Strain release at the graphene-Ni(100) interface investigated by in-situ and operando scanning tunnelling microscopy

Interface strain can significantly influence the mechanical, electronic and magnetic properties of low- dimensional materials. Here we investigated by scanning tunneling microscopy how the stress intro- duced by a mismatched interface affects the structure of a growing graphene (Gr) layer on a Ni(100) surface in real time during the process. Strain release appears to be the main factor governing morphology, with the interplay of two simultaneous driving forces: on the one side the need to obtain two-dimensional best registry with the substrate, via formation of moire patterns, on the other side the requirement of optimal one-dimensional in-plane matching with the transforming nickel carbide layer, achieved by local rotation of the growing Gr flake. Our work suggests the possibility of tuning the local properties of two-dimensional films at the nanoscale through exploitation of strain at a one-dimensional interface

Phenotyping the histopathological subtypes of non-small-cell lung carcinoma: how beneficial is radiomics?

The aim of this study was to investigate the usefulness of radiomics in the absence of well-defined standard guidelines. Specifically, we extracted radiomics features from multicenter computed tomography (CT) images to differentiate between the four histopathological subtypes of non-small-cell lung carcinoma (NSCLC). In addition, the results that varied with the radiomics model were compared. We investigated the presence of the batch effects and the impact of feature harmonization on the models' performance. Moreover, the question on how the training dataset composition influenced the selected feature subsets and, consequently, the model's performance was also investigated. Therefore, through combining data from the two publicly available datasets, this study involves a total of 152 squamous cell carcinoma (SCC), 106 large cell carcinoma (LCC), 150 adenocarcinoma (ADC), and 58 no other specified (NOS). Through the matRadiomics tool, which is an example of Image Biomarker Standardization Initiative (IBSI) compliant software, 1781 radiomics features were extracted from each of the malignant lesions that were identified in CT images. After batch analysis and feature harmonization, which were based on the ComBat tool and were integrated in matRadiomics, the datasets (the harmonized and the non-harmonized) were given as an input to a machine learning modeling pipeline. The following steps were articulated: (i) training-set/test-set splitting (80/20); (ii) a Kruskal-Wallis analysis and LASSO linear regression for the feature selection; (iii) model training; (iv) a model validation and hyperparameter optimization; and (v) model testing. Model optimization consisted of a 5-fold cross-validated Bayesian optimization, repeated ten times (inner loop). The whole pipeline was repeated 10 times (outer loop) with six different machine learning classification algorithms. Moreover, the stability of the feature selection was evaluated. Results showed that the batch effects were present even if the voxels were resampled to an isotropic form and whether feature harmonization correctly removed them, even though the models' performances decreased. Moreover, the results showed that a low accuracy (61.41%) was reached when differentiating between the four subtypes, even though a high average area under curve (AUC) was reached (0.831). Further, a NOS subtype was classified as almost completely correct (true positive rate similar to 90%). The accuracy increased (77.25%) when only the SCC and ADC subtypes were considered, as well as when a high AUC (0.821) was obtained-although harmonization decreased the accuracy to 58%. Moreover, the features that contributed the most to models' performance were those extracted from wavelet decomposed and Laplacian of Gaussian (LoG) filtered images and they belonged to the texture feature class.. In conclusion, we showed that our multicenter data were affected by batch effects, that they could significantly alter the models' performance, and that feature harmonization correctly removed them. Although wavelet features seemed to be the most informative features, an absolute subset could not be identified since it changed depending on the training/testing splitting. Moreover, performance was influenced by the chosen dataset and by the machine learning methods, which could reach a high accuracy in binary classification tasks, but could underperform in multiclass problems.It is, therefore, essential that the scientific community propose a more systematic radiomics approach, focusing on multicenter studies, with clear and solid guidelines to facilitate the translation of radiomics to clinical practice

Carbon dioxide reduction on Ir(111): Stable hydrocarbon surface species at near-ambient pressure

Stable hydrocarbon surface species in the carbon dioxide hydrogenation reaction on Ir(111) were identified by means of infrared-visible sum-frequency generation vibrational spectroscopy and X-ray photoelectron spectroscopy under near-ambient pressure conditions (0.1 mbar). By introducing gas phase binary and ternary mixtures of CO2, CO, and H2 into the reaction chamber, stable ethylidyne and ethynyl species were found at the metal surface above 425 K, in remarkable analogy with that observed during the ethylene decomposition process yielding graphene. In addition, upon increasing temperature (up to 600 K depending on the reaction conditions), vibrational and electronic spectroscopic fingerprints appeared that could be attributed to the nucleation of aromatic hydrocarbons at the edge of metastable graphenic clusters interacting with the metal surface

Temperature-Driven Changes of the Graphene Edge Structure on Ni(111): Substrate vs Hydrogen Passivation

Atomic-scale description of the structure of graphene edges on Ni(111), both during and post growth, is obtained by scanning tunneling microscopy (STM) in combination with density functional theory (DFT). During growth, at 470 \ub0C, fast STM images (250 ms/image) evidence graphene flakes anchored to the substrate, with the edges exhibiting zigzag or Klein structure depending on the orientation. If growth is frozen, the flake edges hydrogenate and detach from the substrate, with hydrogen reconstructing the Klein edges

Operando atomic-scale study of graphene CVD growth at steps of polycrystalline nickel

An operando investigation of graphene growth on (100) grains of polycrystalline nickel (Ni) surfaces was performed by means of variable-temperature scanning tunneling microscopy complemented by density functional theory simulations. A clear description of the atomistic mechanisms ruling the graphene expansion process at the stepped regions of the substrate is provided, showing that different routes can be followed, depending on the height of the steps to be crossed. When a growing graphene flake reaches a monoatomic step, it extends jointly with the underlying Ni layer; for higher Ni edges, a different process, involving step retraction and graphene landing, becomes active. At step bunches, the latter mechanism leads to a peculiar \u2018staircase formation\u2019 behavior, where terraces of equal width form under the overgrowing graphene, driven by a balance in the energy cost between C\u2013Ni bond formation and stress accumulation in the carbon layer. Our results represent a step towards bridging the material gap in searching new strategies and methods for the optimization of chemical vapor deposition graphene production on polycrystalline metal surfaces

1D selective confinement and diffusion of metal atoms on graphene

The role of moiré graphene superstructures in favoring confined adsorption of different metal atoms is an intriguing problem not yet completely solved. Graphene (G) grown on Ni(100) forms a striped moiré pattern of valleys, where G approaches the nickel substrate and interacts with it rather strongly, and ridges, where G stays far away from the substrate and acts almost free-standing. Combining density functional theory (DFT) calculations and scanning-tunneling microscopy (STM) measurements, we show that this peculiar moiré constitutes a regular nanostructured template on a 2D support, confining in 1D trails single metal atoms and few atoms clusters. DFT calculations show that the confinement is selective and highly dependent on the atomic species, with some species preferring to adsorb on ridges and the other showing preference for valleys. Co and Au adsorbates, for instance, have opposite behavior, as predicted by DFT and observed by STM. The origin of such disparate behavior is traced back to the electrostatic interaction between the charged adsorbate and the nickel surface. Moreover, the selectivity is not restricted to the adsorption process only, but persists as adsorbate starts its diffusion, resulting in unidirectional mass transport on a continuous 2D support. These findings hold great promise for exploiting tailored nanostructured templates in a wide range of potential applications involving mass transport along element-specific routes

Exceptionally Stable Cobalt Nanoclusters on Functionalized Graphene

To improve reactivity and achieve a higher material efficiency, catalysts are often used in the form of clusters with nanometer dimensions, down to single atoms. Since the corresponding properties are highly structure-dependent, a suitable support is thus required to ensure cluster stability during operating conditions. Herein, an efficient method to stabilize cobalt nanoclusters on graphene grown on nickel substrates, exploiting the anchoring effect of nickel atoms incorporated in the carbon network is presented. The anchored nanoclusters are studied by in situ variable temperature scanning tunneling microscopy at different temperatures and upon gas exposure. Cluster stability upon annealing up to 200 °C and upon CO exposure at least up to 1 × 10−6 mbar CO partial pressure is demonstrated. Moreover, the dimensions of the cobalt nanoclusters remain surprisingly small (<3 nm diameter) with a narrow size distribution. Density functional theory calculations demonstrate that the interplay between the low diffusion barrier on graphene on nickel and the strong anchoring effect of the nickel atoms leads to the increased stability and size selectivity of these clusters. This anchoring technique is expected to be applicable also to other cases, with clear advantages for transition metals that are usually difficult to stabilize

Nanobubbles at GPa Pressure under Graphene

We provide direct evidence that irradiation of a graphene membrane on Ir with low-energy Ar ions induces formation of solid noble-gas nanobubbles. Their size can be controlled by thermal treatment, reaching tens of nanometers laterally and height of 1.5 nm upon annealing at 1080 \ub0C. Ab initio calculations show that Ar nanobubbles are subject to pressures reaching tens of GPa, their formation being driven by minimization of the energy cost of film distortion and loss of adhesion