Abstract: In the last decades, non-thermal plasma has been extensively investigated as a relevant tool for various biomedical applications, ranging from tissue decontamination to regeneration and from skin treatment to tumor therapies. This high versatility is due to the different kinds and amount of reactive oxygen and nitrogen species that can be generated during a plasma treatment and put in contact with the biological target. Some recent studies report that solutions of biopolymers with the ability to generate hydrogels, when treated with plasma, can enhance the generation of reactive species and influence their stability, resulting thus in the ideal media for indirect treatments of biological targets. The direct effects of the plasma treatment on the structure of biopolymers in water solution, as well as the chemical mechanisms responsible for the enhanced generation of RONS, are not yet fully understood. In this study, we aim at filling this gap by investigating, on the one hand, the nature and extent of the modifications induced by plasma treatment in alginate solutions, and, on the other hand, at using this information to explain the mechanisms responsible for the enhanced generation of reactive species as a consequence of the treatment. The approach we use is twofold: (i) investigating the effects of plasma treatment on alginate solutions, by size exclusion chromatography, rheology and scanning electron microscopy and (ii) study of a molecular model (glucuronate) sharing its chemical structure, by chromatography coupled with mass spectrometry and by molecular dynamics simulations. Our results point out the active role of the biopolymer chemistry during direct plasma treatment. Short-lived reactive species, such as OH radicals and O atoms, can modify the polymer structure, affecting its functional groups and causing partial fragmentation. Some of these chemical modifications, like the generation of organic peroxide, are likely responsible for the secondary generation of long-lived reactive species such as hydrogen peroxide and nitrite ions. This is relevant in view of using biocompatible hydrogels as vehicles for storage and delivery reactive species for targeted therapies.

Abstract: Two electrochemical methodologies, i.e. flowchart and dual-sensor, were developed to aid law enforcement present at festivals to obtain a rapid indication of the presence of four illicit drugs in suspicious samples encountered.

Abstract: Electron tomography is a widely used technique for 3D structural analysis of nanomaterials, but it can cause damage to samples due to high electron doses and long exposure times. To minimize such damage, researchers often reduce beam exposure by acquiring fewer projections through tilt undersampling. However, this approach can also introduce reconstruction artifacts due to insufficient sampling. Therefore, it is important to determine the optimal number of projections that minimizes both beam exposure and undersampling artifacts for accurate reconstructions of beam-sensitive samples. Current methods for determining this optimal number of projections involve acquiring and post-processing multiple reconstructions with different numbers of projections, which can be time-consuming and requires multiple samples due to sample damage. To improve this process, we propose a protocol that combines golden ratio scanning and quasi-3D reconstruction to estimate the optimal number of projections in real-time during a single acquisition. This protocol was validated using simulated and realistic nanoparticles, and was successfully applied to reconstruct two beam-sensitive metal–organic framework complexes.

Abstract: The optical response of metal nanoparticles is governed by plasmonic resonances, which are dictated by the particle morphology. A thorough understanding of the link between morphology and optical response requires quantitatively measuring optical and structural properties of the same particle. Here we present such a study, correlating electron tomography and optical micro-spectroscopy. The optical measurements determine the scattering and absorption cross-section spectra in absolute units, and electron tomography determines the 3D morphology. Numerical simulations of the spectra for the individual particle geometry, and the specific optical set-up used, allow for a quantitative comparison including the cross-section magnitude. Silver nanoparticles produced by photochemically driven colloidal synthesis, including decahedra, tetrahedra and bi-tetrahedra are investigated. A mismatch of measured and simulated spectra is found in some cases when assuming pure silver particles, which is explained by the presence of a few atomic layers of tarnish on the surface, not evident in electron tomography. The presented method tightens the link between particle morphology and optical response, supporting the predictive design of plasmonic nanomaterials.

Abstract: Structural analysis of metal clusters confined in nanoporous materials is typically performed by X-ray-driven techniques. Although X-ray analysis has proved its strength in the characterization of metal clusters, it provides averaged structural information. Therefore, we here present an alternative workflow for bringing the characterization of confined metal clusters towards the local scale. This workflow is based on the combination of aberration-corrected transmission electron microscopy (TEM), TEM image simulations, and powder X-ray diffraction (XRD) with advanced statistical techniques. In this manner, we were able to characterize the clustering of Pb atoms in Linde Type A (LTA) zeolites with Pb loadings as low as 5 wt%. Moreover, individual Pb clusters could be directly detected. The proposed methodology thus enables a local-scale characterization of confined metal clusters in zeolites. This is important for further elucidation of the connection between the structure and the physicochemical properties of such systems.

Abstract: Stimuli-responsive metal-organic frameworks (MOFs) are highly versatile porous materials with the ability to respond to different external stimuli, including temperature, pressure, pH, and light. The MOF properties can switch reversibly under specific light irradiation, opening the doors to various applications. This review focuses on design strategies to obtain photo-responsive MOFs, namely (i) encapsulation of photo-switchable molecules as guests in MOF porous structures, (ii) fabrication of MOF composites, (iii) post-synthesis modification, and (iv) synthesis of MOFs with photo-responsive ligands. The most recent reports from the literature are herein reviewed and analyzed in terms of material chemistry and performance. Comparisons between the different strategies are performed and future challenges are discussed. The critical aspect of the fatigue of photo-responsive MOFs applied for prolonged cycling of irradiation is also discussed.

Abstract: We developed a novel in situ (i.e. inside plasma and during operation) IR dielectric barrier discharge cell allowing investigation of plasma catalysis in transmission mode, atmospheric pressure, flow conditions (WHSV similar to 0-50 000 mL g(-1) h(-1)), at relevant discharge voltages (similar to 0-50 kV) and frequencies (similar to 0-5 kHz). We applied it to study the IR-active surface species formed on a SiO2 support and on a 3 wt% Ru/SiO2 catalyst, which can help to reveal the important surface reaction mechanisms during the plasma-catalytic dry reforming of methane (DRM). Moreover, we present a technique for the challenging task of estimating the temperature of a catalyst sample in a plasma-catalytic system in situ and during plasma operation. We found that during the reaction, water is immediately formed at the SiO2 surface, and physisorbed formic acid is formed with a delay. As Ru/SiO2 is subject to greater plasma-induced heating than SiO2 (with a surface temperature increase in the range of 70-120 degrees C, with peaks up to 150 degrees C), we observe lower amounts of physisorbed water on Ru/SiO2, and less physisorbed formic acid formation. Importantly, the formation of surface species on the catalyst sample in our plasma-catalytic setup, as well as the observed conversions and selectivities in plasma conditions, can not be explained by plasma-induced heating of the catalyst surface, but must be attributed to other plasma effects, such as the adsorption of plasma-generated radicals and molecules, or the occurrence of Eley-Rideal reactions.

Abstract: The (photo)catalytic properties of metal–organic frameworks (MOFs) can be enhanced by post-synthetic inclusion of metallic species in their porosity. Due to their extraordinarily high surface area and well defined porous structure, MOFs can be used for the stabilization of metal nanoparticles with adjustable size within their porosity. Originally, we present here an optimized ultrafast photoreduction protocol for the<italic>in situ</italic>synthesis of tiny and monodisperse silver nanoclusters (AgNCs) homogeneously supported on a photoactive porous titanium carboxylate MIL-125-NH₂MOF. The strong metal–framework interaction between –NH₂and Ag atoms influences the AgNC growth, leading to the surfactant-free efficient catalyst AgNC@MIL-125-NH₂with improved visible light absorption. The potential use of AgNC@MIL-125-NH₂was further tested in challenging applications: (i) the photodegradation of the emerging organic contaminants (EOCs) methylene blue (MB-dye) and sulfamethazine (SMT-antibiotic) in water treatment, and (ii) the catalytic hydrogenation of<italic>p</italic>-nitroaniline (4-NA) to<italic>p</italic>-phenylenediamine (PPD) with industrial interest. It is noteworthy that compared with the pristine MIL-125-NH₂, the composite presents an improved catalytic activity and stability, being able to photodegrade 92% of MB in 60 min and 96% of SMT in 30 min, and transform 100% of 4-NA to PPD in 30 min. Aside from these very good results, this study describes for the first time the use of a MOF in a visible light continuous flow reactor for wastewater treatment. With only 10 mg of AgNC@MIL-125-NH₂, high SMT removal efficiency over 70% is maintained after >2 h under water flow conditions found in real wastewater treatment plants, signaling a future real application of MOFs in water remediation.

Abstract: The combination of energy-dispersive X-ray spectroscopy (EDX) and electron tomography is a powerful approach to retrieve the 3D elemental distribution in nanomaterials, providing an unprecedented level of information for complex, multi-component systems, such as semiconductor devices, as well as catalytic and plasmonic nanoparticles. Unfortunately, the applicability of EDX tomography is severely limited because of extremely long acquisition times and high electron irradiation doses required to obtain 3D EDX reconstructions with an adequate signal-to-noise ratio. One possibility to address this limitation is intelligent denoising of experimental data using prior expectations about the objects of interest. Herein, this approach is followed using the deep learning methodology, which currently demonstrates state-of-the-art performance for an increasing number of data processing problems. Design choices for the denoising approach and training data are discussed with a focus on nanoparticle-like objects and extremely noisy signals typical for EDX experiments. Quantitative analysis of the proposed method demonstrates its significantly enhanced performance in comparison to classical denoising approaches. This allows for improving the tradeoff between the reconstruction quality, acquisition time and radiation dose for EDX tomography. The proposed method is therefore especially beneficial for the 3D EDX investigation of electron beam-sensitive materials and studies of nanoparticle transformations.

Abstract: In this paper, the existence of monolayers with the chemical formula XO2, where X = Zn, Cd, and Hg with hexagonal and tetragonal lattice structures is theoretically predicted by means of first principles calculations. Through cohesive energy calculation and phonon dispersion simulation, it has been proven that the two-dimensional XO2 monolayers proposed are energetically and dynamically stable suggesting their potential experimental realization. Our detailed study demonstrates that these novel newly predicted materials are half-metals and dilute magnetic semiconductors, and they exhibit magnetism in the ground state. The half-metallic character could find many applications in electronic and spintronic devices. Research into the magnetic properties revealed here can enrich theoretical knowledge in this area and provide more potential candidates for XO2 2D-based materials and van der Waals heterostructures.

Abstract: Redox processes activated by environmental factors have been identified as the main cause of the chromatic alterations of a number of artists' pigments, including the yellow pigment orpiment (As2S3). Although a general comprehension of the mechanisms has been provided through characterization of degradation compounds of As2S3, experimental evidences to prove how other paint components and how different environmental agents influence the formation pathways of specific secondary compounds are still lacking. Thus, it becomes fundamental to develop a methodological strategy which enable achieving a discrimination among the causes affecting the chemical stability of more heterogenous As2S3-based paints and defining the mechanism through which the alteration establishes and evolves, with the ultimate goal of optimizing the preventive conservation measures of unique masterpieces. In this paper, we propose a comprehensive multi-material and multi-method approach based on the combination of synchrotron radiation X-ray micro-analytical techniques (i.e., X-ray diffraction, X-ray fluorescence and X-ray absorption near edge structure spectroscopy at S K-/Ag L-3-/As K-edges) and vibrational micro-spectroscopy methods to unveil the causes and mechanism of darkening of “fake-gilded” decorations in tempera paintings, originally consisting of an unusual mixture of As2S3 and metallic silver (Ag-0). Such degradation process is a not yet understood phenomenon threatening a series of Old Master paintings, including those by the Italian painters Cimabue and Pietro Lorenzetti. The high specificity, sensitivity and lateral resolution of the employed analytical methods allowed providing first-time evidence for the presence of black acanthite (alpha-Ag2S), mimetite [Pb-5(AsO4)(3)Cl] and syngenite [K2Ca(SO4)(2)center dot H2O] as degradation products of the “fake-gilded” decorations in the Maesta by Cimabue (Church of Santa Maria dei Servi, Bologna, Italy). Furthermore, the study of the painting combined with that of tempera paint mock-ups permitted to explore and define the environmental agents and internal factors causing the darkening, by proving that: (i) Ag-0 and moisture are key-factors for triggering the transformation of As2S3 to alpha-Ag2S and As-oxides; (ii) S2--ions arising from the degradation of As2S3 are the main responsible for the formation of alpha-Ag2S; (iii) light exposure strengthens the tendency of the paint components towards alteration. Based on our findings, we finally propose a degradation mechanism of As2S3/Ag-0-based tempera paints.

Abstract: A better understanding of the drivers of the economic, environmental, and social sustainability of emerging (biobased) technologies and products in early development phases can help decision-makers to identify sustainability hurdles and opportunities. Furthermore, it guides additional research and development efforts and investment decisions, that will, ultimately, lead to more sustainable products and technologies entering a market. To this end, this study developed a novel techno-sustainability assessment (TSA) framework with a demonstration on a biobased chemical application. The integrated TSA compares the potential sustainability performance of different (technology) scenarios and helps to make better-informed decisions by evaluating and trading-off sustainability impacts in one holistic framework. The TSA combines methods for comprehensive indicator selection and integration of technological and country-specific data with environmental, economic, and social data. Multi-criteria decision analysis (MCDA) is used to address data uncertainty and to enable scenario comparison if indicators are expressed in different units. A hierarchical, stochastic outranking approach is followed that compares different weighting schemes and preference structures to check for the robustness of the results. The integrated TSA framework is demonstrated on an application for which the sustainability of a production and harvesting plant of microalgae-based food colorants is assessed. For a set of scenarios that vary with regard to the algae feedstock, production technology, and location, the sustainability performance is quantified and compared, and the underlying reasons for this performance are explored.

Abstract: KBiNb2O7 was prepared from RbBiNb2O7 by a sequence of cation exchange reactions which first convert RbBiNb2O7 to LiBiNb2O7, before KBiNb2O7 is formed by a further K-for-Li cation exchange. A combination of neutron, synchrotron X-ray and electron diffraction data reveal that KBiNb2O7 adopts a polar, layered, perovskite structure (space group A11m) in which the BiNb2O7 layers are stacked in a (0, ½, z) arrangement, with the K+ cations located in half of the available 10-coordinate interlayer cation sites. The inversion symmetry of the phase is broken by a large displacement of the Bi3+ cations parallel to the y-axis. HAADF-STEM images reveal that KBiNb2O7 exhibits frequent stacking faults which convert the (0. ½, z) layer stacking to (½, 0, z) stacking and vice versa, essentially switching the x- and y-axes of the material. By fitting the complex diffraction peak shape of the SXRD data collected from KBiNb2O7 it is estimated that each layer has approximately an ~11% chance of being defective – a high level which is attributed to the lack of cooperative NbO6 tilting in the material, which limits the lattice strain associated with each fault.

Abstract: Two-dimensional Transition Metal Dichalcogenides (TMDs) possessing extraordinary physical properties at reduced dimensionality have attracted interest due to their promise in electronic and optical device applications. However, TMD monolayers can show a broad range of different properties depending on their crystal phase; for example, H phases are usually semiconductors, while the T phases are metallic. Thus, controlling phase transitions has become critical for device applications. In this study, the energetically low-lying crystal structures of pristine and Janus TMDs are investigated by using ab initio Nudged Elastic Band and molecular dynamics simulations to provide a general explanation for their phase stability and transition properties. Across all materials investigated, the T phase is found to be the least stable and the H phase is the most stable except for WTe2, while the T' and T '' phases change places according to the TMD material. The transition energy barriers are found to be large enough to hint that even the higher energy phases are unlikely to undergo a phase transition to a more stable phase if they can be achieved except for the least stable T phase, which has zero barrier towards the T ' phase. Indeed, in molecular dynamics simulations the thermodynamically least stable T phase transformed into the T ' phase spontaneously while in general no other phase transition was observed up to 2100 K for the other three phases. Thus, the examined T ', T '' and H phases were shown to be mostly stable and do not readily transform into another phase. Furthermore, so-called mixed phase calculations considered in our study explain the experimentally observed lateral hybrid structures and point out that the coexistence of different phases is strongly stable against phase transitions. Indeed, stable complex structures such as metal-semiconductor-metal architectures, which have immense potential to be used in future device applications, are also possible based on our investigation.

Abstract: The added value of supervised Machine Learning (ML) methods to determine the Absolute Configuration (AC) of compounds from their Vibrational Circular Dichroism (VCD) spectra was explored. Among all ML methods considered, Random Forest (RF) and Feedforward Neural Network (FNN) yield the best performance for identification of the AC. At its best, FNN allows near-perfect AC determination, with accuracy of prediction up to 0.995, while RF combines good predictive accuracy (up to 0.940) with the ability to identify the spectral areas important for the identification of the AC. No loss in performance of either model is observed as long as the spectral sampling interval used does not exceed the spectral bandwidth. Increasing the sampling interval proves to be the best method to lower the dimensionality of the input data, thereby decreasing the computational cost associated with the training of the models.