Abstract: Tempering is widely applied to make carbon atoms beneficially rearrange in high strength steel microstructures after quenching; though the nano-scale interaction of carbon atoms with crystallographic defects is hard to experimentally observe. To improve, we investigate the redistribution of carbon atoms along martensite grain boundaries in a quenched and tempered low carbon steel. We observe the tempering-induced microstructural evolution by in-situ heating in a transmission electron microscope (TEM) and by compositional analysis through atom probe tomography (APT). Probe volumes for APT originate from a single martensite packet but in different tempering conditions, which is achieved via a sequential lift-out with in-between tempering treatments. The complementary use of TEM and APT provides crystallographic as well as chemical information on carbon segregation and subsequent carbide precipitation at martensite grain boundaries. The results show that the amount of carbon segregation to martensite grain boundaries is influenced by the boundary type, e.g. low-angle lath or high-angle block boundaries. Also, the growth behavior of cementite precipitates from grain boundary nucleation sites into neighboring martensite grains differs at low- and high-angle grain boundaries. This is due to the crystallographic constraints arising from the semi-coherent orientation relationship between cementite and adjacent martensite. We also show that slower quenching stabilizes thin retained austenite films between martensite grains because of enhanced carbon segregation during cooling. Finally, we demonstrate the effect of carbon redistribution along martensite grain boundaries on the mechanical properties. Here, we compare micro-scale Vickers hardness results from boundary-containing probe volumes to nanoindentation results from pure bulk martensite (boundary-free) probe volumes.

Abstract: Binary ZrCu nanocomposite amorphous films are synthetized by pulsed laser deposition (PLD) under vac-uum (2 x 10-3 Pa) and 10 Pa He pressure, leading to fully amorphous compact and nanogranular mor-phologies, respectively. Then, post-thermal annealing treatments are carried out to explore thermal stability and crystallization phenomena together with the evolution of mechanical properties. Compact films exhibit larger thermal stability with partial crystallization phenomena starting at 420 degrees C, still to be completed at 550 degrees C, while nanogranular films exhibit early-stage crystallization at 300 degrees C and com-pleted at 485 degrees C. The microstructural differences are related to a distinct evolution of mechanical

Abstract: Low temperature plasmas (LTPs) enable to create a highly reactive environment at near ambient temperatures due to the energetic electrons with typical kinetic energies in the range of 1 to 10 eV (1 eV = 11600K), which are being used in applications ranging from plasma etching of electronic chips and additive manufacturing to plasma-assisted combustion. LTPs are at the core of many advanced technologies. Without LTPs, many of the conveniences of modern society would simply not exist. New applications of LTPs are continuously being proposed. Researchers are facing many grand challenges before these new applications can be translated to practice. In this paper, we will discuss the challenges being faced in the field of LTPs, in particular for atmospheric pressure plasmas, with a focus on health, energy and sustainability.

Abstract: All-inorganic lead-free halide double perovskites offer a promising avenue toward non-toxic, stable optoelec-tronic materials, properties that are missing in their prominent lead-containing counterparts. Their large ther-mopowers and high carrier mobilities also make them promising for thermoelectric applications. Here, we present a first-principles study of the lattice vibrations and thermal transport behaviors of Cs2SnI6 and gamma-CsSnI3, two prototypical compounds in this materials class. We show that conventional static zero temperature density functional theory (DFT) calculations severely underestimate the lattice thermal conductivities (kappa l) of these compounds, indicating the importance of dynamical effects. By calculating anharmonic renormalized phonon dispersions, we show that some optic phonons significantly harden with increasing temperature (T), which reduces the scattering of heat carrying phonons and enhances calculated kappa l values when compared with standard zero temperature DFT. Furthermore, we demonstrate that coherence contributions to kappa l, arising from wave like phonon tunneling, are important in both compounds. Overall, calculated kappa l with temperature-dependent inter-atomic force constants, built from particle and coherence contributions, are in good agreement with available measured data, for both magnitude and temperature dependence. Large anharmonicity combined with low phonon group velocities yield ultralow kappa l values, with room temperature values of 0.26 W/m-K and 0.72 W/m-K predicted for Cs2SnI6 and gamma-CsSnI3, respectively. We further show that the lattice dynamics of these compounds are highly anharmonic, largely mediated by rotation of the SnI6 octahedra and localized modes originating from Cs rattling motion. These thermal characteristics combined with their previously computed excellent electronic properties make these perovskites promising candidates for optoelectronic and room temperature thermoelectric applications.

Abstract: This study presents a novel method for the detection and quantification of atmospheric corrosion products on carbon steel. Using hyperspectral imaging (HSI) in the short-wave infrared range (SWIR) (900-1700 nm), we are able to identify the most common corrosion minerals such as: alpha-FeO(OH) (goethite), gamma-FeO(OH) (lepidocrocite), and gamma-Fe2O3 (maghemite). Six carbon steel samples were artificially corroded in a salt spray chamber, each sample with a different duration (between 1 h and 120 hours). These samples were analysed by scanning X-ray diffraction (XRD) and also using a SWIR HSI system. The XRD data is used as baseline data. A random forest regression algorithm is used for training on the combined XRD and HSI data set. Using the trained model, we can predict the abundance map based on the HSI images alone. Several image correlation metrics are used to assess the similarity between the original XRD images and the HSI images. The overall abundance is also calculated and compared for XRD and HSI images. The analysis results show that we are able to obtain visually similar images, with error rates ranging from 3.27 to 13.37%. This suggests that hyperspectral imaging could be a viable tool for the study of corrosion minerals.

Abstract: Using a particle based model, we investigate the skyrmion dynamical behavior in a channel where the upper wall contains divots of one depth and the lower wall contains divots of a different depth. Under an applied driving force, skyrmions in the channels move with a finite skyrmion Hall angle that deflects them toward the upper wall for -x direction driving and the lower wall for +x direction driving. When the upper divots have zero height, the skyrmions are deflected against the flat upper wall for -x direction driving and the skyrmion velocity depends linearly on the drive. For +x direction driving, the skyrmions are pushed against the lower divots and become trapped, giving reduced velocities and a nonlinear velocity-force response. When there are shallow divots on the upper wall and deep divots on the lower wall, skyrmions get trapped for both driving directions; however, due to the divot depth difference, skyrmions move more easily under -x direction driving, and become strongly trapped for +x direction driving. The preferred -x direction motion produces what we call a Magnus diode effect since it vanishes in the limit of zero Magnus force, unlike the diode effects observed for asymmetric sawtooth potentials. We show that the transport curves can exhibit a series of jumps or dips, negative differential conductivity, and reentrant pinning due to collective trapping events. We also discuss how our results relate to recent continuum modeling on a similar skyrmion diode system.

Abstract: Bamboo is an important component in subtropical and tropical forest communities. The plant has characteristic long lanceolate leaves with parallel venation. Prior studies have shown that the leaf shapes of this plant group can be well described by a simplified version (referred to as SGE-1) of the Gielis equation, a polar coordinate equation extended from the superellipse equation. SGE-1 with only two model parameters is less complex than the original Gielis equation with six parameters. Previous studies have seldom tested whether other simplified versions of the Gielis equation are superior to SGE-1 in fitting empirical leaf shape data. In the present study, we compared a three-parameter Gielis equation (referred to as SGE-2) with the two-parameter SGE-1 using the leaf boundary coordinate data of six bamboo species within the same genus that have representative long lanceolate leaves, with >300 leaves for each species. We sampled 2000 data points at approximately equidistant locations on the boundary of each leaf, and estimated the parameters for the two models. The root–mean–square error (RMSE) between the observed and predicted radii from the polar point to data points on the boundary of each leaf was used as a measure of the model goodness of fit, and the mean percent error between the RMSEs from fitting SGE-1 and SGE-2 was used to examine whether the introduction of an additional parameter in SGE-1 remarkably improves the model’s fitting. We found that the RMSE value of SGE-2 was always smaller than that of SGE-1. The mean percent errors among the two models ranged from 7.5% to 20% across the six species. These results indicate that SGE-2 is superior to SGE-1 and should be used in fitting leaf shapes. We argue that the results of the current study can be potentially extended to other lanceolate leaf shapes.

Abstract: Consortia of aerobic heterotrophic bacteria (AHB) are appealing as sustainable alternative protein ingredient for aquaculture given their high nutritional qualities, and their production potential on feed-grade industrial wastewater. Today, the impacts of pre-treatment, bioprocess choice and key parameter settings on AHB productivity and nutritional properties are unknown. This study investigated for the first time AHB microbial protein production effects based on (i) raw vs anaerobically fermented brewery wastewater, (ii) high-rate activated sludge (HRAS) without vs with feast-famine conditions, and (iii) three short solid retention time (SRT): 0.25, 0.50 and 1.00 d. High biomass (4.4–8.0 g TSS/L/d) and protein productivities (1.9–3.2 g protein/L/d) were obtained while achieving COD removal efficiencies up to 98 % at SRT 0.50 d. The AHB essential amino acid (EAA) profiles were above rainbow trout requirements, excluding the S-containing EAA, highlighting the AHB biomass replacement potential for unsustainable fishmeal in salmonid diets.

Abstract: MicroRNAs (miRNAs) are small oligonucleotides (18-25 bases) that play a key role in epigenetic regulation. Since the discovery of miRNAs in 1993, their role in biological processes has been extensively investigated. By now, it has been evidenced that monitoring and detection of miRNAs can improve (early) disease diagnosis. The existing diagnostic approaches have limitations such as the need for complex multistep protocols for sample preparation, analysis, data interpretation, high cost of equipment, the need for highly qualified personnel, and high laboratory standards. As opposed to this, point-of-care biosensors and chips aim to facilitate the procedure and avoid sending samples into centralized laboratories, which saves time, reduces the chance of sample degradation, and enables analysis of patient samples in remote areas, directly at home or primary and secondary care facilities (i.e. general practitioners and specialists). The latter is essential for therapy assessment and follow-up monitoring of patients with chronic diseases and cancer. Nevertheless, the development of such sensors is lagging compared to the projections of 10 – 20 years ago, mainly due to insufficient sensitivity, poor reproducibility, and the complexity of the sensors’ design reported in the research literature. This motivates the development of new detection strategies and technologies such as photoelectrochemical sensors that combine the best features of different sensing approaches. The primary concern when developing detection technologies for miRNA is the need for a highly sensitive and selective platform. This thesis explores a novel photoelectrochemical (PEC) method that is distinctive owing to its sensitive nature and simple and robust design. Firstly, we focused on the usage of recently emerging commercial gold-sputtered electrode systems for the detection of short nucleic acid with enzymatic amplification. Importantly, cleaning such electrodes is a challenge since the standard procedures known for regular disk electrodes such as polishing cannot be employed here, since it will damage the protective layer on the electrode. However, the electrodes can be washed and pretreated chemically and/or electrochemically. Thus, a procedure to effectively clean and modify the gold-sputtered electrode has been developed, resulting in high-performance gold-sputtered nucleic acid sensors. Next, the usage of molecular photosensitizers as an alternative to enzymatic amplification has been evaluated. We took advantage of the singlet oxygen production by photosensitizers upon photoexcitation, leading to a photocurrent response due to the singlet oxygen-induced (electro)chemical conversions. Following the demonstration of the detection strategy, the analytical performance of the sensing system was evaluated using magnetic beads-based nucleic acid assay on disposable electrode platforms, with a focus to enhance the sensitivity and robustness of the technique in detecting complementary nucleic acid targets. Following the fundamental evaluation of the singlet oxygen-based PEC detection of nucleic acids, we further optimized the assay and measurement parameters and employed the sensing strategy for a polymerase chain reaction-free (PCR-free) quantification of miRNAs related to prostate cancer. By successfully detecting and quantifying low-picomolar range concentrations (< 10 pM) in plasma samples from prostate cancer patients, we successfully showed the applicability of the novel sensing strategy. We have also compared and positioned the performance of our developed PEC strategy with an existing state-of-art technique, i.e. electrochemiluminescence (ECL). Our PEC strategy performed on par with ECL, both yielding low-picomolar detection limits in serum matrices, however quicker and cheaper than ECL. Owing to the versatility of this PEC technique, the final study explored its multiplexing capability. As a starting point in this branch of the research, we have investigated two possible ways for multiplexing. To perform multiple measurements at the same time, constructing calibration plots and quantifying unknown miRNA concentrations in patient samples at the same time, we have developed a high-throughput detection with 96X multi-channel electrode systems and in-house designed and constructed 96XLED illumination sources. Secondly, to detect more than one target miRNA in a single measurement, intra-vial multiplexing where the samples were analyzed for different targets in one vial was also explored. Altogether, this thesis presents the fundamentals, development and application of a novel PEC strategy for detecting short (< 25 bases) nucleic acid sequences, in particular, miRNA. With an aim to serve as a distinctive technique to function as a clinical testing platform without any need for PCR, this work adds value to the development of nucleic acid-based sensors for miRNAs and other short-stranded nucleic acid biomarkers, and benefits in the early detection of diseases like cancer.

Abstract: The WHO estimates that 8–10% of couples are facing fertility problems, often due to inaccuracy in predicting the female's ovulation period controlled by four key hormones. The quantification and monitoring of such key hormones are crucial for the early identification of infertility, but also in improving therapeutic management associated with hormonal imbalance. In this review, we extensively summarize and discuss: i) drawbacks of laboratory methods for fertility testing (costly, invasive, complex) and commercially available point-of-care tests (measuring only one/two of the four key hormones), ii) the understanding of different biosensors for fertility monitoring, and iii) an in-depth classification and overview of aptamer-based sensing of the hormones of interest. This review provides insights on hormone detection strategies for fertility, with a focus on the classification of the current ‘aptasensing’ strategies, aiming to assist as a basic guide for the development of accurate fertility window monitoring tools based on aptamers.

Abstract: Circular economy (CE) principles distinguish between technical and biological cycles. Technical cycles involve the management of stocks of non-renewable abiotic resources that cannot be appropriately returned to the biosphere, whereas, biological cycles involve the flows of renewable biotic resources that can safely cycle in and out of the biosphere. Despite this distinction, existing CE monitors are typically developed for technical cycles, and focus mainly on the extent to which resources are looped back in the technosphere. These monitors seem less apt to assess the circularity of biological cycles. This study aims to identify this gap by critically reviewing the CE monitoring criteria and CE assessment tools, and evaluate if they include the four key characteristics of biological cycles. Firstly, biotic resources, although renewable, require to be harvested sustainably. Secondly, while abiotic resources can be restored and recycled to their original quality, biotic resources degrade in quality with every subsequent use and are, hence, cascaded in use. Thirdly, biotic resources should safely return as nutrients to the biosphere to support the regeneration of ecosystems. Fourthly, biological cycles have environmental impacts due to resource extraction, resulting from land-use and resource-depletion and biogenic carbon flows. The CE monitoring criteria lack in thoroughly assessing these characteristics. With the growing demand for biotic resources, the gap in the assessment could exacerbate the overexploitation of natural resources and cause the degradation of ecosystems. The study discusses measures to bridge this gap and suggests ways to design a CE assessment framework that is also apt for biological cycles.

Abstract: The bio-nanohybrid gelatin protein/cadmium sulfide (Gel/CdS) quantum dots (QDs) have been designed via a facile one-pot strategy. The amino acids group of gelatin chelate Cd2+ and grow CdS QDs without any agglomeration. The H-1 NMR spectra indicate that during the above process there are no alterations of the gelatin protein structure conformation and chemical functionalities. The prepared Gel/CdS QDs were characterized and their potential as a system for cellular imaging and the electrochemical sensor for hydrogen peroxide (H2O2) detection applications were investigated. The obtained results demonstrate that the developed Gel/CdS QDs system could offer a simple and convenient operating strategy both for the class of contrast agents for cell labeling and electrochemical sensors purposes.

Abstract: We compare the image-force barrier lowering (IFBL) and calculate the resulting contact resistance for four different metal-dielectric-two-dimensional (2D) material configurations. We analyze edge contacts in three different geometries (a homogeneous dielectric throughout, including the 2D layer; a homogeneous dielectric surrounding the 2D layer, both ungated and back gated) and also a top-contact assuming a homogeneous dielectric. The image potential energy of each configuration is determined and added to the Schottky energy barrier which is calculated assuming a textbook Schottky potential. For each configuration, the contact resistivity is calculated using the WKB approximation and the effective mass approximation using either SiO2 or HfO2 as the surrounding dielectric. We obtain the lowest contact resistance of 1 k Omega mu m by n-type doping an edge contacted transition metal-dichalcogenide (TMD) monolayer, sandwiched between SiO2 dielectric, with similar to 1012 cm-2 donor atoms. When this optimal configuration is used, the contact resistance is lowered by a factor of 50 compared to the situation when the IFBL is not considered.

Abstract: Advances in multi-spectral detectors are causing a paradigm shift in x-ray computed tomography (CT). Spectral information acquired from these detectors can be used to extract volumetric material composition maps of the object of interest. If the materials and their spectral responses are known a priori, the image reconstruction step is rather straightforward. If they are not known, however, the maps as well as the responses need to be estimated jointly. A conventional workflow in spectral CT involves performing volume reconstruction followed by material decomposition, or vice versa. However, these methods inherently suffer from the ill-posedness of the joint reconstruction problem. To resolve this issue, we propose 'A Dictionary-based Joint reconstruction and Unmixing method for Spectral Tomography' (ADJUST). Our formulation relies on forming a dictionary of spectral signatures of materials common in CT and prior knowledge of the number of materials present in an object. In particular, we decompose the spectral volume linearly in terms of spatial material maps, a spectral dictionary, and the indicator of materials for the dictionary elements. We propose a memory-efficient accelerated alternating proximal gradient method to find an approximate solution to the resulting bi-convex problem. From numerical demonstrations on several synthetic phantoms, we observe that ADJUST performs exceedingly well compared to other state-of-the-art methods. Additionally, we address the robustness of ADJUST against limited and noisy measurement patterns. The demonstration of the proposed approach on a spectral micro-CT dataset shows its potential for real-world applications. Code is available at https://github.com/mzeegers/ADJUST.

Abstract: The COVID-19 pandemic had large repercussions for our economy and organizations. Improved resilience can give organizations the ability to withstand crises and build back better and faster. This article assesses resilience of organizations and sole proprietorships in the context of the COVID-19 pandemic with eight circular strategies as explanatory variables. Furthermore, these eight circular strategies are also used to assess the organizations' and sole proprietorships' resilience outside of the COVID-19 pandemic. This analysis is conducted to explain how circular strategies can help companies and sole proprietorships maintain stability. The analysis was performed by means of a survey conducted between May and June 2020 in Flanders (Belgium), using a sample of 542 respondents. After performing a regression analysis combined with expert opinions collected through interviews, we find that companies and sole proprietorships with a higher circularity score have a significantly higher resilience score during crises and during normal times, compared to less circular companies. Furthermore, we find that the size of the company does not matter during a crisis to adapt and react flexibly, while it is important when there is no crisis. Finally, we argue that it is the combination of different circular strategies which yields to the highest results for the organizations' resilience and we provide policy recommendations based on the most asked support measures.