Abstract: We calculate bubble nucleation rates in a Lennard-Jones fluid through explicit molecular dynamics simulations. Our approach-based on a recent free energy method (dubbed reweighted Jarzynski sampling), transition state theory, and a simple recrossing correction-allows us to probe a fairly wide range of rates in several superheated and cavitation regimes in a consistent manner. Rate predictions from this approach bridge disparate independent literature studies on the same model system. As such, we find that rate predictions based on classical nucleation theory, direct brute force molecular dynamics simulations, and seeding are consistent with our approach and one another. Published rates derived from forward flux sampling simulations are, however, found to be outliers. This study serves two purposes: First, we validate the reliability of common modeling techniques and extrapolation approaches on a paradigmatic problem in materials science and chemical physics. Second, we further test our highly generic recipe for rate calculations, and establish its applicability to nucleation processes.

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: Chiroptically active, hierarchically structured materials are difficult to accurately characterize due to linear anisotropic contributions (i.e., linear dichroism (LD) and linear birefringence (LB)) and parasitic ellipticities that produce artifactual circular dichroism (CD) signals, in addition to chiral analyte contributions ranging from molecular-scale clusters to micron-sized assemblies. Recently, we have shown that CdS magic-sized clusters (MSC) can self-assemble into ordered films that have a hierarchical structure spanning seven orders of length-scale. These films have a strong CD response, but the chiral origins are obfuscated by the hierarchical architecture and LDLB contributions. Here, we derive and demonstrate a method for extracting the “pure” CD signal (CD generated by structural dissymmetry) from hierarchical MSC films and identified the chiral origin. The theory behind the method is derived using Mueller matrix and Stokes vector conventions and verified experimentally before being applied to hierarchical MSC and nanoparticle films with varying macroscopic orderings. Each film's extracted “true CD” shares a bisignate profile aligned with the exciton peak, indicating the assemblies adopt a chiral arrangement and form an exciton coupled system. Interestingly, the linearly aligned MSC film possesses one of the highest g-factors (0.05) among semiconducting nanostructures reported. Additionally, we find that films with similar electronic transition dipole alignment can possess greatly different g-factors, indicating chirality change rather than anisotropy is the cause of the difference in the CD signal. The difference in g-factor is controllable via film evaporation geometry. This study provides a simple means to measure “true” CD and presents an example of experimentally understanding chiroptic interactions in hierarchical nanostructures.

Abstract: Vanadium dioxide (VO2) is a popular candidate for electronic and optical switching applications due to its well-known semiconductor-metal transition. Its study is notoriously challenging due to the interplay of long- and short-range elastic distortions, as well as the symmetry change and the electronic structure changes. The inherent coupling of lattice and electronic degrees of freedom opens the avenue toward mechanical actuation of single domains. In this work, we show that we can manipulate and monitor the reversible semiconductor-to-metal transition of VO2 while applying a controlled amount of mechanical pressure by a nanosized metallic probe using an atomic force microscope. At a critical pressure, we can reversibly actuate the phase transition with a large modulation of the conductivity. Direct tunneling through the VO2-metal contact is observed as the main charge carrier injection mechanism before and after the phase transition of VO2. The tunneling barrier is formed by a very thin but persistently insulating surface layer of the VO2. The necessary pressure to induce the transition decreases with temperature. In addition, we measured the phase coexistence line in a hitherto unexplored regime. Our study provides valuable information on pressure-induced electronic modifications of the VO2 properties, as well as on nanoscale metal-oxide contacts, which can help in the future design of oxide electronics.

Abstract: The introduction of Na+ is considered as an effective way to improve the performance of Ni-rich cathode materials. However, the direct structure-property correlation for Na+ doped NCM-based cathode materials remain unclear, due to the difficulty of local and accurate structural characterization for light elements such as Li and Na. Moreover, there is the complexity of the modeling for the whole Li ion battery (LIB) system. To tackle the above-mentioned issues, we prepared Na+-doped LiNi0.6Co0.2Mn0.2O2 (Na-NCM622) material. The crystal structure change and the lattice distortion with picometers precision of the Na+-doped material is revealed by Cs-corrected scanning transmission electron microscopy (STEM). Density functional theory (DFT) and the recently proposed electrochemical model, i.e., modified Planck-Nernst-Poisson coupled Frumkin-Butler-Volmer (MPNP-FBV), has been applied to reveal correlations between the activation energy and the charge transfer resistance at multiscale. It is shown that Na+ doping can reduce the activation energy barrier from. G = 1.10 eV to 1.05 eV, resulting in a reduction of the interfacial resistance from 297 O to 134 Omega. Consequently, the Na-NCM622 cathode delivers a superior capacity retention of 90.8 % (159 mAh.g(-1)) after 100 cycles compared to the pristine NCM622 (67.5 %, 108 mAh.g(-1)). Our results demonstrate that the kinetics of Li+ diffusion and the electrochemical reaction can be enhanced by Na+ doping the cathode material.

Abstract: This study reports the low temperature and low pressure conversion (up to 160 °C, p = 3.5 bar) of CO2 and H2 to CO using plasmonic Au/TiO2 nanocatalysts and mildly concentrated artificial sunlight as the sole energy source (up to 13.9 kW·m-2 = 13.9 suns). To distinguish between photothermal and non-thermal contributors, we investigated the impact of the Au nanoparticle size and light intensity on the activity and selectivity of the catalyst. A comparative study between P25 TiO2-supported Au nanocatalysts of a size of 6 nm and 16 nm displayed a 15 times higher activity for the smaller particles, which can only partially be attributed to the higher Au surface area. Other factors that may play a role are e.g., the electronic contact between Au and TiO2 and the ratio between plasmonic absorption and scattering. Both catalysts displayed ≥84% selectivity for CO (side product is CH4). Furthermore, we demonstrated that the catalytic activity of Au/TiO2 increases exponentially with increasing light intensity, which indicated the presence of a photothermal contributor. In dark, however, both Au/TiO2 catalysts solely produced CH4 at the same catalyst bed temperature (160 °C). We propose that the difference in selectivity is caused by the promotion of CO desorption through charge transfer of plasmon generated charges (as a non-thermal contributor).

Abstract: Bimetallic electrocatalysts have emerged as a viable strategy to tune the electrocatalytic CO2 reduction reaction (eCO2RR) for the selective production of valuable base chemicals and fuels. However, obtaining high product selectivity and catalyst stability remain challenging, which hinders the practical application of eCO2RR. In this work, it was found that a small doping concentration of tin (Sn) in copper oxide (CuO) has profound influence on the catalytic performance, boosting the Faradaic efficiency (FE) up to 98% for carbon monoxide (CO) at -0.75 V versus RHE, with prolonged stable performance (FE > 90%) for up to 15 h. Through a combination of ex situ and in situ characterization techniques, the in situ activation and reaction mechanism of the electrocatalyst at work was elucidated. In situ Raman spectroscopy measurements revealed that the binding energy of the crucial adsorbed *CO intermediate was lowered through Sn doping, thereby favoring gaseous CO desorption. This observation was confirmed by density functional theory, which further indicated that hydrogen adsorption and subsequent hydrogen evolution were hampered on the Sn-doped electrocatalysts, resulting in boosted CO formation. It was found that the pristine electrocatalysts consisted of CuO nanoparticles decorated with SnO2 domains, as characterized by ex situ high-resolution scanning transmission electron microscopy and X-ray photoelectron spectroscopy measurements. These pristine nanoparticles were subsequently in situ converted into a catalytically active bimetallic Sn-doped Cu phase. Our work sheds light on the intimate relationship between the bimetallic structure and catalytic behavior, resulting in stable and selective oxide-derived Sn-doped Cu electrocatalysts.

Abstract: Transition metal (TM) doped two-dimensional single-atom catalysts are known as a promising class of catalysts for electrocatalytic gas conversion. However, the detailed mechanisms that occur at the surface of these catalysts are still unknown. In the present work, we simulate three Mo-doped nitrogenated graphene structures. In each catalyst, the position of the Mo active site and the corresponding local carbon topologies are different, i.e. MoN4C10 with in-plane Mo atom, MoN4C8 in which Mo atom bridges two adjacent armchair-like graphitic edges, and MoN2C3 in which Mo is doped at the edge of the graphene sheet. Using Density Functional Theory (DFT) calculations we discuss the electrocatalytic activity of Mosingle bondNsingle bondC structures for nitrogen reduction reaction (NRR) with a focus on unraveling the corresponding mechanisms concerning different Mo site positions and C topologies. Our results indicate that the position of the active site centers has a great effect on its electrocatalytic behavior. The gas phase N2 efficiently reduces to ammonia on MoN4C8 via the distal mechanism with an onset potential of −0.51 V. We confirm that the proposed pyridinic structure, MoN4C8, can catalyze NRR effectively with a low overpotential of 0.35 V.

Abstract: Recent experiments demonstrated that interfacial water dissociation (H2O ⇆ H+ + OH-) could be accelerated exponentially by an electric field applied to graphene electrodes, a phenomenon related to the Wien effect. Here we report an order-of-magnitude acceleration of the interfacial water dissociation reaction under visible-light illumination. This process is accompanied by spatial separation of protons and hydroxide ions across one-atom-thick graphene and enhanced by strong interfacial electric fields. The found photoeffect is attributed to the combination of graphene's perfect selectivity with respect to protons, which prevents proton-hydroxide recombination, and to proton transport acceleration by the Wien effect, which occurs in synchrony with the water dissociation reaction. Our findings provide fundamental insights into ion dynamics near atomically thin proton-selective interfaces and suggest that strong interfacial fields can enhance and tune very fast ionic processes, which is of relevance for applications in photocatalysis and designing reconfigurable materials.

Abstract: High-performance rechargeable batteries are becoming very important for high-end technologies with their ever increasing application areas. Hence, improving the performance of such batteries has become the main bottleneck to transferring high-end technologies to end users. In this study, we propose an argon intercalation strategy to enhance battery performance via engineering the interlayer spacing of honeycomb structures such as graphite, a common electrode material in lithium-ion batteries (LIBs). Herein, we systematically investigated the LIB performance of graphite and hexagonal boron nitride (h-BN) when argon atoms were sent into between their layers by using first-principles density-functional-theory calculations. Our results showed enhanced lithium binding for graphite and h-BN structures when argon atoms were intercalated. The increased interlayer space doubles the gravimetric lithium capacity for graphite, while the volumetric capacity also increased by around 20% even though the volume was also increased. The ab initio molecular dynamics simulations indicate the thermal stability of such graphite structures against any structural transformation and Li release. The nudged-elastic-band calculations showed that the migration energy barriers were drastically lowered, which promises fast charging capability for batteries containing graphite electrodes. Although a similar level of battery promise was not achieved for h-BN material, its enhanced battery capabilities by argon intercalation also support that the argon intercalation strategy can be a viable route to enhance such honeycomb battery electrodes.

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: Background Patient-derived organoids are invaluable for fundamental and translational cancer research and holds great promise for personalized medicine. However, the shortage of available analysis methods, which are often single-time point, severely impede the potential and routine use of organoids for basic research, clinical practise, and pharmaceutical and industrial applications. Methods Here, we developed a high-throughput compatible and automated live-cell image analysis software that allows for kinetic monitoring of organoids, named Organoid Brightfield Identification-based Therapy Screening (OrBITS), by combining computer vision with a convolutional network machine learning approach. The OrBITS deep learning analysis approach was validated against current standard assays for kinetic imaging and automated analysis of organoids. A drug screen of standard-of-care lung and pancreatic cancer treatments was also performed with the OrBITS platform and compared to the gold standard, CellTiter-Glo 3D assay. Finally, the optimal parameters and drug response metrics were identified to improve patient stratification. Results OrBITS allowed for the detection and tracking of organoids in routine extracellular matrix domes, advanced Gri3D (R)-96 well plates, and high-throughput 384-well microplates, solely based on brightfield imaging. The obtained organoid Count, Mean Area, and Total Area had a strong correlation with the nuclear staining, Hoechst, following pairwise comparison over a broad range of sizes. By incorporating a fluorescent cell death marker, infra-well normalization for organoid death could be achieved, which was tested with a 10-point titration of cisplatin and validated against the current gold standard ATP-assay, CellTiter-Glo 3D. Using this approach with OrBITS, screening of chemotherapeutics and targeted therapies revealed further insight into the mechanistic action of the drugs, a feature not achievable with the CellTiter-Glo 3D assay. Finally, we advise the use of the growth rate-based normalised drug response metric to improve accuracy and consistency of organoid drug response quantification. Conclusion Our findings validate that OrBITS, as a scalable, automated live-cell image analysis software, would facilitate the use of patient-derived organoids for drug development and therapy screening. The developed wet-lab workflow and software also has broad application potential, from providing a launching point for further brightfield-based assay development to be used for fundamental research, to guiding clinical decisions for personalized medicine.

Abstract: The Stöber method is one of the most important and fundamental processes for the synthesis of inorganic (nano)materials but has the drawback of using a large amount of organic solvent. Herein, ethanol was used as an example to explore if the organic solvent in a typical Stöber method can be omitted. It was found that ethanol increases the particle size of the obtained silica spheres and aids the formation of uniform silica particles rather than forming a gel. Nevertheless, the results indicated that an organic solvent in the initial synthesis mixture is not indispensable. An initially immiscible synthesis method was discovered, which can replace the organic solvent-based Stöber method to successfully synthesize silica particles with the same size ranges as the original Stöber process without addition of organic solvents. Moreover, this process can be of further value for the extension to synthesis processes of other materials based on the Stöber process.

Abstract: Accurate barriers for rate controlling elementary reactions on metal surfaces are key to understanding, controlling, and predicting the rate of heterogeneously catalyzed processes. While barrier heights for gas phase reactions have been extensively benchmarked, dissociative chemisorption barriers for the reactions of molecules on metal surfaces have received much less attention. The first database called SBH10 and containing 10 entries was recently constructed based on the specific reaction parameter approach to density functional theory (SRP-DFT) and experimental results. We have now constructed a new and improved database (SBH17) containing 17 entries based on SRP-DFT and experiments. For this new SBH17 benchmark study, we have tested three algorithms (high, medium, and light) for calculating barrier heights for dissociative chemisorption on metals, which we have named for the amount of computational effort involved in their use. We test the performance of 14 density functionals at the GGA, GGA+vdW-DF, and meta-GGA rungs. Our results show that, in contrast with the previous SBH10 study where the BEEF-vdW-DF2 functional seemed to be most accurate, the workhorse functional PBE and the MS2 density functional are the most accurate of the GGA and meta-GGA functionals tested. Of the GGA+vdW functionals tested, the SRP32-vdW-DF1 functional is the most accurate. Additionally, we found that the medium algorithm is accurate enough for assessing the performance of the density functionals tested, while it avoids geometry optimizations of minimum barrier geometries for each density functional tested. The medium algorithm does require metal lattice constants and interlayer distances that are optimized separately for each functional. While these are avoided in the light algorithm, this algorithm is found not to give a reliable description of functional performance. The combination of relative ease of use and demonstrated reliability of the medium algorithm will likely pave the way for incorporation of the SBH17 database in larger databases used for testing new density functionals and electronic structure methods.

Abstract: In this work, the magnetic properties of superparamagnetic iron oxide nanoparticles (SPIONs) submitted to an external magnetic field are studied using a Metropolis algorithm. The influence on the M(B) curves of the size distribution of the nanoparticles, of uniaxial anisotropy, and of dipolar interaction between the cores are examined, as well as the influence of drying the samples under a zero or non-zero magnetic field. It is shown that the anisotropy impacts the shape of the magnetization curves, which then deviate from a pure Langevin behaviour, whereas the dipolar interaction has no influence on the curves at 300 K for small particles (with a radius of 3 nm). The fitting of the magnetization curves of particles with magnetic anisotropy to a Langevin model (including a size distribution of the particles) can then lead to erroneous values of the distribution parameters. The simulation results are qualitatively compared to experimental results obtained for iron oxide nanoparticles (with a 3.21 nm median radius).