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Westborough, MA, United States

Rawle A.F.,Malvern Instruments Inc.
Procedia Engineering | Year: 2015

This paper deals the determination of the particle size distribution of a material nominally (and inadequately) described as "0 - 10 silica". First, we will outline the routes to obtaining a stable set of light scattering data via wet and dry laser diffraction determination. These stable data are a prerequisite for the deconvolution to a particle size distribution. Second, we will focus on the importance of using the correct optical constants (real and imaginary parts of the refractive index) in order that the derived particle size distribution is correctly stated. In particular this robustness study will show how incorrect optical properties can influence the form (shape) of the derived distribution, plus assignation of key points in the distribution (in particular, x10). Certain supplementary information is essential in confirming the correct optical propertiesDensity via helium gas pycnometryX-ray diffraction (XRD) for polymorph confirmationBecke lines for bracketing the real part of the refractive indexVolume concentration experiment for measurement of the imaginary/absorptive part of the refractive index In this manner we can then be confident of a stated particle size distribution and an awareness of the factors that can significantly affect this derived distribution. © 2015 The Authors.

Rawle A.F.,Malvern Instruments Inc.
Procedia Engineering | Year: 2015

Like most things in life one gets out what one puts in and this is no truer than in modern instrumental particle size distribution techniques. The mantra of 'garbage in = garbage out' is meant to convey that the apparently complicated laser diffraction, dynamic light scattering, and electron microscopy techniques measure faithfully what they are given, but what they are given may not be representative of the entire product or material on which significant economic decisions will be made. Tiny samples down to pg in size on electron microscopes are somehow meant to be able to generate information on what may be many tonnes of heterogeneous sample. The bad news is that distribution and heterogeneity imply that statistical methods must be employed in order to obtain accurate and reproducible information. The good news is that representative sampling is amenable to simple statistical evaluation and 2 major predictions can be made:Based on a required or specified precision or standard error (SE), the point at the top end of the distribution to be specified to this degree of precision, and the density of the sample, then a simple prediction of the minimum mass, required to meet this required level of precision, can be calculatedSimilarly, if the mass utilized in the particle size distribution determination is known and the point in the distribution to be specified (plus the density of the sample again), then we can calculate a theoretical best achievable precision based solely on the heterogeneity of the sample This paper will illustrate the 2 points above with practical examples. © 2015 The Authors.

Petzetakis N.,University of California at Berkeley | Stone G.M.,Malvern Instruments Inc. | Balsara N.P.,University of California at Berkeley | Balsara N.P.,Lawrence Berkeley National Laboratory
Macromolecules | Year: 2014

Polyethylene, PE, is a crystalline solid with a relatively high melting temperature, and it exhibits excellent solvent resistance at room temperature. In contrast, polydimethylsiloxane, PDMS, is a rubbery polymer with an ultralow glass transition temperature and poor solvent resistance. PE-PDMS block copolymers have the potential to synergistically combine these disparate properties. In spite of this potential, synthesis of PE-PDMS block copolymers has not been widely explored. We report a facile route for the synthesis of well-defined polyethylene-b-polydimethylsiloxane-b-polyethylene (EDE) triblock copolymers. Poly(1,4-butadiene)-b-polydimethylsiloxane-b-poly(1,4-butadiene) (BDB) copolymer precursors were synthesized by anionic polymerization, followed by diimide-based hydrogenation. Under the standard hydrogenation conditions established by the work of Hahn, the siloxane bond undergoes scission resulting into significant degradation of the PDMS block. Our main accomplishment is the discovery of reaction conditions that avoid PDMS degradation. We used mechanistic insight into arrive at the optimal hydrogenation conditions, and we established the efficacy of our approach by successfully synthesizing a wide variety of block copolymers with total molecular weights ranging from 124 to 340 kg/mol and PDMS volume fractions ranging from 0.22 to 0.77. © 2014 American Chemical Society.

Switching from off-line analysis to continuous particle size measurement can substantially improve the yield of metal powder production processes and at the same time enhance product quality. Process analytical technologies of proven value in metal powder production are explored, along with the gains that accrue from the adoption of continuous particle sizing. These include improved product quality, higher yields and enhanced process efficiency. Fully automated, continuous analysis also frees up labor for higher value tasks and potentially opens up new business opportunities on the basis of a tightly controlled product specification.

Denigris J.,Malvern Instruments Inc.
Advances in Powder Metallurgy and Particulate Materials - Proceedings of the 2015 International Conference on Powder Metallurgy and Particulate Materials, PowderMet 2015 | Year: 2015

Gas Atomization, typically used for high quality spherical particles required for applications such as Metal Injection Molding (MIM), has process variables that alter the resulting PSD of the powder. However, off-line lab testing reduces the response time of that control, and hence quality and yield suffer. OnLine systems, using the proven technique of Laser Diffraction, can report full particle size distributions (ie. D10, D50, D90) automatically, 24/7 and can enhance the knowledge of process performance for both technicians and management. From batch- to batch quality to process development of gas atomization pressure settings, variables can be controlled with continuous real time data. Optimization trials can also be monitored, so real knowledge of the process performance can be learned without sacrificing additional production run time, which can be costly. Monitoring PSD in real time can result in new product offerings, tighter quality specifications, increased yield and ultimately lower cost of manufacturing.

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