Abstract: Infrared thermography is a fast, non-destructive and contactless testing technique which is increasingly used in heritage science. The aim of this study was to assess the ability of infrared thermography, in combination with a data clustering approach, to differentiate between the different types of historical glass that were included in a colorless leaded-glass windows during previous restoration interventions. Inspection of the thermograms and the application of two data mining techniques on the thermal data, i.e., k-means clustering and hierarchical clustering, allowed identifying different groups of window panes that show a different thermal behavior. Both clustering approaches arrive at similar groupings of the glass with a clear separation of three types. However, the lead cames that hold the glass panes appear to have a substantial impact on the thermal behavior of the surrounding glass, thus preventing classification of the smallest glass panes. For the larger panes, this was not a critical issue as the center of the glass remained unaffected. Subtle visual color differences between panes, implying a variation in coloring metal ions, was not always distinguished by IRT. Nevertheless, data clustering assisted infrared thermography shows potential as an efficient and swift method for documenting the material intervention history of leaded-glass windows during or in preparation of conservation treatments.

Abstract: We explore the consequences of nonthermal plasma-activation on product yields in catalytic ammonia synthesis, a reaction that is equilibrium-limited at elevated temperatures. We employ a minimal microkinetic model that incorporates the influence of plasma-activation on N2 dissociation rates to predict NH3 yields into and across the equilibrium-limited regime. NH3 yields are predicted to exceed bulk thermodynamic equilibrium limits on materials that are thermal-rate-limited by N2 dissociation. In all cases, yields revert to bulk equilibrium at temperatures at which thermal reaction rates exceed plasma-activated ones. Beyond-equilibrium NH3 yields are observed in a packed bed dielectric barrier discharge reactor and exhibit sensitivity to catalytic material choice in a way consistent with model predictions. The approach and results highlight the opportunity to exploit synergies between nonthermal plasmas and catalysts to affect transformations at conditions inaccessible through thermal routes.

Abstract: High tunability of two-dimensional magnetic materials (by strain, gating, heterostructuring, or otherwise) provides unique conditions for studying versatile magnetic properties and controlling emergent magnetic phases. Expanding the scope of achievable magnetic phenomena in such materials is important for both fundamental and technological advances. Here we perform atomistic spin-dynamics simulations to explore the (chiral) magnetic phases of atomic monolayers in the limit of suppressed first-neighbors exchange interaction. We report the rich phase diagram of exotic magnetic configurations, obtained for both square and honeycomb lattice symmetries, comprising coexistence of ferromagnetic and antiferromagnetic spin cycloids, as well as multiple types of magnetic skyrmions. We perform a minimum-energy path analysis for the skyrmion collapse to evaluate the stability of such topological objects and reveal that magnetic monolayers could be good candidates to host the antiferromagnetic skyrmions that are experimentally evasive to date.

Abstract: Nonmetal doping is an effective approach to modify the electronic band structure and enhance the photocatalytic performance of bismuth oxyhalides. Using density functional theory, we systematically examine the fundamental properties of single-layer BiOBr doped with boron (B) and phosphorus (P) atoms. The stability of the doped models is investigated based on the formation energies, where the substitutional doping is found to be energetically more stable under O-rich conditions than under Bi-rich ones. The results showed that substitutional doping of P atoms reduced the bandgap of pristine BiOBr to a greater extent than that of boron substitution. The calculation of the effective masses reveals that B doping can render the electrons and holes of pristine BiOBr lighter and heavier, respectively, resulting in a slower recombination rate of photoexcited electron-hole pairs. Based on the results of HOMO-LUMO calculations, the introduction of B atoms tends to increase the number of photocatalytically active sites. The top of the valence band and the conduction band bottom of the B doped BiOBr monolayer match well with the water redox potentials in an acidic environment. The absorption spectra propose that B(P) doping causes a red-shift. Overall, the results predict that nonmetal-doped BiOBr monolayers have a reduced bandgap, a slow recombination rate, more catalytically active sites, enhanced optical absorption edges, and reduced work functions, which will contribute to superior photocatalytic performance.

Abstract: Global environmental problems have urged the need for developing sustainable technologies. However, new technologies that enter the market have often higher economic costs and potentially higher environmental impacts than conventional technologies. This can be explained by learning effects: a production process that is performed for the first time runs less smooth than a production process that has been in operation for years. To obtain a fair estimation of the potential of a new technology, learning effects need to be included. A review on the current literature on learning effects was conducted in order to provide guidelines on how to include learning effects in prospective technology assessment. Based on the results of this review, five recommendations have been formulated and an integration of learning effects in the structure of prospective technology assessment has been proposed. These five recommendations include the combined use of learning effects on the component level and on the end product level; the combined use of learning effects on the technical, economic and environmental level; the combined use of extrapolated values and expert estimates; the combined use of learning-by-doing and learning-by-searching effects and; a tier-based method, including quality criteria, to calculate the learning effect. These five complementary strategies could lead to a clearer perspective on the environmental impact and cost structure of the new technology and a fairer comparison base with conventional technologies, potentially resulting in a faster adoption and a shorter time-to-market for sustainable technologies.

Abstract: When monolayer (ML) MoS2 is placed on a substrate, the proximity-induced interactions such as the Rashba spin-orbit coupling (RSOC) and exchange interaction (EI) can be introduced. Thus, the electronic system can behave like a spintronic device. In this study, we present a theoretical study on how the presence of the RSCO and EI can lead to the band splitting, the lifting of the valley degeneracy and to the spin polarization in n- and p-type ML MoS2. We find that the maxima of the in-plane spin orientation in the conduction and valence bands in ML MoS2 depend on the Rashba parameter and the effective Zeeman field factor. At a fixed Rashba parameter, the minima of the split conduction band and the maxima of the split valence band along with the spin polarization in ML MoS2 can be tuned effectively by varying the effective Zeeman field factor. On the basis that the EI can be induced by placing the ML MoS2 on a ferromagnetic substrate or by magnetic doping in ML MoS2, we predict that the interesting spintronic effects can be observed in n- and p-type ML MoS2. This work can be helpful to gain an in-depth understanding of the basic physical properties of ML MoS2 for application in advanced electronic and optoelectronic devices.

Abstract: We used atom probe tomography to complement electron microscopy for the investigation of spinodal decomposition in alkali feldspar. To this end, gem-quality alkali feldspar of intermediate composition with a mole fraction of a(K) = 0.43 of the K end-member was prepared from Madagascar orthoclase by ion-exchange with (NaK)Cl molten salt. During subsequent annealing at 550 degrees C and close to ambient pressure the ion-exchanged orthoclase unmixed producing a coherent lamellar intergrowth of Na-rich and K-rich lamellae. The chemical separation was completed, and equilibrium Na-K partitioning between the different lamellae was attained within four days, which was followed by microstructural coarsening. After annealing for 4 days, the wavelength of the lamellar microstructure was approximate to 17 nm and it increased to approximate to 30 nm after annealing for 16 days. The observed equilibrium compositions of the Na-rich and K-rich lamellae are in reasonable agreement with an earlier experimental determination of the coherent solvus. The excess energy associated with compositional gradients at the lamellar interfaces was quantified from the initial wavelength of the lamellar microstructure and the lamellar compositions as obtained from atom probe tomography using the Cahn-Hilliard theory. The capability of atom probe tomography to deliver quantitative chemical compositions at nm resolution opens new perspectives for studying the early stages of exsolution. In particular, it helps to shed light on the phase relations in nm scaled coherent intergrowth.

Abstract: CO2 dissociation and its subsequent conversion into added-value chemicals is a promising strategy for recycling CO2 gas into reusable products. One of the possible methods is direct plasma-induced dissociation. In this work we study the efficiency of CO2 dissociation in pulsed atmospheric-pressure gas discharge between two conducting electrodes by a 0-D numerical plasma model. The purpose of the study is to provide results on the optimal conditions of CO2 conversion with respect to the energy efficiency and dissociation by varying the maximum power density value and the pulse length. The power density is directly related to the discharge current and the reduced electric field in the discharge. We consider pulse lengths in the range from hundreds of nanosecond up to milliseconds. The results obtained show that the dissociation degree and energy efficiency are sensitive to the pulse length (duration) and the power density, so that a considerable improvement of the discharge performance can be achieved by fine-tuning these parameters. The study is intended to provide guidance in designing an experimental set-up and a power supply with the characteristics necessary to achieve optimal conversion.

Abstract: The penetration of reactive oxygen and nitrogen species (RONS) across the stratum corneum (SC) is a necessary and crucial process in many skin‐related plasma medical applications. To gain more insights into this penetration behavior, we combined experimental measurements of the permeability of dry and moist SC layers with computer simulations of model lipid membranes. We measured the permeation of relatively stable molecules, which are typically generated by plasma, namely H2O2, NO3−, and NO2−. Furthermore, we calculated the permeation free energy profiles of the major plasma‐generated RONS and their derivatives (i.e., H2O2, OH, HO2, O2, O3, NO, NO2, N2O4, HNO2, HNO3, NO2−, and NO3−) across native and oxidized SC lipid bilayers, to understand the mechanisms of RONS permeation across the SC. Our results indicate that hydrophobic RONS (i.e., NO, NO2, O2, O3, and N2O4) can translocate more easily across the SC lipid bilayer than hydrophilic RONS (i.e., H2O2, OH, HO2, HNO2, and HNO3) and ions (i.e., NO2− and NO3−) that experience much higher permeation barriers. The permeability of RONS through the SC skin lipids is enhanced when the skin is moist and the lipids are oxidized. These findings may help to understand the underlying mechanisms of plasma interaction with a biomaterial and to optimize the environmental parameters in practice in plasma medical applications.

Abstract: The nanoscale morphology of polymer blends is a key parameter to reach high efficiency in bulk heterojunction solar cells. Thereby, research typically focusing on optimal blend morphologies while studying nonoptimized blends may give insight into blend designs that can prove more robust against morphology defects. Here, we focus on the direct correlation of morphology and device performance of thieno[3,4-b]-thiophene-alt-benzodithiophene (PTB7):[6,6]phenyl C-71 butyric acid methyl ester (PC71BM) bulk heterojunction (BHJ) blends processed without additives in different donor/acceptor weight ratios. We show that while blends of a 1:1.5 ratio are composed of large donor-enriched and fullerene domains beyond the exciton diffusion length, reducing the ratio below 1:0.5 leads to blends composed purely of polymer-enriched domains. Importantly, the photocurrent density in such blends can reach values between 45 and 60% of those reached for fully optimized blends using additives. We provide here direct visual evidence that fullerenes in the donor-enriched domains are not distributed homogeneously but fluctuate locally. To this end, we performed compositional nanoscale morphology analysis of the blend using spectroscopic imaging of low-energy-loss electrons using a transmission electron microscope. Charge transport measurement in combination with molecular dynamics simulations shows that the fullerene substructures inside the polymer phase generate efficient electron transport in the polymer-enriched phase. Furthermore, we show that the formation of densely packed regions of fullerene inside the polymer phase is driven by the PTB7:PC71BM enthalpy of mixing. The occurrence of such a nanoscale network of fullerene clusters leads to a reduction of electron trap states and thus efficient extraction of photocurrent inside the polymer domain. Suitable tuning of the polymer-acceptor interaction can thus introduce acceptor subnetworks in polymer-enriched phases, improving the tolerance for high-efficiency BHJ toward morphological defects such as donor-enriched domains exceeding the exciton diffusion length.

Abstract: In recent decades new recycling technologies for mixed plastic waste have emerged. In pyrolysis, the polymer chains are thermally broken (pyrolyzed) to obtain hydrocarbon materials of different molecular weights such as naphtha, oil or waxes, whose yields can be controlled by varying the reaction parameters. Naphtha represents a closed-loop recycling process as it is a feedstock for (poly)olefins; while the co-production of waxes, having several applications in e.g. the construction industry, exemplifies an open-loop recycling process. This paper compares the economic performance of the pyrolysis of mixed polyolefin waste in a closed-loop and open-loop scheme, including a probabilistic approach to the most important variables. From an economic perspective, open-loop pyrolysis as presented outperforms closed-loop recycling, due to the high prices of wax. However, the results present a high dispersion caused by the volatility of the prices of crude oil and its derivates. Considering the current oil price projections, our case study analysis showed that for open-loop recycling there is a future probability of almost a 98 % of observing positive results and around 57 % of probability in the case of closed-loop recycling, under the assumptions made. Yet, in a future scenario where decarbonized electricity would decrease oil prices, the probability of a positive outcome reduces to 57 % for the open-loop case and to less than 8 % in the case of closed-loop recycling. To make these pathways attractive to investors, the nameplate capacity should be at least 70 kt/year for open-loop recycling and 115 kt/year for closed-loop recycling. A 120 kt/year plant should operate minimally at 80 % of its capacity for open-loop recycling, while closed-loop recycling would demand running close to maximum capacity. Security of feedstock supply therefore is required.

Abstract: The autoregressive neural networks are emerging as a powerful computational tool to solve relevant problems in classical and quantum mechanics. One of their appealing functionalities is that, after they have learned a probability distribution from a dataset, they allow exact and efficient sampling of typical system configurations. Here we employ a neural autoregressive distribution estimator (NADE) to boost Markov chain Monte Carlo (MCMC) simulations of a paradigmatic classical model of spin-glass theory, namely, the two-dimensional Edwards-Anderson Hamiltonian. We show that a NADE can be trained to accurately mimic the Boltzmann distribution using unsupervised learning from system configurations generated using standard MCMC algorithms. The trained NADE is then employed as smart proposal distribution for the Metropolis-Hastings algorithm. This allows us to perform efficient MCMC simulations, which provide unbiased results even if the expectation value corresponding to the probability distribution learned by the NADE is not exact. Notably, we implement a sequential tempering procedure, whereby a NADE trained at a higher temperature is iteratively employed as proposal distribution in a MCMC simulation run at a slightly lower temperature. This allows one to efficiently simulate the spin-glass model even in the low-temperature regime, avoiding the divergent correlation times that plague MCMC simulations driven by local-update algorithms. Furthermore, we show that the NADE-driven simulations quickly sample ground-state configurations, paving the way to their future utilization to tackle binary optimization problems.

Abstract: Sustainable development is driving a rapid focus shift in the wastewater and organic waste treatment sectors, from a “removal and disposal” approach towards the recovery and reuse of water, energy and materials (e.g. carbon or nutrients). Purple phototrophic bacteria (PPB) are receiving increasing attention due to their capability of growing photoheterotrophically under anaerobic conditions. Using light as energy source, PPB can simultaneously assimilate carbon and nutrients at high efficiencies (with biomass yields close to unity (1 g CODbiomass·g CODremoved−1)), facilitating the maximum recovery of these resources as different value-added products. The effective use of infrared light enables selective PPB enrichment in non-sterile conditions, without competition with other phototrophs such as microalgae if ultraviolet-visible wavelengths are filtered. This review reunites results systematically gathered from over 177 scientific articles, aiming at producing generalized conclusions. The most critical aspects of PPB-based production and valorisation processes are addressed, including: (i) the identification of the main challenges and potentials of different growth strategies, (ii) a critical analysis of the production of value-added compounds, (iii) a comparison of the different value-added products, (iv) insights into the general challenges and opportunities and (v) recommendations for future research and development towards practical implementation. To date, most of the work has not been executed under real-life conditions, relevant for full-scale application. With the savings in wastewater discharge due to removal of organics, nitrogen and phosphorus as an important economic driver, priorities must go to using PPB-enriched cultures and real waste matrices. The costs associated with artificial illumination, followed by centrifugal harvesting/dewatering and drying, are estimated to be 1.9, 0.3–2.2 and 0.1–0.3 $·kgdry biomass−1. At present, these costs are likely to exceed revenues. Future research efforts must be carried out outdoors, using sunlight as energy source. The growth of bulk biomass on relatively clean wastewater streams (e.g. from food processing) and its utilization as a protein-rich feed (e.g. to replace fishmeal, 1.5–2.0 $·kg−1) appears as a promising valorisation route.

Abstract: STEM imaging is typically performed by raster scanning a focused electron probe over a sample. Here we investigate and compare three different scan patterns, making use of a programmable scan engine that allows to arbitrarily set the sequence of probe positions that are consecutively visited on the sample. We compare the typical raster scan with a so-called ‘snake’ pattern where the scan direction is reversed after each row and a novel Hilbert scan pattern that changes scan direction rapidly and provides an homogeneous treatment of both scan directions. We experimentally evaluate the imaging performance on a single crystal test sample by varying dwell time and evaluating behaviour with respect to sample drift. We demonstrate the ability of the Hilbert scan pattern to more faithfully represent the high frequency content of the image in the presence of sample drift. It is also shown that Hilbert scanning provides reduced bias when measuring lattice parameters from the obtained scanned images while maintaining similar precision in both scan directions which is especially important when e.g. performing strain analysis. Compared to raster scanning with flyback correction, both snake and Hilbert scanning benefit from dose reduction as only small probe movement steps occur.

Abstract: We investigate experimentally the terahertz (THz) optoelectronic properties of monolayer (ML) tungsten disulfide (WS2) placed on different substrates using THz time-domain spectroscopy (TDS). We find that the THz optical response of n-type ML WS2 depends sensitively on the choice of the substrate. This dependence is found to be a consequence of substrate induced charge transfer, extra scattering centers, and electronic localization. Through fitting the experimental results with the Drude-Smith formula, we can determine the key sample parameters (e.g., the electronic relaxation time, electron density, and electronic localization factor) of ML WS2 on different substrates. The temperature dependence of these parameters is examined. Our results show that the THz TDS technique is an efficient non-contact method that can be utilized to characterize and investigate the optoelectronic properties of nano-devices based on ML WS2.