Quantum sensing of microRNAs with nitrogen-vacancy centers in diamond.

Label-free detection of nucleic acids such as microRNAs holds great potential for early diagnostics of various types of cancers. Measuring intrinsic biomolecular charge using methods based on field effect has been a promising way to accomplish label-free detection. However, the charges of biomolecules are screened by counter ions in solutions over a short distance (Debye length), thereby limiting the sensitivity of these methods. Here, we measure the intrinsic magnetic noise of paramagnetic counter ions, such as Mn2+, interacting with microRNAs using nitrogen-vacancy (NV) centers in diamond. All-atom molecular dynamics simulations show that microRNA interacts with the diamond surface resulting in excess accumulation of Mn ions and stronger magnetic noise. We confirm this prediction by observing an increase in spin relaxation contrast of the NV centers, indicating higher Mn2+ local concentration. This opens new possibilities for next-generation quantum sensing of charged biomolecules, overcoming limitations due to the Debye screening.

Deconstructing Electrostatics of Functionalized Metal Nanoparticles from Molecular Dynamics Simulations.

Gold nanoparticles (NPs) with different surface functionalizations can selectively interact with specific proteins, allowing a wide range of possible applications in biotechnology and biomedicine. To prevent their tendency to aggregate and to modulate their interaction with charged biomolecules or substrates (e.g., for biosensing applications), they can be functionalized with charged groups, introducing a mutual interaction which can be modulated by changing the ionic strength of the solvent. In silico modeling of these systems is often addressed with low-resolution models, which must account for these effects in the, often implicit, solvent representation. Here, we present a systematic conformational dynamic characterization of ligand-coated gold nanoparticles with different sizes, charges, and functionalizations by means of atomistic molecular dynamics simulations. Based on these, we deconstruct their electrostatic properties and propose a general representation of their average-long-range interactions extendable to different sizes, charges, and ionic strengths. This study clarifies in detail the role of the different features of the NP (charge, size, structure) and of the ionic strength in determining the details of the interparticle interaction and represents the first step toward a general strategy for the parametrization of NP coarse-grained models able to account for varying ionic strengths.

Heat-Driven Iontronic Nanotransistors.

Thermoelectric polyelectrolytes are emerging as ideal material platform for self-powered bio-compatible electronic devices and sensors. However, despite the nanoscale nature of the ionic thermodiffusion processes underlying thermoelectric efficiency boost in polyelectrolytes, to date no evidence for direct probing of ionic diffusion on its relevant length and time scale has been reported. This gap is bridged by developing heat-driven hybrid nanotransistors based on InAs nanowires embedded in thermally biased Na+ -functionalized (poly)ethyleneoxide, where the semiconducting nanostructure acts as a nanoscale probe sensitive to the local arrangement of the ionic species. The impact of ionic thermoelectric gating on the nanodevice electrical response is addressed, investigating the effect of device architecture, bias configuration and frequency of the heat stimulus, and inferring optimal conditions for the heat-driven nanotransistor operation. Microscopic quantities of the polyelectrolyte such as the ionic diffusion coefficient are extracted from the analysis of hysteretic behaviors rising in the nanodevices. The reported experimental platform enables simultaneously the ionic thermodiffusion and nanoscale resolution, providing a framework for direct estimation of polyelectrolytes microscopic parameters. This may open new routes for heat-driven nanoelectronic applications and boost the rational design of next-generation polymer-based thermoelectric materials.

Aggregation behavior of nanoparticles: Revisiting the phase diagram of colloids.

Surface functionalization of metal nanoparticles (NPs), e.g., using peptides and proteins, has recently attracted a considerable attention in the field of design of therapeutics and diagnostics. The possibility of diverse functionalization allows them to selectively interact with proteins, while the metal core ensures solubility, making them tunable therapeutic agents against diseases due to mis-folding or aggregation. On the other hand, their action is limited by possible self-aggregation, which could be, however, prevented based on the full understanding of their phase diagram as a function of the environmental variables (temperature, ionic strength of the solution, concentration) and intrinsic characteristics (size, charge, amount, and type of functional groups). A common modeling strategy to study the phase behavior is to represent the NPs as spheres interacting via effective potentials implicitly accounting for the solvation effects. Their size put the NPs into the class of colloids, albeit with particularly complex interactions including both attractive and repulsive features, and a consequently complex phase diagram. In this work, we review the studies exploring the phases of these systems starting from those with only attractive or repulsive interactions, displaying a simpler disperse-clustered-aggregated transitions. The phase diagram is here interpreted focusing on the universal aspects, i.e., those dependent on the general feature of the potentials, and available data are organized in a parametric phase diagram. We then consider the potentials with competing attractive short range well and average-long-range repulsive tail, better representing the NPs. Through the proper combination of the attractive only and repulsive only potentials, we are able to interpret the appearance of novel phases, characterized by aggregates with different structural characteristics. We identify the essential parameters that stabilize the disperse phase potentially useful to optimize NP therapeutic activity and indicate how to tune the phase behavior by changing environmental conditions or the NP chemical-physical properties.

Evaluating the performance of ReaxFF potentials for sp2 carbon systems (graphene, carbon nanotubes, fullerenes) and a new ReaxFF potential.

We study the performance of eleven reactive force fields (ReaxFF), which can be used to study sp2 carbon systems. Among them a new hybrid ReaxFF is proposed combining two others and introducing two different types of C atoms. The advantages of that potential are discussed. We analyze the behavior of ReaxFFs with respect to 1) the structural and mechanical properties of graphene, its response to strain and phonon dispersion relation; 2) the energetics of (n, 0) and (n, n) carbon nanotubes (CNTs), their mechanical properties and response to strain up to fracture; 3) the energetics of the icosahedral C60 fullerene and the 40 C40 fullerene isomers. Seven of them provide not very realistic predictions for graphene, which made us focusing on the remaining, which provide reasonable results for 1) the structure, energy and phonon band structure of graphene, 2) the energetics of CNTs versus their diameter and 3) the energy of C60 and the trend of the energy of the C40 fullerene isomers versus their pentagon adjacencies, in accordance with density functional theory (DFT) calculations and/or experimental data. Moreover, the predicted fracture strain, ultimate tensile strength and strain values of CNTs are inside the range of experimental values, although overestimated with respect to DFT. However, they underestimate the Young's modulus, overestimate the Poisson's ratio of both graphene and CNTs and they display anomalous behavior of the stress - strain and Poisson's ratio - strain curves, whose origin needs further investigation.

Covalent organic functionalization of graphene nanosheets and reduced graphene oxide via 1,3-dipolar cycloaddition of azomethine ylide.

Organic functionalization of graphene is successfully performed via 1,3-dipolar cycloaddition of azomethine ylide in the liquid phase. The comparison between 1-methyl-2-pyrrolidinone and N,N-dimethylformamide as dispersant solvents, and between sonication and homogenization as dispersion techniques, proves N,N-dimethylformamide and homogenization as the most effective choice. The functionalization of graphene nanosheets and reduced graphene oxide is confirmed using different techniques. Among them, energy-dispersive X-ray spectroscopy allows to map the pyrrolidine ring of the azomethine ylide on the surface of functionalized graphene, while micro-Raman spectroscopy detects new features arising from the functionalization, which are described in agreement with the power spectrum obtained from ab initio molecular dynamics simulation. Moreover, X-ray photoemission spectroscopy of functionalized graphene allows the quantitative elemental analysis and the estimation of the surface coverage, showing a higher degree of functionalization for reduced graphene oxide. This more reactive behavior originates from the localization of partial charges on its surface due to the presence of oxygen defects, as shown by the simulation of the electrostatic features. Functionalization of graphene using 1,3-dipolar cycloaddition is shown to be a significant step towards the controlled synthesis of graphene-based complex structures and devices at the nanoscale.

Correction: Covalent organic functionalization of graphene nanosheets and reduced graphene oxide via 1,3-dipolar cycloaddition of azomethine ylide.

[This corrects the article DOI: 10.1039/D1NA00335F.].

In silico design, building and gas adsorption of nano-porous graphene scaffolds.

Graphene-based nano-porous materials (GNM) are potentially useful for all those applications needing a large specific surface area (SSA), typical of the bidimensional graphene, yet realized in the bulk dimensionality. Such applications include for instance gas storage and sorting, catalysis and electrochemical energy storage. While a reasonable control of the structure is achieved in micro-porous materials by using nano-micro particles as templates, the controlled production or even characterization of GNMs with porosity strictly at the nano-scale still raises issues. These are usually produced using dispersion of nano-flakes as precursors resulting in little control on the final structure, which in turn reflects in problems in the structural model building for computer simulations. In this work, we describe a strategy to build models for these materials with predetermined structural properties (SSA, density, porosity), which exploits molecular dynamics simulations, Monte Carlo methods and machine learning algorithms. Our strategy is inspired by the real synthesis process: starting from randomly distributed flakes, we include defects, perforation, structure deformation and edge saturation on the fly, and, after structural refinement, we obtain realistic models, with given structural features. We find relationships between the structural characteristics and size distributions of the starting flake suspension and the final structure, which can give indications for more efficient synthesis routes. We subsequently give a full characterization of the models versus H2 adsorption, from which we extract quantitative relationship between the structural parameters and the gravimetric density. Our results quantitatively clarify the role of surfaces and edges relative amount in determining the H2 adsorption, and suggest strategies to overcome the inherent physical limitations of these materials as adsorbers. We implemented the model building and analysis procedures in software tools, freely available upon request.

Engineering 3D Graphene-Based Materials: State of the Art and Perspectives.

Graphene is the prototype of two-dimensional (2D) materials, whose main feature is the extremely large surface-to-mass ratio. This property is interesting for a series of applications that involve interactions between particles and surfaces, such as, for instance, gas, fluid or charge storage, catalysis, and filtering. However, for most of these, a volumetric extension is needed, while preserving the large exposed surface. This proved to be rather a hard task, especially when specific structural features are also required (e.g., porosity or density given). Here we review the recent experimental realizations and theoretical/simulation studies of 3D materials based on graphene. Two main synthesis routes area available, both of which currently use (reduced) graphene oxide flakes as precursors. The first involves mixing and interlacing the flakes through various treatments (suspension, dehydration, reduction, activation, and others), leading to disordered nanoporous materials whose structure can be characterized a posteriori, but is difficult to control. With the aim of achieving a better control, a second path involves the functionalization of the flakes with pillars molecules, bringing a new class of materials with structure partially controlled by the size, shape, and chemical-physical properties of the pillars. We finally outline the first steps on a possible third road, which involves the construction of pillared multi-layers using epitaxial regularly nano-patterned graphene as precursor. While presenting a number of further difficulties, in principle this strategy would allow a complete control on the structural characteristics of the final 3D architecture.

Evolutionary Switches Structural Transitions via Coarse-Grained Models.

Transitions between different conformational states are ubiquitous in proteins. A vast class of conformation-changing proteins includes evolutionary switches, which vary their conformation as an effect of few mutations or weak environmental variations. However, modeling those processes is extremely difficult due to the need of efficiently exploring a vast conformational space to look for the actual transition path. In this study, we report a strategy that simplifies this task attacking the complexity on several sides. We first apply a minimalist coarse-grained model to the protein, based on an empirical force field with a partial structural bias toward one or both the reference structures. We then explore the transition paths by means of stochastic molecular dynamics and select representative structures by means of a principal path-based clustering algorithm. We finally compare this trajectory with that produced by independent methods adopting a morphing-oriented approach. Our analysis indicates that the minimalist model returns trajectories capable of exploring intermediate states with physical meaning, retaining a very low computational cost, which can allow systematic and extensive exploration of the multistable proteins transition pathways.

Structural Transition States Explored With Minimalist Coarse Grained Models: Applications to Calmodulin.

Transitions between different conformational states are ubiquitous in proteins, being involved in signaling, catalysis, and other fundamental activities in cells. However, modeling those processes is extremely difficult, due to the need of efficiently exploring a vast conformational space in order to seek for the actual transition path for systems whose complexity is already high in the stable states. Here we report a strategy that simplifies this task attacking the complexity on several sides. We first apply a minimalist coarse-grained model to Calmodulin, based on an empirical force field with a partial structural bias, to explore the transition paths between the apo-closed state and the Ca-bound open state of the protein. We then select representative structures along the trajectory based on a structural clustering algorithm and build a cleaned-up trajectory with them. We finally compare this trajectory with that produced by the online tool MinActionPath, by minimizing the action integral using a harmonic network model, and with that obtained by the PROMPT morphing method, based on an optimal mass transportation-type approach including physical constraints. The comparison is performed both on the structural and energetic level, using the coarse-grained and the atomistic force fields upon reconstruction. Our analysis indicates that this method returns trajectories capable of exploring intermediate states with physical meaning, retaining a very low computational cost, which can allow systematic and extensive exploration of the multi-stable proteins transition pathways.

Low-Resolution Models for the Interaction Dynamics of Coated Gold Nanoparticles with ß2-microglobulin.

A large number of low-resolution models have been proposed in the last decades to reduce the computational cost of molecular dynamics simulations for bio-nano systems, such as those involving the interactions of proteins with functionalized nanoparticles (NPs). For the proteins, "minimalist" models at the one-bead-per residue (Cα-based) level and with implicit solvent are well established. For the gold NPs, widely explored for biotechnological applications, mesoscale (MS) models treating the NP core with a single spheroidal object are commonly proposed. In this representation, the surface details (coating, roughness, etc.) are lost. These, however, and the specificity of the functionalization, have been shown to have fundamental roles for the interaction with proteins. We presented a mixed-resolution coarse-grained (CG) model for gold NPs in which the surface chemistry is reintroduced as superficial smaller beads. We compared molecular dynamics simulations of the amyloid ß2-microglobulin represented at the minimalist level interacting with NPs represented with this model or at the MS level. Our finding highlights the importance of describing the surface of the NP at a finer level as the chemical-physical properties of the surface of the NP are crucial to correctly understand the protein-nanoparticle association.

Intrinsic structural and electronic properties of the Buffer Layer on Silicon Carbide unraveled by Density Functional Theory.

The buffer carbon layer obtained in the first instance by evaporation of Si from the Si-rich surfaces of silicon carbide (SiC) is often studied only as the intermediate to the synthesis of SiC supported graphene. In this work, we explore its intrinsic potentialities, addressing its structural and electronic properties by means of Density Functional Theory. While the system of corrugation crests organized in a honeycomb super-lattice of nano-metric side returned by calculations is compatible with atomic microscopy observations, our work reveals some possible alternative symmetries, which might coexist in the same sample. The electronic structure analysis reveals the presence of an electronic gap of ~0.7 eV. In-gap states are present, localized over the crests, while near-gap states reveal very different structure and space localization, being either bonding states or outward pointing p orbitals and unsaturated Si dangling bonds. On one hand, the presence of these interface states was correlated with the n-doping of the monolayer graphene subsequently grown on the buffer. On the other hand, the correlation between their chemical character and their space localization is likely to produce a differential reactivity towards specific functional groups with a spatial regular modulation at the nano-scale, opening perspectives for a finely controlled chemical functionalization.

Electronic properties of single-layer tungsten disulfide on epitaxial graphene on silicon carbide.

This work reports an electronic and micro-structural study of an appealing system for optoelectronics: tungsten disulfide (WS2) on epitaxial graphene (EG) on SiC(0001). The WS2 is grown via chemical vapor deposition (CVD) onto the EG. Low-energy electron diffraction (LEED) measurements assign the zero-degree orientation as the preferential azimuthal alignment for WS2/EG. The valence-band (VB) structure emerging from this alignment is investigated by means of photoelectron spectroscopy measurements, with both high space and energy resolution. We find that the spin-orbit splitting of monolayer WS2 on graphene is of 462 meV, larger than what is reported to date for other substrates. We determine the value of the work function for the WS2/EG to be 4.5 ± 0.1 eV. A large shift of the WS2 VB maximum is observed as well, due to the lowering of the WS2 work function caused by the donor-like interfacial states of EG. Density functional theory (DFT) calculations carried out on a coincidence supercell confirm the experimental band structure to an excellent degree. X-ray photoemission electron microscopy (XPEEM) measurements performed on single WS2 crystals confirm the van der Waals nature of the interface coupling between the two layers. In virtue of its band alignment and large spin-orbit splitting, this system gains strong appeal for optical spin-injection experiments and opto-spintronic applications in general.

Optimization of Analytical Potentials for Coarse-Grained Biopolymer Models.

The increasing trend in the recent literature on coarse grained (CG) models testifies their impact in the study of complex systems. However, the CG model landscape is variegated: even considering a given resolution level, the force fields are very heterogeneous and optimized with very different parametrization procedures. Along the road for standardization of CG models for biopolymers, here we describe a strategy to aid building and optimization of statistics based analytical force fields and its implementation in the software package AsParaGS (Assisted Parameterization platform for coarse Grained modelS). Our method is based on the use and optimization of analytical potentials, optimized by targeting internal variables statistical distributions by means of the combination of different algorithms (i.e., relative entropy driven stochastic exploration of the parameter space and iterative Boltzmann inversion). This allows designing a custom model that endows the force field terms with a physically sound meaning. Furthermore, the level of transferability and accuracy can be tuned through the choice of statistical data set composition. The method-illustrated by means of applications to helical polypeptides-also involves the analysis of two and three variable distributions, and allows handling issues related to the FF term correlations. AsParaGS is interfaced with general-purpose molecular dynamics codes and currently implements the "minimalist" subclass of CG models (i.e., one bead per amino acid, Cα based). Extensions to nucleic acids and different levels of coarse graining are in the course.

2D materials. Graphene, related two-dimensional crystals, and hybrid systems for energy conversion and storage.

Graphene and related two-dimensional crystals and hybrid systems showcase several key properties that can address emerging energy needs, in particular for the ever growing market of portable and wearable energy conversion and storage devices. Graphene's flexibility, large surface area, and chemical stability, combined with its excellent electrical and thermal conductivity, make it promising as a catalyst in fuel and dye-sensitized solar cells. Chemically functionalized graphene can also improve storage and diffusion of ionic species and electric charge in batteries and supercapacitors. Two-dimensional crystals provide optoelectronic and photocatalytic properties complementing those of graphene, enabling the realization of ultrathin-film photovoltaic devices or systems for hydrogen production. Here, we review the use of graphene and related materials for energy conversion and storage, outlining the roadmap for future applications.

Diffusion within the cytoplasm: a mesoscale model of interacting macromolecules.

Recent experiments carried out in the dense cytoplasm of living cells have highlighted the importance of proteome composition and nonspecific intermolecular interactions in regulating macromolecule diffusion and organization. Despite this, the dependence of diffusion-interaction on physicochemical properties such as the degree of poly-dispersity and the balance between steric repulsion and nonspecific attraction among macromolecules was not systematically addressed. In this work, we study the problem of diffusion-interaction in the bacterial cytoplasm, combining theory and experimental data to build a minimal coarse-grained representation of the cytoplasm, which also includes, for the first time to our knowledge, the nucleoid. With stochastic molecular-dynamics simulations of a virtual cytoplasm we are able to track the single biomolecule motion, sizing from 3 to 80 nm, on submillisecond-long trajectories. We demonstrate that the size dependence of diffusion coefficients, anomalous exponents, and the effective viscosity experienced by biomolecules in the cytoplasm is fine-tuned by the intermolecular interactions. Accounting only for excluded volume in these potentials gives a weaker size-dependence than that expected from experimental data. On the contrary, adding nonspecific attraction in the range of 1-10 thermal energy units produces a stronger variation of the transport properties at growing biopolymer sizes. Normal and anomalous diffusive regimes emerge straightforwardly from the combination of high macromolecular concentration, poly-dispersity, stochasticity, and weak nonspecific interactions. As a result, small biopolymers experience a viscous cytoplasm, while the motion of big ones is jammed because the entanglements produced by the network of interactions and the entropic effects caused by poly-dispersity are stronger.

A multi-scale-multi-stable model for the rhodopsin photocycle.

We report a multi-scale simulation study of the photocycle of the rhodopsins. The quasi-atomistic representation ("united atoms" UA) of retinal is combined with a minimalist coarse grained (CG, one-bead-per amino acid) representation of the protein, in a hybrid UA/CG approach, which is the homolog of QM/MM, but at lower resolution. An accurate multi-stable parameterization of the model allows simulating each state and transition among them, and the combination of different scale representation allows addressing the entire photocycle. We test the model on bacterial rhodopsin, for which more experimental data are available, and then also report results for mammalian rhodopsins. In particular, the analysis of simulations reveals the spontaneous appearance of meta-stable states in quantitative agreement with experimental data.