Electrostatic potentials of atomic nanostructures at metal surfaces quantified by scanning quantum dot microscopy.

The discrete and charge-separated nature of matter - electrons and nuclei - results in local electrostatic fields that are ubiquitous in nanoscale structures and relevant in catalysis, nanoelectronics and quantum nanoscience. Surface-averaging techniques provide only limited experimental access to these potentials, which are determined by the shape, material, and environment of the nanostructure. Here, we image the potential over adatoms, chains, and clusters of Ag and Au atoms assembled on Ag(111) and quantify their surface dipole moments. By focusing on the total charge density, these data establish a benchmark for theory. Our density functional theory calculations show a very good agreement with experiment and allow a deeper analysis of the dipole formation mechanisms, their dependence on fundamental atomic properties and on the shape of the nanostructures. We formulate an intuitive picture of the basic mechanisms behind dipole formation, allowing better design choices for future nanoscale systems such as single-atom catalysts.

Kinetic Trapping of Charge-Transfer Molecules at Metal Interfaces.

Despite the common expectation that conjugated organic molecules on metals adsorb in a flat-lying layer, several recent studies have found coverage-dependent transitions to upright-standing phases, which exhibit notably different physical properties. In this work, we argue that from an energetic perspective, thermodynamically stable upright-standing phases may be more common than hitherto thought. However, for kinetic reasons, this phase may often not be observed experimentally. Using first-principles kinetic Monte Carlo simulations, we find that the structure with lower molecular density is (almost) always formed first, reminiscent of Ostwald's rule of stages. The phase transitions to the upright-standing phase are likely to be kinetically hindered under the conditions typically used in surface science. The simulation results are experimentally confirmed for the adsorption of tetracyanoethylene on Cu(111) using infrared and X-ray photoemission spectroscopy. Investigating both the role of the growth conditions and the energetics of the interface, we find that the time for the phase transition is determined mostly by the deposition rate and, thus, is mostly independent of the nature of the molecule.

Exploring in-plane interactions beside an adsorbed molecule with lateral force microscopy.

Atomic force microscopy with a CO-functionalized tip can be used to directly image the internal structure of a planar molecule and to characterize chemical bonds. However, hydrogen atoms usually cannot be directly observed due to their small size. At the same time, these atoms are highly important, since they can direct on-surface chemical reactions. Measuring in-plane interactions at the sides of PTCDA (3,4,9,10-perylenetetracarboxylic dianhydride) molecules with lateral force microscopy allowed us to directly identify hydrogen atoms via their repulsive signature, which we confirmed with a model incorporating radially symmetric atomic interactions. Additional features were observed in the force data and could not be explained by H-bonding of the CO tip with the PTCDA sides. Instead, they are caused by electrostatic interaction of the large dipole of the metal apex, which we verified with density functional theory. This calculation allowed us to estimate the strength of the dipole at the metal tip apex. To further confirm that this dipole generally affects measurements on weakly polarized systems, we investigated the archetypical surface adsorbate of a single CO molecule. We determined the radially symmetric atomic interaction to be valid over a large solid angle of 5.4 sr, corresponding to 82°. We therefore find that in both the PTCDA and CO systems, the underlying interaction preventing direct observations of H-bonding and causing a collapse of the radially symmetric model is the dipole at the metal apex, which plays a significant role when approaching closer than standard imaging heights.

Partial restoration of aromaticity of pentacene-5,7,12,14-tetrone on Cu(111).

The π-conjugation of organic molecules can be strongly influenced when functional groups are added to a molecule, for example when pentacene is converted into pentacene-5,7,12,14-tetrone (P4O) by substitution of four H-atoms with four O-atoms, leading to four CO double bonds. In fact, although free P4O resembles the parent hydrocarbon pentacene structurally at a first glance, its electronic properties differ drastically and can be more accurately described by three benzene units connected via four carbonyl groups. If P4O is deposited onto Cu(111), the electronic interaction across the interface has previously been reported to fully restore the π-conjugation through a weakening of the CO double bonds and a redistribution of electrons, both of which have been explained with the model of surface-induced aromatic stabilization. Here, we observe for the case of P4O on Cu(111) that the molecule does not exhibit full π-conjugation upon interaction with the surface, likely because of the special electronic nature of the hybridized P4O on Cu(111). Our results are derived from CO-functionalized noncontact atomic force microscopy measurements in combination with dispersion-corrected density functional theory calculations yielding bond lengths and molecular geometries. To characterize the aromaticity, we apply the harmonic oscillator model of aromaticity.

Interplay of Adsorption Geometry and Work Function Evolution at the TCNE/Cu(111) Interface.

The adsorption of organic electron acceptors on metal surfaces is a powerful way to change the effective work function of the substrate through the formation of charge-transfer-induced dipoles. The work function of the interfaces is hence controlled by the redistribution of charges upon adsorption of the organic layer, which depends not only on the electron affinity of the organic material but also on the adsorption geometry. As shown in this work, the latter dependence controls the work function also in the case of adsorbate layers exhibiting a mixture of various adsorption geometries. Based on a combined experimental (core-level and infrared spectroscopy) and theoretical (density functional theory) study for tetracyanoethylene (TCNE) on Cu(111), we find that TCNE adsorbs in at least three different orientations, depending on TCNE coverage. At low coverage, flat lying TCNE dominates, as it possesses the highest adsorption energy. At a higher coverage, additionally, two different standing orientations are found. This is accompanied by a large increase in the work function of almost 3 eV at full monolayer coverage. Our results suggest that the large increase in work function is mainly due to the surface dipole of the free CN groups of the standing molecules and less dependent on the charge-transfer dipole of the differently oriented and charged molecules. This, in turn, opens new opportunities to control the work function of interfaces, e.g., by synthetic modification of the adsorbates, which may allow one to alter the adsorption geometries of the molecules as well as their contributions to the interface dipoles and, hence, the work function.

Impact of Static Distortion Waves on Superlubricity.

Friction is a major source of energy loss in mechanical devices. This energy loss may be minimized by creating interfaces with extremely reduced friction, i.e., superlubricity. Conventional wisdom holds that incommensurate interface structures facilitate superlubricity. Accurately describing friction necessitates the precise modeling of the interface structure. This, in turn, requires the use of accurate first-principles electronic structure methods, especially when studying organic/metal interfaces, which are highly relevant due to their tunability and propensity to form incommensurate structures. However, the system size required to calculate incommensurate structures renders such calculations intractable. As a result, studies of incommensurate interfaces have been limited to very simple model systems or strongly simplified methodology. We overcome this limitation by developing a machine-learned interatomic potential that is able to determine energies and forces for structures containing thousands to tens of thousands of atoms with an accuracy comparable to conventional first-principles methods but at a fraction of the cost. Using this approach, we quantify the breakdown of superlubricity in incommensurate structures due to the formation of static distortion waves. Moreover, we extract design principles to engineer incommensurate interface systems where the formation of static distortion waves is suppressed, which facilitates low friction coefficients.

A Matched Molecular and Clinical Analysis of the Epithelioid Haemangioendothelioma Cohort in the Stafford Fox Rare Cancer Program and Contextual Literature Review.

BACKGROUND: Epithelioid haemangioendothelioma (EHE) is an ultra-rare malignant vascular tumour with a prevalence of 1 per 1,000,000. It is typically molecularly characterised by a WWTR1::CAMTA1 gene fusion in approximately 90% of cases, or a YAP1::TFE3 gene fusion in approximately 10% of cases. EHE cases are typically refractory to therapies, and no anticancer agents are reimbursed for EHE in Australia. METHODS: We report a cohort of nine EHE cases with comprehensive histologic and molecular profiling from the Walter and Eliza Hall Institute of Medical Research Stafford Fox Rare Cancer Program (WEHI-SFRCP) collated via nation-wide referral to the Australian Rare Cancer (ARC) Portal. The diagnoses of EHE were confirmed by histopathological and immunohistochemical (IHC) examination. Molecular profiling was performed using the TruSight Oncology 500 assay, the TruSight RNA fusion panel, whole genome sequencing (WGS), or whole exome sequencing (WES). RESULTS: Molecular analysis of RNA, DNA or both was possible in seven of nine cases. The WWTR1::CAMTA1 fusion was identified in five cases. The YAP1::TFE3 fusion was identified in one case, demonstrating unique morphology compared to cases with the more common WWTR1::CAMTA1 fusion. All tumours expressed typical endothelial markers CD31, ERG, and CD34 and were negative for pan-cytokeratin. Cases with a WWTR1::CAMTA1 fusion displayed high expression of CAMTA1 and the single case with a YAP1::TFE3 fusion displayed high expression of TFE3. Survival was highly variable and unrelated to molecular profile. CONCLUSIONS: This cohort of EHE cases provides molecular and histopathological characterisation and matching clinical information that emphasises the molecular patterns and variable clinical outcomes and adds to our knowledge of this ultra-rare cancer. Such information from multiple studies will advance our understanding, potentially improving treatment options.

Sensor Fusion for the Robust Detection of Facial Regions of Neonates Using Neural Networks.

The monitoring of vital signs and increasing patient comfort are cornerstones of modern neonatal intensive care. Commonly used monitoring methods are based on skin contact which can cause irritations and discomfort in preterm neonates. Therefore, non-contact approaches are the subject of current research aiming to resolve this dichotomy. Robust neonatal face detection is essential for the reliable detection of heart rate, respiratory rate and body temperature. While solutions for adult face detection are established, the unique neonatal proportions require a tailored approach. Additionally, sufficient open-source data of neonates on the NICU is lacking. We set out to train neural networks with the thermal-RGB-fusion data of neonates. We propose a novel indirect fusion approach including the sensor fusion of a thermal and RGB camera based on a 3D time-of-flight (ToF) camera. Unlike other approaches, this method is tailored for close distances encountered in neonatal incubators. Two neural networks were used with the fusion data and compared to RGB and thermal networks. For the class "head" we reached average precision values of 0.9958 (RetinaNet) and 0.9455 (YOLOv3) for the fusion data. Compared with the literature, similar precision was achieved, but we are the first to train a neural network with fusion data of neonates. The advantage of this approach is in calculating the detection area directly from the fusion image for the RGB and thermal modality. This increases data efficiency by 66%. Our results will facilitate the future development of non-contact monitoring to further improve the standard of care for preterm neonates.

Targeting homologous recombination deficiency in uterine leiomyosarcoma.

BACKGROUND: Uterine leiomyosarcoma (uLMS) is a rare and aggressive gynaecological malignancy, with individuals with advanced uLMS having a five-year survival of < 10%. Mutations in the homologous recombination (HR) DNA repair pathway have been observed in ~ 10% of uLMS cases, with reports of some individuals benefiting from poly (ADP-ribose) polymerase (PARP) inhibitor (PARPi) therapy, which targets this DNA repair defect. In this report, we screened individuals with uLMS, accrued nationally, for mutations in the HR repair pathway and explored new approaches to therapeutic targeting. METHODS: A cohort of 58 individuals with uLMS were screened for HR Deficiency (HRD) using whole genome sequencing (WGS), whole exome sequencing (WES) or NGS panel testing. Individuals identified to have HRD uLMS were offered PARPi therapy and clinical outcome details collected. Patient-derived xenografts (PDX) were generated for therapeutic targeting. RESULTS: All 13 uLMS samples analysed by WGS had a dominant COSMIC mutational signature 3; 11 of these had high genome-wide loss of heterozygosity (LOH) (> 0.2) but only two samples had a CHORD score > 50%, one of which had a homozygous pathogenic alteration in an HR gene (deletion in BRCA2). A further three samples harboured homozygous HRD alterations (all deletions in BRCA2), detected by WES or panel sequencing, with 5/58 (9%) individuals having HRD uLMS. All five individuals gained access to PARPi therapy. Two of three individuals with mature clinical follow up achieved a complete response or durable partial response (PR) with the subsequent addition of platinum to PARPi upon minor progression during initial PR on PARPi. Corresponding PDX responses were most rapid, complete and sustained with the PARP1-specific PARPi, AZD5305, compared with either olaparib alone or olaparib plus cisplatin, even in a paired sample of a BRCA2-deleted PDX, derived following PARPi therapy in the patient, which had developed PARPi-resistance mutations in PRKDC, encoding DNA-PKcs. CONCLUSIONS: Our work demonstrates the value of identifying HRD for therapeutic targeting by PARPi and platinum in individuals with the aggressive rare malignancy, uLMS and suggests that individuals with HRD uLMS should be included in trials of PARP1-specific PARPi.

Polymorphism mediated by electric fields: a first principles study on organic/inorganic interfaces.

Organic/inorganic interfaces are known to exhibit rich polymorphism, where different polymorphs often possess significantly different properties. Which polymorph forms during an experiment depends strongly on environmental parameters such as deposition temperature and partial pressure of the molecule to be adsorbed. To prepare desired polymorphs these parameters are varied. However, many polymorphs are difficult to access within the experimentally available temperature-pressure ranges. In this contribution, we investigate how electric fields can be used as an additional lever to make certain structures more readily accessible. On the example of tetracyanoethylene (TCNE) on Cu(111), we analyze how electric fields change the energy landscape of interface systems. TCNE on Cu(111) can form either lying or standing polymorphs, which exhibit significantly different work functions. We combine first-principles calculations with a machine-learning based structure search algorithm and ab initio thermodynamics to demonstrate that electric fields can be exploited to shift the temperature of the phase transition between standing and lying polymorphs by up to 100 K.

Australian Genomics: Outcomes of a 5-year national program to accelerate the integration of genomics in healthcare.

Australian Genomics is a national collaborative partnership of more than 100 organizations piloting a whole-of-system approach to integrating genomics into healthcare, based on federation principles. In the first five years of operation, Australian Genomics has evaluated the outcomes of genomic testing in more than 5,200 individuals across 19 rare disease and cancer flagship studies. Comprehensive analyses of the health economic, policy, ethical, legal, implementation and workforce implications of incorporating genomics in the Australian context have informed evidence-based change in policy and practice, resulting in national government funding and equity of access for a range of genomic tests. Simultaneously, Australian Genomics has built national skills, infrastructure, policy, and data resources to enable effective data sharing to drive discovery research and support improvements in clinical genomic delivery.

Autonomous Single-Molecule Manipulation Based on Reinforcement Learning.

Building nanostructures one-by-one requires precise control of single molecules over many manipulation steps. The ideal scenario for machine learning algorithms is complex, repetitive, and time-consuming. Here, we show a reinforcement learning algorithm that learns how to control a single dipolar molecule in the electric field of a scanning tunneling microscope. Using about 2250 iterations to train, the algorithm learned to manipulate the molecule toward specific positions on the surface. Simultaneously, it generates physical insights into the movement as well as orientation of the molecule, based on the position where the electric field is applied relative to the molecule. This reveals that molecular movement is strongly inhibited in some directions, and the torque is not symmetric around the dipole moment.

Long-range dispersion-inclusive machine learning potentials for structure search and optimization of hybrid organic-inorganic interfaces.

The computational prediction of the structure and stability of hybrid organic-inorganic interfaces provides important insights into the measurable properties of electronic thin film devices, coatings, and catalyst surfaces and plays an important role in their rational design. However, the rich diversity of molecular configurations and the important role of long-range interactions in such systems make it difficult to use machine learning (ML) potentials to facilitate structure exploration that otherwise requires computationally expensive electronic structure calculations. We present an ML approach that enables fast, yet accurate, structure optimizations by combining two different types of deep neural networks trained on high-level electronic structure data. The first model is a short-ranged interatomic ML potential trained on local energies and forces, while the second is an ML model of effective atomic volumes derived from atoms-in-molecules partitioning. The latter can be used to connect short-range potentials to well-established density-dependent long-range dispersion correction methods. For two systems, specifically gold nanoclusters on diamond (110) surfaces and organic π-conjugated molecules on silver (111) surfaces, we train models on sparse structure relaxation data from density functional theory and show the ability of the models to deliver highly efficient structure optimizations and semi-quantitative energy predictions of adsorption structures.

Note: Reproducibility of potential energy surfaces of organic/metal interfaces on the example of PTCDA on Ag(111).

We recently published a benchmark study of common local, semi-local, and non-local exchange correlation functionals in combination with various van der Waals (vdW) corrections, where we investigated the reproducibility of the potential energy surface of perylenetetracarboxylic dianhydride on Ag(111). This Note presents an additional benchmark of the recently developed non-local many body dispersion (MBD-NL) vdW correction, coupled with the Perdew-Burke-Ernzerhof (PBE) functional. We find that this computation method shows similar performance as the established approaches. Notably, it yields very similar results as PBE + MBD.

Role of Adatoms for the Adsorption of F4TCNQ on Au(111).

Organic adlayers on inorganic substrates often contain adatoms, which can be incorporated within the adsorbed molecular species, forming two-dimensional metal-organic frameworks at the substrate surface. The interplay between native adatoms and adsorbed molecules significantly changes various adlayer properties such as the adsorption geometry, the bond strength between the substrate and the adsorbed species, or the work function at the interface. Here, we use dispersion-corrected density functional theory to gain insight into the energetics that drive the incorporation of native adatoms within molecular adlayers based on the prototypical, experimentally well-characterized system of F4TCNQ on Au(111). We explain the adatom-induced modifications in the adsorption geometry and the adsorption energy based on the electronic structure and charge transfer at the interface.

Correlation between two- and three-dimensional crystallographic lattices for epitaxial analysis. II. Experimental results.

While the crystal structure of the polymorph phase can be studied in three dimensions conveniently by X-ray methods like grazing-incidence X-ray diffraction (GIXD), the first monolayer is only accessible by surface-sensitive methods that allow the determination of a two-dimensional lattice. Here, GIXD measurements with sample rotation are compared with distortion-corrected low-energy electron diffraction (LEED) experiments on conjugated molecules: 3,4;9,10-perylenetetracarboxylic dianhydride (PTCDA), 6,13-pentacenequinone (P2O), 1,2;8,9-dibenzopentacene (trans-DBPen) and dicyanovinyl-quaterthiophene (DCV4T-Et2) grown by physical vapor deposition on Ag(111) and Cu(111) single crystals. For these molecular crystals, which exhibit different crystallographic lattices and crystal orientations as well as epitaxial properties, the geometric parameters of the three-dimensional lattice are compared with the corresponding geometry of the first monolayer. A comparison of the monolayer lattice from LEED investigations with the multilayer lattices determined by rotated GIXD experiments reveals a correlation between the first monolayer and the epitaxial growth of three-dimensional crystals together with lattice distortions and re-alignment of molecules. The selected examples show three possible scenarios of crystal growth on top of an ordered monolayer: (i) growth of a single polymorph, (ii) growth of three different polymorphs; in both cases the first monolayer serves as template. In the third case (iii) strong lattice distortion and distinct molecular re-alignments from the monolayer to epitaxially grown crystals are observed. This is the second part of our work concerning the correlation between two- and three-dimensional crystallographic lattices for epitaxial analysis. In the first part, the theoretical basis has been derived which provides a mathematical relationship between the six lattice parameters of the three-dimensional case and the three parameters obtained for the two-dimensional surface unit cell, together with their orientation to the single-crystalline substrate. In this work, a combined experimental approach of GIXD and LEED is introduced which can be used to investigate the effect of the epitaxial monolayer on the structural properties of molecular crystals grown on top.

From a bistable adsorbate to a switchable interface: tetrachloropyrazine on Pt(111).

Virtually all organic (opto)electronic devices rely on organic/inorganic interfaces with specific properties. These properties are, in turn, inextricably linked to the interface structure. Therefore, a change in structure can introduce a shift in function. If this change is reversible, it would allow constructing a switchable interface. We accomplish this with tetrachloropyrazine on Pt(111), which exhibits a double-well potential with a chemisorbed and a physisorbed minimum. These minima have significantly different adsorption geometries allowing the formation of switchable interface structures. Importantly, these structures facilitate different work function changes and coherent fractions (as would be obtained from X-ray standing wave measurements), which are ideal properties to read out the interface state. We perform surface structure search using a modified version of the SAMPLE approach and account for thermodynamic conditions using ab initio thermodynamics. This allows investigating millions of commensurate as well as higher-order commensurate interface structures. We identify three different classes of structures exhibiting different work function changes and coherent fractions. Using temperature and pressure as handles, we demonstrate the possibility of reversible switching between those different classes, creating a dynamic interface for potential applications in organic electronics.

Interfacial Charge Transfer Influences Thin-Film Polymorphism.

The structure and chemical composition are the key parameters influencing the properties of organic thin films deposited on inorganic substrates. Such films often display structures that substantially differ from the bulk, and the substrate has a relevant influence on their polymorphism. In this work, we illuminate the role of the substrate by studying its influence on para-benzoquinone on two different substrates, Ag(111) and graphene. We employ a combination of first-principles calculations and machine learning to identify the energetically most favorable structures on both substrates and study their electronic properties. Our results indicate that for the first layer, similar structures are favorable for both substrates. For the second layer, we find two significantly different structures. Interestingly, graphene favors the one with less, while Ag favors the one with more electronic coupling. We explain this switch in stability as an effect of the different charge transfer on the two substrates.

Toward Targeted Kinetic Trapping of Organic-Inorganic Interfaces: A Computational Case Study.

Properties of inorganic-organic interfaces, such as their interface dipole, strongly depend on the structural arrangements of the organic molecules. A prime example is tetracyanoethylene (TCNE) on Cu(111), which shows two different phases with significantly different work functions. However, the thermodynamically preferred phase is not always the one that is best suited for a given application. Rather, it may be desirable to selectively grow a kinetically trapped structure. In this work, we employ density functional theory and transition state theory to discuss under which conditions such a kinetic trapping might be possible for the model system of TCNE on Cu. Specifically, we want to trap the molecules in the first layer in a flat-lying orientation. This requires temperatures that are sufficiently low to suppress the reorientation of the molecules, which is thermodynamically more favorable for high dosages, but still high enough to enable ordered growth through diffusion of molecules. On the basis of the temperature-dependent diffusion and reorientation rates, we propose a temperature range at which the reorientation can be successfully suppressed.