Subject Number :S-17-MS-2017
Support Type :Common use (including technical support necessary for the training),
Proposal Title (English) :Chemical Mapping Individual Atmospheric Nanoparticles
Username (English) : X. Kong1*, M. Huttula1, J. J. Lin1, P. Corral Arroyo2, T. Ohigashi3, N. Kosugi3,4, Z. Wu5 and N. L. Prisle1
Affiliation (English) : 1Nano and Molecular Systems Research Unit, University of Oulu, Oulu 90041, Finland
2Laboratory of Environmental Chemistry, Paul Scherrer Institute, CH-5232 Villigen PSI, Switzerland
3UVSOR Synchrotron, Institute for Molecular Science, Okazaki 444-8585, Japan
4School of Physical Sciences, The Graduate University for Advanced Studies, Okazaki 444-8585, Japan
5State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, 100871, China
*now at Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden

During the STXM beamtime, chemical maps were drawn for two laboratory-generated aerosol samples and one ambient urban aerosol sample.
The laboratory-generated aerosols were produced by the atomization of solutions of sodium n-decanoate and sodium chloride. Particles of 150, 200, and 300 nm were generated from solutions with 0.5 and 0.8 mass fraction of sodium n-decanoate and particles of 30, 50, 80 nm, and polydisperse samples were generated from solutions of 0.25 mass fraction of sodium n-decanoate.
The urban aerosol samples were collected from October 2017 to January 2018 in a winter campaign jointly operated by Peking University and the University of Gothenburg. Sampling was carried out at the Peking University Atmosphere Environment Monitoring Station on the roof of a six-floor building on the campus of Peking University located in the northwestern urban area of Beijing. A SKC 4-stage cascade impactor was used to collect particles with cut-off sizes of 250 and 500 nm that were impacted onto Formvar films.

Fig. 1. Optical density at various photon energies. The bright areas indicate where the excited functional groups are located. (a) soot; (b) NH4+; (c) NO3-.

The urban aerosol samples were examined at the adsorption edges of carbon, nitrogen, sulfur, silicon, chlorine, and potassium. Figure 1 shows the optical density of a particle at the carbon and nitrogen edges. At 284.5 eV, the C=C double bond is shown as the bright area [1], which indicates two soot components located at the two poles of the particle. The spatial resolution of the image is 30 nm. The detailed carbon edge spectrum indicates that the soot particles are well aged. In addition, the potassium L-edge indicates that K+ ions are present and located in the area between the soot particles. The absorption maxima of the ammonium functional group are around 401 eV, and according to the panel (b) the NH4+ mainly stays between the soot particles, like K+. From panel (c) it seems that the NO3- is spread all over the particle. Further quantitative analysis will be performed to calculate the mixing ratio.

Fig. 2. (a) carbon K-edge adsorption spectra of sampled urban particles; (b) STXM spectra of laboratory-generated soot coated by α-pinene (red) and naphthalene SOA (black) [2]; (c) STXM image of the particles at 286.6 eV.

The carbon edge adsorption spectrum contains rich information about the functional groups and the oxidation states. Figure 2 shows (a) the carbon spectra of aged soot particles at the carbon edge from 280 eV to 320 eV, where the spectra of different colors are taken from different places of the particle, but they are very consistent except for the amplitudes that is correlated to the particle thickness of the examined regions. Panel (b) shows the spectra of laboratory-generated soot coated by α-pinene (in red) and naphthalene SOA (in black) from literature [2], where the featured peaks excellently match the sampled particle shown in panel (a). The STXM method at BL4U seems to be perfectly suited for the study of both ambient and laboratory nanoparticles.

[1] R. C. Moffet, A.V. Tivanski, and M.K. Gilles, Fundamentals and Applications in Aerosol Spectroscopy (CRC Press; Boca Raton, Fl, 2011).
[2] Charnawskas, J.C., et al., Faraday Discussions, 200 (2017) 165-194.

Subject Number :S-17-MS-2017-2
Support Type :Common use (including technical support necessary for the training),
Proposal Title (English) : Novel insights to cloud water microphysics using synchrotron-excited XAS
Username (English) : J. J. Lin1, G. Michailoudi1, H. Yuzawa2, H. Iwayama2,3, M. Nagasaka2,3, M. Huttula1, N. Kosugi2,3 and N. L. Prisle1
Affiliation (English) : 1Nano and Molecular Systems Research Unit, University of Oulu, Oulu 90041, Finland
2UVSOR Synchrotron, Institute for Molecular Science, Okazaki 444-8585, Japan
3School of Physical Sciences, The Graduate University for Advanced Studies (SOKENDAI), Okazaki 444-8585, Japan

Cloud droplets in the atmosphere are complex aqueous mixtures of organic and inorganic constituents, but their exact composition and the nature of aqueous phase molecular interactions are extremely challenging to quantify [1]. Amphiphilic compounds present in the organic aerosol fraction are known to self assemble into micelles, but direct chemical information on the aqueous phase interactions of amphiphilic monomers and their aggregate structures has been lacking.
Two types of samples were analyzed with synchrotron radiation X-ray absorption spectroscopy using the liquid cell setup at BL3U [2]: 1) cloud water collected from October 10-14 at the Finnish Meteorological Institute Pallas-Sodankylä Global Atmosphere Watch station in the sub-Arctic region in Finnish Lapland as part of the 7th Pallas Cloud Experiment from September 1 to November 30; and 2) organic micellar systems composed of n-octanoic acid, n-decanoic acid, and sodium n-decanoate dissolved in water at varying multiples of their critical micelle concentration (CMC). A summary of the samples measured is given in Table 1.

Table 1. Summary of samples measured
Sample CMC [mM] Concentrations Number of scans
Cloud Water – – 5
n-octanoic acid 140.
[3] 0.75, 1.25, 2, 3, 4, 6, and 8 times CMC; pure 15
Sodium n-octanoate 340.0 [4] 0.75, 2, 3, 4, and 5 times CMC 7
Sodium n-decanoate 94.0
[4] 2, 3, 4, 6, and 8 times CMC 10
For the cloud water samples, both the oxygen K-edge in the 528-550 eV range and the carbon K-edge in the 280-300 eV range were studied. Unfortunately, due to the apparently very low concentration of organic material in the cloud water, presumably from very clean Arctic conditions, any difference between cloud water spectra and pure water spectra could not be detected. Another batch of the cloud water sample will be subject to various complementary analyses to confirm this.
For the aqueous organic micellar solutions, the carbon K-edge in the 280-300 eV range was studied. Photon energies in the 280-285 and 290-300 eV range were scanned at 0.1 eV resolution while photon energies in the 285-290 eV range were scanned at 0.02 eV resolution in order to specifically capture the aliphatic R(CH)R’ and carboxylic C(=O)OH carbon absorption peaks from monomers to identify shifts in peak location due to the difference in chemical environment between monomers and micelles in solution.
Fig. 1. Preliminary absorption spectra for sodium n-octanoate solutions at various concentrations together with that of pure octanoic acid.

Preliminary absorption spectra at the C 1s absorption edge for sodium n-octanoate in water at various concentrations along with pure octanoic acid are shown in Fig. 1. Background absorption due to water and the cell membranes have been removed. For all the samples, one main peak is observed at around 289 eV. For sodium n-octanoate solution below the CMC, the absorption spectra before and after the main feature are less than those solutions above the CMC. Interestingly, the higher the sodium n-octanoate concentration in solution, the more the absorption spectra start to resemble that of pure octanoic acid. The measurements hold promise for detecting the aqueous-phase interactions of organic monomer and micellar structures compounds in solution.

[1] T. Kurtén et al., J. Phys. Chem. A, 119 (2014) 4509-4514.
[2] M. Nagasaka et al., J. Electron Spectrosc., 177 (2010) 130-134.
[3] A. N. Campbell and G. R. Lakshminarayanan, Can. J. Chem., 43 (1965) 1729-1737.
[4] K. Quast, Miner. Eng., 19 (2010) 582-597.

Subject Number :S-17-MS-2017-3
Support Type :Common use (including technical support necessary for the training),
Proposal Title (English) : STXM Studies of Abnormal Ultrastructural Features in FINCA disease
Username (English) : E.-V. Immonen1, 2, T. Ohigashi3, I. Miinalainen4, M. Patanen1, R. Hinttala 2,4, J. Uusimaa2,4, N. Kosugi3, M. Huttula1
Affiliation (English) : 1Nano and Molecular Systems research unit, P.O. Box 3000, 90014 University of Oulu, Finland
2PEDEGO Research Unit and Medical Research Center Oulu, University of Oulu and Oulu University Hospital, PO Box 5000, 90014 University of Oulu, Finland
3UVSOR Facility, Institute for Molecular Science, Okazaki 444-8585, Japan
4 Biocenter Oulu, PO Box 5000, 90014 University of Oulu, Finland

In this work, we have used scanning transmission X-ray imaging technique at BL4U beamline to study human tissue samples exhibiting abnormal ultrastructural features related to FINCA disease. Recently, a novel, fatal cerebropulmonary disease in children was characterized with fibrosis, neuro- degeneration and cerebral angiomatosis (FINCA disease) [1]. The disease course is progressive with multiorgan manifestations, leading to death before the age of two years. Studies on autopsy samples revealed interstitial fibrosis and previously undescribed granuloma-like lesions in the lungs, and hepatomegaly related to widespread microvacuolar hepatocyte fatty degeneration and hepatocellular necrosis. All patients had a combination of mutations in the NHLRC2 gene encoding an NHL repeat containing protein 2 that is ubiquitously present in various types of tissues, from animals to bacteria. Still, no published results exist on the functional role of the NHLRC2.
In order to learn more about the pathomechanism behind FINCA disease, multidisciplinary approaches are called for. These, among others, include morphological and chemical analysis of the subcellular features in the liver autopsy samples taken from the FINCA patients. However, conventional imaging techniques, such as electron microscopy, possess severe pitfalls when it comes to chemical imaging, requiring the utilization of the STXM method. Thus, we have performed the first proof-of-principle STXM studies on subcellular structural characteristics related to storage diseases.
The samples (approximately 150-200 nm thick) were prepared at Biocenter Oulu, Finland using standard methods in transmission electron microscopy (TEM), and placed on Butvar film coated copper grids. In addition, sections adjacent to ones selected for STXM imaging were imaged using TEM, allowing the comparison of these methods as shown in Fig. 1.
The measurements concentrated on C 1s edge, and we have observed several abnormal ultrastructural features in various tissues of a FINCA patient. Figure 1 shows an example of a liver sample, where these abnormal features include large vacuoles of unknown origin (marked as U in Fig. 1). According to carbon K-edge absorption profile, these vacuoles have a chemical signature that differs from other organelles (Fig. 1C). Further studies will focus on verifying the origin and identifying the accumulated compounds. This can be useful in finding novel biomarkers in future for example storage diseases.

Fig. 1. A) A TEM image showing organelles (lipid vesicles L, their membrane MR, mitochondria M, and an unknown structure U, within a hepatocyte. B) An STXM image of the same region with hν = 287 eV. Subcellular features used for collecting region specific absorption spectra are marked with colors. C) Absorption spectra from regions in B. The arrows indicate the most prominent spectral differences. D) STXM image stack from an energy range 285-286 eV (1st arrow in C). E) Energy range 286-288 eV (2nd arrow in C). F) 288-297 (3rd arrow in C).

[1] J. Uusimaa, R. Kaarteenaho, T. Paakkola, H.Tuominen, M.K. Karjalainen, J. Nadaf, T. Varilo, M. Uusi-Mäkelä, M. Suo-Palosaari, I. Pietilä, A.E. Hiltunen, L. Ruddock, H. Alanen, E. Biterova, I.Miinalainen, A.Salminen, R. Soininen, A. Manninen, R. Sormunen, M.Kaakinen, R. Vuolteenaho, R. Herva, P. Vieira, T. Dunder, H. Kokkonen, J. S. Moilanen, H. Rantala, L. M. Nogee, J. Majewski, M. Rämet, M. Hallman, R. Hinttala, Acta Neuropathol. (2018). In press. https://doi.org/10.1007/s00401-018-1