Distinct Particle Research Articles

Quantum cellular automata and quantum field theory in two spatial dimensions

Quantum walks on lattices can give rise to one-particle relativistic wave equations in the long-wavelength limit. In going to multiple particles, quantum cellular automata (QCA) are natural generalizations of quantum walks. In one spatial dimension, the quantum walk can be "promoted" to a QCA that, in the long-wavelength limit, gives rise to the Dirac quantum field theory (QFT) for noninteracting fermions. This QCA/QFT correspondence has both theoretical and practical applications, but there are obstacles to similar constructions in two or more spatial dimensions. Here we show that a method of construction employing distinguishable particles confined to the completely antisymmetric subspace yields a QCA in two spatial dimensions that gives rise to the 2D Dirac QFT. Generalizing to 3D will entail some additional complications, but no conceptual barriers. We examine how this construction evades the "no go" results in earlier work.

Nanoemulsions containing plant oils: How do they influence hair treatment?

Cosmetic and dermatological products, mainly the hair treatments, are projected to have remarkable growing in coming years. Nanotechnology, specifically nanoemulsions, has potential to be used in several hair products, owing to their beneficial properties. Concurrently, plant-derived cosmetics have become more popular over the years to consumers who prefer a safer, natural and sustainable approach. There is a lack of studies combining plant oils and nanotechnology for haircare formulations. In this work, different plant oil-loaded nanoemulsions were prepared to investigate the influence of their particle size, zeta potential and composition on hair treatment efficacy. Coconut, olive and Abyssinian oils, alone or in combination, were loaded into nanoemulsions by high-pressure homogenization method (HPH). The mean particle size, polydispersity index and zeta potential were measured by the dynamic light scattering (DLS) method, and a stability test was performed for five months. A sensorial screening evaluation performed by the analyst and the combing test using Dia-Stron® instrumentation were applied on hair tresses treated by these nanoemulsions. The use of different plant oils for nanoemulsion resulted in distinct final particle sizes and zeta potential. However, results suggested no significant difference between them in hair tresses combing efficacy compared by Dia-Stron® instrument testing. Moreover, the plant-loaded nanoemulsions with increased concentration of cationic surfactant indicated a 50% reduction in combing force using this device when compared to control, in addition to better sensory results by screening test compared to other nanoemulsions and control. The composition of plant oils, particle size or zeta potential of the prepared nanoemulsions does not seem to significantly influence hair performance. Thus, we suggest that finding the right balance between cationic surfactant and plant oils may be the most appropriate path to develop effective nanoemulsions in hair treatment.

Study of Cassava Starch Layer on Zinc Anode by Electrochemistry Method for Zinc-air Fuel Cell System

In this study, cassava starch was prepared as conductive biodegradable material on zinc anode in zinc-air fuel cell system by using electrochemistry method. Successful formation of cassava layer on zinc anode was determined by its conductivity values by using four-point probe instrument and the enhancement of conductivity of anode after the deposition of cassava starch was proven. The optimization of conductivity study was further carried out in different concentrations of cassava. It was shown the increment about 30% from 0.079 to 0.105 Scm−1 of conductivity values from original conductivity of pure zinc. Moreover, SEM analysis exhibits the morphology of cassava coated on zinc plate with distinct particle structure was observed, proving the successful formation of cassava layer via electrochemical method. Hence, the discharge profile analysis highlighted the composition of C3 of CS performed best performance in ZAFC system.

Discharge properties of a coaxial plasma jet at different microwave frequencies

This article investigated the discharge characteristics of Ar plasma sustained by a coaxial plasma jet under different microwave frequencies (433 MHz, 915 MHz, 2450 MHz, and 5800 MHz). Clear distinctions of the discharge characteristics such as excitation and plasma-sustaining power, shape, electron number density, electron temperature, and so on were experimentally obtained under these operating frequencies. The results demonstrated that microwave powers at different frequencies could excite distinct particles. The higher the microwave frequency, the lower the excitation and the plasma-sustaining power. However, these powers did not decline linearly to the increase in microwave frequency. The Ar plasma at a frequency of 5800 MHz exhibited an upside-down “bell” shape and a larger cross section, while those at other three frequencies seemed to have thin and long plumes. The investigation also revealed that the electron temperature increased and then decreased, while the electron number density and gas temperature elevated monotonously with increasing frequency. This study is intended to be helpful in determining the optimal frequency and design microwave plasma sources for certain applications.

Entanglement Enabled Intensity Interferometry of different wavelengths of light

We propose methods to perform intensity interferometry of photons having two different wavelengths. Distinguishable particles typically cannot interfere with each other, but we overcome that obstacle by processing the particles via entanglement and projection so that they lead to the same final state at the detection apparatus. Specifically, we discuss how quasi-phase-matched nonlinear crystals can be used to convert a quantum superposition of light of different wavelengths onto a common wavelength, while preserving the phase information essential for their meaningful interference. We thereby gain access to a host of new observables, which can probe subtle frequency correlations and entanglement. Further, we generalize the van Cittert–Zernike formula for the intensity interferometry of extended sources, demonstrate how our proposal supports enhanced resolution of sources with different spectral character, and suggest potential applications.

Theoretical performance of multiple size-exclusion chromatography columns connected in series

The fundamental relationships are derived for the retention, peak width, and peak capacity of non-retained polymers eluting from multiple standard size-exclusion chromatography (SEC) columns connected in series. The standard SEC columns may have different dimensions and are packed with particles having distinct average particle diameters (APDs) and average mesopore sizes (AMSs). The performances (peak capacity, local resolution power, and sensitivity) of three standard SEC columns connected in series (called a tri-SEC column) packed with bridged-ethylene-hybrid (BEH) fully porous particles (FPPs) having three different APDs (1.7, 2.5, and 3.5 μm) and AMSs (200, 450, and 900 Å, respectively) are calculated as a function of the applied flow rate and size of polystyrene standards. Irrespective of the APD and AMS, the present investigation assumes isomorphological materials relative to the mesopore space of the three different BEH particles. The advantage of a 15 cm long tri-SEC column over a single reference SEC column (APD=3.5 μm, AMS=900 Å), which generates the same back pressure and separation window as those of the tri-SEC column, is expected at flow rates larger than the optimum flow rate generating the maximum peak capacity. The calculations predict a significant relative increase of the peak capacity (from +25% to +85%), resolution of small molecules (from +75% to +225%), and of the detection limit of intermediate size (from +15% to +70%) and largest polymers (from +25 to +110%). This is explained by 1) the exclusion of the largest polymers from the internal volume of the particles having the smallest mesopores (restricted access media) and 2) the minimum dispersion along the columns packed with the smallest particle sizes in the tri-SEC column. The main benefit of multi-SEC columns is to easily adjust the desired pore size distribution by properly selecting the lengths of each individual SEC column. The user can then control the pore size distribution for any specific separation problem. A potential application is theoretically demonstrated for the fast purification of monoclonal antibodies from metabolites, host cell proteins, aggregated forms, and from virus-like particles.

Hyper-hybrid entanglement, indistinguishability, and two-particle entanglement swapping

Hyper-hybrid entanglement for two indistinguishable bosons has been recently proposed by Li \textit{et al.} [Y. Li, M. Gessner, W. Li, and A. Smerzi, \href{https://doi.org/10.1103/PhysRevLett.120.050404}{Phys. Rev. Lett. 120, 050404 (2018)}]. In the current paper, we show that this entanglement exists for two indistinguishable fermions also. Next, we establish two {\em no-go} results: no hyper-hybrid entanglement for two {\em distinguishable} particles, and no unit fidelity quantum teleportation using {\em indistinguishable} particles. If either of these is possible, then the {\em no-signaling principle} would be violated. While several earlier works have attempted extending many results on distinguishable particles to indistinguishable ones, and vice versa, the above two no-go results establish a nontrivial separation between the two domains. Finally, we propose an efficient entanglement swapping using only two indistinguishable particles, whereas a minimum number of either three distinguishable or four indistinguishable particles is necessary for existing protocols.

Generalized friction and dilatancy laws for immersed granular flows consisting of large and small particles

The motion of fully immersed granular materials, composed of two distinct particle sizes, flowing down rough inclined planes is studied through fluid–particle numerical simulations. We focus on the effect of ambient fluids, as well as their interplay with particle size segregation, on the steady-state kinematic and rheological profiles of the granular-fluid mixture flow. Simulation results are analyzed in the framework of a visco-inertial rheological model, which is first validated in monodisperse flows with a wide range of the ambient fluid viscosity (i.e., from air to water and slurry) and then generalized for size-bidisperse mixtures. It is found that the local effective friction and volume fraction of mixtures with different particle sizes can be approximated from the rheology of single-component flows. While the presence of viscous ambient fluids slows down size segregation (perpendicular to the flow) depending on the mixture composition and flow viscosity, the effective bulk friction is shown to be independent of the state and progress of segregation.

P.732 Use of optical coherence tomography for the diagnosis of deterioration of functionality in bipolar disorder

Copper-doped hydroxyapatite (CuHAP) mesoporous nanopowders (NPs) with distinct particle morphologies of nanorods-like (CuHAP-NR) and hierarchically assembled micro-spherical (CuHAP-MS) particles were comprehensively characterized for drug delivery applications. Both products were monolithic consisted of HAP phase only. Rietveld refinement suggested deformation led straining of HAP nanocrystals due to substitution of Cu ions in lattice structure of HAP. CuHAP-NR was more crystalline than CuHAP-MS NP. CuHAP-NR and CuHAP-MS NPs were having nanorods-like and microspherical ended hierarchically assembled nanostructured particles, respectively. Atomic structures proposed by XRD and HRTEM corroborated each other. Elemental examination confirmed formation of CuHAP composition in both NPs. Isotherms affirmed mesoporous structure of both products. Surface area of CuHAP-NR was significantly bigger and porous than CuHAP-MS NP. Consequently, former adsorbed 2.5 times more drug than latter. Furthermore, CuHAP-NR NP released drug in sustained and controlled manner, whereas, CuHAP-MS NP exhibited burst-release of drug. Critical analysis concluded that particle architecture of drug carriers significantly influenced their drug loading and releasing abilities.

Theory for hierarchical assembly with dielectrophoresis and the role of particle anisotropy.

Nonuniform electric fields cause polarizable particles to move through an effect known as dielectrophoresis (DEP). Additionally, the particles themselves create nonuniform fields due to their induced dipoles. When the nonuniform field of one particle causes another to move, it represents a path to hierarchical assembly termed mutual DEP (mDEP). Anisotropic particles potentially provide further opportunities for assembly through intense and intricate local field profiles. Here, we construct a theoretical framework for describing anisotropic particles as templates for assembly through mDEP by considering the motion of small nanoparticles near larger anisotropic nanoparticles. Using finite element analysis, we study eight particle shapes and compute their field enhancement and polarizability in an orientation-specific manner. Strikingly, we find a more than tenfoldenhancement in the field near certain particle shapes, potentially promoting mDEP. To more directly relate the field intensity to the anticipated assembly outcome, we compute the volume experiencing each field enhancement versus particle shape and orientation. Finally, we provide a framework for predicting how mixtures of two distinct particle species will begin to assemble in a manner that allows for the identification of conditions that kinetically bias assembly toward specific hierarchical outcomes.

Mechanical behavior of solid helium: Elasticity, plasticity, and defects

At low temperatures, atoms in classical crystals are localized at lattice sites and can be regarded as distinguishable particles rendering quantum statistics unimportant. In solid helium, however, this situation is different: helium's small mass and weak interatomic potentials allow the exchange of neighboring atoms and the emergence of statistics dominated by quantum effects. This review covers the concepts and properties of solid helium and how crystalline properties are affected by structure, defects, vacancies, and dislocations. Recent experimental results on plastic deformation give promise to a new understanding of the mechanical properties of quantum solids.

Numerical Solution of Stagnation Point Flow and Heat Transfer over a Nonlinear Stretching/Shrinking Sheet in Hybrid Nanofluid: Stability Analysis

The steady, laminar, stagnation point flow of hybrid nanofluid past a nonlinearly stretching and shrinking sheet is studied. Hybrid nanofluid is regarded by disseminated two distinct nano-sized particles, silver (Ag) and copper oxide (CuO) in pure water. Similarity technique was used for the transformation of partial differential equations (PDEs) into an ordinary differential equations (ODEs). Obtained ODEs were solved using Matlab’s built in function (bvp4c). The results of important governing parameters which are nonlinear parameter, stretching/shrinking parameter and nanoparticle volume fraction are evaluated and discussed in graphical and tabular form for the velocity and temperature profiles, along with local skin friction, local Nusselt number. Nonunique solutions (first and second branch) are visible for some limit of shrinking parameter. It is noticed that nonlinear parameter hastens flow separations. Hence, a stability analysis is executed to identify which solutions are stable and physically feasible.

Using Dimensionality Reduction and Clustering Techniques to Classify Space Plasma Regimes

Collisionless space plasma environments are typically characterised by distinct particle populations. Although moments of their velocity distribution functions help in distinguishing different plasma regimes, the distribution functions themselves provide more comprehensive information about the plasma state, especially at times when the distribution function includes non-thermal effects. Unlike moments, however, distribution functions are not easily characterised by a small number of parameters, making their classification more difficult to achieve. In order to perform this classification, we propose to distinguish between the different plasma regions by applying dimensionality reduction and clustering methods to electron distributions in pitch angle and energy space. We utilise four separate algorithms to achieve our plasma classifications: autoencoders, principal component analysis, mean shift, and agglomerative clustering. We test our classification algorithms by applying our scheme to data from the Cluster-PEACE instrument measured in the Earth's magnetotail. Traditionally, it is thought that the Earth's magnetotail is split into three different regions (the plasma sheet, the plasma sheet boundary layer, and the lobes), that are primarily defined by their plasma characteristics. Starting with the ECLAT database with associated classifications based on the plasma parameters, we identify 8 distinct groups of distributions, that are dependent upon significantly more complex plasma and field dynamics. By comparing the average distributions as well as the plasma and magnetic field parameters for each region, we relate several of the groups to different plasma sheet populations, and the rest we attribute to the plasma sheet boundary layer and the lobes. We find clear distinctions between each of our classified regions and the ECLAT results.

The Bose-Einstein statistics: Remarks on Debye, Natanson, and Ehrenfest contributions and the emergence of indistinguishability principle for quantum particles

The principal mathematical idea behind the statistical properties of black-body radiation (photons) was introduced already by L. Boltzmann (1877/2015) and used by M. Planck (1900; 1906) to derive the frequency distribution of radiation (Planck’s law) when its discrete (quantum) structure was additionally added to the reasoning. The fundamental physical idea – the principle of indistinguishability of the quanta (photons) – had been somewhat hidden behind the formalism and evolved slowly. Here the role of P. Debye (1910), H. Kamerlingh Onnes and P. Ehrenfest (1914) is briefly elaborated and the crucial role of W. Natanson (1911a; 1911b; 1913) is emphasized. The reintroduction of this Natanson’s statistics by S. N. Bose (1924/2009) for light quanta (called photons since the late 1920s), and its subsequent generalization to material particles by A. Einstein (1924; 1925) is regarded as the most direct and transparent, but involves the concept of grand canonical ensemble of J. W. Gibbs (1902/1981), which in a way obscures the indistinguishability of the particles involved. It was ingeniously reintroduced by P. A. M. Dirac (1926) via postulating (imposing) the transposition symmetry onto the many-particle wave function. The above statements are discussed in this paper, including the recent idea of the author (Spałek 2020) of transformation (transmutation) – under specific conditions – of the indistinguishable particles into the corresponding to them distinguishable quantum particles. The last remark may serve as a form of the author’s post scriptum to the indistinguishability principle.

The Curious Strategy of Multipartite Viruses.

Multipartite virus genomes are composed of several segments, each packaged in a distinct viral particle. Although this puzzling genome architecture is found in ∼17% of known viral species, its distribution among hosts or among distinct types of genome-composing nucleic acid remains poorly understood. No convincing advantage of multipartitism has been identified, yet the maintenance of genomic integrity appears problematic. Here we review recent studies shedding light on these issues. Multipartite viruses rapidly modify the copy number of each segment/gene from one host species to another, a putative benefit if host switches are common. One multipartite virus functions in a multicellular way: The segments do not all need to be present in the same cell and can functionally complement across cells, maintaining genome integrity within hosts. The genomic integrity maintenance during host-to-host transmission needs further elucidation. These features challenge several virology foundations and could apply to other multicomponent viral systems.

Measuring grain size fractions of bidisperse granular materials using X-ray radiography.

The mechanical properties of granular materials such as sand, snow and rice are inherently tied to the size of the constituent particles. When a system is composed of particles of various sizes, it is common for these particles to segregate by size when disturbed. There is therefore a need to measure the particle size distribution within granular media as it evolves over time. However, there are very few experimental techniques available which can measure the particle sizes in situ without disturbing the medium. Here we present a technique to determine the volume fractions of the grain sizes in bidisperse granular materials with the aid of dynamic X-ray radiography. As a result of the penetration of the X-rays into the medium, radiography minimises the effect of walls and boundaries on experimental measurements, which typically dominate optical measurements. The technique proposed here is based on using Fourier transforms of X-ray radiographs to extract local measurements evolving over time that can be related to the particle size distribution. For the case of bidisperse granular media, with two distinct particle sizes, we show that this technique can measure the relative concentration of the two species, which we determine via a heuristic calibration parameter. We validate this technique by comparing discrete element simulations of mixtures of known concentration with experimental measurements derived from X-ray radiography of glass beads. In the future, this technique could be used to measure the grain size distribution in systems of bidisperse dense granular media where the concentration of particles is not known a priori. Additionally, the technique can be used to analyse granular segregation as it evolves dynamically.

Optimized CNN model for identifying similar 3D wear particles in few samples

Typical wear particles can be considered as distinctive indicators of on-going wear faults in machines. However, the small number of samples has limited the identification accuracy for similar fault particles. Besides, the three-dimensional (3D) characterization of wear particles may face huge challenges due to excessive surface parameters. Focusing on the problems of high similarity particles and few samples, a CNN-based particle classification method is developed with an example set of fatigue and severe sliding particles, which are the product of severe wear of machines. For data reduction, imaged 3D particle surfaces are firstly converted into 2D depth maps without losing surface information. Virtual fault particle images are then synthesized using a Conditional Generative Adversarial Networks (CGAN), according to the particle generation mechanism and distinctive particle features. Furthermore, a non-parametric particle identification model is established with the optimization of the CNN structures and training method, and the network is further optimized with the particle image standardization and the network visualization. Validation experiments reveal that the proposed method can accurately identify all tested fatigue and severe sliding particles with their typical characteristics.

Notes on statistical ensembles in the Cell Model

Properties of basic statistical ensembles in the Cell Model are discussed. The simplest version of the model with a fixed total number of particles is considered. The microcanonical ensembles of distinguishable and indistinguishable particles, with and without a limit on the maximum number of particles in a single cell, are discussed. The joint probability distributions of particle multiplicities in cells for different ensembles are derived, and their second moments are calculated. The results for infinite volume limit are calculated. The obtained results resemble those in the statistical physics of bosons, fermions and boltzmanions.

Polyaniline intercalated with Ag1.2V3O8 nanorods based electrochemical sensor

A sensitive electrochemical based sensor was demonstrated for the determination of uric acid (UA) by polyaniline (PANI) intercalated with Ag1.2V3O8-PANI (AgV-PANI). In this study, a simple ultrasonication method has been employed to prepare AgV-PANI nanorods and the crystalline structure, surface topography, and elemental status of the as-synthesized nanocomposites were investigated using various analytical techniques. These analyses revealed the formation of a good crystalline and ∼50nm diameter with length of ∼100–300nm AgV-PANI nanorod-like structure. Distinguishable PANI particles were apparently placed on the Ag1.2V3O8 surface, and the surface of the nanorods was exceptionally smooth. The incorporation of PANI with Ag1.2V3O8 significantly improved the charge-transfer characteristics and enhanced the peak current in the cyclic voltammogram compared with the case of pristine Ag1.2V3O8 nanorods. The lowest threshold of UA detection was observed to be 0.017μM (S/N=3), and the detection range was wide (0.1–10μM). Moreover, the AgV-PANI-modified electrode demonstrated excellent selectivity and sensitivity towards UA detection in the existence of other biologically important interferents. The present study confirmed that the proposed Ag1.2V3O8-PANI based electrochemical sensor has good reproducibility and a long shelf life (40d).

Machine learning and statistical analyses for extracting and characterizing "fingerprints" of antibody aggregation at container interfaces from flow microscopy images.

Therapeutic proteins are exposed to numerous stresses during their manufacture, shipping, storage and administration to patients, causing them to aggregate and form particles through a variety of different mechanisms. These varied mechanisms generate particle populations with characteristic morphologies, creating "fingerprints" that are reflected in images recorded using flow imaging microscopy. Particle population fingerprints in test samples can be extracted and compared against those of particles produced under baseline conditions using an algorithm that combines machine learning tools such as convolutional neural networks with statistical tools such as nonparametric density estimation and Rosenblatt transform-based goodness-of-fit hypothesis testing. This analysis provides a quantitative method with user-specified type 1 error rates to determine whether the mechanisms that produce particles in test samples differ from particle formation mechanisms operative under baseline conditions. As a demonstration, this algorithm was used to compare particles within intravenous immunoglobulin formulations that were exposed to freeze-thawing and shaking stresses within a variety of different containers. This analysis revealed that seemingly subtle differences in containers (e.g., glass vials from different manufacturers) generated distinguishable particle populations after the stresses were applied. This algorithm can be used to assess the impact of process and formulation changes on aggregation-related product instabilities.