilizing XUV reflection–absorption spectroscopy to understand magnetization dynamics and spin transport at interfaces. This is a direction that builds naturally from studies of charge transport, but now the focus is placed on the spin rather than the charge of the electron. Developing SFG in the XUV spectral region is another exciting direction. This is a technique that has potential to reveal new details about dynamics at buried interfaces that are hard to probe by traditional surface spectroscopies.

(1) S. Bandaranayake, E. Hruska, S. Londo, S. Biswas, and L.R. Baker, Small Polarons and Surface Defects in Metal Oxide Photocatalysts Studied Using XUV Reflection–Absorption Spectroscopy. 

(2) S. Wallentine, S. Bandaranayake, S. Biswas, and L.R. Baker, Direct Observation of Carbon Dioxide Electroreduction on Gold: Site Blocking by the Stern Layer Controls CO

(3) (7) L.R. Baker, G. Kennedy, M.Van Spronsen, A. Hervier, X. Cai, S. Chen, L.W. Wang, and G.A. Somorjai, Furfuraldehyde hydrogenation on titanium oxide-supported platinum nanoparticles studied by sum frequency generation vibrational spectroscopy: acid–base catalysis explains the molecular origin of strong metal–support interactions, 

L. Robert Baker, the 2021 winner of the Emerging Leader in Atomic Spectroscopy Award, is an Associate Professor of chemistry at The Ohio State University where he leads research on X-ray spectroscopy, nonlinear and time-resolved spectroscopy, the chemistry of surfaces and interface science, and energy conversion and catalysis. Baker earned his BS degree in chemistry and his MS in physical chemistry from Brigham Young University and received his PhD in 2012 in physical chemistry from University of California, Berkeley under Professor Gabor A. Somorjai. Following postdoctoral research at University of California at Berkeley (mentored by Prof. Stephen R. Leone), he then became an assistant professor at The Ohio State University, Department of Chemistry and Biochemistry in 2014. In 2020, he was appointed an Associate Professor of Chemistry. He serves as principal investigator of the NSF National eXtreme Ultrafast Science (NeXUS) facility. His awards include the Camille Dreyfus Teacher-Scholar Award, Air Force Office of Scientific Research Young Investigator Award, Department of Energy Early Career Award, and the Young Innovator Award in NanoEnergy.

L. Robert Baker is an associate professor at The Ohio State University in the Department of Chemistry & Biochemistry. His research focuses on X-ray spectroscopy, nonlinear and time-resolved spectroscopy, the chemistry of surfaces and interface science, and energy conversion and catalysis—work that may lead to better solar energy conversion materials. He is the winner of the 2021 Emerging Leader in Atomic Spectroscopy Award, which is presented by Spectroscopy magazine. This annual award, begun in 2017, recognizes the achievements and aspirations of a talented young atomic spectroscopist, selected by an independent scientific committee.

The 2021 Emerging Leader in Atomic Spectroscopy: Using X-ray Spectroscopy to Understand Surface Science and Energy Conversion

In this study, several strains of yeast cells, both wild-type (WT) and engineered, were analyzed by Raman imaging microscopy. Two biologically active compounds produced within the engineered yeast cells were readily identified by their characteristic Raman spectral peaks. The locales and the relative abundance of those biologically active compounds were visualized from the Raman microscopic images of the individual cells. Raman imaging microscopy further reveals the differences in efficiencies with which the biologically active compounds were produced by different yeast cell strains.

In recent years, bioengineering of yeast cells has made them a “biological cell factory” for producing a range of chemicals, including biologically active compounds through fermentation processes. This “biological cell factory” can efficiently convert starch, sucrose, or lignocellulose-based raw materials into precursor metabolites, which can further be converted into the products of interest (1). Bulk chemical analyses, such as gas chromatography–mass spectrometry (GC–MS) and nuclear magnetic resonance (NMR), can be used for screening fermentation cultures, assessing product yield, and delivering process optimization. These methods, however, are destructive, time-consuming, and insufficient, because they fail to provide specificity at the cellular level (2). Fluorescence microscopy-based imaging is capable of visualizing large or small molecules at the cellular level, but the success of this approach largely hinges on the photostability and brightness of fluorescent labels, their possible perturbations to the biological system, and the methods by which the labels are attached (3). Confocal Raman microscopy-based imaging has emerged as a great alternative (3,4). Raman spectroscopy provides chemical annotations to the constituents within the cell, and the high spatial resolution intrinsic to confocal microscopy provides cellular level information. In contrast to the fluorescence microscopy-based imaging approach, Raman imaging is nondestructive and requires minimal sample preparation.

In this study, wild-type (WT) and several strains of engineered yeast cells were analyzed by Raman imaging. Two biologically active compounds produced within the engineered yeast strains were readily identified. Furthermore, it was revealed that different strains produced the two compounds with different efficiencies. Raman images of the individual cells show the locales as well as the relative abundance of those compounds. The information extracted provides valuable information that helps optimize the fermentation process.

A 2 μL sample of the WT and each engineered yeast cell strain sample was transferred to an aluminum-coated, 12-positions slide, and then it was air dried prior to the Raman imaging experiments. The two biologically active reference compounds are denoted as “compound 1” and “compound 2”, respectively. Compound 1 is a terpene and compound 2 is a sterol. For the two reference compounds in solid powder form, a small amount of each was transferred to a glass slide, and the Raman spectra were collected.

The Raman spectra and the Raman area images (for an area containing one or two cells within each sample) were acquired using a Thermo Scientific DXR3xi Raman Imaging Microscope. The key experimental parameters for Raman imaging are listed below:

Raman images were obtained by the correlation profiling option of the OM-NICxi software, using a representative Raman spectrum from within a cell as the reference. The correlation profile shows the correlation between the spectrum at each pixel (sampling point) in the Raman image and the specified reference spectrum. Each color in the rainbow-colored intensity scale represents a degree of correlation with the reference spectrum. The higher the intensity value, the greater similarity to the reference spectrum.

Figure 1 shows the representative Raman spectra of the two biologically active compounds, compound 1 (pink trace, top) and compound 2 (red trace, bottom). Both compounds have prominent features in the C-H stretching region (~2900 cm

). The two strong peaks attributed to the C=C stretch- ing vibrations are located at 1667 cm

, respectively, for compound 1 and compound 2. The downshift of the C=C stretching peak in compound 2 (1605 cm

) is because of a ring structure. There are no strong interfering Raman peaks from the cellular matrices in this frequency range. Hence, these peaks are chosen as the Raman markers to distinguish the two biologically active compounds in the yeast cell samples by Raman imaging.

FIGURE 1: Raman spectra of the pure biologically active compounds, (a) compound 1, and (b) compound 2. These two compounds can be clearly distinguished by the presence of strong C=C peaks at 1667 cm

Figure 2 shows the Raman image of a WT yeast cell (Figure 2a), the representative Raman spectra from the different locations of the imaged region (Figure 2b), and the representative Raman spectra of compound 1 and compound 2. As can be seen in Figure 2a, the cells (in red) can be spectroscopically differentiated from the surrounding media (in blue), with a clearly delineated contour in green color. As expected, the cells are devoid of either compound 1 or compound 2 (Figure 2c). Raman image of the strain B yeast cells is shown in Figure 3. Both compound 1 and compound 2 are produced in the cell, as evidenced by Figures 3b and 3c. Interestingly, for the same strain B, there are cell-to-cell variations in the level of produced compounds. Based on the relative peak intensities, the cell in the upper- left corner appears to produce more compound 1 than compound 2, whereas the cell in the center produces similar amounts of compound 1 and compound 2.

FIGURE 2: Raman image of a wild-type (WT) yeast cell. (a) Raman image of a selected region for the WT yeast cells. The Raman image was generated by correlation profiling. (b) Raman spectra from the spots S-1 and S-2 on the Raman image. (c) Raman spectra of S-1 and S-2, as well as compounds 1 and 2 displayed in the 1540–1750 cm

 region. The Raman spectra for the cells were normalized with respect to the C-H stretching peaks near 3000 cm

FIGURE 3: Raman image of the strain B yeast cells. (a) Raman image of a selected region for the strain B yeast cells. The Raman image was generated by correlation profiling. (b) Raman spectra from the spots S-1, S-2 and S-3 on the Raman image. (c) Raman spectra of S-1, S-2 and S-3, as well as compounds 1 and 2 displayed in the 1540–1750 cm

 region. The Raman spectra for the cells are normalized with respect to the C-H stretching peaks near 3000 cm

Figure 4 summarizes the Raman imaging analyses of the three different strains of yeast cells—strain A, strain C, and strain D—in terms of their yield in producing compound 1 and compound 2. At a quick glance, strain A appears to be a low producer of compound 1 and does not produce compound 2. Strains C and D, on the other hand, appear to produce both compound 1 and compound 2, but strain D has a higher yield for compound 1 than for compound 2.

FIGURE 4: Raman images of three different strains of yeast cells. (Top row) Raman images from the selected regions for the three different strains: strain A, strain C, and strain D. The Raman images were generated using correlation profiling. (Bottom row) Raman spectra (1540–1750 cm

 region) from the spots on the top corresponding Raman images. The Raman spectra are normalized with respect to the C-H stretching peaks near 3000 cm

Figure 5 summarizes the yield with which compound 1 and compound 2 are produced by the four different engineered strains of yeast cells (strains A–D). Strain A (blue) appears to be a low producer of compound 1 and does not produce compound 2. Strains B, C, and D all produce both compound 1 and compound 2. Strain B displays nonuniform levels of produced compounds from cell-to-cell, some cells (pink) producing higher level of compound 1 than the others (cyan). Strain C (green) seems to produce higher level of compound 2, and strain D (red) has the highest yield for compound 1. Caution should be exercised when assessing the product yields from the Raman peak intensity. A more rigorous, quantitative analysis requires the respective Raman cross sections for the compounds 1 and 2 be properly accounted for.

FIGURE 5: Raman spectra of the strains A–D of the yeast cells. (a) Raman spectra (1540–1750 cm

 region) from the cells of the strains A–D, shown in the offset mode, and (b) in the overlayed mode. The Raman spectra are normalized with respect to the C-H stretching peaks near 3000 cm

In this study, Raman imaging was successfully applied to analyze different strains of yeast cells, both WT and engineered. The target bioactive compounds produced by the engineered yeast cells can be readily identified and differentiated by their respective Raman spectra, allowing for assessment of the product yields of different yeast strains by Raman imaging. Raman images of the individual cells from the four engineered yeast cell strains indicate that these strains produce compounds 1 and 2 with different efficiencies. Raman imaging can also be used to estimate the relative yield of the bioactive compounds. A quantitative analysis of product yield, however, requires a more systematic study on the Raman cross-sections of those compounds. The information obtained from the Raman imaging will be beneficial for a deeper understanding of the fermentation process and for optimization of the process for higher product yield.

The authors would like to acknowledge Dr. Michael Leavell and his colleagues at Amyris for providing the samples and many insightful discussions.

(1) Y. Chen, L. Daviet, M. Schalk, V. Siewers, and J. Nielsen, 

(2) R. Miyaoka, M. Hosokawa, M. Ando, T. Mori, H.O. Hamaguchi, and H. Takeyama, 

(4) L. Mikoliunaite, R.D. Rodriguez, E. Sheremet, V. Kolchuzhin, J. Mehner, A. Ramanavicius, and D.R.T. Zahn, 

 are with Thermo Fisher Scientific in Madison, Wisconsin. 

 was formerly a scientist with Amyris in Emeryville, California, when this work was being conducted. Direct correspondence to: 

Using Raman imaging, wild-type and engineered yeast cells were compared for their ability to produce bioactive compounds. Raman imaging microscopy is able to visualize locales, relative abundance, and production efficiencies of biologically active compounds for the individual yeast cells.

Tracking Bioactive Compounds Produced by Genetically Engineered Yeast Cells Using Raman Imaging

Surface enhanced Raman spectroscopy (SERS), although popular in research, has not yet been adopted in industrial applications. This is largely because of the challenge of manufacturing cost-effective, reproducible SERS substrates in volume that can be read by a compact reader for practical deployment in the field. Here we demonstrate and characterize a new dual-nanostructured SERS substrate approach that could be amenable to large-scale production. This dual-nanostructured surface combines long-range ordering of silver nanowires (AgNWs) with dense non-ordered decoration via silver nanoparticles (AgNPs) to improve on the SERS response characteristics of the individual structures, using fabrication methods that could be scaled for deposition via roll-to-roll Mayer rod coating. Characterization of the substrates’ SERS response using 638 nm Raman spectroscopy and ATP as the reporter molecule found the dual-nanostructured SERS substrate to offer both greater spatial uniformity and a consistently higher SERS enhancement factor (EF) than the nanowires alone.

Surface-enhanced Raman spectroscopy (SERS) is a technique whereby the Raman effect is enhanced by a factor of 10

 or more through interaction of the analyte with noble metal nanostructures (1). This increases the limit of detection of Raman significantly for both quantitative and qualitative applications, and reduces performance demands on the associated instrumentation to allow greater deployment in the field. In SERS, the Raman signal enhancement originates from a combination of electromagnetic (EM) field enhancement because of the interaction of light with the nanostructures and the chemical enhancement (CE) that occurs when the analyte is chemisorbed on a SERS-active surface.

SERS can amplify Raman signals, but to make the technique practical for industrial use, large quantities of substrate are needed. The approach described here could enable cost-effective, reproducible manufacturing of SERS substrates at large scale.

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