Solute transport in orthorhombic lysozyme crystals: a molecular simulation study

Long-time equilibrium molecular dynamics simulations were performed to study the passage of a substrate, l-arabinose, through nanopores of orthorhombic hen egg white lysozyme crystals. Cross-linked protein crystals (CLPC), as novel biological nanoporous media, consist of an extensive regular matrix of chiral solvent-filled nanopores via which ions and solutes, e.g. sugars and amino acids, travel in and out. We studied the diffusive motion of arabinose inside protein channels. The computed diffusion coefficients within the crystal were orders of magnitudes lower relative to the diffusion coefficient of the solute in water. This study is valuable for understanding the nature of solute–protein interactions and transport phenomena in CLPCs and provides an understanding of biocatalytic and bioseparation processes using CLPC

Typology of Business Models for Emerging Grid-scale Energy Storage Technologies

The main goals of this thesis are to develop, validate, and analyze emerging business models to ensure near-term market success of the grid-scale Energy Storage (ES) technologies. The main research contributions are a typology (i.e. classification according to general type) of emerging business models for investment and operational viability of grid–scale storage, validation of business models for valuation analysis of diverse grid-scale storage, and a unique technology management framework for value analysis of emerging technologies. It is widely accepted that the intermittency of primary renewable energy sources is a limiting factor for inclusion of these technologies in autonomous power applications. ES technologies can be seen as valuable flexibility assets with their capabilities to control grid power intermittency or power quality services in generation, transmission, and distribution, as well as in end-user consumption side. When combined with sophisticated and reliable business models, grid-scale storage technologies can contribute significantly to enhance asset utilization rate and reliability of the power systems. The latter is particularly critical for deployment of regional and national energy policies of implementing renewable sources. Despite the fact that energy storage systems increase operational cost of the distributed electricity system, energy storage technologies can play a vital role in reducing overall upgrade cost of the electricity grids when renewable sources need to be integrated locally. The main challenge of adopting ES technologies among utilities is how to match the right energy storage technology to appropriate business-operation models for a site-specific grid configuration. Current know-how and assessment tools provide substantial information around technical specifications and requirements for adopting ES technologies for various grid configurations. However, only few of the existing approaches use market driven information. The majority of the tools also suffer from a lack of detailed information relevant for business managers for decision making purposes. Currently, none of the existing tools and investment methodologies evaluate the benefits of electricity storage from the perspective of a detailed techno-economic and business-operation models. The choice of appropriate business model, complexity of regulatory and policy environment, ownership and governance structure of storage asset, financing strategies, managing revenue streams, and associated operational risks are critical for providing an accurate assessment of the viability of the emerging ES technologies. In order to fully assess the value proposition of ES technologies, formulate their risks and opportunities profile, and develop implementation plans, a comprehensive analysis framework is needed to support integration of technical, economic and business operation perspectives. This research aims to develop a typology of different business models in the context of grid-scale ES technologies. A bottom-up approach is proposed, demonstrated, and validated to identify a generalized business model framework. The business model framework is tailored to provide a customized analysis platform for adopting emerging energy storage technologies. Several case studies are carried out based on the proposed business model framework and energy storage valuation analysis therein. Each business model, combined with thorough valuation analysis, provides insights on when deployment of individual storage technologies can be economically and technically viable. For industry looking to adapt new energy storage technologies, such analysis can provide multi-dimension considerations (cost, efficiency, reliability, best practice business operation model, and policy instruments), which can potentially lead to complete insights for strategic decision-making purposes

Benchmark Analysis and Business Operation Model of a Clean Energy Commercialization Accelerator

The Clean Technology Community Gateway (CTCG) is a not-for-profit organization which was established to coordinate clean energy project consortia in BC for end users such as on- and off-grid communities and municipalities. The core business strategy of CTCG is to focus initially on remote communities as its target market. The initiative is designed to close the commercialization gap between emerging clean energy technologies and community needs by managing and implementing large-scale demonstration projects. In order to develop and implement the best business practices for CTCG, this report explores different business operational models which were adopted by different non-profit clean energy commercialization organizations. A two-stage approach is employed where, in the first stage, over fifteen organizations (including twelve non-profit organizations and three university research parks) in Canada, the U.S., and Europe are selected for benchmark analysis. Four distinct business operational models are identified based upon an in-depth analysis: incubation focused, technology-enabled, market-enabled, and strategic partnership. Thereafter, a typology of organizations is proposed, based on four discriminating models: governance, finance, operation, and revenue. In the second stage, the typological analysis is employed to unravel best business practices for CTCG in view of governance structure, management practice, community impacts, overall business model and performance, strategic plan, and operation

Electrochemical materials discovery and intelligence

Design and implementation of efficient and cost-effective electrochemical devices is a complex challenge. It hinges on big-data driven knowledge at the frontiers of multi-disciplinary efforts in materials discovery and design. These massive data–driven processes, however, require intensive cognitive, yet expensive systems, including human, to determine the best design decisions. A novel approach towards Artificial Intelligence (AI) and Machine Learning (ML) algorithms can overcome the complexity of selecting advanced new materials with the predictable and desired properties. Focusing on advanced electrocatalysts for CO2 conversion as a use case, we demonstrate an AI-driven “Virtual Materials Intelligence” platform (beta) for materials data management and intelligent design equipped with an advanced user interface and predictive capabilities in view of materials properties and function. The platform combines information originating from large data sets of different origins. The data storage, data analysis, and advanced analysis algorithms enable efficient and secure data flow between several different simulation and characterization activities. The cloud-based platform ultimately aims to manage all available materials databases and relevant modeling, simulation, performance, cost, and characterization data and how they can be communicated to materials fabrication and design teams

Characterization of pore network structure in catalyst layers of polymer electrolyte fuel cells

We model and validate the effect of ionomer content and Pt nanoparticles on nanoporous structure of catalyst layers in polymer electrolyte fuel cells. By employing Pore network modeling technique and analytical solutions, we analyze and reproduce experimental N2-adsorption isotherms of carbon, Pt/ carbon and catalyst layers with various ionomer contents. The porous catalyst layer structures comprise of Ketjen Black carbon, Pt and Nafion ionomer. The experimental pore size distributions obtained by N2- adsorption are used as an input to generate porous media using the pore network approach. Subsequently, the simulated porous structures are used to produce simulated N2-adsorption isotherms, which are then compared to the experimentally measured isotherms. The results show a good agreement in the prediction of the effect of the ionomer content on the microstructure of catalyst layers. Moreover, the analysis of the isotherms confirms the hypothesis of ionomer distribution on the surface of agglomerates as well as the existence of different sorption regimes in primary and secondary pores of fuel cell catalyst layers

Morphology of supported polymer electrolyte ultra-thin films: a numerical study

Morphology of polymer electrolytes membranes (PEM), e.g., Nafion, inside PEM fuel cell catalyst layers has significant impact on the electrochemical activity and transport phenomena that determine cell performance. In those regions, Nafion can be found as an ultra-thin film, coating the catalyst and the catalyst support surfaces. The impact of the hydrophilic/hydrophobic character of these surfaces on the structural formation of the films has not been sufficiently explored yet. Here, we report about Molecular Dynamics simulation investigation of the substrate effects on the ionomer ultra-thin film morphology at different hydration levels. We use a mean-field-like model we introduced in previous publications for the interaction of the hydrated Nafion ionomer with a substrate, characterized by a tunable degree of hydrophilicity. We show that the affinity of the substrate with water plays a crucial role in the molecular rearrangement of the ionomer film, resulting in completely different morphologies. Detailed structural description in different regions of the film shows evidences of strongly heterogeneous behavior. A qualitative discussion of the implications of our observations on the PEMFC catalyst layer performance is finally proposed