A Miniaturized Method for Evaluating the Dynamic Gas-Phase Adsorption and Degradation of Sarin on Porous Adsorbents at Different Humidity Levels.

Metal organic frameworks based on zirconium nodes (Zr-MOFs) have impressive adsorption capacities, and many can rapidly hydrolyze toxic organophosphorus nerve agents. They could thus potentially replace commonly used adsorbents in respiratory filters. However, current test methodologies are poorly adapted to screen the large number of available MOFs, and data for nerve agent adsorption by MOFs are scarce. This paper presents a miniaturized method for assessing the capacity of Zr-MOFs for dynamic gas phase adsorption and degradation of sarin (GB) into the primary hydrolysis product isopropyl methyl phosphonic acid (IMPA). The method was validated by comparing the dynamic adsorption capacities of activated carbon (AC) and NU-1000 for GB under dry and humid conditions. Under dry conditions, unimpregnated AC had a greater capacity for GB uptake (0.68 ± 0.06 g/g) than pelletized NU-1000 (0.36 ± 0.03 g/g). At 55% relative humidity (RH), the capacity of AC was largely unchanged (0.72 ± 0.10 g/g) but that of NU-1000 increased slightly, to 0.46 ± 0.10 g/g. However, NU-1000 exhibited poor water retention at 55% RH. For both adsorbents, the degree of hydrolysis of GB into IMPA was significantly greater at 55% RH than under dry conditions, but the overall degree of hydrolysis was limited in both cases. Further tests at higher relative humidities are needed to fully evaluate the ability of NU-1000 to degrade GB after adsorption from the gas phase. The proposed experimental setup uses very small amounts of both adsorbent material (20 mg) and toxic agent, making it ideal for assessing new MOFs. However, future methodological challenges are reliable generation of sarin at higher RH and exploring sensitive methods to monitor degradation products from nerve agents in real-time.

Route Determination of Sulfur Mustard Using Nontargeted Chemical Attribution Signature Screening.

Route determination of sulfur mustard was accomplished through comprehensive nontargeted screening of chemical attribution signatures. Sulfur mustard samples prepared via 11 different synthetic routes were analyzed using gas chromatography/high-resolution mass spectrometry. A large number of compounds were detected, and multivariate data analysis of the mass spectrometric results enabled the discovery of route-specific signature profiles. The performance of two supervised machine learning algorithms for retrospective synthetic route attribution, orthogonal partial least squares discriminant analysis (OPLS-DA) and random forest (RF), were compared using external test sets. Complete classification accuracy was achieved for test set samples (2/2 and 9/9) by using classification models to resolve the one-step routes starting from ethylene and the thiodiglycol chlorination methods used in the two-step routes. Retrospective determination of initial thiodiglycol synthesis methods in sulfur mustard samples, following chlorination, was more difficult. Nevertheless, the large number of markers detected using the nontargeted methodology enabled correct assignment of 5/9 test set samples using OPLS-DA and 8/9 using RF. RF was also used to construct an 11-class model with a total classification accuracy of 10/11. The developed methods were further evaluated by classifying sulfur mustard spiked into soil and textile matrix samples. Due to matrix effects and the low spiking level (0.05% w/w), route determination was more challenging in these cases. Nevertheless, acceptable classification performance was achieved during external test set validation: chlorination methods were correctly classified for 12/18 and 11/15 in spiked soil and textile samples, respectively.

On the use of spectra from portable Raman and ATR-IR instruments in synthesis route attribution of a chemical warfare agent by multivariate modeling.

Collecting data under field conditions for forensic investigations of chemical warfare agents calls for the use of portable instruments. In this study, a set of aged, crude preparations of sulfur mustard were characterized spectroscopically without any sample preparation using handheld Raman and portable IR instruments. The spectral data was used to construct Random Forest multivariate models for the attribution of test set samples to the synthetic method used for their production. Colored and fluorescent samples were included in the study, which made Raman spectroscopy challenging although fluorescence was diminished by using an excitation wavelength of 1064 nm. The predictive power of models constructed with IR or Raman data alone, as well as with combined data was investigated. Both techniques gave useful data for attribution. Model performance was enhanced when Raman and IR spectra were combined, allowing correct classification of 19/23 (83%) of test set spectra. The results demonstrate that data obtained with spectroscopy instruments amenable for field deployment can be useful in forensic studies of chemical warfare agents.

Comparing results of X-ray diffraction, µ-Raman spectroscopy and neutron diffraction when identifying chemical phases in seized nuclear material, during a comparative nuclear forensics exercise.

This work presents the results for identification of chemical phases obtained by several laboratories as a part of an international nuclear forensic round-robin exercise. In this work powder X-ray diffraction (p-XRD) is regarded as the reference technique. Neutron diffraction produced a superior high-angle diffraction pattern relative to p-XRD. Requiring only small amounts of sample, µ-Raman spectroscopy was used for the first time in this context as a potentially complementary technique to p-XRD. The chemical phases were identified as pure UO2 in two materials, and as a mixture of UO2, U3O8 and an intermediate species U3O7 in the third material.

Noise Removal with Maintained Spatial Resolution in Raman Images of Cells Exposed to Submicron Polystyrene Particles.

The biodistribution of 300 nm polystyrene particles in A549 lung epithelial cells has been studied with confocal Raman spectroscopy. This is a label-free method in which particles and cells can be imaged without using dyes or fluorescent labels. The main drawback with Raman imaging is the comparatively low spatial resolution, which is aggravated in heterogeneous systems such as biological samples, which in addition often require long measurement times because of their weak Raman signal. Long measurement times may however induce laser-induced damage. In this study we use a super-resolution algorithm with Tikhonov regularization, intended to improve the image quality without demanding an increased number of collected pixels. Images of cells exposed to polystyrene particles have been acquired with two different step lengths, i.e., the distance between pixels, and compared to each other and to corresponding images treated with the super-resolution algorithm. It is shown that the resolution after application of super-resolution algorithms is not significantly improved compared to the theoretical limit for optical microscopy. However, to reduce noise and artefacts in the hyperspectral Raman images while maintaining the spatial resolution, we show that it is advantageous to use short mapping step lengths and super-resolution algorithms with appropriate regularization. The proposed methodology should be generally applicable for Raman imaging of biological samples and other photo-sensitive samples.

Large uptake of titania and iron oxide nanoparticles in the nucleus of lung epithelial cells as measured by Raman imaging and multivariate classification.

It is a challenging task to characterize the biodistribution of nanoparticles in cells and tissue on a subcellular level. Conventional methods to study the interaction of nanoparticles with living cells rely on labeling techniques that either selectively stain the particles or selectively tag them with tracer molecules. In this work, Raman imaging, a label-free technique that requires no extensive sample preparation, was combined with multivariate classification to quantify the spatial distribution of oxide nanoparticles inside living lung epithelial cells (A549). Cells were exposed to TiO2 (titania) and/or α-FeO(OH) (goethite) nanoparticles at various incubation times (4 or 48 h). Using multivariate classification of hyperspectral Raman data with partial least-squares discriminant analysis, we show that a surprisingly large fraction of spectra, classified as belonging to the cell nucleus, show Raman bands associated with nanoparticles. Up to 40% of spectra from the cell nucleus show Raman bands associated with nanoparticles. Complementary transmission electron microscopy data for thin cell sections qualitatively support the conclusions.

Visualization of custom-tailored iron oxide nanoparticles chemistry, uptake, and toxicity.

Nanoparticles of iron oxide generated by wearing of vehicles have been modelled with a tailored solution of size-uniform engineered magnetite particles produced by the Bradley reaction, a solvothermal metal-organic approach rendering hydrophilic particles. The latter does not bear any pronounced surface charge in analogy with that originating from anthropogenic sources in the environment. Physicochemical properties of the nanoparticles were thoroughly characterized by a wide range of methods, including XPD, TEM, SEM, DLS and spectroscopic techniques. The magnetite nanoparticles were found to be sensitive for transformation into maghemite under ambient conditions. This process was clearly revealed by Raman spectroscopy for high surface energy magnetite particles containing minor impurities of the hydromaghemite phase and was followed by quantitative measurements with EXAFS spectroscopy. In order to assess the toxicological effects of the produced nanoparticles in humans, with and without surface modification with ATP (a model of bio-corona formed in alveolar liquid), a pathway of potential uptake and clearance was modelled with a sequence of in vitro studies using A549 lung epithelial cells, lymphocyte 221-B cells, and 293T embryonal kidney cells, respectively. Raman microscopy unambiguously showed that magnetite nanoparticles are internalized within the A549 cells after 24 h co-incubation, and that the ATP ligand is retained on the nanoparticles throughout the uptake process. The toxicity of the nanoparticles was estimated using confocal fluorescence microscopy and indicated no principal difference for unmodified and modified particles, but revealed considerably different biochemical responses. The IL-8 cytokine response was found to be significantly lower for the magnetite nanoparticles compared to TiO(2), while an enhancement of ROS was observed, which was further increased for the ATP-modified nanoparticles, implicating involvement of the ATP signalling pathway in the epithelium.

Polymorph- and size-dependent uptake and toxicity of TiO2 nanoparticles in living lung epithelial cells.

The cellular uptake and distribution of five types of well-characterized anatase and rutile TiO(2) nanoparticles (NPs) in A549 lung epithelial cells is reported. Static light scattering (SLS), in-vitro Raman microspectroscopy (µ-Raman) and transmission electron spectroscopy (TEM) reveal an intimate correlation between the intrinsic physicochemical properties of the NPs, particle agglomeration, and cellular NP uptake. It is shown that µ-Raman facilitates chemical-, polymorph-, and size-specific discrimination of endosomal-particle cell uptake and the retention of particles in the vicinity of organelles, including the cell nucleus, which quantitatively correlates with TEM and SLS data. Depth-profiling µ-Raman coupled with hyperspectral data analysis confirms the location of the NPs in the cells and shows that the NPs induce modifications of the biological matrix. NP uptake is found to be kinetically activated and strongly dependent on the hard agglomeration size-not the primary particle size-which quantitatively agrees with the measured intracellular oxidative stress. Pro-inflammatory responses are also found to be sensitive to primary particle size.