Carbon nanotubes: are they dispersed or dissolved in liquids?

Carbon nanotubes (CNTs) constitute a novel class of nanomaterials with remarkable applications in diverse domains. However, the main intrincsic problem of CNTs is their insolubility or very poor solubility in most of the common solvents. The basic key question here is: are carbon nanotubes dissolved or dispersed in liquids, specifically in water? When analyzing the scientific research articles published in various leading journals, we found that many researchers confused between "dispersion" and "solubilization" and use the terms interchangeably, particularly when stating the interaction of CNTs with liquids. In this article, we address this fundamental issue to give basic insight specifically to the researchers who are working with CNTs as well asgenerally to scientists who deal with nano-related research domains.Among the various nanomaterials, CNTs gained widespread attention owing to their exceptional properties, good chemical stability, and large surface area [1,2]. CNTs are extremely thin tubes and feature an extremely enviable combination of mechanical, thermal, electrical, and optical properties. Their size, shape, and properties construct them as prime contenders for exploiting the growth of a potentially revolutionary material for diverse applications.Nevertheless, the main intrinsic drawback of CNTs is their insolubility or extremely poor solubility in most of the common solvents due to their hydrophobicity, thus creating it tricky to explore and understand the chemistry of such material at the molecular level and device applications. Though diverse approaches [3] have been introduced to improve the dispersion of CNTs in different solvents including water, challenges still remain in developing simple, green, facile, and effective strategies for a large-scale production of CNT dispersions. To this end, in many studies a wide range of agents have been used. To give a few examples: solvents [4], biopolymers [5], and surfactants [6]. Meanwhile, when analyzing the scientific research articles published in various leading journals, regarding the dispersion of CNTs, it is really puzzling owing to the usage of different terminologies with respect to the dispersion of CNTs. Most of the studies indicated "dispersion"; however, considerable quantities of articles were published with the term "solubilization", which can be evidently seen from the literature analysis [7]. Hence, many researchers confound "dispersion" and "solubilization" and use the terms interchangeably, especially when describing the interaction of CNTs with solvents. Many scientists have mentioned that CNTs can be "solubilized in water or organic solvents" by means of polymers and/or surfactants, which is ambiguous. It is evident that there is, as a result of that, a lot of confusion regarding this fundamental matter. The basic and fundamental key question here is: are CNTs dissolved or dispersed in a liquid?Basically, "dispersion" and "solubilization" are different phenomena. Dispersion and solubilization can be defined as "a system, in which particles of any nature (e.g., solid, liquid, or gas) are dispersed in a continuous phase of a different composition (or state)" [8] and a "process, by which an agent increases the solubility or the rate of dissolution of a solid or liquid solute" [9], respectively. Hence, in general, the dispersion of solute particles in solvents leads to the formation of colloids or suspensions, and solutions may be obtained as a result of solubilization of solute molecules or ions in the specified solvent. Furthermore, dispersion is mostly related to solute particles, whereas solubility or solubilization is generally connected with solute molecules or ions.The main differences between a colloid and a solution are: A solution is homogenous and remains stable and does not separate after standing for any period of time. Further it cannot be separated by standard separation techniques such as filtration or centrifugation. A solution looks transparent and it can transmit the light. Also, solutions contain the solute in a size at the molecular or ionic level, typically less than 1 nm or maximum a few nm in all dimensions. A colloid is a mixture with particles sizes between 1 and 1000 nm in at least one dimension. It is opaque, non-transparent, and the particles are large enough to scatter light. Colloids are not as stable as solutions and the dispersed particles (comparatively larger-sized particles) may be conveniently separated by standard separation techniques such as (ultra)centrifugation or filtration. Frequently, dispersed particles in colloidal systems may slowly agglomerate owing to inter-particle attractions over prolonged periods of time and, as a result, colloidal dispersions may form flocs or flakes.As far as CNTs are concerned, even though the diameter of the tubes is in the nanometer range (approximately between 0.4 and 3 nm for single-walled carbon nanotubes, and 1.4 and 100 nm for multi-walled carbon nanotubes) [10], their length can be up to several micrometers to millimeters. Further, it is a well-known fact that CNTs are not equal in size with respect to both diameter and length. Hence, the result of dispersion techniques mostly used and adopted to produce well-dispersed CNTs in either aqueous and/or organic media are typically dispersions of differently sized tubes. Consequently, based on the definition [6,7] and the abovementioned points, the mixture of CNTs and water or organic solvents, whether in the presence or non-presence of dispersing agents such as surfactants or polymers, is just a colloidal dispersion and not a solution. Figure ​Figure11 shows the schematic illustration for the formation of dispersed CNTs in a liquid with the aid of a dispersing agent. Simultaneously, the dispersion can result in a debundling or individualization of the bundled CNTs.Open in a separate windowFigure 1Schematic showing the transition of the bundled to the individualized, dispersed state of carbon nanotubes in a liquid with the aid of a dispersing agent.Therefore, "solubilization" is a process to achieve a stable solution, whereas "dispersion" is a form of colloidal system. Here we conclude that the mixture obtained by using CNTs and a liquid medium (water or organic solvents) with or without surfactants or polymers is a dispersion of CNTs in the medium, but not a solution. Further, in our opinion, one cannot solubilize CNTs in water or organic solvents. Hence, we recommend to restrict the use of the term "solubilization" or "solution," instead we should use the term "dispersion" or "colloid," when dealing with CNTs. Further, we think that this should be also applicable for nanoparticles of comparable dimensions such as metal and metal oxide nanoparticles, polymer nanoparticles, etc., if the criteria of the definitions given above are fulfilled.In short, the term "dispersion" should exclusively be used as far as CNTs are concerned, and the use of the term "solution" should be avoided or restricted.     

Self- and mutual-diffusion coefficients in the dodecane/polystyrene system

The self-diffusion coefficient of dodecane in cross-linked polystyrene was measured using pulsed gradient spin echo nuclear magnetic resonance (PGSE–NMR) spectroscopy. The concentration and temperature dependence of the diffusion coefficient was analyzed by the Fujita and Vrentas–Duda models. Parameters describing the Fujita model were determined from fitting of diffusion data to the PVT behavior of the system. Parameters describing the Vrentas–Duda model were determined from the analysis of the viscosity of dodecane, the viscoelastic relaxation properties, and the glass transition temperature of polystyrene as well as from the diffusion coefficient of the system, measured from independent experiments. Both the Fujita and Vrentas–Duda models described well the concentration and temperature dependence of the diffusion coefficient. Mutual diffusion coefficients were determined from these results. © 1994 John Wiley & Sons, Inc.     

Atomic Friction Investigations on Ordered Superstructures

We review recent friction measurements on ordered superstructures performed by atomic force microscopy. In particular, we consider ultrathin KBr films on NaCl(001) and Cu(001) surfaces, single and bilayer graphene on SiC(0001), and the herringbone reconstruction of Au(111). Atomically resolved friction images of these systems show periodic features spanning across several unit cells. Although the physical mechanisms responsible for the formation of these superstructures are quite different, the experimental results can be interpreted within the same phenomenological framework. A comparison between experiments and modeling shows that, in the cases of KBr films on NaCl(001) and of graphene films, the tip-surface interaction is well described by a potential with the periodicity of the substrate which is modulated or, respectively, superimposed with a potential with the symmetry of the superstructure.     

Associative Grammar Combination Operators for Tree-Based Grammars

Polarized unification grammar (PUG) is a linguistic formalism which uses polarities to better control the way grammar fragments interact. The grammar combination operation of PUG was conjectured to be associative. We show that PUG grammar combination is not associative, and even attaching polarities to objects does not make it order-independent. Moreover, we prove that no non-trivial polarity system exists for which grammar combination is associative. We then redefine the grammar combination operator, moving to the powerset domain, in a way that guarantees associativity. The method we propose is general and is applicable to a variety of tree-based grammar formalisms.     

Where the wind blows

The power systems in Denmark, Spain, Ireland, and New Zealand have some of the highest wind penetrations in the world (see Table 1). The management of the different power systems to date, with increasing amounts of wind energy, has been successful. There have been no incidents in which the wind has directly or indirectly been a major factor causing operational problems for the system. However, there are a number of parameters that are being monitored that indicate the need for active management in the near future (and in some cases already today). In this article, we briefly describe the situations in these four countries, giving special emphasis to the market integration of wind power, the use of wind forecasting, and curtailment experience. The final section provides an overview of the main wind forecasting methodologies and challenges.     

Parameter reference immutability: formal definition, inference tool, and comparison

Knowing which method parameters may be mutated during a method’s execution is useful for many software engineering tasks. A parameter reference is immutable if it cannot be used to modify the state of its referent object during the method’s execution. We formally define this notion, in a core object-oriented language. Having the formal definition enables determining correctness and accuracy of tools approximating this definition and unbiased comparison of analyses and tools that approximate similar definitions. We present Pidasa, a tool for classifying parameter reference immutability. Pidasa combines several lightweight, scalable analyses in stages, with each stage refining the overall result. The resulting analysis is scalable and combines the strengths of its component analyses. As one of the component analyses, we present a novel dynamic mutability analysis and show how its results can be improved by random input generation. Experimental results on programs of up to 185 kLOC show that, compared to previous approaches, Pidasa increases both run-time performance and overall accuracy of immutability inference.     

The principle cause for lower plate numbers in capillary zone electrophoresis with most organic solvents.

With longitudinal diffusion as an unavoidable source of peak broadening, the peak efficiency (expressed by the plate number, N) in capillary zone electrophoresis depends on the ratio of electrophoretic mobility, mu, and tracer- or self-diffusion coefficient, D. Both parameters are functions of the ionic strength of the electrolyte solution. According to theory, the mobility is decreased with increasing ionic strength by the relaxation effect (depending on the relative permittivity) and the electrophoretic effect (depending on the relative permittivity and the viscosity of the solvent), whereas the diffusion coefficient is decreased only by the relaxation effect. This allows the theoretical predictions that the plate number, which is proportional to the ratio mu/D, decreases with increasing ionic strength and that the magnitude of this reduction depends on the solvent. Taking the values for relative permittivity and viscosity allows forecasting that, in general, water as a solvent exhibits the smallest lowering of the plate number, as compared to organic solvents. The theoretical predictions are confirmed by the data for the ratio calculated from measured mobilities and diffusion coefficients for iodide as the analyte ion in water, methanol, and acetonitrile with ionic strength of the background electrolyte varying between 0.005 and 0.080 mol L(-1). Whereas the experimentally observed plate number per volt is reduced from its "ultimate value" of about 20 (analyte charge number z = 1, zero ionic strength) in water by only 10%, the decrease at the same ionic strength in methanol and acetonitrile reaches 25 to 30%. Thus, the maximum plate number should read Nmax approximately equals 13 zU (with U being the effective voltage) for these solvents with ionic strengths normally applied in capillary electrophoresis. This reduction is not stemming from inappropriate experimental conditions, but has fundamental physicochemical causes.     

Characterization of seasonal variation of forest canopy in a temperate deciduous broadleaf forest, using daily MODIS data

In this paper, we present an improved procedure for collecting no or little atmosphere- and snow-contaminated observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The resultant time series of daily MODIS data of a temperate deciduous broadleaf forest (the Bartlett Experimental Forest) in 2004 show strong seasonal dynamics of surface reflectance of green, near infrared and shortwave infrared bands, and clearly delineate leaf phenology and length of plant growing season. We also estimate the fractions of photosynthetically active radiation (PAR) absorbed by vegetation canopy (FAPAR_canopy), leaf (FAPAR_leaf), and chlorophyll (FAPAR_chl), respectively, using a coupled leaf-canopy radiative transfer model (PROSAIL-2) and daily MODIS data. The Markov Chain Monte Carlo (MCMC) method (the Metropolis algorithm) is used for model inversion, which provides probability distributions of the retrieved variables. A two-step procedure is used to estimate the fractions of absorbed PAR: (1) to retrieve biophysical and biochemical variables from MODIS images using the PROSAIL-2 model; and (2) to calculate the fractions with the estimated model variables from the first step. Inversion and forward simulations of the PROSAIL-2 model are carried out for the temperate deciduous broadleaf forest during day of year (DOY) 184 to 201 in 2005. The reproduced reflectance values from the PROSAIL-2 model agree well with the observed MODIS reflectance for the five spectral bands (green, red, NIR₁, NIR₂, and SWIR₁). The estimated leaf area index, leaf dry matter, leaf chlorophyll content and FAPAR_canopy values are close to field measurements at the site. The results also showed significant differences between FAPAR_canopy and FAPAR_chl at the site. Our results show that MODIS imagery provides important information on biophysical and biochemical variables at both leaf and canopy levels.     

An overview of JML tools and applications

The Java Modeling Language (JML) can be used to specify the detailed design of Java classes and interfaces by adding annotations to Java source files. The aim of JML is to provide a specification language that is easy to use for Java programmers and that is supported by a wide range of tools for specification typechecking, runtime debugging, static analysis, and verification.This paper gives an overview of the main ideas behind JML, details about JML’s wide range of tools, and a glimpse into existing applications of JML.     

Sliding simulation for adhesion problems in micro gear trains based on an atomistic simplified model

The objective of this research work is to provide a systematic method to perform molecular dynamics simulation or evaluation for adhesion of micro/nano gear train during sliding friction in MEMS. In this paper, molecular dynamics simulations of adhesion problems in micro gear train are proposed. The perfect MEMS gear train model is very complicated by considering the computing time. A simplified model to simulate surface sliding between metals by molecular dynamics (MD) is proposed because the surface property is a dominant factor for the performance of gear systems. Based on analysis of sliding friction and the transmitting characteristics of micro gear train, a model is established by utilizing the Morse potential function. The Verlet algorithm is employed to solve atom trajectories. The simulation results show that adhesion tends to occur between two micro gears after certain cycles and such adhesion accounts for the friction force and the temperature increase. The simulation results are in consistence with the experimental results in the literature. The model is meaningful to prolong the lifetime of micro gear train by selecting proper parameters.