SEED Teams & Topics

Precious metals including silver, gold, platinum and palladium are highly important catalytic materials for various green applications, e.g. water purification and hydrogen production. However, their price remains high hindering the production possibilities of cheaper catalytic materials. On the other hand, these precious metals are found in low concentrations in many waste effluents including municipal wastewater. Capturing these metals selectively on suitable supports could offer a sustainable way for catalyst production by utilizing minor contents of precious metals, which otherwise would be wasted. By preparing the supports by 3D-printing from recycled raw materials would make the catalysts production even more sustainable. The DC can freely propose raw materials and innovative 3D-printing technologies for study.

Currently community-led groups, such as grassroot civic tech groups, create monitoring devices and share measurement outcomes as open data through citizen science. These efforts include air quality measurement and crowdsourcing lake water quality measurement. The data is published openly, but the groups’ software development or data management practises are rarely interlinked, which limits their effectiveness. For example, measurement firmware is developed individually, and the open data is collected in separate pools. To address the gaps in integration, publishing and interoperation, open science tools and new community approaches such as open-source software ecosystems can be developed and deployed. The DC can freely propose interesting approaches to overcome the presented challenge.

The low temperature Fischer-Tropsch (FT) process can be used to manufacture distillate fuels (diesel and/or kerosene) using CO2 and hydrogen produced using renewable electricity to promote carbon neutrality. The process requires the use of catalysts and with multifunctional catalysts good conversion and product yield can be achieved at moderate process conditions. The challenge for the DC is to find suitable catalyst combinations and optimized process conditions for the production of kerosene and diesel (C9-C16 paraffinic hydrocarbons) using FT-synthesis when starting from CO2 as a feedstock.

In many industries, production is localized to the Far East, because of low production costs. As companies move their upstream supply chain partners closer, this causes major changes in financial flows. Companies that will replace the giants of the Far East are often weaker in financial standing. For localization to be successful, a company should be prepared to develop a comprehensive working capital management strategy for the supply chain in its quest for a less risky supply chain. The DC will examine how working capital management changes as companies move from global supply chains to local supply chains, and how the companies manage working capital finance at different stages of the supply chain.

The project will address the characterization of electrode structures, charge and discharge mechanism and redox processes aimed to improve the performance of new energy batteries and to develop the design of electrode materials. The main topic is the study of structural defects induced in the battery cathode by migration of lithium and sodium ions. Especially, the effect of sodium will be considered since the intercalation of this element can introduce more defects in the cathode matrix than lithium.

There are many countries in the world in which most of dry waste is landfilled instead of recycling.  Thus, millions of tons of material wait for a reasonable treatment technology because incineration emits into the atmosphere dioxins, furans, and oxides of nitrogen and sulphur. In this research, scientifically sound tools for the evaluation of new technology and development of innovations are built utilizing a waste treatment case study.

Measures for urban regeneration are essential to provide citizens with sustainable and sound living conditions, including, very importantly, better air quality. Simultaneous promotion of environmental sustainability and socio-digital innovations is needed. There are multiple underlying dimensions, within the concept of socio-digital participation, such as communication-oriented participation and knowledge-oriented participation. Beyond merely “using technologies”, socio-digital participation is identified in this research as informally emerging social participation that provides affordances for “connected learning”. Socio-digital technologies offer profound new approaches for people to access connected learning.

While climate change continues harming the global environment and communities, there is a growing demand for advancing the sustainability of industrial facilities, including lowering emissions. The key hypothesis is that novel technologies will enable the provision of information to direct staff behaviors, which will enable the seamless connection between physical and digital assets and allow the sustainable transformation of industrial facilities. While the awareness and the perceived importance of lowering emissions have increased, a gap exists between an organization's aspiration towards lowering emissions and the actual managerial initiatives. More precisely, gaps exist both in the theoretical and empirical understanding of how the managerial initiatives towards emission reduction are realized and implemented in practice. The DC can freely present ways to approach the presented challenge.

How to facilitate the cooperation - especially the information and knowledge flows - between the manufacturing organization, recycling organizations and other stakeholders in a zero-waste manufacturing ecosystem? Lifecycle thinking aims at reduction of resource use and emissions, as well as improvement of the social-economic performance of a product through its lifecycle. Research tends to focus on beginning-of-life and middle-of-life, with less emphasis on end-of-life, which could play a significant role in reducing negative environmental impacts. A new value ecosystem linking different stakeholders across the product life cycle is beneficial to sustainable manufacturing, especially from ‘zero-waste’ and smart recycling perspectives.

Decentralization of energy resources is an ongoing trend, supporting the decarbonization of energy system. Distributed energy resources, such as microgeneration, energy storages, and electric vehicles, provide the flexibility for the power system, which is required for system stability. However, the challenge here is to forecast the availability of the resources, and optimally control the active resources based on the system demands. The objective of this study is to develop and test modern machine learning methodology for probabilistic forecasting the system state and resource availability, and based on these, allow optimised allocation of distributed energy resources. Real-life data of energy production and consumption will be applied in the testing of the methodology. Practical implementation of the research results will promote the cost-efficiency, reliability, and sustainability of the power system and provide added value for the owners of the energy resources by improved market access potential.