Structure and Defect Identification at Self-Assembled Islands of CO Using Scanning Probe Microscopy.

Understanding how carbon dioxide (CO) behaves and interacts with surfaces is paramount for the development of sensors and materials to attempt CO mitigation and catalysis. Here, we combine simultaneous atomic force microscopy (AFM) and scanning tunneling microscopy (STM) using CO-functionalized probes with density functional theory (DFT)-based simulations to gain fundamental insight into the behavior of physisorbed CO molecules on a gold(111) surface that also contains one-dimensional metal-organic chains formed by 1,4-phenylene diisocyanide (PDI) bridged by gold (Au) adatoms. We resolve the structure of self-assembled CO islands, both confined between the PDI-Au chains as well as free-standing on the surface and reveal a chiral arrangement of CO molecules in a windmill-like structure that encloses a standing-up CO molecule and other foreign species existing at the surface.

Efficient Electron Hopping Transport through Azurin-Based Junctions.

We conducted a theoretical study of electron transport through junctions of the blue-copper azurin from . We found that single-site hopping can lead to either higher or lower current values compared to fully coherent transport. This depends on the structural details of the junctions as well as the alignment of the protein orbitals.

Molecular Identification from AFM Images Using the IUPAC Nomenclature and Attribute Multimodal Recurrent Neural Networks.

Spectroscopic methods─like nuclear magnetic resonance, mass spectrometry, X-ray diffraction, and UV/visible spectroscopies─applied to molecular ensembles have so far been the workhorse for molecular identification. Here, we propose a radically different chemical characterization approach, based on the ability of noncontact atomic force microscopy with metal tips functionalized with a CO molecule at the tip apex (referred as HR-AFM) to resolve the internal structure of individual molecules. Our work demonstrates that a stack of constant-height HR-AFM images carries enough chemical information for a complete identification (structure and composition) of quasiplanar organic molecules, and that this information can be retrieved using machine learning techniques that are able to disentangle the contribution of chemical composition, bond topology, and internal torsion of the molecule to the HR-AFM contrast.

Experimental Data Confirm Carrier-Cascade Model for Solid-State Conductance across Proteins.

The finding that electronic conductance across ultrathin protein films between metallic electrodes remains nearly constant from room temperature to just a few degrees Kelvin has posed a challenge. We show that a model based on a generalized Landauer formula explains the nearly constant conductance and predicts an Arrhenius-like dependence for low temperatures. A critical aspect of the model is that the relevant activation energy for conductance is either the difference between the HOMO and HOMO-1 or the LUMO+1 and LUMO energies instead of the HOMO-LUMO gap of the proteins.

Revisiting Vibrational Spectroscopy to Tackle the Chemistry of ZrO Metal-Organic Framework Nodes.

The metal-organic framework MOF-808 contains ZrO nodes with a high density of vacancy sites, which can incorporate carboxylate-containing functional groups to tune chemical reactivity. Although the postsynthetic methods to modify the chemistry of the ZrO nodes in MOFs are well known, tackling these alterations from a structural perspective is still a challenge. We have combined infrared spectroscopy experiments and first-principles calculations to identify the presence of node vacancies accessible for chemical modifications within the MOF-808.

QUAM-AFM: A Free Database for Molecular Identification by Atomic Force Microscopy.

This paper introduces Quasar Science Resources-Autonomous University of Madrid atomic force microscopy image data set (QUAM-AFM), the largest data set of simulated atomic force microscopy (AFM) images generated from a selection of 685,513 molecules that span the most relevant bonding structures and chemical species in organic chemistry. QUAM-AFM contains, for each molecule, 24 3D image stacks, each consisting of constant-height images simulated for 10 tip-sample distances with a different combination of AFM operational parameters, resulting in a total of 165 million images with a resolution of 256 × 256 pixels. The 3D stacks are especially appropriate to tackle the goal of the chemical identification within AFM experiments by using deep learning techniques.

Hydrogen bonded trimesic acid networks on Cu(111) reveal how basic chemical properties are imprinted in HR-AFM images.

High resolution non-contact atomic force microscopy measurements characterize assemblies of trimesic acid molecules on Cu(111) and the link group interactions, providing the first fingerprints utilizing CO-based probes for this widely studied paradigm for hydrogen bond driven molecular self assembly. The enhanced submolecular resolution offered by this technique uniquely reveals key aspects of the competing interactions. Accurate comparison between full-density-based modeled images and experiment allows to identify key structural elements in the assembly in terms of the electron-withdrawing character of the carboxylic groups, interactions of those groups with Cu atoms in the surface, and the valence electron density in the intermolecular region of the hydrogen bonds.

A Deep Learning Approach for Molecular Classification Based on AFM Images.

In spite of the unprecedented resolution provided by non-contact atomic force microscopy (AFM) with CO-functionalized and advances in the interpretation of the observed contrast, the unambiguous identification of molecular systems solely based on AFM images, without any prior information, remains an open problem. This work presents a first step towards the automatic classification of AFM experimental images by a deep learning model trained essentially with a theoretically generated dataset. We analyze the limitations of two standard models for pattern recognition when applied to AFM image classification and develop a model with the optimal depth to provide accurate results and to retain the ability to generalize.

Impact of Small Adsorbates in the Vibrational Spectra of Mg- and Zn-MOF-74 Revealed by First-Principles Calculations.

In this work, we analyze the influence of small adsorbates on the vibrational spectra of Mg- and Zn-metal-organic framework MOF-74 by means of first-principles calculations. In particular, we consider the adsorption of four representative species of different interaction strengths: Ar, CO, HO, and NH. Apart from a comprehensive characterization of the structural and energetic aspects of empty and loaded MOFs, we use a fully quantum approach to evaluate the Raman and IR activities of the normal modes, leading to the construction of the whole vibrational spectra.

Surface-controlled reversal of the selectivity of halogen bonds.

Intermolecular halogen bonds are ideally suited for designing new molecular assemblies because of their strong directionality and the possibility of tuning the interactions by using different types of halogens or molecular moieties. Due to these unique properties of the halogen bonds, numerous areas of application have recently been identified and are still emerging. Here, we present an approach for controlling the 2D self-assembly process of organic molecules by adsorption to reactive vs.

Mechanical Deformation and Electronic Structure of a Blue Copper Azurin in a Solid-State Junction.

Protein-based electronics is an emerging field which has attracted considerable attention over the past decade. Here, we present a theoretical study of the formation and electronic structure of a metal-protein-metal junction based on the blue-copper azurin from pseudomonas aeruginosa. We focus on the case in which the protein is adsorbed on a gold surface and is contacted, at the opposite side, to an STM (Scanning Tunneling Microscopy) tip by spontaneous attachment.

A comprehensive study of the molecular vibrations in solid-state benzylic amide [2]catenane.

The interpretation of vibrational spectra is often complex but a detailed knowledge of the normal modes responsible for the experimental bands provides valuable information about the molecular structure of the sample. In this work we record and assign in detail the infrared (IR) spectrum of the benzylic amide [2]catenane, a complex molecular solid displaying crimped mechanical bonds like the links of a chain. In spite of the large size of the unit cell, we calculate all the vibrational modes of the catenane crystal using quantum first-principles calculations.

A Solid-State Protein Junction Serves as a Bias-Induced Current Switch.

A sample-type protein monolayer, that can be a stepping stone to practical devices, can behave as an electrically driven switch. This feat is achieved using a redox protein, cytochrome C (CytC), with its heme shielded from direct contact with the solid-state electrodes. Ab initio DFT calculations, carried out on the CytC-Au structure, show that the coupling of the heme, the origin of the protein frontier orbitals, to the electrodes is sufficiently weak to prevent Fermi level pinning.

Ab initio electronic structure calculations of entire blue copper azurins.

We present a theoretical study of the blue-copper azurin extracted from Pseudomonas aeruginosa and several of its single amino acid mutants. For the first time, we consider the whole structure of this kind of protein rather than limiting our analysis to the copper complex only. This is accomplished by combining fully ab initio calculations based on density functional theory with atomic-scale molecular dynamics simulations.

High-accuracy large-scale DFT calculations using localized orbitals in complex electronic systems: the case of graphene-metal interfaces.

Over many years, computational simulations based on density functional theory (DFT) have been used extensively to study many different materials at the atomic scale. However, its application is restricted by system size, leaving a number of interesting systems without a high-accuracy quantum description. In this work, we calculate the electronic and structural properties of a graphene-metal system significantly larger than in previous plane-wave calculations with the same accuracy.

Substrate-induced enhancement of the chemical reactivity in metal-supported graphene.

Graphene is commonly regarded as an inert material. However, it is well known that the presence of defects or substitutional hetero-atoms confers graphene promising catalytic properties. In this work, we use first-principles calculations to show that it is also possible to enhance the chemical reactivity of a graphene layer by simply growing it on an appropriate substrate.

Unveiling the atomistic mechanisms for oxygen intercalation in a strongly interacting graphene-metal interface.

The atomistic mechanisms involved in the oxygen (O) intercalation in the strongly interacting graphene (G) on Rh(111) system are characterized in a comprehensive experimental and theoretical study, combining scanning tunneling microscopy and density functional theory (DFT) calculations. Experimental evidence points out that the G areas located just above the metallic steps of the substrate are the active sites for initializing the intercalation process when some micro-etching points appear after molecular oxygen gas exposure. These regions are responsible for both the dissociation of the oxygen molecules and the subsequent penetration to the G-metal interface.

Detecting Mechanochemical Atropisomerization within an STM Break Junction.

We have employed the scanning tunneling microscope break-junction technique to investigate the single-molecule conductance of a family of 5,15-diaryl porphyrins bearing thioacetyl (SAc) or methylsulfide (SMe) binding groups at the ortho position of the phenyl rings (S2 compounds). These ortho substituents lead to two atropisomers, cis and trans, for each compound, which do not interconvert in solution under ambient conditions; even at high temperatures, isomerization takes several hours (half-life 15 h at 140 °C for SAc in CClD). All the S2 compounds exhibit two conductance groups, and comparison with a monothiolated (S1) compound shows the higher group arises from a direct Au-porphyrin interaction.

Purely substitutional nitrogen on graphene/Pt(111) unveiled by STM and first principles calculations.

Nitrogen doping of graphene can be an efficient way of tuning its pristine electronic properties. Several techniques have been used to introduce nitrogen atoms on graphene layers. The main problem in most of them is the formation of a variety of C-N species that produce different electronic and structural changes on the 2D layer.