Analysis of Carbon Nanoparticle Coatings via Wettability.

Wettability, typically estimated through the contact angle, is a fundamental property of surfaces with wide-ranging implications in both daily life and industrial processes. Recent scientific interest has been paid to the surfaces exhibiting extreme wettability: superhydrophobic and superhydrophilic surfaces, characterized by high water repellency and exceptional water wetting, respectively. Both chemical composition and morphology play a role in the determination of the wettability "performance" of a surface. To tune surface-wetting properties, we considered coatings of carbon nanoparticles (CNPs) in this study. They are a new class of nanomaterials synthesized in flames whose chemistry, dimension, and shape depend on combustion conditions. For the first time, we systematically studied the wettability of CNP coatings produced in a controlled rich ethylene/air flame stabilized over a McKenna burner. A selected substrate was intermittently inserted in the flame at 15 mm above the burner to form a thin coating thanks to a thermophoretic-driven deposition mechanism. The chemical-physical quality and the deposed quantity of the CNPs were varied by opportunely combing the substrate flame insertion number (from 1 to 256) and the carbon-to-oxygen ratio, C/O (from 0.67 to 0.87). The wettability of the coatings was evaluated by measuring the contact angle, CA, with the sessile drop method. When the C/O = 0.67, the CNPs were nearly spherical, smaller than 8 nm, and always generated hydrophilic coatings (CA < 35°). At higher C/O ratios, the CNPs reached dimensions of 100 nm, and fractal shape aggregates were formed. In this case, either hydrophilic (CA < 76°) or superhydrophobic (CA ~166°) behavior was observed, depending on the number of carbon nanoparticles deposed, i.e., film thickness. It is known that wettability is susceptible to liquid surface tension, and therefore, tests were conducted with different fluids to establish a correlation between the flame conditions and the nanostructure of the film. This method offers a fast and simple approach to determining mesoscale information for coating roughness and topographical homogeneity/inhomogeneity of their surfaces.

Machine Learning-Driven Multiscale Modeling: Bridging the Scales with a Next-Generation Simulation Infrastructure.

Interdependence across time and length scales is common in biology, where atomic interactions can impact larger-scale phenomenon. Such dependence is especially true for a well-known cancer signaling pathway, where the membrane-bound RAS protein binds an effector protein called RAF. To capture the driving forces that bring RAS and RAF (represented as two domains, RBD and CRD) together on the plasma membrane, simulations with the ability to calculate atomic detail while having long time and large length- scales are needed. The Multiscale Machine-Learned Modeling Infrastructure (MuMMI) is able to resolve RAS/RAF protein-membrane interactions that identify specific lipid-protein fingerprints that enhance protein orientations viable for effector binding. MuMMI is a fully automated, ensemble-based multiscale approach connecting three resolution scales: (1) the coarsest scale is a continuum model able to simulate milliseconds of time for a 1 µm2 membrane, (2) the middle scale is a coarse-grained (CG) Martini bead model to explore protein-lipid interactions, and (3) the finest scale is an all-atom (AA) model capturing specific interactions between lipids and proteins. MuMMI dynamically couples adjacent scales in a pairwise manner using machine learning (ML). The dynamic coupling allows for better sampling of the refined scale from the adjacent coarse scale (forward) and on-the-fly feedback to improve the fidelity of the coarser scale from the adjacent refined scale (backward). MuMMI operates efficiently at any scale, from a few compute nodes to the largest supercomputers in the world, and is generalizable to simulate different systems. As computing resources continue to increase and multiscale methods continue to advance, fully automated multiscale simulations (like MuMMI) will be commonly used to address complex science questions.

Exploring CRD mobility during RAS/RAF engagement at the membrane.

During the activation of mitogen-activated protein kinase (MAPK) signaling, the RAS-binding domain (RBD) and cysteine-rich domain (CRD) of RAF bind to active RAS at the plasma membrane. The orientation of RAS at the membrane may be critical for formation of the RAS-RBDCRD complex and subsequent signaling. To explore how RAS membrane orientation relates to the protein dynamics within the RAS-RBDCRD complex, we perform multiscale coarse-grained and all-atom molecular dynamics (MD) simulations of KRAS4b bound to the RBD and CRD domains of RAF-1, both in solution and anchored to a model plasma membrane. Solution MD simulations describe dynamic KRAS4b-CRD conformations, suggesting that the CRD has sufficient flexibility in this environment to substantially change its binding interface with KRAS4b. In contrast, when the ternary complex is anchored to the membrane, the mobility of the CRD relative to KRAS4b is restricted, resulting in fewer distinct KRAS4b-CRD conformations. These simulations implicate membrane orientations of the ternary complex that are consistent with NMR measurements. While a crystal structure-like conformation is observed in both solution and membrane simulations, a particular intermolecular rearrangement of the ternary complex is observed only when it is anchored to the membrane. This configuration emerges when the CRD hydrophobic loops are inserted into the membrane and helices α3-5 of KRAS4b are solvent exposed. This membrane-specific configuration is stabilized by KRAS4b-CRD contacts that are not observed in the crystal structure. These results suggest modulatory interplay between the CRD and plasma membrane that correlate with RAS/RAF complex structure and dynamics, and potentially influence subsequent steps in the activation of MAPK signaling.

Asynchronous Reciprocal Coupling of Martini 2.2 Coarse-Grained and CHARMM36 All-Atom Simulations in an Automated Multiscale Framework.

The appeal of multiscale modeling approaches is predicated on the promise of combinatorial synergy. However, this promise can only be realized when distinct scales are combined with reciprocal consistency. Here, we consider multiscale molecular dynamics (MD) simulations that combine the accuracy and macromolecular flexibility accessible to fixed-charge all-atom (AA) representations with the sampling speed accessible to reductive, coarse-grained (CG) representations. AA-to-CG conversions are relatively straightforward because deterministic routines with unique outcomes are achievable. Conversely, CG-to-AA conversions have many solutions due to a surge in the number of degrees of freedom. While automated tools for biomolecular CG-to-AA transformation exist, we find that one popular option, called Backward, is prone to stochastic failure and the AA models that it does generate frequently have compromised protein structure and incorrect stereochemistry. Although these shortcomings can likely be circumvented by human intervention in isolated instances, automated multiscale coupling requires reliable and robust scale conversion. Here, we detail an extension to Multiscale Machine-learned Modeling Infrastructure (MuMMI), including an improved CG-to-AA conversion tool called sinceCG. This tool is reliable (∼98% weakly correlated repeat success rate), automatable (no unrecoverable hangs), and yields AA models that generally preserve protein secondary structure and maintain correct stereochemistry. We describe how the MuMMI framework identifies CG system configurations of interest, converts them to AA representations, and simulates them at the AA scale while on-the-fly analyses provide feedback to update CG parameters. Application to systems containing the peripheral membrane protein RAS and proximal components of RAF kinase on complex eight-component lipid bilayers with ∼1.5 million atoms is discussed in the context of MuMMI.

Machine learning-driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins.

RAS is a signaling protein associated with the cell membrane that is mutated in up to 30% of human cancers. RAS signaling has been proposed to be regulated by dynamic heterogeneity of the cell membrane. Investigating such a mechanism requires near-atomistic detail at macroscopic temporal and spatial scales, which is not possible with conventional computational or experimental techniques. We demonstrate here a multiscale simulation infrastructure that uses machine learning to create a scale-bridging ensemble of over 100,000 simulations of active wild-type KRAS on a complex, asymmetric membrane. Initialized and validated with experimental data (including a new structure of active wild-type KRAS), these simulations represent a substantial advance in the ability to characterize RAS-membrane biology. We report distinctive patterns of local lipid composition that correlate with interfacially promiscuous RAS multimerization. These lipid fingerprints are coupled to RAS dynamics, predicted to influence effector binding, and therefore may be a mechanism for regulating cell signaling cascades.

Testing Surgical Face Masks in an Emergency Context: The Experience of Italian Laboratories during the COVID-19 Pandemic Crisis.

The first wave of the COVID-19 pandemic brought about a broader use of masks by both professionals and the general population. This resulted in a severe worldwide shortage of devices and the need to increase import and activate production of safe and effective surgical masks at the national level. In order to support the demand for testing surgical masks in the Italian context, Universities provided their contribution by setting up laboratories for testing mask performance before releasing products into the national market. This paper reports the effort of seven Italian university laboratories who set up facilities for testing face masks during the emergency period of the COVID-19 pandemic. Measurement set-ups were built, adapting the methods specified in the EN 14683:2019+AC. Data on differential pressure (DP) and bacterial filtration efficiency (BFE) of 120 masks, including different materials and designs, were collected over three months. More than 60% of the masks satisfied requirements for DP and BFE set by the standard. Masks made of nonwoven polypropylene with at least three layers (spunbonded-meltblown-spunbonded) showed the best results, ensuring both good breathability and high filtration efficiency. The majority of the masks created with alternative materials and designs did not comply with both standard requirements, resulting in suitability only as community masks. The effective partnering between universities and industries to meet a public need in an emergency context represented a fruitful example of the so-called university "third-mission".

[Validation of surgical masks during COVID19 emergency: activities at the University of Napoli Federico II]. / Validazione di maschere chirurgiche nella fase di emergenza COVID19: l'esperienza dell'Università degli Studi di Napoli Federico II.

SUMMARY: During COVID-19 pandemic crisis, Italian Government has approved Law Decree no. 18 of 17 march 2020, in which art. 15 allows enterprises to produce, import and commercialize surgical masks notwithstanding the current rules of product certification. It is just required that the interested enterprises send to the Italian National Institute of Health a selfcertification in which they declare the technical characteristics of the masks and that masks are produced according to the safety requirements. In this context, a technical-scientific unit was established at the University of Napoli Federico II to provide interested enterprises with state-of-the-art consultancy, testing and measurement services, adhering to rigorous scientific protocols. Characterization tests were carried out on 163 surgical masks and/or materials for their construction and they have enabled the identification of pre-screening criteria to simplify the procedure for evaluating surgical masks using methods for assessing the filtration efficiency of particles and aerosols. Based on experimental results, it has been observed that a filtration efficiency for particles with sizes larger that 650 nm (PFE>650) exceeding 35% might guarantees a bacterial filtration efficiency (BFE) higher than 95% while BFE values higher than 98% are obtained when the PFE>650 is larger than 40%. PFE measurement is extremely simpler with respect to BFE, the latter being time-consuming and requiring specific equipment and methods for its realization. Many tested materials have shown the capability to assure high filtration efficiencies but Spundonded-Meltblown-Spunbonded (SMS), that are layers of non-woven fabric with different weights of Meltblown, can simultaneously guarantee high particle filtration efficiencies with pressure drop values (breathability) in the limits to classify the surgical masks as Type II/IIR. In fact, the fabric products analyzed so far have not been able to simultaneously guarantee adequate BFE and breathability values. On the contrary, Spunbonds of adequate weights can virtually verify both requirements and accredit themselves as possible materials for the production of surgical masks, at least of Type I. Further studies are needed to verify the possibility of producing low-cost, reusable surgical masks that could meet the criteria of circular economy.

Capturing Phase Behavior of Ternary Lipid Mixtures with a Refined Martini Coarse-Grained Force Field.

Whether lipid rafts are present in the membranes of living cells remains hotly disputed despite their incontrovertible existence in liposomes at 298 K. In attempts to resolve this debate, molecular dynamics (MD) simulations have been extensively used to study lipid phase separation at high resolution. However, computation has been of limited utility in this respect because the experimental distributions of phases in lamellar lipid mixtures are poorly reproduced by simulations. In particular, all-atom (AA) approaches suffer from restrictions on accessible time scales and system sizes whereas the more efficient coarse-grained (CG) force fields remain insufficiently accurate to achieve correspondence with experiment. In this work, we refine the CG Martini parameters for the high- and low-melting temperature ( Tm) lipids 1,2-dipalmitoyl- sn-glycero-3-phosphatidylcholine (DPPC) and 1,2-dioleoyl- sn-glycero-3-phosphatidylcholine (DOPC). Our approach involves the modification of bonded Martini parameters based on fitting to atomistic simulations conducted with the CHARMM36 lipid force field. The resulting CG parameters reproduce experimental structural and thermodynamic properties of homogeneous lipid membranes while concurrently improving simulation fidelity to experimental phase diagrams of DPPC, DOPC, and cholesterol lipid mixtures. Importantly, the refined parameters provide much better phase accuracy for regions near the critical point that mimic the lipid concentrations under physiological conditions.

Sacrificial photocatalysis: removal of nitrate and hydrogen production by nano-copper-loaded P25 titania. A kinetic and ecotoxicological assessment.

The photocatalytic removal of nitrate with simultaneous hydrogen generation was demonstrated using zero-valent nano-copper-modified titania (P25) as photocatalyst in the presence of UV-A-Vis radiation. Glycerol, a by-product in biodiesel production, was chosen as a hole scavenger. Under the adopted experimental conditions, a nitrate removal efficiency up to 100% and a simultaneous hydrogen production up to 14 µmol/L of H2 were achieved (catalyst load = 150 mg/L, initial concentration of nitrate = 50 mg/L, initial concentration of glycerol = 0.8 mol/L). The reaction rates were independent of the starting glycerol concentration. This process allows accomplishing nitrate removal, with the additional benefit of producing hydrogen under artificial UV-A radiation. A kinetic model was also developed and it may represent a benchmark for a detailed understanding of the process kinetics. A set of acute and chronic bioassays (Vibrio fischeri, Raphidocelis subcapitata, and Daphnia magna) was performed to evaluate the potential ecotoxicity of the nitrate/by-product mixture formed during the photocatalytic process. The ecotoxicological assessment indicated an ecotoxic effect of oxidation intermediates and by-products produced during the process.

Cadmium adsorption by coal combustion ashes-based sorbents--relationship between sorbent properties and adsorption capacity.

A very interesting possibility of coal combustion ashes reutilization is their use as adsorbent materials, that can also take advantage from proper beneficiation techniques. In this work, adsorption of cadmium from aqueous solutions was taken into consideration, with the emphasis on the intertwining among waste properties, beneficiation treatments, properties of the beneficiated materials and adsorption capacity. The characterization of three solid materials used as cadmium sorbents (as-received ash, ash sieved through a 25 µm-size sieve and demineralized ash) was carried out by chemical analysis, infrared spectroscopy, laser granulometry and mercury porosimetry. Cadmium adsorption thermodynamic and kinetic tests were conducted at room temperature, and test solutions were analyzed by atomic absorption spectrophotometry. Maximum specific adsorption capacities resulted in the range 0.5-4.3 mg g(-1). Different existing models were critically considered to find out an interpretation of the controlling mechanism for adsorption kinetics. In particular, it was observed that for lower surface coverage the adsorption rate is governed by a linear driving force while, once surface coverage becomes significant, mechanisms such as the intraparticle micropore diffusion may come into play. Moreover, it was shown that both external fluid-to-particle mass transfer and macropore diffusion hardly affect the adsorption process, which was instead regulated by intraparticle micropore diffusion: characteristic times for this process ranged from 4.1 to 6.1d, and were fully consistent with the experimentally observed equilibrium times. Results were discussed in terms of the relationship among properties of beneficiated materials and cadmium adsorption capacity. Results shed light on interesting correlations among solid properties, cadmium capture rate and maximum cadmium uptake.