Elsa Olivetti

Design for Recycling

As design for recycling becomes more broadly applied in material and product design, analytical tools to quantify the environmental implications of design choices will become a necessity. Currently, few systematic methods exist to measure and direct the metallurgical alloy design process to create alloys that are most able to be produced from scrap. This is due, in part, to the difficulty in evaluating such a context-dependent property as recyclability of an alloy, which will depend on the types of scraps available to producers, the compositional characteristics of those scraps, their yield, and the alloy specification itself. This article explores the use of a chance-constrained based optimization model, similar to models used in operational planning in secondary production today, to (1) characterize the challenge of developing recycling-friendly alloys due to the contextual sensitivity of recycling, (2) demonstrate how such models can be used to evaluate the potential scrap usage of alloys, and (3) explore the value of sensitivity analysis information to proactively identify effective alloy modifications that can drive increased potential scrap use. These objectives are demonstrated through two cases that involve the production of a broad range of alloys utilizing representative scraps from three classes of industrial end uses.

Rubbery Graft Copolymer Electrolytes for Solid-State, Thin-Film Lithium Batteries

Graft copolymer electrolytes (GCEs) of poly[(oxyethylene)9 methacrylate]-g-poly(dimethyl siloxane) (POEM-g-PDMS) (70:30) have been synthesized by simple free radical polymerization using a macromonomer route. Differentialscanning calorimetry, transmission electron microscopy, and small angle neutron scattering confirmed the material to be microphase-separated with a domain periodicity of ∼25 nm. Over the temperature range 290 200 cycles) at a discharge rate of 2/3 C and could be cycled (charged and discharged) at subambient temperature (0°C).

End-of-life LCA allocation methods: Open loop recycling impacts on robustness of material selection decisions

Materials selection decisions exhibit great influence on the environmental performance of firms through their impact on processing technology, product form, and supply chain configuration. Consequently, materials dictate a products environmental profile via the burden associated with extraction and refining, transformation from material to product, product performance characteristics during use, and potential recovery at end-of-life (EOL). While lifecycle assessment (LCA) methods provide quantitative input to a product designers materials selection decision, LCA implementations are evolving and disparate. This work explores several analytical variations of LCA related to the allocation of recycling impacts at product EOL and the implications of these variants across a range of contexts. Stylized analyses across a range of materials are presented, focusing on materials with varying primary and secondary materials production burdens. This work illustrates that a) the application of distinct EOL allocation methods give different values of cumulative environmental impact for the same material, b) these impacts change at differing rates between the various methods, and c) these disparities can result in different rank ordering of materials preference. Characterizing this behavior over a range of parameters illustrates the potential trends in allocation method bias for or against particular materials classes.

Doping level and work function control in oxidative chemical vapor deposited poly (3,4-ethylenedioxythiophene)

Control over doping level and work function is achieved for poly(3,4-ethylenedioxythiophene) (PEDOT) films deposited by oxidative chemical vapor deposition (oCVD). Surface analysis reveals the equivalence of the surface and bulk compositions for the oCVD films. The oCVD PEDOT polymer chains doped solely with Cl− ions. By increasing the substrate temperature used for deposition, the doping level was monotonically increased from 17to33at.%, resulting in a corresponding ability to tune the work function from 5.1to5.4eV. The controllability of doping level and work function of oCVD PEDOT offers great potential advantages for organic devices.

Toward sustainable material usage: evaluating the importance of market motivated agency in modeling material flows.

Increasing recycling will be a key strategy for moving toward sustainable materials usage. There are many barriers to increasing recycling, including quality issues in the scrap stream. Repeated recycling can compound this problem through the accumulation of tramp elements over time. This paper explores the importance of capturing recycler decision-making in accurately modeling accumulation and the value of technologies intended to mitigate it. A method was developed combining dynamic material flow analysis with allocation of those materials into production portfolios using blending models. Using this methodology, three scrap allocation methods were explored in the context of a case study of aluminum use: scrap pooling, pseudoclosed loop, and market-based. Results from this case analysis suggest that market-driven decisions and upgrading technologies can partially mitigate the negative impact of accumulation on scrap utilization, thereby increasing scrap use and reducing greenhouse gas emissions. A market-based allocation method for modeling material flows suggests a higher value for upgrading strategies compared to a pseudoclosed loop or pooling allocation method for the scenarios explored.

A Methodology for Robust Comparative Life Cycle Assessments Incorporating Uncertainty

We propose a methodology for conducting robust comparative life cycle assessments (LCA) by leveraging uncertainty. The method evaluates a broad range of the possible scenario space in a probabilistic fashion while simultaneously considering uncertainty in input data. The method is intended to ascertain which scenarios have a definitive environmentally preferable choice among the alternatives being compared and the significance of the differences given uncertainty in the parameters, which parameters have the most influence on this difference, and how we can identify the resolvable scenarios (where one alternative in the comparison has a clearly lower environmental impact). This is accomplished via an aggregated probabilistic scenario-aware analysis, followed by an assessment of which scenarios have resolvable alternatives. Decision-tree partitioning algorithms are used to isolate meaningful scenario groups. In instances where the alternatives cannot be resolved for scenarios of interest, influential parameters are identified using sensitivity analysis. If those parameters can be refined, the process can be iterated using the refined parameters. We also present definitions of uncertainty quantities that have not been applied in the field of LCA and approaches for characterizing uncertainty in those quantities. We then demonstrate the methodology through a case study of pavements.

Exploring the viability of probabilistic under-specification to streamline life cycle assessment.

Life cycle assessment (LCA) is a technique used to assess the environmental impact of products, processes, or materials. Recently, its importance as a decision-making tool to help evaluate current inventories and innovation of environmentally responsible products has grown; however, the amount of information needed to completely assess even the simplest products environmental impact may require significant time and resources. Myriad quantitative and qualitative effort-reducing strategies have been considered to accelerate the pace and reduce the cost of LCA. Although these streamlining methodologies reduce the time and effort of conducting LCA, they introduce variability and uncertainty into the results, creating a challenge for stakeholders who may need to make decisions based on the information. This Article explores the impact of streamlining on the credibility of LCA results given the uncertainty in the context of several case studies related to materials production in common consumer products. A technique for the structured analysis of the bill of materials is proposed, which leverages statistical analysis in the context of uncertainty.

Increasing Secondary and Renewable Material Use: A Chance Constrained Modeling Approach To Manage Feedstock Quality Variation

The increased use of secondary (i.e., recycled) and renewable resources will likely be key toward achieving sustainable materials use. Unfortunately, these strategies share a common barrier to economical implementation - increased quality variation compared to their primary and synthetic counterparts. Current deterministic process-planning models overestimate the economic impact of this increased variation. This paper shows that for a range of industries from biomaterials to inorganics, managing variation through a chance-constrained (CC) model enables increased use of such variable raw materials, or heterogeneous feedstocks (hF), over conventional, deterministic models. An abstract, analytical model and a quantitative model applied to an industrial case of aluminum recycling were used to explore the limits and benefits of the CC formulation. The results indicate that the CC solution can reduce cost and increase potential hF use across a broad range of production conditions through raw materials diversification. These benefits increase where the hFs exhibit mean quality performance close to that of the more homogeneous feedstocks (often the primary and synthetic materials) or have large quality variability. In terms of operational context, the relative performance grows as intolerance for batch error increases and as the opportunity to diversify the raw material portfolio increases.

Machine-learned and codified synthesis parameters of oxide materials

Predictive materials design has rapidly accelerated in recent years with the advent of large-scale resources, such as materials structure and property databases generated by ab initio computations. In the absence of analogous ab initio frameworks for materials synthesis, high-throughput and machine learning techniques have recently been harnessed to generate synthesis strategies for select materials of interest. Still, a community-accessible, autonomously-compiled synthesis planning resource which spans across materials systems has not yet been developed. In this work, we present a collection of aggregated synthesis parameters computed using the text contained within over 640,000 journal articles using state-of-the-art natural language processing and machine learning techniques. We provide a dataset of synthesis parameters, compiled autonomously across 30 different oxide systems, in a format optimized for planning novel syntheses of materials.

Modeling the economic and environmental performance of recycling systems

A general model for evaluating the economic and environmental performance of electronics recycling systems is developed. This model comprehends the three main functions in a recycling system - collection, processing, and system management - and aims to enable quantification of the impact of context and system architecture on the performance of electronics recycling systems. Different modeling techniques are used, including process-based cost models, to evaluate economic performance, and life cycle assessment tools, to evaluate environmental performance. A case study, based loosely on Maine, is presented to show the utility of such a model in evaluating electronics recycling systems.