Connecting Higher-Order Topology with the Orbital Hall Effect in Monolayers of Transition Metal Dichalcogenides.

Monolayers of transition metal dichalcogenides (TMDs) in the 2H structural phase have been recently classified as higher-order topological insulators (HOTIs), protected by C_{3} rotation symmetry. In addition, theoretical calculations show an orbital Hall plateau in the insulating gap of TMDs, characterized by an orbital Chern number. We explore the correlation between these two phenomena in TMD monolayers in two structural phases: the noncentrosymmetric 2H and the centrosymmetric 1T. Using density functional theory, we confirm the characteristics of 2H TMDs and reveal that 1T TMDs are identified by a Z_{4} topological invariant. As a result, when cut along appropriate directions, they host conducting edge states, which cross their bulk energy-band gaps and can transport orbital angular momentum. Our linear response calculations thus indicate that the HOTI phase is accompanied by an orbital Hall effect. Using general symmetry arguments, we establish a connection between the two phenomena with potential implications for orbitronics and spin orbitronics.

How lignin sticks to cellulose-insights from atomic force microscopy enhanced by machine-learning analysis and molecular dynamics simulations.

Elucidating cellulose-lignin interactions at the molecular and nanometric scales is an important research topic with impacts on several pathways of biomass valorization. Here, the interaction forces between a cellulosic substrate and lignin are investigated. Atomic force microscopy with lignin-coated tips is employed to probe the site-specific adhesion to a cellulose film in liquid water. Over seven thousand force-curves are analyzed by a machine-learning approach to cluster the experimental data into types of cellulose-tip interactions. The molecular mechanisms for distinct types of cellulose-lignin interactions are revealed by molecular dynamics simulations of lignin globules interacting with different cellulose Iß crystal facets. This unique combination of experimental force-curves, data-driven analysis, and molecular simulations opens a new approach of investigation and updates the understanding of cellulose-lignin interactions at the nanoscale.

Stability and Rupture of an Ultrathin Ionic Wire.

Using a combination of in situ high-resolution transmission electron microscopy and density functional theory, we report the formation and rupture of ZrO_{2} atomic ionic wires. Near rupture, under tensile stress, the system favors the spontaneous formation of oxygen vacancies, a critical step in the formation of the monatomic bridge. In this length scale, vacancies provide ductilelike behavior, an unexpected mechanical behavior for ionic systems. Our results add an ionic compound to the very selective list of materials that can form monatomic wires and they contribute to the fundamental understanding of the mechanical properties of ceramic materials at the nanoscale.

High-throughput inverse design and Bayesian optimization of functionalities: spin splitting in two-dimensional compounds.

The development of spintronic devices demands the existence of materials with some kind of spin splitting (SS). In this Data Descriptor, we build a database of ab initio calculated SS in 2D materials. More than that, we propose a workflow for materials design integrating an inverse design approach and a Bayesian inference optimization. We use the prediction of SS prototypes for spintronic applications as an illustrative example of the proposed workflow. The prediction process starts with the establishment of the design principles (the physical mechanism behind the target properties), that are used as filters for materials screening, and followed by density functional theory (DFT) calculations. Applying this process to the C2DB database, we identify and classify 358 2D materials according to SS type at the valence and/or conduction bands. The Bayesian optimization captures trends that are used for the rationalized design of 2D materials with the ideal conditions of band gap and SS for potential spintronics applications. Our workflow can be applied to any other material property.

Machine Learning of Microscopic Ingredients for Graphene Oxide/Cellulose Interaction.

Understanding the role of microscopic attributes in nanocomposites allows one to control and, therefore, accelerate experimental system designs. In this work, we extracted the relevant parameters controlling the graphene oxide binding strength to cellulose by combining first-principles calculations and machine learning algorithms. We were able to classify the systems among two classes with higher and lower binding energies, which are well defined based on the isolated graphene oxide features. Using theoretical X-ray photoelectron spectroscopy analysis, we show the extraction of these relevant features. In addition, we demonstrate the possibility of refined control within a machine learning regression between the binding energy values and the system's characteristics. Our work presents a guiding map to control graphene oxide/cellulose interaction.

At the Verge of Topology: Vacancy-Driven Quantum Spin Hall in Trivial Insulators.

Vacancies in materials structure─lowering its atomic density─take the system closer to the atomic limit, to which all systems are topologically trivial. Here we show a mechanism of mediated interaction between vacancies inducing a topologically nontrivial phase. Within an ab initio approach we explore topological transition dependence with the vacancy density in transition metal dichalcogenides. As a case of study, we focus on the PtSe2, for which the pristine form is a trivial semiconductor with an energy gap of 1.2 eV. The vacancies states lead to a large topological gap of 180 meV within the pristine system gap. We derive an effective model describing this topological phase in other transition metal dichalcogenide systems. The mechanism driving the topological phase allows the construction of backscattering protected metallic channels embedded in a semiconducting host.

Conformational analysis of tannic acid: Environment effects in electronic and reactivity properties.

Polyphenols are natural molecules of crucial importance in many applications, of which tannic acid (TA) is one of the most abundant and established. Most high-value applications require precise control of TA interactions with the system of interest. However, the molecular structure of TA is still not comprehended at the atomic level, of which all electronic and reactivity properties depend. Here, we combine an enhanced sampling global optimization method with density functional theory (DFT)-based calculations to explore the conformational space of TA assisted by unsupervised machine learning visualization and then investigate its lowest energy conformers. We study the external environment's effect on the TA structure and properties. We find that vacuum favors compact structures by stabilizing peripheral atoms' weak interactions, while in water, the molecule adopts more open conformations. The frontier molecular orbitals of the conformers with the lowest harmonic vibrational free energy have a HOMO-LUMO energy gap of 2.21 (3.27) eV, increasing to 2.82 (3.88) eV in water, at the DFT generalized gradient approximation (and hybrid) level of theory. Structural differences also change the distribution of potential reactive sites. We establish the fundamental importance of accurate structural consideration in determining TA and related polyphenol interactions in relevant technological applications.

Room-Temperature Negative Differential Resistance in Surface-Supported Metal-Organic Framework Vertical Heterojunctions.

The advances of surface-supported metal-organic framework (SURMOF) thin-film synthesis have provided a novel strategy for effectively integrating metal-organic framework (MOF) structures into electronic devices. The considerable potential of SURMOFs for electronics results from their low cost, high versatility, and good mechanical flexibility. Here, the first observation of room-temperature negative differential resistance (NDR) in SURMOF vertical heterojunctions is reported. By employing the rolled-up nanomembrane approach, highly porous sub-15 nm thick HKUST-1 films are integrated into a functional device. The NDR is tailored by precisely controlling the relative humidity (RH) around the device and the applied electric field. The peak-to-valley current ratio (PVCR) of about two is obtained for low voltages (<2 V). A transition from a metastable state to a field emission-like tunneling is responsible for the NDR effect. The results are interpreted through band diagram analysis, density functional theory (DFT) calculations, and ab initio molecular dynamics simulations for quasisaturated water conditions. Furthermore, a low-voltage ternary inverter as a multivalued logic (MVL) application is demonstrated. These findings point out new advances in employing unprecedented physical effects in SURMOF heterojunctions, projecting these hybrid structures toward the future generation of scalable functional devices.

Recent Advances in Immunosafety and Nanoinformatics of Two-Dimensional Materials Applied to Nano-imaging.

Two-dimensional (2D) materials have emerged as an important class of nanomaterials for technological innovation due to their remarkable physicochemical properties, including sheet-like morphology and minimal thickness, high surface area, tuneable chemical composition, and surface functionalization. These materials are being proposed for new applications in energy, health, and the environment; these are all strategic society sectors toward sustainable development. Specifically, 2D materials for nano-imaging have shown exciting opportunities in in vitro and in vivo models, providing novel molecular imaging techniques such as computed tomography, magnetic resonance imaging, fluorescence and luminescence optical imaging and others. Therefore, given the growing interest in 2D materials, it is mandatory to evaluate their impact on the immune system in a broader sense, because it is responsible for detecting and eliminating foreign agents in living organisms. This mini-review presents an overview on the frontier of research involving 2D materials applications, nano-imaging and their immunosafety aspects. Finally, we highlight the importance of nanoinformatics approaches and computational modeling for a deeper understanding of the links between nanomaterial physicochemical properties and biological responses (immunotoxicity/biocompatibility) towards enabling immunosafety-by-design 2D materials.

Disassembly of TEMPO-Oxidized Cellulose Fibers: Intersheet and Interchain Interactions in the Isolation of Nanofibers and Unitary Chains.

Cellulose disassembly is an important issue in designing nanostructures using cellulose-based materials. In this work, we present a combination of experimental and theoretical study addressing the disassembly of cellulose nanofibrils. Through 2,2,6,6-tetramethylpiperidine-1-oxyl-mediated oxidation processes, combined with atomic force microscopy results, we show the formation of nanofibers with diameters corresponding to that of a single-cellulose polymer chain. The formation of these polymer chains is controlled by repulsive electrostatic interactions between the oxidized chains. Further, first-principles calculations have been performed in order to provide an atomistic understanding of the cellulose disassembling processes, focusing on the balance between the interchain (IC) and intersheet (IS) interactions upon oxidation. First, we analyze these interactions in pristine systems, where we found the IS interaction to be stronger than the IC interaction. In the oxidized systems, we have considered the formation of (charged) carboxylate groups along the inner sites of elementary fibrils. We show a net charge concentration on the carboxylate groups, supporting the emergence of repulsive electrostatic interactions between the cellulose nanofibers. Indeed, our total energy results show that the weakening of the binding strength between the fibrils is proportional to the concentration and net charge density of the carboxylate group. Moreover, by comparing the IC and IS binding energies, we found that most of the disassembly processes should take place by breaking the IC O-H···O hydrogen bond interactions and thus supporting the experimental observation of single- and double-cellulose polymer chains.

Converging Multidimensional Sensor and Machine Learning Toward High-Throughput and Biorecognition Element-Free Multidetermination of Extracellular Vesicle Biomarkers.

Extracellular vesicles (EVs) are a frontier class of circulating biomarkers for the diagnosis and prognosis of different diseases. These lipid structures afford various biomarkers such as the concentrations of the EVs (CV) themselves and carried proteins (CP). However, simple, high-throughput, and accurate determination of these targets remains a key challenge. Herein, we address the simultaneous monitoring of CV and CP from a single impedance spectrum without using recognizing elements by combining a multidimensional sensor and machine learning models. This multidetermination is essential for diagnostic accuracy because of the heterogeneous composition of EVs and their molecular cargoes both within the tumor itself and among patients. Pencil HB cores acting as electric double-layer capacitors were integrated into a scalable microfluidic device, whereas supervised models provided accurate predictions, even from a small number of training samples. User-friendly measurements were performed with sample-to-answer data processing on a smartphone. This new platform further showed the highest throughput when compared with the techniques described in the literature to quantify EVs biomarkers. Our results shed light on a method with the ability to determine multiple EVs biomarkers in a simple and fast way, providing a promising platform to translate biofluid-based diagnostics into clinical workflows.

Pair Distribution Function from Electron Diffraction in Cryogenic Electron Microscopy: Revealing Glassy Water Structure.

In recent years, cryogenic electron microscopy (Cryo-EM) has revolutionized the structure determination of wet samples and especially that of biological macromolecules. The glassy-water medium in which the molecules are embedded is considered an almost in vivo environment for biological samples. The local structure of amorphous ice is known from neutron- and X-ray-diffraction studies, techniques appropriate for much larger volumes than those used in cryo-EM. We here present a first study of the pair-distribution function g(r) of glassy water under cryo-EM conditions using electron diffraction data. We found g(r) to be between that of low-density amorphous ice and that of supercooled water. Under electron exposure, cubic-ice regions were found to nucleate in thicker glassy-water samples. Our work enables to obtain quantitative structural information using g(r) from cryo-EM.

Ambipolar Resistive Switching in an Ultrathin Surface-Supported Metal-Organic Framework Vertical Heterojunction.

Memristors (MRs) are considered promising devices with the enormous potential to replace complementary metal-oxide-semiconductor (CMOS) technology, which approaches the scale limit. Efforts to fabricate MRs-based hybrid materials may result in suitable operating parameters coupled to high mechanical flexibility and low cost. Metal-organic frameworks (MOFs) arise as a favorable candidate to cover such demands. The step-by-step growth of MOFs structures on functionalized surfaces, called surface-supported metal-organic frameworks (SURMOFs), opens the possibility for designing new applications in strategic fields such as electronics, optoelectronics, and energy harvesting. However, considering the MRs architecture, the typical high porosity of these hybrid materials may lead to short-circuited devices easily. In this sense, here, it is reported for the first time the integration of SURMOF films in rolled-up scalable-functional devices. A freestanding metallic nanomembrane provides a robust and self-adjusted top mechanical contact on the SURMOF layer. The electrical characterization reveals an ambipolar resistive switching mediated by the humidity level with low-power consumption. The electronic properties are investigated with density functional theory (DFT) calculations. Furthermore, the device concept is versatile, compatible with the current parallelism demands of integration, and transcends the challenge in contacting SURMOF films for scalable-functional devices.

Exploring Two-Dimensional Materials Thermodynamic Stability via Machine Learning.

The increasing interest and research on two-dimensional (2D) materials has not yet translated into a reality of diverse materials applications. To go beyond graphene and transition metal dichalcogenides for several applications, suitable candidates with desirable properties must be proposed. Here we use machine learning techniques to identify thermodynamically stable 2D materials, which is the first essential requirement for any application. According to the formation energy and energy above the convex hull, we classify materials as having low, medium, or high stability. The proposed approach enables the stability evaluation of novel 2D compounds for further detailed investigation of promising candidates, using only composition properties and structural symmetry, without the need for information about atomic positions. We demonstrate the usefulness of the model generating more than a thousand novel compounds, corroborating with DFT calculations the classification for five of these materials. To illustrate the applicability of the stable materials, we then perform a screening of electronic materials suitable for photoelectrocatalytic water splitting, identifying the potential candidate Sn2SeTe generated by our model, and also PbTe, both not yet reported for this application.

Ab Initio Simulations and Materials Chemistry in the Age of Big Data.

In this perspective, we discuss computational advances in the last decades, both in algorithms as well as in technologies, that enabled the development, widespread use, and maturity of simulation methods for molecular and materials systems. Such advances led to the generation of large amounts of data, which required the creation of several computational databases. Within this scenario, with the democratization of data access, the field now encounters several opportunities for data-driven approaches toward chemical and materials problems. Specifically, machine learning methods for predictions of novel materials or properties are being increasingly used with great success. However, black box usage fails in many instances; several technical details require expert knowledge in order for the predictions to be useful, such as with descriptors and algorithm selection. These approaches represent a direction for further developments, notably allowing advances for both developed and emerging countries with modest computational infrastructures.

Toward Realistic Amorphous Topological Insulators.

The topological properties of materials are, until now, associated with the features of their crystalline structure, although translational symmetry is not an explicit requirement of the topological phases. Recent studies of hopping models on random lattices have demonstrated that amorphous model systems show a nontrivial topology. Using ab initio calculations, we show that two-dimensional amorphous materials can also display topological insulator properties. More specifically, we present a realistic state-of-the-art study of the electronic and transport properties of amorphous bismuthene systems, showing that these materials are topological insulators. These systems are characterized by the topological index [Formula: see text]2 = 1 and bulk-edge duality, and their linear conductance is quantized, [Formula: see text], for Fermi energies within the topological gap. Our study opens the path to the experimental and theoretical investigation of amorphous topological insulator materials.

Electronic transport properties of MoS2 nanoribbons embedded in butadiene solvent.

Transition metal dichalcogenides (TMDCs) are promising materials for applications in nanoelectronics and correlated fields, where their metallic edge states play a fundamental role in the electronic transport. In this work, we investigate the transport properties of MoS2 zigzag nanoribbons under a butadiene (C4H6) atmosphere, as this compound has been used to obtain MoS2 flakes by exfoliation. We use density functional theory combined with non-equilibrium Green's function techniques, in a methodology contemplating disorder and different coverages. Our results indicate a strong modulation of the TMDC electronic transport properties driven by butadiene molecules anchored at their edges, producing the suppression of currents due to a backscattering process. Our results indicate a high sensitivity of the TMDC edge states. Thus, the mechanisms used to reduce the dimensionality of MoS2 considerably modify its transport properties.

Decreasing Nanocrystal Structural Disorder by Ligand Exchange: An Experimental and Theoretical Analysis.

Nanocrystals (NCs) present unique physicochemical properties arising from their size and the presence of ligands. Comprehending and controlling the ligand-crystal interactions as well as the ligand exchange process is one of the central themes in NC science nowadays. However, the relationship between NC structural disorder and the ligand exchange effect in the NC atomic structure is not yet sufficiently understood. Here we combine pair distribution function analysis from electron diffraction data, extended X-ray absorption fine structure, and high-resolution transmission electron microscopy as experimental techniques and first-principles density functional theory calculations to elucidate the ligand exchange effects in the ZrO2 NC structure. We report a substantial decrease in the structural disorder for ZrO2 NCs caused by strain rearrangements during the ligand exchange process. These results can have a direct impact on the development of functional nanomaterials, especially in properties controlled by structural disorder.

Spin-Polarization Control Driven by a Rashba-Type Effect Breaking the Mirror Symmetry in Two-Dimensional Dual Topological Insulators.

Three-dimensional topological insulators protected by both the time reversal (TR) and mirror symmetries were recently predicted and observed. Two-dimensional materials featuring this property and their potential for device applications have been less explored. We find that, in these systems, the spin polarization of edge states can be controlled with an external electric field breaking the mirror symmetry. This symmetry requires that the spin polarization is perpendicular to the mirror plane; therefore, the electric field induces spin-polarization components parallel to the mirror plane. Since this field preserves the TR topological protection, we propose a transistor model using the spin direction of protected edge states as a switch. In order to illustrate the generality of the proposed phenomena, we consider compounds protected by mirror planes parallel and perpendicular to the structure, e.g., Na_{3}Bi and half-functionalized (HF) hexagonal compounds, respectively. For this purpose, we first construct a tight-binding effective model for the Na_{3}Bi compound and predict that HF-honeycomb lattice materials are also dual topological insulators.