In order to make practical devices using nanodiamond, highly efficient and directional emission of single photons in well-defined modes, either collimated into free space or waveguides are essential. This is a Herculean task as the photoluminescence of the NV centers is associated with two orthogonal dipoles arranged in a plane perpendicular to the NV defect symmetry axis. Here, we report on a micro-concave waveguide antenna design, which can effectively direct single photons from any emitter into either free space or into waveguides in a narrow cone angle with more than 80% collection efficiency irrespective of the dipole orientation. The device also enhances the spontaneous emission rate which further increases the number of photons available for collection. The waveguide antenna has potential applications in quantum cryptography, quantum computation, spectroscopy and metrology.The negatively charged nitrogen-vacancy colour center (NV In systems with very small mode volumes, for example in plasmonics34Here, we report on a theoretical study of a novel feasible micro-cavity waveguide antenna design, which can effectively direct photons from a single NV center in a nanodiamond into free space or a waveguides in a very narrow cone angle with more than 80% collection efficiency irrespective of the dipole orientations. The proposed antenna enhances the spontaneous emission rate which further increases the number of photons available for collection. It is also insensitive to the location of the dipole in the antenna over a range of distances which are well within any fabrication tolerances.Rs covered with a silver film of thickness TAg to form a cavity with radius of curvature, RAg(RAg = Rs  –  TAg). The thickness is chosen to be much greater than the skin depth of silver to act as a mirror. The silver is then covered with first layer of silicon nitride (refractive index 2) of thickness TSiNx which is approximately equal to RAg/2. Silicon nitride is chosen because of its low loss in the visible region and refractive index close to diamond (refractive index 2.4) to reduce light scattering due to the refractive index mismatch in addition to diamond’s compatibility with silicon nitride from a fabrication point of viewRAg/2n from the silver surface. This first silicon nitride layer of thickness (TSiNx ≥ De)sets the height of the emitter from the bottom of the concave mirror.De is the geometric distance from top surface of concave mirror to the location of the NV center in the nanodiamond sphere. This dielectric thickness is important to act as a mirror for the dipole and also to prevent any nonradiative decay channels of the emitter with the metallic mirror. In practical applications, the nanodiamond can be placed at any specified location using an atomic force microscope (AFM) tip or tungsten probe attached in a SEM37T'SiNx is deposited on the top of the emitter to form a waveguide in silicon nitride of thickness TSiNx + T'SiNx.We start our study by showing how the emission from a single emitter with different dipole orientations behaves in a planar slab waveguide of thickness (Tw=300nm), length (Lw = 3 µm) and a refractive index of 1.5. This is followed by a gradual evolution of the planar slab into our novel micro-cavity waveguide antenna in three steps. The emitter is designed as a dipole, which emits at 700 nm vacuum wavelength embedded in a sphere of 40 nm radius and with refractive index 2.4 . The detailed computational aspects are given in the Methods. Only two cases have to be considered viz, a parallel and a perpendicular dipole due to the symmetry of the problem. As a first step, we transform the planar waveguide into a curved waveguide , the light directed towards the mirror is collimated. But he light directed into half sphere not containing the mirror is diverging. Hence the total radiation coming out of the mirror is sum of these two contributions. When (De > f), the emitted light is directed into a cone angle. De > f)for perpendicular dipole orientation.If the emitter is placed at the focal length  and θ is the half-angle of the maximum cone of light that exit the antenna. The dotted line in the polar plots  (details are given in Methods). The simulation geometry is shown in lots the  shows co┴ as given in De greater or smaller than the focal length of the cavity (f), it is possible to fine tune the emission  depending on refractive index of the dielectric used for making the antenna.The emission intensity pattern is well within this cone angle. We have calculated the collection efficiency for different NAs starting from 0.95 to 0.4 for both ǁ and and 82% respectively (again referring to a 90° full cone angle). Furthermore we take a look at the enhancement of the spontaneous emission rate (SPE) of the nanodiamond in the antenna. The total emitted power from the dipole is calculated by integrating the radial component of the Poynting vector over a closed spherical surface centred on emitterE) in the cavity to the emitted power of the same nanodiamond in air (PAir) (SPE = PE/PAir). Here, the radial component of Poynting vector is integrated over a closed surface of same radius to find the emitted power in the above two cases. The SPE varies with respect to position of the dipole in the antenna. Thus, the enhancement and emission angle have been calculated at various dipole positions. A maximum enhancement of SPE = 20 is obtained without losing the high collection efficiency of more than 80% for ǁ, ┴ (and 45°) dipole orientations.The collection efficiencies calculated for parallel, perpendicular and 45 degrees dipole orientations are 85%, 81%, Z axis as shown in the z axis) as shown in z axis the variation in the peak emission enhancement is less with full width at half maximum (FWHM) 195 nm (┴) and 144 nm (ǁ) respectively. Over 80 nm offset in x axis, the reduction in the SPE is less than 10% and 20% for ǁ and ┴ dipole orientations respectively.As a next step, we have varied the position of the nanodiamond vertically . The distance of the dipole from the base of the cavity (z axis distance) is 315 nm and 215 nm for ǁ and ┴ dipole respectively. Since NV centre is made of combination of two orthogonal dipoles, a suitable cavity parameter  can be obtained with maximum enhancement for both parallel and perpendicular dipoles from ┴) and ~20 with a FWHM of 20 nm (ǁ). The geometry determined is suitable for any emitter, and the peak operating wavelength and the emission enhancement can be tuned by varying the waveguide parameters such as curvature, position of the dipole and refractive index of the waveguide used.These results show that the antenna is less sensitive to the position of the dipole over a range, which is well within any fabrication tolerances and this tolerance is enough to take into account random sizes of nanodiamonds with variable position of the dipole inside the nanodiamond, and also positioning of the nanodiamond via AFM or tungsten probe. To investigate the spectral response of the antenna, we scan the emission wavelength of the dipole in the cavity from 650 to 750 nm to determine the maximum enhancement. In summary, we have presented a novel micro-cavity waveguide antenna to enhance extraction of photons from NV center in nanodiamonds. The antenna manipulates the emission direction by making use of a combination of waveguide and a dielectric silver mirror. The antenna also enhances the spontaneous emission rate. Furthermore the geometry presented here is a unique solution to the difficulty in collecting photons from NV centres in nanodiamond which is associated with two orthogonal dipoles arranged in a plane perpendicular to the NV defect symmetry axis. Collection efficiencies of more than 80% are demonstrated irrespective of the dipole orientations using the antenna with an accompanying increase in the spontaneous emission rate by a factor of eighteen. We have also shown that the antenna geometry is robust against offsets in dipole positioning over a range of distances which falls well within any fabrication tolerances. The concave waveguide antenna will find potential applications in quantum cryptography, quantum computation, spectroscopy and metrology.Rs) on a semi- infinite semispherical silicon and covered with 300 nm thick silver layer (TAg). The top surface of the silver coating has a curvature reduces of 900 nm (RAg). The silver is covered with silicon nitride of thickness 615 nm and the nanodiamond is placed at 315 nm above bottom of the cavity inside the silicon nitride. Another semi-infinite half sphere of air is placed above the semi-infinite hemisphere silicon concave cavity. A scattering boundary condition is used around the sphere to truncate the simulation geometry. The radiated power intensity and far field radiation pattern is calculated using hollow spheres inside the geometry with the center of the sphere as the centre of the nanodiamond.The micro-cavity mirror antenna is computationally investigated using the finite element method (FEM) implemented in COMSOL MULTIPHYSICS 4.3b. A spherical simulation volume in 3D is used to find the emission characteristics such as the far field radiation profile, emission cone angle and enhancement in the spontaneous emission rate from an NV centre nanodiamond in the micro-concave waveguide antenna. In all cases, the nanodiamond is modelled as a point located at the centre of a sphere of radius 40 nm and refractive index 2.4. The emission wavelength is chosen at 700 nm vacuum wavelength . The emission for parallel, perpendicular and 45 degrees dipole orientations are studied. The geometry consists of a concave cavity with radius of curvature 1200 nm .


To investigate the mechanisms for the perception of relative numerosity, we used two-interval forced-choice  to measure thresholds for area, density and numerosity differences between dot textures, and a 2 × 2 FC task to measure the ability of observers to distinguish changes in area from changes in density. To prevent the use of a one-dimensional size signal we used textures in which dots were scattered within irregular polygonal areas. Numerosity thresholds were similar in the area and density-varying conditions, consistent with a single numerosity mechanism. Thresholds for area and density discriminations were raised when number was held constant, consistent with numerosity thresholds being lower than those for size and density. Also, area thresholds for polygonal outlines were increased when no dots were present in the outline. However, a single numerosity mechanism cannot account for all the data, because we find that observers in randomly-interleaved size-varying and density-varying conditions are also able to discriminate between changes in size and density with a precision predicted from independently-noisy size and density channels that have similar noise to that in the putative numerosity channel. A complication, previously noted with circular shapes, is that denser textures tend to be confused with larger textures, and vice versa. This could explain why thresholds rise when density and size changes are in opposition, in the constant-number case. These findings taken together do not rule out an independent numerosity mechanism, but they are equally compatible with a flexible computation of numerosity from size and density cues. We addressed this question by comparing two-channel vs three channel fits to the combined data in all conditions.22.12. Stimulus presentation was controlled by MATLAB and the PTB3 version of the Psychtoolbox. On each trial subjects saw consecutively two stimuli, which they were required to compare for number, density or size.Examples of the stimuli are shown in 2) or positive contrast . The dots were randomly positioned within notional polygons without overlap. The irregular polygon shapes were generated by an algorithm that pseudo-randomly varied the position and number of vertices of each polygon in any trial. In all conditions the standard stimulus contained 64 dots within the standard area of 50,000 pixel, which corresponds to a circular area of 2.63° radius. An example is shown in the center panel of μ) and the standard deviation (σ) of the psychometric function efficiently by concentrating cue values at μ ± σ.Each stimulus contained a number of fuzzy dots with a diameter of 10 arcmin and a Gaussian envelope with a space constant of 2.5 arcmin. Each dot was randomly assigned a negative  the following conditions and Trial Types were used. We use ‘Condition’ to refer to a block of trials containing the same Task and one or two Trial Types and, ‘Trial Type’ to refer to the kinds of trial within a block.The ‘Density Condition’ consisted of blocked density varying trials where the area of the test was the same as the standard and the density of dots co-varied with the number. Observers estimated the differences in density between the test and standard patch. Similarly, the ‘Size Condition’ consisted of size varying trials where the density of the dots in the test was the same as in the standard, and the area was adjusted to accommodate the greater or smaller number of dots at that fixed density. Here, observers were asked to estimate the differences in texture area. In both conditions, size varying and density varying trials were presented in separate blocks and observers made a binary choice: ‘denser’/‘less dense’ and ‘larger’/‘smaller’. In a modified Size Condition, the ‘Outline Size Condition’ only the outline of the polygon shape was shown but no dots. Here, observers compared area size of the test stimulus with the area size of the standard.In the ‘Mixed Task Condition’ and in the ‘Numerosity Condition’ trials of size and density varying cues were randomly interleaved. In the ‘Mixed Task Condition’ observers were asked which kind of difference (size or density) was present, and the direction of change. In the ‘Number Condition’ the observers had only two keys available, to indicate which stimulus had more dots (numerosity discrimination).Since we cannot prevent observers in the density and size conditions using numerosity as a cue , we introduced a further condition to estimate size (‘Extended Size Condition’) and density (‘Extended Density Condition’) changes alone. Here, we introduced a trial-type for which the number of dots was kept constant at 64 dots in each stimulus with size and density of the test varying oppositely to each other. Hence, in the Extended Size Condition in half of the trials a larger stimulus coincided with less density and in the 50% of trials a larger stimulus coincided with higher numerosity, but constant density compared to the standard. The aim of this arrangement is to prevented observers from using numerosity as a reliable cue to estimate the density or size of the texture.An overview of all conditions is given in Prior to the experiments the observers were shown examples of the stimuli and were told about the relationship between density, size and number of dots in the different conditions.Each observer completed 5 sessions of all conditions; only of the Mixed Task Condition 7 sessions were done. In the Single Trial Type conditions  the sessions contained 128 trials summing up to 640 trials per observer and condition. In the Mixed Task, Extended Size and Density and Number Conditions two different Trial Types were interleaved summing to at least 1280 trials per observer and condition.Five observers took part in the experiments; three with experience in psychophysical experiments  and two with no previous psychophysical experience (Observer 4 and 5 of age 19 and 39). The vision of subjects 1, 3, 4 and 5 was normal or corrected to normal. Subject 2 was a myope with −0.75D/−0.75D. Subjects 1–4 took also part in the numerosity experiments presented in 2.2μ (50% point) and spread σ (standard deviation). 95% confidence limits for the individual points on the psychometric functions and those for the fitted parameters of the psychometric functions were obtained by a bootstrapping procedure with 640 simulations. The best-fitting parameters of the psychometric functions and their confidence intervals for all subjects and tasks are given in Individual psychometric functions representing percent ‘larger’/‘denser’/‘more’ responses of each condition and Trial Type were fit, using the MATLAB ‘fminsearch’ procedure, by cumulative Gaussian functions with parameters σ for the two Types was compared to a fit using the same value of σ. To asses the differences in only the slope of the psychometric functions (σ), the PMFs were allowed to have different μ’s. The likelihoods and the derived chi-squared values of these pairwise comparisons are shown in σ fit is significantly better than a one-σ fit we can conclude that the thresholds for the two trial-types (density or size changes with numerosity) are significantly different.To see if there were any statistically significant effects of Trial Type a Chi-squared test based on likelihood ratios was used. A fit to the combined data over Trial Type using different values of Signal detection models were fit to all the data of a single subject under all the experimental conditions. Details are given at the appropriate point in the paper.3σ) are shown by circles with error bars indicating 95% confidence limits. The colored bars show predictions of a 2-channel size-density model, to be described below. The horizontal lines depict predictions of a 3-channel size-density-numerosity model.Thresholds (Weber Fractions) are shown separately for the different conditions, Trial Types and observers in For a given observer, thresholds are similar across tasks and conditions, and observers who are relatively good at one task are also good at the others. The most obvious feature of the data is that thresholds are similar for size, density and numerosity, both in Single Trial and mixed conditions . Unlike the previously reported results for circular textures  numerosity thresholds were not lower in the size-varying vs the density-varying conditions. The exception to this simple rule is in the ‘Extended Size’ and ‘Extended Density’ conditions. Here, thresholds are lower when size/density co-varies with numerosity than when size and density change in opposite directions, so as to keep number constant. Finally, thresholds are higher in the Outline Size condition, when only the outline of the polygon changes in size than in all the other conditions when dots are present within the outline.These findings are confirmed by the statistical pairwise comparisons in In When only the outlines of the polygons but no dots were shown the threshold for Size judgment increases markedly (significantly in 3 out of 4 observers) relative to the case where changes in size co-vary with dot number. The Weber Fractions in the outline Size Condition resemble the thresholds of the Extended Size Condition when numerosity is kept constant and does not co-vary with patch size. This suggests that the outline condition gives a true measure of size discrimination, when it is not aided by concomitant changes in density and/or number. Size discrimination of polygons thus appears to be relatively poor, as would be expected from the finding that the most accurate forms of 2-D size discrimination are obtained by combining 1-D estimates .The simplest explanation of the identity of thresholds between size- density- and number-varying conditions is that the same mechanism is being used in each case: a relative numerosity system. However, a single-channel model for all the data is ruled out by a number of facts. One is that observers were able to discriminate changes in size and density in the Mixed Task Condition see . PerformThese considerations suggest that a hybrid model may be required to explain the full range of data, incorporating size, density and numerosity mechanisms (channels). But if this approach is to be taken, it is necessary to show that a 3-channel model is significantly better than a 2-channel (size/density) model, taking into account the greater number of parameters in the 3-channel case. We address this question rigorously in the following Modelling section.44.1μ) and the sigma (σ) of the psychometric function  and chooses the one that deviates most from its reference value  as in the Single Trial Type condition. In the size-outline condition, the observer monitors only the size channel.m in the density channel, a signal mp (p < 1) would be added to the size channel. This is equivalent to introducing a bias μ in the psychometric function. If p > 0 this ensures that observers will be above chance at detecting the direction of the numerosity difference even if they incorrectly identify its source. The same cross talk will also improve performance in the numerosity condition where observers do not have to identify the source. The cross-talk was also assumed to be present in the extended conditions, leading to a larger signal in the case where size and density co-varied and to a smaller signal when they counter-varied. There was no cross-talk in the outline size condition, because the density cue was absent.To improve the fit of the two-channel model and to compensate for two conspicuous failures, two further parameters were included as in . The firA second major drawback of the simple 2-channel model is that it assumes observers choose density or size equally often if there is no noticeable cue available, hence there is no bias in the identification of the source of change. However, some observers  show a significant bias as can be seen in the upshift or downshift of the response probability for small cues in the 5th row of μ, σ) psychometric function to all empirical functions where numerosity varied and could be used as a cue. However, a single mechanism could not discriminate between size and density changes in the Mixed Task, so we added two further channels for Size and Density, each with its own μ and σ, and deemed that observers used the Max rule to make their choice. This gave a 6-parameter model in total. The size and density channels are used only to identify the source of the numerosity difference in the Mixed Task condition and in the Extended Conditions. More details of the models are given in  while the two-channel model with leakage and bias had four. The different degrees of freedom allow a statistical comparison between the fits . Moreover, changes in density tend to be confused with changes in size. Whether these findings support the idea of a special numerosity mechanism, distinct from size and density, is a complex question, which we now address.μ) of the psychometric function is constrained to be zero, the resulting 3 parameter version of the three-channel model is significantly inferior to the 4-parameter version of the two-channel model for all observers. Further, the ability of observers to report the direction of a numerosity change correctly when they are unable to identify its origin (size vs. density) can be explained by a bias towards seeing denser patterns as larger and vice versa. This bias has been independently confirmed  than the 3-channel model and is more successful. If the mean (onfirmed  and coulonfirmed  or decoronfirmed . The biaOur view is that the case for a distinct numerosity mechanism is ‘not proven’ by the present experiment. It cannot be rejected, nor is there any compelling reason to accept it. Lacking so far is a model for how numerosity is computed if it is indeed true that ‘Vision senses number directly’ . If coun


In isolated bimetallic nanoscale systems the limit amount of matter and surface-induced size effects can change the thermodynamics of first-order phase transformation. In this paper we present theoretical modification of Gibbs free energy concept describing first-order phase transformation of binary alloyed nanoparticles taking into account size effects as well as depletion and hysteresis effects. In such a way the hysteresis in a form of nonsymmetry for forth and back transforming paths takes place; compositional splitting and the loops-like splitted path on the size dependent temperature–composition phase diagram occur. Our calculations for individual Cu–Ni nanoparticle show that one must differentiate the solubility curves and the equilibrium loops (discussed here in term of solidification and melting loops). For the first time we have calculated and present here on the temperature–composition phase diagram the nanomelting loop at the size of 80 nm and the nanosolidification loop at the size of 25 nm for an individual Cu–Ni nanoparticle. So we observe the difference between the size-dependent phase diagram and solubility diagram, between two-phase equilibrium curves and solubility curves; also intersection of nanoliquidus and nanosolidus is available. These findings lead to the necessity to reconsider such basic concepts in materials science as phase diagram and solubility diagram. One of the key questions in nanoscience is related to the problem of equilibrium phase diagrams variation for multicomponent finite systems with size decreasing. One of the most extensively studied size effects  is the size-dependent melting temperature shift which is usually observed and explained in accordance to the so called capillary effect (surface-to-volume ratio or Laplace pressure) –3. SomewT–X) phase diagram has been substantiated [Size effects in multicomponent nanomaterials, where the first-order phase transformation starts from a nucleation and includes a change of composition of chemical elements, are accompanied with the less known “chemical depletion” effect: the amount of one of the chemical components may not be sufficient for the formation of a new phase nucleus of the different composition –13. The antiated –21. UnfoThe present communication is directed to gaining the new knowledge of basic principles and specific features of materials stabilization processes related to the non-negligible size and depletion effects  providing new insights into first-order phase transition problem for nanosystems and basic concepts of phase diagram and solubility diagram in materials science.T–X diagrams for an individual nanoparticle in solid–liquid two-phase region based upon the condition of the energy minimum are constructed and the difference between the equilibrium and solubility curves is explained. The concluding remarks are given in last part of the manuscript.The structure of the paper is as follows. First we give in brief the general thermodynamic approach explaining the influence of sizes and depletion on modification of Gibbs thermodynamics for multicomponent nanoscale systems. For simplicity we restrict the consideration by bimetallic individual nanoparticles. Next the Let us first briefly remember the size effect on the shift of phase diagram curves based on general thermodynamic approach. For a bulk material classical thermodynamics finds the equilibrium states related to the concavity (or convexity) of energy potentials after the so called Gibbs method of geometrical thermodynamics: first one has to plot the Gibbs free energy densities as functions of composition and then find the conditions for minimal energy of a given system by using the rule of common tangent .g (energy per number of atoms in a system): g1 < g2. Subindexes 1 and 2 are referred to the phase 1 or phase 2, respectively. In a multicomponent system it also depends on such factors as molar fraction of a chemical element X (composition), temperature T, so that one can write:Corresponding modification and theoretical descriptions for nanosystems may be done taking into account the additional surface energies of nanometric systems. Such approach explains, for example, the ‘anomalous’ appearance of metastable phases (from bulk point of view) in nanosized systems which are related to the change of the conditions for the phase equilibrium. Stable state exists when the system is in its lowest energy condition. As result the stable phase in bulk material  is one which has the lowest bulk Gibbs free energy density gbulk dependence for both phases:This condition has another form if one deals with a solution which is described by single Gibbs free energy density X1 and X2 are the molar fractions of a component in phase 1 and phase 2, respectively.Here σ, which can dramatically change the equilibrium conditions [N0 and the surface area can be defined as:In a nanometric particle one has to take into consideration the surface and interface free energies, nditions . Then Gifor phase 1 case –for phase 2 case –for solution model –f1, f2 and f are shape factors and the value f/N01/3 represents the well-known surface-to-volume ratio; σ1 and σ2 are surface energy functions of phase 1 and phase 2, respectively. As one can see in Equations 3–5 to the origin of size effect belong the finite volume or number of atoms, the surface area and surface energy. One may observe also that the Gibbs free energy density in a nanoscale system is increased by the surface energy input.Here the 2 < σ1, then so it may become the stable one  = g2nano:The critical number of atoms gnano = gnano for solution case:or the condition N0) the nanophase 2 will be advantageous as compared with nanophase 1 when: N0 < N0*. The experimental examples of such kind surface-induced size effect have been found for different systems  [For a small volume of such a system  –24. FiguG* is very high  then the phase transformation will be suppressed. That is why the nucleation must be taken into account in the thermodynamic calculations and it is critical for multicomponent nanosystems in terms of chemical depletion. In this respect the cases  in The first-order phase transformations are described as transformations following the nucleation of new phase clusters . If the Xn and number of atoms Nn are formed at the surface or inside an isolated nanoparticle  then there exist the compositional changes X0 → Xp around the nucleus and the chemical depletion appears ΔX = X0– Xp  has the form:In our case (fixed pressure and temperature) the thermodynamic potential will be the Gibbs free energy of a nanosystem being the sum of two parts: the bulk and the surface contributions. For chosen phase transition mode from single-phase state to two-phase morphology ,b the GiS1 is the external surface area of phase 1, S2 is the external surface area of phase 2, S12 is the interphase area, σ12 is interphase energy being the function of both compositions Xn, Xp and the temperature. That expression represents the modification of Gibbs thermodynamics for muticomponent nanosystems and differs from commonly known and used expression of classic nucleation theory for Gibbs free energy change. The main difference is in the second term Np·g1 which is outgoing in a bulk and accounts the chemical depletion effect in a nano. As result it changes the thermodynamics of phase transformation as compared with classical Gibbs theory.In T, composition X0, particle size N0 and other parameters the system of G as the function of number of atoms in a new phase Nn . The function ΔG can be found and depicted by the direct calculation for reasonable compositions Xn . Doing that one shall obtain the curves shown in T of the particle (by other fixed parameters) or changing the number N0 (by other fixed parameters) or alternatively changing only the composition X0 (by other fixed parameters) it is possible to achieve all states shown in At fixed temperature G < 0) and each phase has different composition. From this one has to distinguish: 1) initial composition X0 functioning at the same time as the limit solubility; 2) new phase composition Xn; 3) composition of depleted phase Xp. If one changes the temperature T the curves ΔG changes and the minimum point moves as well  function one can find all stable states and plot the equilibrium T–X nanophase diagram [In two-phase states  the nano diagram . The letX0 at low values T. If one increases the temperature the melting starts when a liquid part appears. This is the solidus temperature (above which both components are no more miscible). By solidus temperature the energy change ΔG has two minima ΔG = 0: first for starting point Nn = 0 and the second for new two-phase state with Nn ≠ 0  < 0 is already satisfied at the beginning . Let us call it the transition criterion. If one plots that composition X0 at T–X diagram then it will be point with coordinates . Changing the initial composition X0 and repeating the thermodynamic analysis  generating the solubility curve at the T–X diagram. In that interpretation solidus is the solubility curve for a solid nanosystem and is the “curve representing in a temperature–concentration diagram the line connecting the temperatures at which fusion is just started for various compositions of a starting solid phase” [Let us look first on the initial points of the phase transformation by example of melting of the solid nanoparticle of composition analysis  one can d phase” . The solT and decreases it then solidification appears. The corresponding thermodynamic analysis gives shifted and size-dependent liquidus curve (or nanoliquidus) which is “in a temperature–concentration diagram the curve connecting the temperatures at which freezing is just started for various compositions of a starting liquid phase”. The detailed calculation of the nanosolidus and nanoliquidus for particular case of Cu–Ni nanosystem has been done in one of our previous works [In opposite way if one starts from entire liquid nanoparticle at high temperature us works .X0 and number of atoms N0. The solid-to-liquid transformation starts at nanosolidus and goes to new two-phase equilibrium when two phases (depleted phase 1 with changed composition Xp and new phase 2 with composition Xn) co-exist  curves and can be shown on T–X diagram as equilibrium curves. If one plots equilibrium concentrations Xn, Xp for any fixed T, one will obtain a loop-like splitted path .Let us look now on two-phase stable states during the melting of that binary nanoparticle by fixed initial composition co-exist . If one T–X diagram whereas the letter corresponds to the stability or equilibrium T–X phase diagram. In order to see the difference in R = 80 nm of the Cu–Ni spherical nanoparticle and one nanomelting loop for initial composition X0 = 0.2 found by most probable cap-like transformation mechanism  nanosolidus and nanoliquidus can overlap and intersect; iv) the forms (curvature) of the solubility curves on the diagram change; v) solubility limits, eutectic and peritectic points shift in composition and are size-dependent. In Next we consider only the solubility curves on osystems  at diffeThe application of the nucleation and the modification of Gibbs thermodynamics for multicomponent isolated nanosystems are discussed here for the particular case of transforming individual bimetallic nanoparticle. The new-model developments are more general and rigorous than the text-book case which is applicable only for large system sizes where interface contributions can be disregarded.T–X diagram nanomelting loop starts at nanosolidus and ends at temperatures essentially different from nanoliquidus and nanosolidification loop starts at nanoliquidus and ends at temperatures different from nanosolidus. This called thermodynamic hysteresis and it relies on symmetry violation of forth and back transformations in nanosystems. The increasing of the sizes leads to the vanishing of last effect due to decreasing of the surface energy input and vanishing the chemical depletion.For the first time, to our knowledge, we calculate and present for individual Cu–Ni nanoparticle the nanomelting loop at size 80 nm and the nanosolidification loop at size 25 nm on temperature–composition phase diagram and generalize the results showing that the nanosolidus and nanoliquidus curves indicate only the starts of nanomelting and nanosolidification but not the two-phase equilibrium. Next new result is that at The concept of equilibrium phase diagram has to be revised, due to the fact that the amount of matter is limited in small isolated systems and one needs a new physically acceptable explanation for the purpose of adapting to nanosystems.G have to be evaluated. A relevant way is using the Gibbs free energy densities g1, g2 of the Cu–Ni system and surface (interphase) energy functions of respective phases σ1, σ2 and σ12. In our case the temperature interval where liquid-to-solid and vice-versa transitions are investigated is from 1000 to 1700 K. The corresponding thermodynamic data and parameters are used from the literature [To calculate the nanomelting and the nanosolidification loops, the Gibbs free energy change Δterature –39 and lThe calculation of the Gibbs free energy densities of the Cu–Ni binary system is based on the thermodynamic data of the CALPHAD for subregular solutions (taken directly from ) and pre


Many studies have been performed on exploring the effects of radio-frequency (RF) energy on biological function in vivo. In particular, gene expression results have been inconclusive due, in part, to a lack of a standardized experimental procedure. This research describes a new far field RF exposure system for unrestrained murine models that reduces experimental error. The experimental procedure includes the materials used, the creation of a patch antenna, the uncertainty analysis of the equipment, characterization of the test room, experimental equipment used and setup, power density and specific absorption rate experiment, and discussion. The result of this research is an experimental exposure system to be applied to future biological studies. Radio-frequency (RF) energy is nearly everywhere, it is used in cell phones, wireless internet and many other sources. These RF energy levels used by common devices are below the threshold level which does not produce heating of cells in living tissues. However, this low-level exposure of RF energy has still raised concerns over its possible effects on human health, specifically, genetic alterations. Researchers have investigated if RF energy can induce changes in biological function  were solved for by using the equations below. The power received at the patch antenna is:Ae is the area of the effective aperture, computed by:Dr is the directivity of the patch antenna with an efficiency of 95 % and λ is the wavelength and defined by:c is the speed of light in m/s and f is the frequency in hertz. The uncertainty of λ can be computed byThen the power density incident on the antenna can then be calculated using Eq. Then the uncertainty of the effective aperture can be calculated as:UPR is dependent on the value recorded by the spectrum analyzer (PR) and its respective measurement tolerance (TPR) given by the manufacturer:Now the uncertainty of the power received at the spectrum analyzer can be computed. This value Knowing that the transmission lines are identical the equation can be simplified to:Knowing the uncertainty for the effective area of the aperture and the power received at the patch antenna. The uncertainty of the power density can be determined as:These equations were used for the test equipment and the uncertainty of the power density was calculated. Figure PrdB is the power received at the spectrum analyzer, PRba is the power received at the biconical antenna. Knowing the uncertainty of both the biconcial antenna and the transmission lines, we can calculate the total uncertainty as follows:The spectrum analyzer has a frequency range from 9 kHz to 3.0 GHz, and the biological antenna has a frequency range of 25 MHz–4 GHz  are commonly used to characterize RF energy exposure in the aforementioned exposure systems. Power density is the amount of power (in W) per unit area (in m). It can be calculated byDifferential power measured in a closed exposure system.Rate of temperature change in the biological test subject measured with noninterfering probes.Calorimetric techniques.Thermographic techniques.Implantable E-field probes.SAR is a measure of electromagnetic (EM) energy absorbed by a body. SAR calculation can be accomplished through theoretical, experimental and empirical techniques. The Radio Frequency Radiation Dosimetry Handbook (Fifth Edition)  Pe is the power absorbed by the patch antenna in the empty enclosure; PI is the input power; PO is the output power; PR is the reflected power; and Ps is the power absorbed by the patch antenna while the sample is present in the enclosure in Watts. After Pe and Ps are measured, the SAR is calculated by usingFinally, Durney et al.  describe2009Then the uncertainty for SAR is:In Eq. The microstrip patch antenna is widely used because of its low volume and thin profile characteristics Balanis . Also th2 which is matched with 50 Ω using an inset-fed microstrip line. A detailed picture of the manufactured microstrip-fed rectangular patch antenna is shown in Fig. A rectangular MPA with a microstrip feed is designed so its pattern maximum is normal to the top patch surface i.e., broadside radiator. Next, the actual length and width of the radiating patch are calculated using the design equations given in . A horn antenna was used to measure the reflected power at eight different locations in a circular plane with a radius of 38.0 cm at the level of the transmit antenna  to ensure that mice remain in their respective cages. For radio-frequency exposure experiments BALB/c mice (6–9 weeks of age) were obtained from Jackson laboratory . Animals were housed on Alpha-dri™ paper bedding  in micro filter-topped cages  in a specific pathogen-free facility with ad libitum access to food and water.t) of the Horn antenna and power transmitted (Pt) from the Horn antenna. Equation The mice are then separated into treatment and control groups respectively. Anechoic material is used in both the treatment and control group to limit the exposure of background RF and to ensure that the control group is not radiated with stray test RF energy. Figure 2 for 2.45 GHz .An improvement to the exposure system reported here includes an independent estimate of SAR based on full-wave electromagnetic simulations and theoretical computations on a 3D whole mouse model. This would allow for independent verification of the experimental differential power procedure used here to estimate SAR and found in the Radio Frequency Radiation Dosimetry Handbook (Fifth Edition) This manuscript describes a new far field RF exposure system for unrestrained murine models that reduces experimental error. The steps to reduce experimental error were described and the result of this manuscript is an experimental exposure system to be applied to future biological studies.


Normally these SiCO compositions would be expected to crystallize. Instead, calorimetric measurements have shown that the amorphous phase is thermodynamically stable. In this article we employ first-principles calculations to estimate how the interfacial energy of the graphene networks is favorably influenced by having mixed bonds attached to them. We analyze the ways in which this reduction in interfacial energy can stabilize the amorphous phase. The approach highlights how density functional theory computations can be combined with the classical analysis of phase transformations to explain the behavior of a complex material. In addition we discover a two-dimensional lattice structure, with the composition Si2C4O3 that is constructed from a single layer of graphene congruent with silicon and oxygen bonds on either side.A model for the thermodynamic stability of amorphous silicon oxycarbide (SiCO) is presented. It builds upon the reasonably accepted model of SiCO which is conceived as a nanodomain network of graphene. The domains are expected to be filled with SiO In the early 1950s attempts were made to introduce carbon into SiO3) groupHowever, more recently, it has been shown that large mole fractions of carbon can be incorporated into compounds of silicon, carbon and oxygen if they are synthesized via the chemical routeThe carbon content in these materials, processed from the polymer route, can be varied over a wide range by changing the composition of the organic precursors2 carbon within the structure13C confirmed the presence of sp2 carbon, but, in addition, the 29Si spectra showed the presence of mixed bonds of SiC4-nOn for n = 1, 2 and 3Fig. 1(a)Insights into the molecular structure of PDCs began to emerge from the following observations: (i) Although devoid of wide-angle X-ray diffraction peaks, the SiCO materials exhibit strong signals in Small Angle X-ray Scattering (SAXS) suggesting the presence of short range orderThe validity of the graphene network model has been confirmed by molecular dynamics calculations122 interface, which is calculated from first principles.The most remarkable recent development has been measurements of the enthalpy of formation of SiCO compounds by high temperature calorimetry which asserts that the amorphous state of these materials has a lower enthalpy than the crystalline stateWe seek to determine whether or not the amorphous phase of silica in the nanodomain model shown in We follow the hypothesis that the mixed bonds of Si present at the graphene interface lowers its energy. These mixed bonds are silicon-centered tetrahedra consisting of Si bonded to oxygen on one side and the carbon atoms in graphene on the other. These bonds are the source of interfacial bonding and a lower interfacial energy. We estimate the change in the interfacial energy in terms of the cost of breaking C-Si bond in these mixed bond tetrahedra.2 to sp3; thus the C-Si bond induces strain in the adjacent C-C bonds which leads to a minimum in the interfacial energy when calculated as a function of the density of Si bonds on the graphene face.The unusual feature of the C-Si bonds is that they force the carbon atoms in graphene to change their hybridization from spThe graphene segments in the nanodomain structure would contain edges with the spatial frequency of the nanodomain size. The edges are defect sites where the mixed bond interactions will be stronger. We shall consider both the face and the edge coordinations of the C-Si-O mixed bonds at the interface. In this section a framework for this analysis is developed which can lead to a quantitative estimate of the domain size which can be stabilized in this way.xCyOz, as determined from high temperature calorimetric measurementsThe argument for the thermodynamic stability of the amorphous nanodomain structure of SiGII-I. We assume that the mixed bonds are present in the amorphous state but broken in the crystalline state, that is, the interfacial energy of graphene, with the mixed bonds attached to it is lower than without the mixed bonds. ΔGII-I is then given byd′ is the size of the nanodomains. Here The mixed-bonds are attached to graphene in two ways: on the face of the graphene segments or along its edges as shown schematically in γf and γe, where the subscript f refers to the graphene face and e to the graphene edges. They are equal to the energy of the bonded state where silicon forms mixed bonds with the carbon atoms in graphene, minus the energy of the separated state where the silicon-carbon bonds have been broken. Defined in this way, both are negative quantities.The differences in the interfacial energy of the amorphous state and the crystalline state are written as d′. Each face of the cube is shared by two adjacent domains, thus overall three of such faces belong to one cube, hence the pre-factor of three in the d′2γf term. The other interface term refers to the edges of the graphene segments that form the cube. Here there are different possible values for the pre-factor. At one extreme it can be assumed that there is only one edge that is shared by the four cubes that meet there, in which case each edge would be shared between four cubes; since there are twelve edges, three of them would belong to a cube. For this case the pre-factor ne = 3.The pre-factors in the interfacial energy terms, in Eq. segments of graphene form the faces of the cubes, each with their own edges that do not dovetail with the edges from the orthogonally aligned adjacent faces. In this instance four edges are shared by the four cubes that meet there, and the total number of edges per cube becomes ne = 12. Thus a pre-factor three would be the minimum value for neand twelve its highest possible value. In the following analysis we compute the predictions for the model for ne = 3 → 12.It is however, difficult to imagine the edges of the graphene from four adjacent cubes meeting to form a one line of a coherent carbon structure. It is more likely that d′, exhibits a minimum. Its position is analyzed by differentiating the right hand side with respect to d′ and setting the result equal to zero. The solution to this equation gives the value for d′ where the total free energy reaches its minimum value. We call it d′*.The form of Eq. Differentiating the right hand side of Eq. γf < 0. The quantity in Eq. α, is positive since both γe and γf are negative quantities.This quadratic equation has two solutions. One of them, which is necessarily negative, is not admissible. The viable solution for Eq. α recognizes the influence of edge energy. If the edge energy goes to zero, that is, there are no mixed bonds present at the edges, then α → 0, and α represents the contribution of the edge mixed bonds to the calculation of the domain size where the free energy. Note that a larger domain size implies that crystallization becomes more difficult, or, in other words, the interfacial energy opposition to crystallization grows stronger.A physical interpretation of the right hand side of Eq. α from Eq. We now normalize the variables in Eq. α = 0.25 to 2.0 are shown in α increases, reflecting the increasing role of the edge bonds. The values for α are derived from first principles calculations, as described in the next section.Plots of Eq. In order to determine the relative significance of the mixed bonds formed at the graphene face and the graphene edges, we consider the case where the only contribution to interfacial energy is provided by the mixed bonds present at the edges, without any mixed bonds being present on the face atoms.For this purpose we omit the face energy term from Eq. Differentiating and equating to zero gives the following result for the domain size when it is stabilized solely the interfacial energy of the graphene edgesIn the next section we compute the magnitude of the binding energy of the mixed bonds to the face atoms, and to the edges of graphene. The interfacial energy depends not only on the energy per bond, but also on the concentration of the Si-C bonds, defined as the number of Si-C bonds as a fraction of the total number of carbon bonds on the surface. For example if one out of ten carbon atoms on the surface contains a Si-bond then the concentration is given by the Si/C ratio is equal to 0.1. Since the binding energy of one mixed-bond is distributed over several carbon atoms, the gain in energy per carbon atom is equal to the product of the binding energy of one bond times the concentration of the bonds. For example if the binding energy of one Si-bond is a quantity X then the binding energy per carbon atom, for a Si/C ratio of 0.1, would be 0.1X. We call this latter quantity α, may be set equal to a3 = Ω, where Ω is the effective volume of carbon atom in graphite. Then the following relationships applyThe interfacial parameters are related to 3, assuming the density of graphite to be equal to 2.09 g cm–3, gives a = 0.21 nm. Note that γf being face energy has units of energy per unit area while γe has units of energy per unit length.The effective volume per carbon atom is Ω = 0.0095 nm−1.The first-principles calculations were performed using density functional theory (DFT) and plane-wave projector augmented-wave method3 molecules or 1–8 Si(OH)3 molecules attached to it. The graphene sheets were separated by 20 Å vacuum in the supercell. For the edges both armchair and zigzag configurations were considered.The binding energy of a Si bonded to the graphene surface as a function Si-C bond concentration was calculated by using supercell approximation. The supercell consists of 60-atom graphene with 1–16 SiH2C4O3, that is there are two carbon atoms and 1.5 oxygen atoms for every silicon atom. The mixed bond molecules, per silicon atom, have the composition SiC1/4O3/2, with the silicon being coordinated to one carbon and three oxygen atoms. Thus one half of all carbon atoms change their electronic structure from sp2 to sp3 in order to form a bond with silicon. The binding energy per Si bond is calculated to be −1.2 eV. Therefore the quantity –2. This would be the work required per unit area to separate the Si-O bonds from the graphene (the separated state is the zero energy state). This magnitude of this interfacial energy is very high . The implication is the the 2-D structure shown in When exploring the various geometries for attaching Si-O molecules to graphene surfaces, the periodic structure, shown in The question arises whether it is possible to synthesize this 2D lattice. The chemical route to the synthesis of silicon-oxycarbide is likely to yield a distribution of different kinds of interface structures, some of whom may indeed have the arrangement shown in 3 nature of the Si orbitals is expected to yield reasonable values for the bonding energy of the Si-C bond when the valency of the remaing three orbitals is satisfied by connecting them to OH or to -H atoms. The energy of the Si-C bond is calculated for both –OH and –H configurations, and is found to be essentially similar for both of them, providing evidence for the local nature of the Si-C bond.We have conducted approximate calculations for the binding energy of mixed bonds of Si to graphene for the case where Si is bonded to one carbon and three oxygen atoms. The highly covalent nature of the Si-C bond and the singular sp3 Si-C bond pulls the carbon atoms upwards from the graphene plane, thereby inducing strain in the neighboring carbon atoms. The strain extends to the first nearest carbon. Thus if two silicon bonds were to be brought closer than the spacing between two nearest neighbor carbon atoms then the strain fields will overlap, thereby placing a penalty on the binding energy of the Si molecule to the graphene layer.  In summary, the energy does indeed decline linearly up to the point that silicon atoms are spaced two carbon atoms apart, but then rises when the strain fields begin to overlap.The calculated change in a = 0.21 nm, gives that γf = −0.76 J m–2. As a comparison the typical value for the grain boundaries in ceramics is ~1 J m–2. The grain boundary energy is defined as the work that would be required to separate the boundary to free surfaces. A similar definition is used for γf, that is, it is the work needed to break the silicon-carbon bond to create two free surfaces.The magnitude of 1/4O3/2 chemistry, that is, where Si is coordinated to one carbon (in the graphene) and three oxygen atoms. Attempts to model a situation where one silicon is bonded to two carbon atoms on the graphene face were unsuccessful, yielding very weak bonds. However, strong interaction was obtained if a carbon atom in the graphene face was removed creating a carbon-vacancy, and the Si was inserted into the vacancy to form three bonds with carbon, with the one Si-O bond lying normal to the graphene plane. In this instance a very high value of the binding energy (−3.14 eV) between the Si and graphene was obtained. If these bonds do indeed form while the polymer is converting into the ceramic then their influence on the interfacial energy, and thereby on the stability of the amorphous phase is likely to be profound.In the above discussion we have considered mixed bonds of the SiCγe is a line energy, and would be equal to the work required to separate the interface  interfacial energy is likely to rise linearly with the number concentration of Si atoms present at the edges. However, visual inspection of the molecular configurations suggests that steric hindrance will prevent the Si atoms being closer than approximately 4 carbon atom-spacings for the single Si-C bonds, and 8 carbon atom-spacings for the double Si-C bonds. Substituting the calculated values for the binding energy of the Si mixed-bonds into the relationship for It is possible to seek a comparison between the measurements of the average size of the nanodomains in the amorphous structure of SiCO, measured by SAXS, which have been reported in ref. The high temperature calorimetric measurementsBelow we estimate the size of the nanodomains assuming that the free energy is equal to the enthalpy measured from calorimetric experiments. It is immediately evident from Eq. γf and γe are obtained from the computed values for γf and γe for comparison with experimental data. The lowest value for γf, as calculated above is −0.76 J m–2, and the lowest value for γe is −0.77 × 10–10 J m–1.The procedure and the equations that lead to the comparison between theory and experiment are outlined in –1. It was converted to –3, by calculating the molar volume of SixCyOz from its composition and assuming a density of 2 g cm–3  and a higher (more negative) interfacial energy between silica and carbon would therefore promote the stability of the amorphous phase. The credibility of this concept could be established only by quantitative analysis. The estimates of the interfacial energy from first principles calculation and the measurements of the free energy of crystallization could now be molded into a model to compare theory with experiment. The results in d′*]edge, which considers only the edge bonds, underestimate the experimental values of the domain size. Clearly both faces and edges play a significant role in the stabilization of the nanodomain structure; this fact had not been recognized in the original modelThe exercise of comparing theory with experiment reveals new insights into the importance of the interface structure. The bonds between silicon and carbon can occur in two configurations: at the faces, and at the edges of the graphene segments. The results in 2C4O3, and its interfacial energy is calculated to be −2.1 J m–2; this would be the work required to separate the graphene from the silicon-oxygen layers. It is intriguing to consider if such stand-alone 2-D structures can be synthesized from the chemical route. They probably exist, sporadically, in the conventionally processed polymer derived ceramics; for example the band gap estimated for this structure, 2.5 eV, agrees well with the experimental measurement of the absorption edgeThe computations have revealed interesting new scenarios for the graphene-silica interface structure and energetics. A most remarkable prediction is of a self-standing two-dimensional lattice, which given in The computational results raise further questions about the C-Si-O coordination of the mixed bonds that form the interface. Here we have considered Si bonded to one carbon on the graphene faces with the remaining three bonds being occupied by oxygen. Attempts to connect Si to two carbon atoms on the graphene surface and then to two oxygen atoms yielded very weak bonding. However, if the a Si was inserted to fill a defect site where a carbon atom in the graphene was absent, thereby bonding to three carbon atoms and then to one oxygen atoms then a binding energy of −3.21 eV was achieved. Therefore the genesis of how Si mixed-bonds may become incorporated into defect sites in graphene as the nanodomain structure evolves from the polymer is an intriguing question. Certainly the edges of graphene present ready-made defect sites where the mixed-bonds can attach, and we have considered this issue in detail in the present analysis.2 to sp3 hybridization, has a decay length of about one carbon spacing. The overlap of the strains from adjacent Si atoms then causes the overall binding energy to weaken.An interesting feature of the face-bonds is dependence of the interfacial energy on the density of the Si bonds on the surface. The energy is lowest for silicon-to-carbon ratio of 0.25. At lower concentrations the energy is weaker because there are fewer bonds. However, at higher concentrations the strain induced in the graphene when the Si atom attaches to a carbon atom, forcing it to change from sp2 tie line and the carbon apex, which is generally held to be correct. It has been difficult to fabricate glassy PDCs close to the tie-line and also close to the carbon rich portion of the composition diagram.The above result, which is described quantitatively in 2 network within the interior of the domains. This approximation raises two issues: how such a configuration affects the strength of the Si-C bonds, and what may be the effect of the constraint from the silica network on the Si-C bond. The energy of Si-C bond for –OH and –H configurations are essentially the same in Finally, we discuss the approximations that have been used to pose the problems in a way that has made the present first principles computations feasible. We have assumed the Si-molecules to be terminated in –OH or –H bonds on the other side of graphene. Strictly speaking they would be a part of the SiOA fraction of the carbon atoms at the graphene edges will necessarily have dangling bonds since they cannot all be bonded to Si. In the analysis we have terminated them with hydrogen atoms. The other possibility would be that they are bonded, at an angle, to the edges of the graphene from adjacent cells. This complexity is of a high level and unlikely to be successfully modeled by density functional theory calculations. The presence of some hydrogen in the polymer-derived materials is controversial since hydrogen is released over 700 °C to 1250 °C during processing. Therefore residual hydrogen could have a role in stabilizing the nanodomain structure of the amorphous state.The high temperature calorimetry measures the enthalpy of formation, while the thermodynamic stability is related to the Gibbs free energy which includes entropy. Since the entropy of the amorphous state would be greater, the difference in the free energy of the amorphous and the crystalline states would be smaller (entropy lowers the free energy) than estimated here. It also means that the actual values for First principles computations have been combined with classical analysis of phase stability to gain quantitative insights into the stability of polymer derived SiCO. The question of the role of the interfacial energy between graphene and silica in stabilizing the amorphous nature of the nanodomain structure of these materials has forged a confluence between these two approaches. The interfacial energy is calculated from specific configurations of mixed bonds of C-Si-O that create bonding between graphene and silica. Mixed bonds at the faces and at the edges of the graphene segments are considered. It emerges that both types of bonds make meaningful contribution to the interfacial energy. Only when both are considered do we obtain a satisfactory agreement between the measured and predicted values of the size of the nanodomains.The computations have revealed a new two-dimensional lattice of a middle layer of graphene bonded to Si-O layers on either side. This structure has a 2:4:3 (Si:C:O) stoichiometry, which lies close to the middle of the Si-C-O composition diagram. It is energetically highly favored relative to the other cases of interfacial energies that we have considered here. It is intriguing to think if this 2-D lattice can be fabricated from the chemical route and what kinds of new functional properties it may possess.2 tie-line and the C-apex. Thus it is anticipated that the amorphous nanodomain structure is likely to be most stable in the middle regime of the Si-C-O composition diagram.We find that the energy of the graphene-silica interface has its lowest, or the most favored value midway between the SiC-SiOHow to cite this article: Yu, L. and Raj, R. On the thermodynamically stable amorphous phase of polymer-derived silicon oxycarbide. Sci. Rep.5, 14550; doi: 10.1038/srep14550 (2015).


We used a face adaptation paradigm to bias the perception of ambiguous images of faces and study how single neurons in the human medial temporal lobe (MTL) respond to the same images eliciting different percepts. The ambiguous images were morphs between the faces of two familiar individuals, chosen because at least one MTL neuron responded selectively to one but not to the other face. We found that the firing of MTL neurons closely followed the subjects’ perceptual decisions—i.e., recognizing one person or the other. In most cases, the response to the ambiguous images was similar to the one obtained when showing the pictures without morphing. Altogether, these results show that many neurons in the medial temporal lobe signal the subjects’ perceptual decisions rather than the visual features of the stimulus. •We used a face adaptation paradigm to bias the perception of ambiguous images•Neurons in the human MTL fired according to the subjects’ perceptual decisions•In most cases, ambiguous images triggered similar responses as the original pictures•MTL neurons follow subjective perception rather than visual inputs Using an adaptation paradigm to bias the perception of morphed faces, Quian Quiroga et al. show how single neurons in the human medial temporal lobe follow the subjective perception by the subjects rather than the visual features of the stimulus. A key function of the brain is to extract meaning from relatively limited, noisy, and ambiguous sensory information. We indeed perceive—and are aware of seeing—the face of a particular person rather than the combination of pixels and specific features that compose the person’s face. This process of extracting meaning involves categorizations and perceptual decisions , where sIn the quest to understand how the brain constructs meaning from sensory information, several works have studied the firing of single neurons in monkeys using identical but ambiguous stimuli that elicit different perceptual outcomes . This perceptual difference was larger for longer presentations of the adaptors  preceded by an adaptor (the picture of Clinton or the one of Bush) and had to respond whether the ambiguous picture corresponded to one or the other A. Figureadaptors C.Figure Given the different perceptual outcomes using the same set of ambiguous images, we then asked whether the firing of single neurons in the medial temporal lobe was entirely driven by visual features or whether it was modulated by the subjects’ decision (picture A or B). Altogether, we obtained 81 significant responses . Note, however, that in this case there is also a lower response to picture A compared to the ambiguous picture recognized as A, which was statistically significant . Given these results, it seems plausible to argue that the lack of statistical significance when analyzing the whole response strength  but not for “adaptor” or for the interaction between both factors.Finally, to disentangle whether the differential responses to the morphed pictures (according to the subjects’ perception) could, at least in part, be explained by a modulation in the firing of the neurons given by the presentation of the preceding adaptors, we performed a two-way ANOVA with “decision” (recognized A or B) and “adaptor” (picture A or B) as independent factors. This analysis showed that the differential firing of MTL neurons was due to the decision and not due to the preceding adaptor. In fact, there was a significant effect for the factor “decision”  were shown for 1 s, six times each in pseudorandom order . The picAfter determining the morphs eliciting an ambiguous perception, subjects performed the adaptation paradigm, in which the perception of the ambiguous images was biased by first showing one of the two original pictures used to generate the morphs A. The baFrom the continuous wide-band data, spike detection and sorting were carried out using “Wave_Clus,” an adaptive and stochastic clustering algorithm . As in pp of getting a hit by chance , the probability of getting k hits by chance in n trials is k hits in n trials. From this, we assessed statistical significance and calculated a p value by adding up the probabilities of getting k or more hits by chance: A linear classifier was used to decode the subjects’ decision upon the presentation of the ambiguous morphed images (recognized picture A or B) in those cases where we had at least five trials for each decision. One at a time, the firing in each trial was used to test the classifier, which was trained with the remaining trials . As in previous works , to evalInstantaneous firing rate curves were calculated by convolving the normalized spike trains with a Gaussian window of 100 ms width. For each response, we estimated the latency onset as the point where the instantaneous firing rate crossed the mean + 2.5 SD of the baseline for at least 100 ms. Similar results were obtained using a threshold of 3 or 4 SD. Statistical differences between the different average firing rate curves were assessed with a Kolmogorov-Smirnov test in the time window from 0 to 1 s after stimulus onset.C.K., I.F., A.K., and R.Q.Q. designed the paradigm; I.F. performed the surgeries; A.K. and F.M. collected the electrophysiological data; R.Q.Q. analyzed the data and wrote the paper; and all authors discussed the results and commented on the manuscript. R.Q.Q. and A.K. contributed equally to the study.


We have developed a system that streams and archives live sound from remote areas across Japan via an unmanned automatic camera. The system was used to carry out pilot bird censuses in woodland; this allowed us to examine the use of live sound transmission and the role of social media as a mediator in remote scientific monitoring. The system has been streaming sounds 8 h per day for more than five years. We demonstrated that: (1) the transmission of live sound from a remote woodland could be used effectively to monitor birds in a remote location; (2) the simultaneous involvement of several participants via Internet Relay Chat to listen to live sound transmissions could enhance the accuracy of census data collection; and (3) interactions through Twitter allowed members of the public to engage or help with the remote monitoring of birds and experience inaccessible nature through the use of novel technologies.The online version of this article (doi:10.1007/s13280-015-0708-y) contains supplementary material, which is available to authorized users. The spread of the Internet and mobile digital devices has given advent to a new scientific era, with scientists as well as members of the public accruing various benefits from rapidly evolving communication technologies. Parr  stressedIn earlier work, Saito et al.  demonstrIn addition, several studies have begun to demonstrate the roles played by social media in the natural science field. Social media allows the rapid spread of information among individuals, a quality that holds the potential to promote participation, social learning, and ultimately alter public opinion Büscher . This thIn this paper, we (1) outline the architecture of the Cyberforest live sound streaming system with its unmanned automatic camera and sound-recording components, (2) determine the utility of live sound recordings for conducting a bird census, and (3) examine the potential role of social media as a platform to disseminate and share such ecological live sounds between academics and non-academics. Finally, we discuss the significance and challenges associated with using live sound and social media for scientific monitoring.Fagus species intermixed with various conifer species. In addition, seven more sites across Japan were equipped for conducting live sound streaming as of November 2014  to listen to and record the sounds in a remote forest via an automatic camera system, and 2) to distribute those sound data to the public via the Internet, so that anyone could listen to the live sounds in the remote forest in real-time. The Live Sound System, depicted in Fig.  to distr2), thus capitalizing on the live distribution capabilities of the existing unmanned recording camera system. The Field Encoding System comprised two blocks: an audio block and a transmission block. The microphones of the existing unmanned cameras were individually connected to the amplifier of the audio block, and their outputs served as input to the audio encoder to convert microphone sounds into MP3, the format used for subsequent digital sound delivery. Because there was no prior Internet connection at the Tetto site, we incorporated a satellite Internet service which was provided by IPSTAR in February 2010. We subscribed to IPSTAR’s ‘Dual’ service plan, which served the largest upload bandwidth of 2 Megabits per second for approximately US $125 a monthThe Field Encoding System Fig.  establisElectrical power to the Field Encoding System was provided by lead-acid batteries charged by a solar cell. Constraints on the power generation capacity meant that continuous operation of the Field Encoding System was not possible, and a control board-computer with a relay switch was incorporated into the power supply to facilitate control of its running time. The running time schedule was controlled by Cron software (a time-based job scheduler run on Linux). It was thus possible to control relay switches connected to the power supply of the Field Encoding System, and alter the operating time remotely if necessary. Four solar panels and two batteries were deployed for the audio and transmission blocks; the equipment used in these are listed A–I in Fig. S1) from 2011 onwards. The longest transmission time each day was during sunrise (03:30–08:00 Japan Standard Time), which was when most bird song could be heard. Transmission also occurred around noon (11:00–13:00) to maintain consistency with the unmanned camera that had been used previously. Further 20-min transmissions occurred at 00:00, 02:00, 16:00, and 22:00 to capture the sounds of the forest and bird song during the evening and at night.We thought it was important for those listening to live transmissions to be able to experience changes in bird song within the day as well as across the seasons. Therefore, live sound was delivered year-round six times per day . Each text posted on the Internet Relay Chat comprised a log file with a date and time stamp. We collected data on the forest avifauna for 70 min each day, starting 10 min before sunrise, between April 1 and June 30, for the years 2011–2013. We remotely listened to birds at the same site every day in 2011, except for eight days when the system temporarily failed because of snow covering the antenna. During the period 2012–2013, the number of Cyberforest study sites increased to three  = total minutes a species vocalized/total minutes all species vocalized × 100; vocalization included drumming and wing beating). We used vocalization rate of a species in a given year, and subsequently took the average of the three years.As the platform for data recording and sharing, we used the #birdresearch channel on an Internet Relay Chat server, where three of the authors (including two ornithologists) accessed and posted the names and other ecological information of bird species and weather conditions while listening to the live sounds , an introduced species which occurs sporadically in more southern regions of Japan, was heard, a birder who had joined the Relay Chat channel identified it because he was more familiar with it. Furthermore, archived files were played when it was necessary to listen to certain sounds again, for example to correct mistakes or determine species that could not be identified during the live delivery period , communication over this channel proved helpful for species identification. For example, when the song of a Red-billed leiothrix (5). All but two species detected during the field census were also recorded and subsequently identified through the audio census. The two species that were not recorded in the audio census were a rare species and a large woodpecker that was difficult to identify to the species level by sound. The 20 most abundant species in the audio census accounted for 90 % of species recorded in the field census. Figure τ = 0.576, n = 20, P < 0.001). Species for which very similar values were obtained with either method , Japanese tit (JT), and Eurasian jay (EJ). Varied and Japanese tits, which are not particularly vocal, were more easily detected in the field census when also using visual cues. Eurasian jays, which typically move around in small flocks, were consistently recorded as one bird in the audio census, thus leading to underestimation. The drumming sound of copper pheasants was around 300 Hz or lower, which fell at the lowest end of the sound-frequency spectrum of target birds in this study. It is high enough for humans to hear in the field, because our auditory range is approximately 20–15 000 Hz. However, if the field conditions are unfavorable for hearing, such as when the site is noisy, the target bird is far away, or a researcher is not used to the drumming sound, it can go unnoticed. The regular built-in speakers of a personal computer are not designed to reproduce this low-frequency sound. When listening to the live sounds of Cyberforest for bird monitoring, therefore, the use of ear-phones or head-phones are recommended because they can reproduce the low-frequency sound.In our second experiment, we wanted to determine whether we could draw in listeners through Twitter by announcing that bird songs in distant natural forests were available to listen to in real-time, provide them with tweets giving information on each of the vocalizations, and encourage them to take part in the bird censusing process.First, we implemented a database in the Streaming/Archiving System to collect tweets. This was done by archiving any tweets that used the #tetto hashtag and storing them on our ‘Twitter #tetto Database Server’ in PostgreSQL database6 show the case, on April 21, 2011 between 5:41 am and 6:07 am, in which one of us . We later uploaded this identification to Twitter with a link to the audio recording posted on the sharing site SoundCloud,There was also a case in which the number of concurrent listeners increased rapidly when unusual ecological sounds were heard in the field over a 10-min period from 6:59 am on February 21, 2012. In this case, corresponding tweets were posted for a 20-min period from the start of the sound; approximately 19 people were recorded listening to the sound delivery during that episode. The unknown sounds ceased without anyone determining what species had produced it or why. We subsequently sent the recorded file to the ornithologist described above, who identified it as the sound of a territorial dispute between white-backed woodpeckers , which allowed listeners to experience bird sound from our focal site since 2011 until today. As system managers and listeners could check the live sound almost every day, any system troubles could be discovered promptly and corrected. Indeed, system maintenance at the Tetto site was performed regularly, at least every two weeks, and we kept improving the Field Encoding System.When ornithologists, based in separate places, used the developed sound system to census woodland birds remotely, more species were detected than during the field-based spot-censuses (36 vs. 28). The finding that the top 20 species in the audio census accounted for 90 % of those in the field census suggests that the Live Sound System has clear potential for conducting bird censuses. It captured well those bird species that were vocal, occurred in relatively large numbers, and produced sounds that were of a sufficiently high frequency for speakers to reproduce well. The successful recording of sounds of an extremely secretive and easily disturbed species suggests that it has also utility for monitoring bird species whose behavior is readily affected by people.There were several differences in methodology employed between the audio census and the field census reported on in this paper. The audio census was carried out around sunrise because it is the time when most of the birds are active. We continued to collect data for 70 min including an hour after sunrise in order to also pick up the vocalization of species that become active in this time zone. The vocalization data were sampled for three continuous months to determine the many phases of bird activity throughout the breeding season. In the field census, on the other hand, the data were collected within 2 h after sunrise, sometimes after the audio census time. The spot census was conducted for 10 min per day and a total of 80 min in four plots per year. This amount of time is much smaller compared to that of the audio census (approximately 2000 min). These differences in methodology between the audio and spot censuses are difficult to evaluate, but likely play a part in the difference in prevalence values of some bird species between the two censuses.We demonstrated that anyone could hear the live sounds of a remote forest wherever they are as long as there is an Internet connection. The Cyberforest Live Sound System also enables city dwellers, for example, to experience bird song in remote natural areas to which they may not otherwise have access. The Live Sound System made it possible for us to conduct the audio census every morning for three months for three consecutive years—something that would have been close to impossible otherwise, given the remoteness of the area and inclement weather conditions hindering physical access to the site. The commenting on the bird song and wider broadcasting was dependent on input by the researchers who simply enjoyed sharing the experience of listening to bird song and other vocalizations with other people. Our study showed that the integration of live sound and social media allowed listeners to learn from other listeners and engage with a remote area, potentially fostering a sense of closeness to some of Japans’ most remote natural places.By incorporating tweets with Twitter hashtags concerning live sounds in a database, we were able to tag some of the (highly variable) characteristics of remote environments as metadata. For example, when a user searches for a Twitter log with the keyword “owl,” a list of places showing where the hooting of owls can be listened to, together with the date and time of the recording, is displayed. The user could then locate the corresponding section of the sound archive file to check. This means that someone, who has never heard the voice of an owl before could get to know the day and time of the owl’s call, and then try to hear it at that same time and place in real time. It is difficult to traverse a remote forest at night, but it is easy to give the Live Sound System a try.Calonectris leucomelas), we started the live sound test operation experiments without maintenance using only the solar panel and battery system developed in October 2014 and, as of October 2015, it is still operating as intended.Finally, applying the technologies developed, we have been expanding the live sound monitoring, as shown in Fig. We have developed a new type of bird census method coined ‘audio census,’ using a Live Sound System and social media (Internet Relay Chat and Twitter). A total of 36 bird species were recorded by several ornithologists in separate places using the audio census method during the 3-months-long breeding seasons of the 3-year study period. We showed that the Live Sound System and social media could contribute to raising public interest in, and realizing auditory real-time experience of, remote natural forest. The Cyberforest Live Sound System is an unmanned automatic system that facilitates information collection in a remote forest without having to visit the site each time, creating a new way to conduct bird monitoring with fewer logistical problems for humans and less disturbance of wildlife. The use of social media enables a large number of participants, including professional and amateur ornithologists, to participate, thus building a base for a large-scale monitoring scheme with participants from all over the country. Expecting that the Cyberforest Live Sound System can contribute both to the monitoring of natural environments and engagement of the wider public in nature conservation, we consider it important to keep pace with the quickly developing digital communication era.Supplementary material 1 (PDF 298 kb)


The purpose of this study was to determine the kinetics of the dissolution of a uranium residue in ammonium carbonate media. The residue is generated in the production of medical isotopes. The effects of parameters, such as varying peroxide and carbonate concentrations, dissolution time as well as temperature on the extraction rate have been separately studied. Results indicate complete dissolution of the residue at 60 °C, after 30 min, in ammonium carbonate solution enriched with hydrogen peroxide. The yield and rate of uranium extraction were found to increase as a function of both temperature, in the range of 25–60 °C, and hydrogen peroxide concentration. The extraction process was governed by chemical reaction as the activation energy was found to be 45.5 kJ/mol. The order of reaction with respect to uranium concentration was found to be approximately first order. The uranium residue consists of insoluble precipitate that forms when target plates of uranium and aluminum alloy are dissolved during the production of molybdenum-99 for use in technetium generators. This process was developed by the Karlsruhe Nuclear Research Center in Germany .During this process, an alloy of enriched uranium and aluminum is irradiated in a thermal neutron flux. After short cooling periods, the plates are dissolved in a strong base medium of sodium hydroxide. This results in the dissolution of the aluminum matrix as well as the fission products: molybdenum, cesium, strontium, barium, antimony, tellurium, iodine and a portion of the ruthenium and zirconium. The insoluble residue that remains contains more than 90 % of uranium that is present in a mixture of oxidation states .The uranium content in the residue was believed to be predominantly in a U(IV) oxidation state. Thus, understanding of the dissolution of the simpler pure uranium dioxide in carbonate solutions was essential for the understanding of the dissolution of the more complex residue. Solid–liquid ratio, type of oxidants and carbonate concentrations were varied to determine the rate laws governing the dissolution. The resulting kinetics model was then applied to the dissolution of the residue from the molybdenum process for uranium recovery.Some studies on the dissolution kinetics of uranium dioxide particles have been conducted by different authors to determine the influence of reagent concentration, type of oxidants and temperature on the dissolution rate of uranium dioxide in alkaline solution.Smith and colleagues reported that the dissolution rate of uranium dioxide powder increases linearly over the temperature range of 15–60 °C in 1 M ammonium carbonate with varied concentrations of hydrogen peroxide . This haThe rate of uranium dioxide dissolution under oxidizing conditions in carbonate/bicarbonate media was found to be directly proportional to the total hydrogen carbonate concentration by Grandstaff . GeneralLiterature suggests an order of 0.5 or 1 when using only dissolved oxygen as an oxidant, in the absence of hydrogen peroxide –8. HiskeInvestigating the oxidative dissolution of uranium dioxide in alkaline media, Clarens et al.  observedHowever, there is a disagreement over the order of hydrogen peroxide while dissolution is done in ammonium carbonate. Smith and colleagues reported that the dissolution rate increased linearly with a slope of 2.41 with hydrogen peroxide concentration in 1 M ammonium carbonate for peroxide concentration between 0.05 and 2 M .The temperature dependence of the dissolution of uranium dioxide in carbonate and peroxide solutions has not been well studied. Casas et al.  reportedThus, it can be seen that the concentration of the reagents and the temperature of the solution have an impact on the dissolution rate of uranium dioxide in carbonate media. However, the working temperature, while leaching with ammonium carbonate in an open beaker, must be kept below 60 °C as its solutions decompose to ammonia and carbon dioxide above this temperature .An ammonium carbonate based dissolution process of the residue using hydrogen peroxide as an oxidant for uranium recovery has been developed and described in the previous paper . TherefoFuel pellets of uranium dioxide (99.9 % uranium dioxide) supplied by the South African Nuclear Energy Corporation (Necsa), were ground in a mortar prior to the start of the experiments. 70 g of the sample with a particle size of 38–106 µm was immersed for 48 h in a solution of 1 M ammonium carbonate, prepared with oxygen free water. The aim was to dissolve oxidized phases that may have formed on the surface of the pellets due to oxidation by air. The sample was then washed with water free of oxygen and leached.100 g of simulated residue, with a particle size of 38–106 µm, was used. The material was supplied by Necsa and was not pre-treated for oxide layers removal prior to leaching. This was due to the fact that the sample was kept in a sealed container and was not exposed to atmospheric air.The experimental approach involved first the dissolution of uranium dioxide samples in an open beaker or in the autoclave. Then, the resultant kinetics model was applied to the simulated residue samples.Leaching variables considered were temperature, solid–liquid ratio and reagent concentration. Single variable testing was used for the optimization of the dissolution parameters. In this approach, a series of leaching tests were performed while changing only one variable at a time and maintaining the other variables at fixed values for a given set of measurements .0.5 g of the uranium residue was transferred to a glass beaker containing ammonium carbonate solution and hydrogen peroxide of an appropriate concentration. The mixture was then heated to the required temperature with continuous stirring until the end of the experiment.Solution pH was measured using a Metrohm 704 pH-meter with a combination pH electrode and temperature probe. The meter and electrode were calibrated using pH 7.00 and 10.00 buffer solutions.After dissolution, the solution was allowed to cool to ambient temperature and then filtered through a weighed No 4 Whatman filter paper. The undissolved residue was collected, washed, dried for 24 h in the oven at 60 °C and then weighed to determine the amount of the undissolved sample.Depending on the expected dissolved uranium, solutions collected were analyzed for uranium by use of the spectrophotometric technique.The autoclave used for the dissolution of the sample was a Parr 4848 reactor controller that was connected to a stainless steel 4597 Micro Reactor with a 100 ml fixed head. The device has a working pressure up to 207 bar and maximum working temperature of 350 °C is permissible.0.5 g of the simulated residue was added into ammonium carbonate and hydrogen peroxide solutions of an appropriate concentration. The mixture was then poured into the reaction vessel, which was sealed, and then heated to the required temperature with continuous stirring (500 rpm) until the end of the experiment. All experiments using ammonium carbonate solutions at 60 °C and above were performed in the leach autoclave as ammonium carbonate decomposes to ammonia and carbon dioxide above this temperature.Sampling was not possible with this autoclave; therefore analysis was carried out after the experiment.The final solution was filtered with Whatman No 4 filter paper and the residue obtained was washed, dried and weighed to determine the amount of the undissolved uranium.The experimental conditions used for batch and autoclave leaching are shown in Table A sample was sent to Pelindaba Analytical Lab for X-ray fluorescence analysis in order to determine the major components present in the simulated residue. The results are indicated in Table A second sample of the simulated residue was sent for X-ray diffraction in order to determine the oxidation state see Fig. .Fig. 1Th2U2O7. These compounds of uranium were expected. The uranium is therefore present in a mixture of oxidation states [The diffractogram of the uranium sample (simulated) matched closely to that of uranium dioxide and Na2], 1/[UO2] or [UO2] versus time is a straight line has to be found, respectively.The first step in understanding how a given chemical reaction occurs is to determine the form of the rate law. To decide whether the rate law for uranium dioxide dissolution in ammonium carbonate is first order, second order or zero order, an indication on whether the plot of ln[UO2] represents the total concentration of uranium dissolved from the uranium dioxide sample.[UOFrom Fig. 2] versus time at 25 °C. This confirms that the relationship between the two variables is certain enough to be useful. Thus the reaction is first order in UO2 under the reaction conditions used in this work. This observation is consistent with that reported by Sharma and colleagues who found the order of reaction to be unity while dissolving uranium dioxide in sodium carbonate-bicarbonate solution containing sodium hypochlorite as an oxidant [It was also observed that the regression line does fit the data in the plot ln[UO oxidant .The chemical reaction of uranium dioxide dissolution in solutions of ammonium carbonate and hydrogen peroxide was studied using experiments in which the reactants were charged to a vessel and maintained at constant and uniform temperature.2] versus time. The value of the rate constant k was determined from the slope of the resultant line.The order of the rate law with respect to uranium dioxide concentration was verified by constructing a plot of ln  versus time . This implies that the dissolution of uranium residue is first order under the conditions investigated. First order reactions with respect to hydrogen peroxide and ammonium carbonate, respectively, were also observed.From the values in Table It can be seen, from Fig. This study has enabled determination of leaching kinetics for various leaching conditions of uranium residue in carbonate media containing hydrogen peroxide oxidant.The activation energy for the dissolution of simulated uranium residue was found to be 45.5 kJ/mol and confirms a chemically controlled process for uranium residue dissolution in ammonium carbonate solution with hydrogen peroxide under the conditions investigated. The order of reaction with respect to uranium concentration, ammonium carbonate solution and hydrogen peroxide concentration each was found to be approximately first order.The kinetics of dissolution of the uranium in the simulated residue was found to be similar to that of uranium dioxide dissolution in the presence of hydrogen peroxide, but with slightly higher activation energy.