Development of a tool for rapidly assessing the implementation difficulty and emissions benefits of innovations

Consumer goods manufacturers aiming to reduce the environmental impact associated with their products commonly pursue incremental change strategies, but more radical approaches may be required if we are to address the challenges of sustainable consumption. One strategy to realize step change reductions is to prepare a portfolio of innovations providing different levels of impact reduction in exchange for different levels of organizational resource commitment. In this research a tool is developed to support this strategy, starting with the assumption that through brainstorming or other eco-innovation approaches, a long-list of candidate innovations has been created. The tool assesses the potential greenhouse gas benefit of an innovative option against the difficulty of its implementation. A simple greenhouse gas benefit assessment method based on streamlined LCA was used to analyze impact reduction potential, and a novel measure of implementation difficulty was developed. The predictions of implementation difficulty were compared against expert opinion, and showed similar results indicating the measure can be used sensibly to predict implementation difficulty. The assessment of the environmental gain versus implementation difficulty is visualized in a matrix, showing the trade-offs of several options. The tool is deliberately simple with scalar measures of CO 2 emissions benefits and implementation difficulty so tool users must remain aware of other potential environmental burdens besides greenhouse gases (e.g. water, waste). In addition, although relative life cycle emissions benefits of an option may be low, the absolute impact of an option can be high and there may be other co-benefits, which could justify higher levels of implementation difficulty. Different types of consumer products (e.g. household, personal care, foods) have been evaluated using the tool. Initial trials of the tool within Unilever demonstrate that the tool facilitates rapid evaluation of low-carbon innovations. © 2011 Elsevier Ltd. All rights reserved.

Simple and functional photonic devices from printable liquid crystal lasers

In this paper we demonstrate laser emission from emulsion-based polymer dispersed liquid crystals. Such lasers can be easily formed on single substrates with no alignment layers. Remarkably, it is shown that there can exist two radically different laser emission profiles, namely, photonic band-edge lasing and non-resonant random lasing. The emission is controlled by simple changes in the emulsification procedure. Low mixing speeds generate larger droplets that favor photonic band edge lasing with the requisite helical alignment produced by film shrinkage. Higher mixing speeds generate small droplets, which facilitate random lasing by a non-resonant scattering feedback process. Lasing thresholds and linewidth data are presented showing the potential of controllable linewidth lasing sources. Sequential and stacked layers demonstrate the possibility of achieving complex, simultaneous multi-wavelength and "white-light" laser output from a wide variety of substrates including glass, metallic, paper and flexible plastic. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

Simple and practical construction of high-performance, low-cost rogowski transducers and accompanying circuitry for research applications

Rogowski transducers have become an increasingly popular method of measuring current within prototyping applications and power electronics equipment due to their significant advantages compared to an equivalent current transformer. This paper presents a simple and practical construction technique of high-performance, low-cost Rogowski transducers and accompanying circuitry. Experimental tests were carried out to show the validity of the proposed construction technique. © 2005 IEEE.

A simple combinatorial method aiding research on single-walled carbon nanotube growth on substrates

Establishing fabrication methods of carbon nanotubes (CNTs) is essential to realize many applications expected for CNTs. Catalytic growth of CNTs on substrates by chemical vapor deposition (CVD) is promising for direct fabrication of CNT devices, and catalyst nanoparticles play a crucial role in such growth. We have developed a simple method called "combinatorial masked deposition (CMD)", in which catalyst particles of a given series of sizes and compositions are formed on a single substrate by annealing gradient catalyst layers formed by sputtering through a mask. CMD enables preparation of hundreds of catalysts on a wafer, growth of single-walled CNTs (SWCNTs), and evaluation of SWCNT diameter distributions by automated Raman mapping in a single day. CMD helps determinations of the CVD and catalyst windows realizing millimeter-tall SWCNT forest growth in 10 min, and of growth curves for a series of catalysts in a single measurement when combined with realtime monitoring. A catalyst library prepared using CMD yields various CNTs, ranging from individuals, networks, spikes, and to forests of both SWCNTs and multi-walled CNTs, and thus can be used to efficiently evaluate self-organized CNT field emitters, for example. The CMD method is simple yet effective for research of CNT growth methods. © 2010 The Japan Society of Applied Physics.

SIMPLE REMESHING SCHEME FOR THE FINITE ELEMENT BASED SIMULATION OF DOPANT DIFFUSION IN SILICON.

Process simulation programs are valuable in generating accurate impurity profiles. Apart from accuracy the programs should also be efficient so as not to consume vast computer memory. This is especially true for devices and circuits of VLSI complexity. In this paper a remeshing scheme to make the finite element based solution of the non-linear diffusion equation more efficient is proposed. A remeshing scheme based on comparing the concentration values of adjacent node was then implemented and found to remove the problems of oscillation.

A simple prediction model for asphalt surface texture incorporating mix gradation and air voids

This paper presents the results of a preliminary study that seeks to show how asphalt grading and air voids are related to the texture depth of asphalt. The fiftieth percentile particle size (D50) is shown to be a good predictor of texture depth measurements from a collected database of field and laboratory studies. The D50 is used to normalise collected texture data and this 'relative texture' is shown to correlate with air voids. Regression analyses confirm that air voids should be included along with a measure of gradation in the interpretation of asphalt surface texture.The derived formulae are used to develop correlation charts.

Highly chiral-selective growth of single-walled carbon nanotubes with a simple monometallic Co catalyst

We report on the growth of single-walled carbon nanotubes from a monometallic Co catalyst on an oxidized Si wafer support by the most simple growth recipe (vacuum annealing, growth by undiluted C 2H 2). Nevertheless, multiwavelength Raman spectroscopy and transmission electron spectroscopy show a remarkable selectivity for chiral indices and thus, e.g., high abundance with a single chirality representing 58% of all semiconducting tubes. In situ x-ray photoelectron spectroscopy monitors the catalyst chemistry during carbon nanotube growth and shows interfacial Co-Si interactions that may help to stabilize the nanoparticle/nanotube diameter. We outline a two-mechanism model explaining the selective growth. © 2012 American Physical Society.

SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases

The Internet has enabled the creation of a growing number of large-scale knowledge bases in a variety of domains containing complementary information. Tools for automatically aligning these knowledge bases would make it possible to unify many sources of structured knowledge and answer complex queries. However, the efficient alignment of large-scale knowledge bases still poses a considerable challenge. Here, we present Simple Greedy Matching (SiGMa), a simple algorithm for aligning knowledge bases with millions of entities and facts. SiGMa is an iterative propagation algorithm which leverages both the structural information from the relationship graph as well as flexible similarity measures between entity properties in a greedy local search, thus making it scalable. Despite its greedy nature, our experiments indicate that SiGMa can efficiently match some of the world's largest knowledge bases with high precision. We provide additional experiments on benchmark datasets which demonstrate that SiGMa can outperform state-of-the-art approaches both in accuracy and efficiency.