Quality comparison of plastic packaging waste from different separation systems: Result enhancement with non-negative matrix factorization of FTIR spectra.

Whether plastic packaging waste is disposed of in different bins (source separation, S) or in a single bin (post source separation, P) is generally assumed to impact the waste stream's quality. To elucidate this question, we evaluated the quality of LDPE, HDPE, and PP plastic waste from both separation systems (S and P) through a concise analytical strategy. The materials received similar treatment after collection (e.g., washing, NIR-sorting). A multivariate approach to ATR-FTIR spectroscopy was developed to assess their material composition and the effect of washing. Results were complemented by TGA, DSC, and py-GC/MS analysis. The material performance was investigated by a lab-scale extrusion and granulation, followed by an assessment of the mechanical properties and the melt volume rate. Our study reveals the HDPE materials to be of good quality, regardless of their source. For LDPE and PP, the P-materials are fractionally more contaminated after washing. Both PP-materials display poor material performance with highly fluctuating elongations-at-break (between 30% and 380%). S-LDPE was found to contain more polymeric impurities than P-LDPE. We conclude that the quality depends strongly on the material type and on the treatment after collection (washing, sorting). The multivariate approach to FTIR data evaluation we propose aims at simplifying the quality evaluation of polyolefin waste plastics and may serve as a basis for future work in this field.

Reconstructing the exit wave of 2D materials in high-resolution transmission electron microscopy using machine learning.

Reconstruction of the exit wave function is an important route to interpreting high-resolution transmission electron microscopy (HRTEM) images. Here we demonstrate that convolutional neural networks can be used to reconstruct the exit wave from a short focal series of HRTEM images, with a fidelity comparable to conventional exit wave reconstruction. We use a fully convolutional neural network based on the U-Net architecture, and demonstrate that we can train it on simulated exit waves and simulated HRTEM images of graphene-supported molybdenum disulphide (an industrial desulfurization catalyst). We then apply the trained network to analyse experimentally obtained images from similar samples, and obtain exit waves that clearly show the atomically resolved structure of both the MoS2 nanoparticles and the graphene support. We also show that it is possible to successfully train the neural networks to reconstruct exit waves for 3400 different two-dimensional materials taken from the Computational 2D Materials Database of known and proposed two-dimensional materials.

Chemometrics for Raman Spectroscopy Harmonization.

Raman spectroscopy is used in a wide variety of fields, and in a plethora of different configurations. Raman spectra of simple analytes can often be analyzed using univariate approaches and interpreted in a straightforward manner. For more complex spetral data such as time series or line profiles (1D), Raman maps (2D), or even volumes (3D), multivariate data analysis (MVDA) becomes a requirement. Even though there are some existing standards for creation, implementation, and validation of methods and models employed in industry and academics, further research and development in the field must contribute to their improvement. This review will cover, in broad terms, existing techniques as well as new developments for MVDA for Raman spectroscopic data, and in particular the use associated with instrumentation and data calibration. Chemometric models are often generated via fusion of analytical data from different sources, which enhances model discrimination and prediction abilities as compared to models derived from a single data source. For Raman spectroscopy, raw or unprocessed data is rarely ever used. Instead, spectra are usually corrected and manipulated,1 often by case-specific rather than universal methods. Calibration models can be used to characterize qualitatively and/or quantitatively samples measured with the same instrumentation that was used to create the model. However, regular validation is required to ensure that aging or incorrect maintenance of the instrument does not alter the model's predictions, particularly when applied in regulated fields such as pharmaceuticals. Furthermore, a model transfer may be required for different reasons, such as replacement or significant repair of the instrumentation. Modeling can also be used to consistently harmonize Raman spectroscopic data across several instrumental designs, accounting for variations in the resulting spectrum induced by different components. Data for Raman harmonization models should be processed in a protocolled manner, and the original data accessible to allow for model reconstruction or transfer when new data is added. Important processing steps will be the calibration of the spectral axes and instrument dependent effects, such as spectral resolution. In addition, data fusion and model transfer are essential for allowing new instrumentation to build on existing models to harmonize their own data. Ideally, an open access database would be created and maintained, for the purpose of allowing for continued harmonization of new Raman instruments using an outlined and accepted protocol.

Probing atom dynamics of excited Co-Mo-S nanocrystals in 3D.

Advances in electron microscopy have enabled visualizations of the three-dimensional (3D) atom arrangements in nano-scale objects. The observations are, however, prone to electron-beam-induced object alterations, so tracking of single atoms in space and time becomes key to unravel inherent structures and properties. Here, we introduce an analytical approach to quantitatively account for atom dynamics in 3D atomic-resolution imaging. The approach is showcased for a Co-Mo-S nanocrystal by analysis of time-resolved in-line holograms achieving ~1.5 Å resolution in 3D. The analysis reveals a decay of phase image contrast towards the nanocrystal edges and meta-stable edge motifs with crystallographic dependence. These findings are explained by beam-stimulated vibrations that exceed Debye-Waller factors and cause chemical transformations at catalytically relevant edges. This ability to simultaneously probe atom vibrations and displacements enables a recovery of the pristine Co-Mo-S structure and establishes, in turn, a foundation to understand heterogeneous chemical functionality of nanostructures, surfaces and molecules.

Analysis of the molecular heterogeneity of poly(lactic acid)/poly(butylene succinate-co-adipate) blends by hyphenating size exclusion chromatography with nuclear magnetic resonance and infrared spectroscopy.

The compositional and stereochemical heterogeneity of copolymers are key molecular metrics, and their knowledge is of pivotal importance for evidence based material development. Yet, while it is state of the art to determine these parameters for many petroleum based polymers, little insight exists in that regard for bio-based materials. Towards this end, size exclusion chromatography (SEC) was hyphenated with nuclear magnetic resonance spectroscopy (NMR) in an offline manner and a blend of poly(lactic acid) (PLA) and poly(butylene succinate-co-adipate) (PBSA) investigated. Thus, the microstructural heterogeneity could be shown with regard to tacticity of the PLA and regioregularity of the PBSA component. The results show, that the highest molar mass fraction differs in stereochemical composition from the others. It may be assumed that this is the result of misinsertions with regard to stereochemistry occurring during the catalytic polymerization of the lactide. While the content of both constituent polymers along the molar mass axis could be well studied using a univariate analysis of the infrared (IR) spectra, this method failed to profile the adipate and succinate content individually. For this purpose, SEC was coupled to IR spectroscopy in online mode and the spectra were evaluated by a multivariate protocol. Thus, the content of each monomer along the molar mass distribution could be mapped with high chromatographic resolution.

New zeolite-like RUB-5 and its related hydrous layer silicate RUB-6 structurally characterized by electron microscopy.

This study made use of a recently developed combination of advanced methods to reveal the atomic structure of a disordered nanocrystalline zeolite using exit wave reconstruction, automated diffraction tomography, disorder modelling and diffraction pattern simulation. By applying these methods, it was possible to determine the so far unknown structures of the hydrous layer silicate RUB-6 and the related zeolite-like material RUB-5. The structures of RUB-5 and RUB-6 contain the same dense layer-like building units (LLBUs). In the case of RUB-5, these building units are interconnected via additional SiO4/2 tetrahedra, giving rise to a framework structure with a 2D pore system consisting of intersecting 8-ring channels. In contrast, RUB-6 contains these LLBUs as separate silicate layers terminated by silanol/sil-oxy groups. Both RUB-6 and RUB-5 show stacking disorder with intergrowths of different polymorphs. The unique structure of RUB-6, together with the possibility for an interlayer expansion reaction to form RUB-5, make it a promising candidate for interlayer expansion with various metal sources to include catalytically active reaction centres.

Accurate determination of the layer thickness of a multilayer polymer film by non-invasive multivariate confocal Raman microscopy.

Plastic films of multiple layers are widely used in packaging. While a detailed spatially resolving analysis is highly relevant, the currently used approaches are either tedious or lack spatial resolution. An advanced method of confocal Raman microscopy was therefore developed in this work, using a water immersion objective combined with multivariate data analysis and ray tracing models for analysing a commercial-grade five-layer plastic film. The water immersion objective and ray tracing models are able to reduce the refraction induced artefacts in depth profiling and ensure a correct and accurate interpretation of each layer thickness. The multivariate data analysis can distinguish each layer without prior knowledge of the sample and unique markers of each composition. Finally, the resulting thicknesses of the film layers could be successfully compared to complementary experimental results. In conclusion, each layer thickness of a multilayer polymer film and its chemical composition could be measured accurately and non-invasively without sample preparation.

Controlling the Morphology of Au-Pd Heterodimer Nanoparticles by Surface Ligands.

Controlling the morphology of noble-metal nanoparticles is mandatory to tune specific properties such as catalytic and optical behavior. Heterodimers consisting of two noble metals have been synthesized, so far mostly in aqueous media using selective surfactants or chemical etching strategies. We report a facile synthesis for Au@Pd and Pd@Au heterodimer nanoparticles (NPs) with morphologies ranging from segregated domains (heteroparticles) to core-shell structures by applying a seed-mediated growth process with Au and Pd seed nanoparticles in 1-octadecene (ODE), which is a high-boiling organic solvent. The as-synthesized oleylamine (OAm) functionalized Au NPs led to the formation of OAm-Au@Pd heteroparticles with a "windmill" morphology, having an Au core and Pd "blades". The multiply twinned structure of the Au seed particles (⌀ ≈ 9-11 nm) is associated with a reduced barrier for heterogeneous nucleation. This leads to island growth of bimetallic Au@Pd heteroparticles with less-regular morphologies. The reaction process can be controlled by tuning the surface chemistry with organic ligands. Functionalization of Au NPs (Ø ≈ 9-11 nm) with 1-octadecanethiol (ODT) led to the formation of ODT-Au@Pd NPs with a closed Pd shell through a strong ligand-metal binding, which is accompanied by a redistribution of the electron density. Experiments with varied Pd content revealed surface epitaxial growth of Pd on Au. For OAm-Pd and ODT-Pd seed particles, faceted, Au-rich domain NPs and impeded core-shell NPs were obtained, respectively. This is related to the high surface energy of the small Pd seed particles (⌀ ≈ 5-7 nm). The metal distribution of all bimetallic NPs was analyzed by extended (aberration-corrected) transmission electron microscopy (HR-TEM, HAADF-STEM, EDX mapping, ED). The Au and Pd NPs, as well as the ODT-Au@Pd and OAm-Pd@Au heteroparticles, catalyze the reduction of 4-nitrophenol to 4-aminophenol with borohydride. The catalytic activity is dictated by the particle structure. OAm-Au@Pd heteroparticles with faceted Au domains had the highest activity because of a mixed Au-Pd surface structure, while ODT-Au@Pd NPs, where the active Au core is covered by a Pd shell, had the lowest activity.

Ab initio structure determination and quantitative disorder analysis on nanoparticles by electron diffraction tomography.

Nanoscaled porous materials such as zeolites have attracted substantial attention in industry due to their catalytic activity, and their performance in sorption and separation processes. In order to understand the properties of such materials, current research focuses increasingly on the determination of structural features beyond the averaged crystal structure. Small particle sizes, various types of disorder and intergrown structures render the description of structures at atomic level by standard crystallographic methods difficult. This paper reports the characterization of a strongly disordered zeolite structure, using a combination of electron exit-wave reconstruction, automated diffraction tomography (ADT), crystal disorder modelling and electron diffraction simulations. Zeolite beta was chosen for a proof-of-principle study of the techniques, because it consists of two different intergrown polymorphs that are built from identical layer types but with different stacking sequences. Imaging of the projected inner Coulomb potential of zeolite beta crystals shows the intergrowth of the polymorphs BEA and BEB. The structures of BEA as well as BEB could be extracted from one single ADT data set using direct methods. A ratio for BEA/BEB = 48:52 was determined by comparison of the reconstructed reciprocal space based on ADT data with simulated electron diffraction data for virtual nanocrystals, built with different ratios of BEA/BEB. In this way, it is demonstrated that this smart interplay of the above-mentioned techniques allows the elaboration of the real structures of functional materials in detail - even if they possess a severely disordered structure.

Glycine-functionalized copper(ii) hydroxide nanoparticles with high intrinsic superoxide dismutase activity.

Superoxide dismutases (SOD) are a group of enzymes that catalyze the dismutation of superoxide (O2-) radicals into molecular oxygen (O2) and H2O2 as a first line of defense against oxidative stress. Here, we show that glycine-functionalized copper(ii) hydroxide nanoparticles (Gly-Cu(OH)2 NPs) are functional SOD mimics, whereas bulk Cu(OH)2 is insoluble in water and catalytically inactive. In contrast, Gly-Cu(OH)2 NPs form water-dispersible mesocrystals with a SOD-like activity that is larger than that of their natural CuZn enzyme counterpart. Based on this finding, we devised an application where Gly-Cu(OH)2 NPs were incorporated into cigarette filters. Cigarette smoke contains high concentrations of toxic reactive oxygen species (ROS, >1016 molecules per puff) including superoxide and reactive nitrogen species which lead to the development of chronic and degenerative diseases via oxidative damage and subsequent cell death. Embedded in cigarette filters Gly-Cu(OH)2 NPs efficiently removed ROS from smoke, thereby protecting lung cancer cell lines from cytotoxic effects. Their stability, ease of production and versatility make them a powerful tool for a wide range of applications in environmental chemistry, biotechnology and medicine.

Anhydrous Amorphous Calcium Oxalate Nanoparticles from Ionic Liquids: Stable Crystallization Intermediates in the Formation of Whewellite.

The mechanisms by which amorphous intermediates transform into crystalline materials are not well understood. To test the viability and the limits of the classical crystallization, new model systems for crystallization are needed. With a view to elucidating the formation of an amorphous precursor and its subsequent crystallization, the crystallization of calcium oxalate, a biomineral widely occurring in plants, is investigated. Amorphous calcium oxalate (ACO) precipitated from an aqueous solution is described as a hydrated metastable phase, as often observed during low-temperature inorganic synthesis and biomineralization. In the presence of water, ACO rapidly transforms into hydrated whewellite (monohydrate, CaC2 O4 ⋅H2 O, COM). The problem of fast crystallization kinetics is circumvented by synthesizing anhydrous ACO from a pure ionic liquid (IL-ACO) for the first time. IL-ACO is stable in the absence of water at ambient temperature. It is obtained as well-defined, non-agglomerated particles with diameters of 15-20 nm. When exposed to water, it crystallizes to give (hydrated) COM through a dissolution/recrystallization mechanism.

Structure-determining step in the hierarchical assembly of peptoid nanosheets.

Organic two-dimensional nanomaterials are of growing importance, yet few general synthetic methods exist to produce them in high yields and to precisely functionalize them. We previously developed an efficient hierarchical supramolecular assembly route to peptoid bilayer nanosheets, where the organization of biomimetic polymer sequences is catalyzed by an air-water interface. Here we determine at which stages of assembly the nanoscale and atomic-scale order appear. We used X-ray scattering, grazing incidence X-ray scattering at the air-water interface, electron diffraction, and a recently developed computational coarse-grained peptoid model to probe the molecular ordering at various stages of assembly. We found that lateral packing and organization of the chains occurs during the formation of a peptoid monolayer, prior to its collapse into a bilayer. Identifying the structure-determining step enables strategies to influence nanosheet order, to predict and optimize production yields, and to further engineer this class of material. More generally, our results provide a guide for using fluid interfaces to catalytically assemble 2D nanomaterials.

Atomic resolution phase contrast imaging and in-line holography using variable voltage and dose rate.

The TEAM 0.5 electron microscope is employed to demonstrate atomic resolution phase contrast imaging and focal series reconstruction with acceleration voltages between 20 and 300 kV and a variable dose rate. A monochromator with an energy spread of ≤0.1 eV is used for dose variation by a factor of 1,000 and to provide a beam-limiting aperture. The sub-Ångstrøm performance of the instrument remains uncompromised. Using samples obtained from silicon wafers by chemical etching, the [200] atom dumbbell distance of 1.36 Å can be resolved in single images and reconstructed exit wave functions at 300, 80, and 50 kV. At 20 kV, atomic resolution <2 Å is readily available but limited by residual lens aberrations at large scattering angles. Exit wave functions reconstructed from images recorded under low dose rate conditions show sharper atom peaks as compared to high dose rate. The observed dose rate dependence of the signal is explained by a reduction of beam-induced atom displacements. If a combined sample and instrument instability is considered, the experimental image contrast can be matched quantitatively to simulations. The described development allows for atomic resolution transmission electron microscopy of interfaces between soft and hard materials over a wide range of voltages and electron doses.

Improved specimen reconstruction by Hilbert phase contrast tomography.

The low signal-to-noise ratio (SNR) in images of unstained specimens recorded with conventional defocus phase contrast makes it difficult to interpret 3D volumes obtained by electron tomography (ET). The high defocus applied for conventional tilt series generates some phase contrast but leads to an incomplete transfer of object information. For tomography of biological weak-phase objects, optimal image contrast and subsequently an optimized SNR are essential for the reconstruction of details such as macromolecular assemblies at molecular resolution. The problem of low contrast can be partially solved by applying a Hilbert phase plate positioned in the back focal plane (BFP) of the objective lens while recording images in Gaussian focus. Images recorded with the Hilbert phase plate provide optimized positive phase contrast at low spatial frequencies, and the contrast transfer in principle extends to the information limit of the microscope. The antisymmetric Hilbert phase contrast (HPC) can be numerically converted into isotropic contrast, which is equivalent to the contrast obtained by a Zernike phase plate. Thus, in-focus HPC provides optimal structure factor information without limiting effects of the transfer function. In this article, we present the first electron tomograms of biological specimens reconstructed from Hilbert phase plate image series. We outline the technical implementation of the phase plate and demonstrate that the technique is routinely applicable for tomography. A comparison between conventional defocus tomograms and in-focus HPC volumes shows an enhanced SNR and an improved specimen visibility for in-focus Hilbert tomography.