Effects of Nanoparticle Geometry and Size Distribution on Diffusion Impedance of Battery Electrodes

The short diffusion lengths in insertion battery nanoparticles render the capacitive behavior of bounded diffusion, which is rarely observable with conventional larger particles, now accessible to impedance measurements. Coupled with improved geometrical characterization, this presents an opportunity to measure solid diffusion more accurately than the traditional approach of fitting Warburg circuit elements, by properly taking into account the particle geometry and size distribution. We revisit bounded diffusion impedance models and incorporate them into an overall impedance model for different electrode configurations. The theoretical models are then applied to experimental data of a silicon nanowire electrode to show the effects of including the actual nanowire geometry and radius distribution in interpreting the impedance data. From these results, we show that it is essential to account for the particle shape and size distribution to correctly interpret impedance data for battery electrodes. Conversely, it is also possible to solve the inverse problem and use the theoretical "impedance image" to infer the nanoparticle shape and/or size distribution, in some cases, more accurately than by direct image analysis. This capability could be useful, for example, in detecting battery degradation in situ by simple electrical measurements, without the need for any imaging.Comment: 30 page

Determining wood chip size: image analysis and clustering methods

One of the standard methods for the determination of the size distribution of wood chips is the oscillating screen method (EN 15149- 1:2010). Recent literature demonstrated how image analysis could return highly accurate measure of the dimensions defined for each individual particle, and could promote a new method depending on the geometrical shape to determine the chip size in a more accurate way. A sample of wood chips (8 litres) was sieved through horizontally oscillating sieves, using five different screen hole diameters (3.15, 8, 16, 45, 63 mm); the wood chips were sorted in decreasing size classes and the mass of all fractions was used to determine the size distribution of the particles. Since the chip shape and size influence the sieving results, Wang’s theory, which concerns the geometric forms, was considered. A cluster analysis on the shape descriptors (Fourier descriptors) and size descriptors (area, perimeter, Feret diameters, eccentricity) was applied to observe the chips distribution. The UPGMA algorithm was applied on Euclidean distance. The obtained dendrogram shows a group separation according with the original three sieving fractions. A comparison has been made between the traditional sieve and clustering results. This preliminary result shows how the image analysis-based method has a high potential for the characterization of wood chip size distribution and could be further investigated. Moreover, this method could be implemented in an online detection machine for chips size characterization. An improvement of the results is expected by using supervised multivariate methods that utilize known class memberships. The main objective of the future activities will be to shift the analysis from a 2-dimensional method to a 3- dimensional acquisition process

Evaluation of deep learning methods for particle characterisation from in-line imaging and chord length distribution measurements

In-line Process Analytical Technologies (PAT) are useful for measurement of particle characteristics (e.g. particle size distribution, PSD) non-destructively and with high time-resolution (inaccessible with off-line techniques) which can be essential for process monitoring and accurate population balance modelling. This work is concerned with assessing in-line imaging and chord length distribution (CLD) for determination of PSD. Imaging is limited by resolution (small particles are difficult to measure), subject focus, and field-of-view (particles touching image frame). CLD sensors (e.g. focused-beam reflectance measurement, FBRM) can detect particles of smaller sizes but struggle with fast flow (undersized chords), large particles (chord splitting), and shiny particles (specular reflection). In-line measurements were taken with the Mettler Toledo FBRM (CLD) and PVM (imaging) probes of Polystyrene Standard Spheres, a mixture of Polystyrene Spheres and Ellipsoids, and Lactose particles. In-line-derived PSDs from a range of crystal sizes and concentrations were compared with ground truth (off-line microscopy or manufacturer specifications) as shown in Figure 1. Image analyses include a machine learning (ML) method (Detectron 2 [1]) and a traditional approach (ImagingApp [2]). CLD analyses are based on a statistical approach [3], and a 1D convolutional neural network method. Sensors and analyses are evaluated using Root Mean Square Error (RMSE) and Integral Absolute Error (IAE) of the Cumulative Density Functions (CDFs). Statistical CLD analysis is found to be sensitive to non-uniform particle shape distributions, and to artefacts introduced by the sensor. ML CLD analysis yields improved results, but is heavily reliant on the training data. The selection of image analysis approach has less of an effect on the resulting PSD than the characteristics of the image sensor itself where resolution and field-of-view limitations play a larger role – impacting measure of small and large particles respectively. Low concentrations present issues with detecting only few particles: limiting ability to form smooth PSDs. References [1] Wu, Y., et al. 2019. Detectron 2. https://github.com/facebookresearch/detectron2 [2] Cardona, J., et al. 2018. Image analysis framework with focus evaluation for in-situ characterisation of particle size and shape attributes. Chemical Engineering Science, 191: 208-231. [3] Agimelen, O. S., et al. 2015. Estimate of particle size distribution and aspect ratio of non-spherical particles from chord length distribution. Chemical Engineering Science, 123: 629-640

Grain size distributions of volcanic particles by CAMSIZER

Grain size distribution is a key parameter in physical volcanology to describe and characterize tephra fall deposits. Walker (1973) used grain size parameters to propose a classification scheme of explosive volcanic eruptions. More recently, the role of grain size populations of eruptive mixtures at the vent has been widely considered a crucial input parameter for the application of numerical models simulating both plume and tephra dispersal (e.g. Cioni et al., 2003; Andronico et al., 2008; Scollo et al., 2008). Grain size analysis can be performed by various techniques that differ in their applicability, technology and affordability. The most commonly used technique is sieving, performed by a nested column of sieves arranged in decreasing order of aperture size (http://www.ivhhn.org/). Sieving can be performed manually or by machine shaking, usually in the particle range from 64 mm to less than 32 µm. Both these procedures are cumbersome, time-consuming and subject to many errors. Here, we present a new methodology to measure the distribution of volcanic particles by CAMSIZER® (Figure 1), an instrument developed by Retsch Technology GmbH (Haan) and Jenoptik AG (Jena) in Germany (see at http://www.retsch.com). CAMSIZER is a compact laboratory instrument for the simultaneous measurement of particle size distribution and particle shape of incoherent materials in the range of 30 µm to 30 mm, based on digital image processing. The sample is fed in from a vibrating feed channel that controls particles falling through the measurement field, where images of the particle flow are recorded by two digital cameras (Basic and Zoom) with different resolutions (Andronico et al., 2009). The Basic camera provides the analysis of the larger particles, while the Zoom camera focuses on smaller particles furnishing high resolution images of the finer classes of the wide measuring range. Software created by Retsch Technology enables processing digital images and providing grain size and shape parameters. Although this instrument is becoming very common in industry for quality control, research and production monitoring of very different kinds of materials, it has never been used before in volcanology. CAMSIZER has recently been installed at Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Catania (INGV-CT) to measure grain-size distribution of volcanic particles within the volcanic monitoring activity of Eastern Sicily (Lo Castro and Andronico, 2008). As is well-known, this area is characterized by the presence of two of the most active volcanoes in the world, Mt. Etna and Stromboli, which commonly produce large quantities of tephra (e.g. Rosi et al., 2000; Alparone et al., 2007). The use of CAMSIZER on volcanic products ranging from fine lapilli to ash have allowed us to obtain detailed particle size analysis and drastically reduce the work and measuring time needed in classical sieve analysis. To optimize these objectives, CAMSIZER has been tested on different materials, not only volcanic, in order to calibrate the instrument and compare results with those obtained by sieving. In particular, we present results derived by two different kinds of test: the first regards repeatability by measuring the same sample several times to determine the accuracy of the instrument, the second concerns the compatibility between sieve analysis and CAMSIZER results. Our work suggests that CAMSIZER may constitute a good tool to improve grain size analysis in volcanology and thus help in tephra hazard assessments

SYNTHESIS OF MESOPOPROUS SILICA NANOPARTICLES USING DROPLET MICROFLUIDICS

Introduction: Nanoparticles are small particles within nanoscale levels. Their size goes up to few hundred nanometers, although some sources state that nanoparticles are up to 100nm in diameter. Structure and function can vary between different nanoparticle models, each depending on physical properties of particles as well as way of their production. Each particle represents one functional unit. One of the most used type of nanoparticle is mesoporous silica nanoparticle. This is round shaped nanoparticle made of mesoporous silica, which makes it widespread when it comes to the drug loading. Despite the thing that nanomedicine sounds perfect in theory and brings enormous potential into targeted drug delivery, in real life it is hard to predict its behavior in vitro, and especially in vivo. Objectives: One way to enhance synthesis of MSNs and improve its efficiency is use of microfluidic chips and techniques. Microfluidic chip brings opportunity to manipulate different fluid flow in order to synthesize nanoparticles inside a picoliter volume droplets. The first objective is to optimize the microfluidic system in order to create stable droplets in order to synthesize MSNs inside a droplet. Second objective was to wash out the sample and measure the particles in order to check their size. Third objective was to image the particles with TEM (Transmission Electron Microscope) to see if their shape and size are suitable for drug loading, coating and similar manipulations, as well as establishing the protocol for the full process. Methods: Optimization is conducted by adjusting different flow rates and concentration of CTAB, TEOS, SPAN65 which are surfactants, precipitates and solvents. The main goal of optimization is to create stable fluid flow and stable droplets. Optimization process is monitored in real time with high speed microscope camera. Fluid flows of each substance were adjusted with fluid flow pumps. The main goal is to create a stable flow thus having a stable droplets in a sample. After the formation and collection of stable droplets, the sample is centrifuged and washed with ammonium hydroxide and ethanol solution for three times. After washing sample should be taken to Zetasizer, in order to measure particle size. If the sample is within certain nanometer range, it will be stored and imaged with Transmission electron microscope. Obtained images will be prone to image analysis with imageJ, from which data analysis will be obtained as well. Keywords: Mesoporous Silica Nanoparticle (MSN), Transmission Electron Microscope (TEM), Cetyl Trimethyl Ammonium Bromide (CTAB), Tetra Orto Silicate (TEOS

Novel application of confocal laser scanning microscopy and 3D volume rendering toward improving the resolution of the fossil record of charcoal.

Published onlineHistorical ArticleResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, Non-P.H.S.This is the final version of the article. It first appeared from PLoS via http://dx.doi.org/10.1371/journal.pone.0072265Variations in the abundance of fossil charcoals between rocks and sediments are assumed to reflect changes in fire activity in Earth's past. These variations in fire activity are often considered to be in response to environmental, ecological or climatic changes. The role that fire plays in feedbacks to such changes is becoming increasingly important to understand and highlights the need to create robust estimates of variations in fossil charcoal abundance. The majority of charcoal based fire reconstructions quantify the abundance of charcoal particles and do not consider the changes in the morphology of the individual particles that may have occurred due to fragmentation as part of their transport history. We have developed a novel application of confocal laser scanning microscopy coupled to image processing that enables the 3-dimensional reconstruction of individual charcoal particles. This method is able to measure the volume of both microfossil and mesofossil charcoal particles and allows the abundance of charcoal in a sample to be expressed as total volume of charcoal. The method further measures particle surface area and shape allowing both relationships between different size and shape metrics to be analysed and full consideration of variations in particle size and size sorting between different samples to be studied. We believe application of this new imaging approach could allow significant improvement in our ability to estimate variations in past fire activity using fossil charcoals.This research was supported by funding from a European Union Marie Curie Intra-European Fellowship (FILE-PIEF-GA-2009-25378 to CMB), a Marie Curie Career Integration Grant (PyroMap PCIG10-GA-2011-303610 to CMB), a University of Exeter Outward Mobility Academic Fellowship (to CMB) and the US National Science Foundation (DBI-1052997 to SWP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Scattering Properties of Suspended Particles

Effective monitoring and modelling of the marine environment is of importance to both the general public and the scientific community, but relies on the ability to obtain accurate measurements of suspended particle characteristics. Many instruments for measuring particles rely on optical and acoustic scattering from the particles and use this information to infer a particle size and concentration. However, assumptions such as spherical particles of a known composition are widely used, both in measurement technology and in numerical modelling. Various imaging techniques have shown great variability in the shape, size and composition of marine particles when measured within their natural environment. Subsequently, there is substantial uncertainty in the response of light scattering instruments to this diverse range of particles. In this study, a holographic camera was modified to simultaneously record in-focus images of marine particles with their forward angle scattering characteristics. This was achieved by combining both laser scattering and transmissometry with digital holography. The results from this system were compared with theoretical models of scattering from spherical particles within the intended size range of both instruments (15-500microns), with particle size information from both techniques agreeing well during these idealised conditions. The combined holographic and light scattering system was then used to investigate the response of the LISST-100 (Sequoia Scientific Inc.) to spherical particles with diameters extending beyond that intended by the instrument 250microns for type-B and 500microns for type-C derivatives), but that have been observed in-situ with imaging methods. This revealed an aliasing of single large particles into multiple smaller particles during the inversion of LISST-100 scattering into a particle size distribution. For spheres greater than the type-C instrument range, the inversion of scattering produces particle volume distributions that peak at varying sizes between 250-400microns. This key finding highlights the need for care to be taken when interpreting particle size distributions from the LISST-100 when there is potential for particles outside of its range limit. Natural particles, extracted from coastal waters, were then recorded by the combined laboratory system. These complex particles produced highly variable scattering properties which were contaminated by asymmetrical features within the azimuthal plane. This observation of strong azimuthal asymmetry is of concern for both measurements and models of optical properties that assume a symmetrical scattering function for natural particle populations. The azimuthal asymmetry in scattering contributed to additional variability in the response of the instrument in comparison to the holographic camera, which was also subjected to apparent particle break-up via segmentation during image processing. A discussion of holographic imaging and laser diffraction for characterising particles in-situ forms the final part of this thesis, which utilises data from a magnified holographic system that covers the same size range of the LISST-100. This final analysis demonstrated the need for future technology to accurately measure size distributions over a much larger range of sizes than is currently possible (e.g. <2microns to 1000microns). In summary, three key factors were identified to cause an increase in the apparent number of small particles reported by the LISST-100: 1) contamination from scattering of particles larger than the intended size range of the instrument; 2) a decrease in refractive index (particle composition); 3) additional scattering from small sub-components of particle geometry. The standard holographic camera systems are capable of accurately obtaining particle size and concentration measurements that are comparable to other techniques such as the LISST-100. However, in situations where background illumination is poor, errors in the image processing routines can cause an apparent particle break-up due to incorrect binarisation. Despite this, the holographic method provides a unique and powerful mechanism that enables images of particles to be analysed within the context of their in-situ environment.NER

Automated Particle Identification through Regression Analysis of Size, Shape and Colour

Rapid point of care diagnostic tests and tests to provide therapeutic information are now available for a range of specific conditions from the measurement of blood glucose levels for diabetes to card agglutination tests for parasitic infections. Due to a lack of specificity these test are often then backed up by more conventional lab based diagnostic methods for example a card agglutination test may be carried out for a suspected parasitic infection in the field and if positive a blood sample can then be sent to a lab for confirmation. The eventual diagnosis is often achieved by microscopic examination of the sample. In this paper we propose a computerized vision system for aiding in the diagnostic process; this system used a novel particle recognition algorithm to improve specificity and speed during the diagnostic process. We will show the detection and classification of different types of cells in a diluted blood sample using regression analysis of their size, shape and colour. The first step is to define the objects to be tracked by a Gaussian Mixture Model for background subtraction and binary opening and closing for noise suppression. After subtracting the objects of interest from the background the next challenge is to predict if a given object belongs to a certain category or not. This is a classification problem, and the output of the algorithm is a Boolean value (true/false). As such the computer program should be able to ”predict” with reasonable level of confidence if a given particle belongs to the kind we are looking for or not. We show the use of a binary logistic regression analysis with three continuous predictors: size, shape and color histogram. The results suggest this variables could be very useful in a logistic regression equation as they proved to have a relatively high predictive value on their own

Caractérisation des dimensions et de la forme des particules de fourrages hachés

La taille et la forme des fourrages hachés influencent la conservation en ensilage et l’utilisation par les vaches laitières. Traditionnellement, la longueur est estimée par tamisage mécanique. La mesure par imagerie proposée ici permet de caractériser des particules individuelles avec une précision accrue. Des fourrages de maïs et de luzerne hachés à trois longueurs théoriques (12,7, 25,4, et 29,6 mm) ont été utilisés. La mesure de la forme a été obtenue par le concept de Normalized Multiscale Bending Energy (NMBE) qui fait appel au traitement de signal digital. À partir de photos de particules, un algorithme développé dans MATLAB® fournit des mesures précises de la longueur, l’aire, l’épaisseur et la surface massique de chaque particule. Le tamisage mécanique sous-estimait la longueur des particules par rapport aux mesures par imagerie. La méthode du NMBE a montré que les particules de luzerne étaient plus irrégulières et plus allongées que celles de maïs.The size and shape of chopped forage particles can influence silage conservation and feed utilization by dairy cows. Particle length is traditionally measured by mechanical sieving. Image analysis is proposed here to measure more precisely individual particles. Corn and alfalfa forages were chopped at three theoretical lengths (12.7, 25.4 and 29.6 mm). Shape measurement was obtained from the concept of Normalized Multiscale Bending Energy (NMBE) which uses digital signal processing. From pictures of chopped particles, an algorithm developed in MATLAB® provided precise measurements of length, area, thickness and area per unit mass for each particle. Mechanical sieving underestimated actual particle length as measured by image analysis. The NMBE method indicated that alfalfa particles were more irregular and elongated compared to corn particles

Incidence of rough and irregular atmospheric ice particles from Small Ice Detector 3 measurements

NERC, NE/E011225/1 © Author(s) 2013. This work is distributed under the Creative Commons Attribution 3.0 LicenseThe knowledge of properties of ice crystals such as size, shape, concavity and roughness is critical in the context of radiative properties of ice and mixed phase clouds. Limitations of current cloud probes to measure these properties can be circumvented by acquiring two-dimensional light scattering patterns instead of particle images. Such patterns were obtained in situ for the first time using the Small Ice Detector 3 (SID-3) probe during several flights in a variety of mid-latitude mixed phase and cirrus clouds. The patterns are analyzed using several measures of pattern texture, selected to reveal the magnitude of particle roughness or complexity. The retrieved roughness is compared to values obtained from a range of well-characterized test particles in the laboratory. It is found that typical in situ roughness corresponds to that found in the rougher subset of the test particles, and sometimes even extends beyond the most extreme values found in the laboratory. In this study we do not differentiate between small-scale, fine surface roughness and large-scale crystal complexity. Instead, we argue that both can have similar manifestations in terms of light scattering properties and also similar causes. Overall, the in situ data is consistent with ice particles with highly irregular or rough surfaces being dominant. Similar magnitudes of roughness were found in growth and sublimation zones of cirrus. The roughness was found to be negatively correlated with the halo ratio, but not with other thermodynamic or microphysical properties found in situ. Slightly higher roughness was observed in cirrus forming in clean oceanic airmasses than in a continental, polluted one. Overall, the roughness and complexity is expected to lead to increased shortwave cloud reflectivity, in comparison with cirrus composed of more regular, smooth ice crystal shapes. These findings put into question suggestions that climate could be modified through aerosol seeding to reduce cirrus cover and optical depth, as the seeding may result in decreased shortwave reflectivity.Peer reviewe