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Bornholm, Færøerne og Island står over for specifikke udfordringer med batteriaffald, og projektet ’Batterier i nordiske småsamfund’ har haft til formål at analysere data, de reguleringsmæssige rammebetingelser, barrierer og muligheder for at styrke miljøvenligt forbrug og bortskaffelse af batterier og akkumulatorer i de tre samfund nu og i fremtiden. Alle fundene og de konkrete anbefalinger til myndighederne i Bornholm, Færøerne og Island er kort opsummeret i disse policy briefs.

Bornholm, Færøerne og Island står over for specifikke udfordringer med batteriaffald, og projektet ’Batterier i nordiske småsamfund’ har haft til formål at analysere data, de reguleringsmæssige rammebetingelser, barrierer og muligheder for at styrke miljøvenligt forbrug og bortskaffelse af batterier og akkumulatorer i de tre samfund nu og i fremtiden. På tværs af de tre nordiske småsamfund konkluderes det, at der er behov for implementering af konkrete visioner og målsætninger for batteriforbrug, indsamling og bortskaffelse, samt styrkelse af affalds monitorering og informationskampagner. Konkrete anbefalinger til myndighederne i Bornholm, Færøerne og Island er ligeledes udformet med initiativer til kort- og langsigtede styrkelse af håndteringen af batteriaffald.

Enhancing cell lifetime is a vital criterion in battery design and development. Because lifetime evaluation requires prolonged cycling experiments, early prediction of cell aging can significantly accelerate both the autonomous discovery of better battery chemistries and their development into production. We demonstrate an early prediction model with reliable uncertainty estimates, which utilizes an arbitrary number of initial cycles to predict the whole battery degradation trajectory. Our autoregressive model achieves an RMSE of 106 cycles and a MAPE of 10.6% when predicting the cell's end of life (EOL). Beyond being a black box, we show evidence through an explainability analysis that our deep model learns the interplay between multiple cell degradation mechanisms. The learned patterns align with existing chemical insights into the rationale for early EOL despite not being trained for this or having received prior chemical knowledge. Our model will enable accelerated battery development via uncertainty-guided truncation of cell cycle experiments once the predictions are reliable., Enhancing cell lifetime is a vital criterion in battery design and development. Because lifetime evaluation requires prolonged cycling experiments, early prediction of cell aging can significantly accelerate both the autonomous discovery of better battery chemistries and their development into production. We demonstrate an early prediction model with reliable uncertainty estimates, which utilizes an arbitrary number of initial cycles to predict the whole battery degradation trajectory. Our autoregressive model achieves an RMSE of 106 cycles and a MAPE of 10.6% when predicting the cell's end of life (EOL). Beyond being a black box, we show evidence through an explainability analysis that our deep model learns the interplay between multiple cell degradation mechanisms. The learned patterns align with existing chemical insights into the rationale for early EOL despite not being trained for this or having received prior chemical knowledge. Our model will enable accelerated battery development via uncertainty-guided truncation of cell cycle experiments once the predictions are reliable.

Silicon–Graphite blended electrodes in Li-ion batteries have been proposed as a way to harness the high capacity of Si as an anode material, while minimising the negative effects of their large volume expansion. NMC 811 is the current state-of-the-art layered oxide cathode material, where the cobalt content of the cathode has been minimised. These are the two of the most promising materials for achieving electric vehicle targets in terms of performance, cyclability and price, however their degradation mechanism is not fully understood. Here these two materials have been used to manufacture 5 Ah prototype multi-layer pouch cells, which are aged and then studied using two complimentary diffraction techniques. Neutron diffraction has enabled a quantitative analysis of phase transitions in Si–Gr anodes in a pristine and degraded cell, and the alloying behaviour of Si and Li has been inferred by comparison of identical cells with either graphite or Si–Gr anodes. Synchrotron X-ray Diffraction has been used to make an operando 2D map of the cathode and anode lithiation in the pouch cell, as well as to map the volume expansion across the cell. This approach has revealed that degradation entails significant inhomogeneities across both electrodes, linked to the inhomogeneous volume expansion of the Si–Gr anodes.

Internet-of-thing (IoT) is an assembly of devices that collect and share data with other devices and communicate via the internet. This massive network of devices, generates and communicates data and is the key to the value in IoT, allowing access to raw information, gaining insight, and making an intelligent decisions. Today, there are billions of IoT devices such as sensors and actuators deployed. Many of these applications are easy to connect, but those tucked away in hard-to-access spots will need to harvest ambient energy. Therefore, the aim is to create devices that are self-report in real-time. Efforts are underway to install a self-powered unit in IoT devices that can generate sufficient power from environmental conditions such as light, vibration, and heat. In this review paper, we discuss the recent progress made in materials and device development in power- and, storage units, and power management relevant for IoT applications. This review paper will give a comprehensive overview for new researchers entering the field of IoT and a collection of challenges as well as perspectives for people already working in this field.

Li-ion batteries are the essential energy-storage building blocks of modern society. However, producing ultra-high electrochemical performance in safe and sustainable batteries for example, e-mobility, and portable and stationary applications, demands overcoming major technological challenges. Materials engineering and new chemistries are key aspects to achieving this objective, intimately linked to the use of advanced characterization techniques. In particular, operando investigations are currently attracting enormous interest. Synchrotron- and neutron-based bulk techniques are increasingly employed as they provide unique insights into the chemical, morphological, and structural changes inside electrodes and electrolytes across multiple length scales with high time/spatial resolutions. However, data acquisition, data analysis, and scientific outcomes must be accelerated to increase the overall benefits to the academic and industrial communities, requiring a paradigm shift beyond traditional single-shot, sophisticated experiments. Here a multi-scale and multi-technique integrated workflow is presented to enhance bulk characterization, based on standardized and automated data acquisition and analysis for high-throughput and high-fidelity experiments, the optimization of versatile and tunable cells, as well as multi-modal correlative characterization. Furthermore, new mechanisms, methods and organizations such as artificial intelligence-aided modeling-driven strategies, coordinated beamtime allocations, and community-unified infrastructures are discussed in order to highlight perspectives in battery research at large scale facilities.

Li-ion batteries are the essential energy-storage building blocks of modern society. However, producing ultra-high electrochemical performance in safe and sustainable batteries for example, e-mobility, and portable and stationary applications, demands overcoming major technological challenges. Materials engineering and new chemistries are key aspects to achieving this objective, intimately linked to the use of advanced characterization techniques. In particular, operando investigations are currently attracting enormous interest. Synchrotron- and neutron-based bulk techniques are increasingly employed as they provide unique insights into the chemical, morphological, and structural changes inside electrodes and electrolytes across multiple length scales with high time/spatial resolutions. However, data acquisition, data analysis, and scientific outcomes must be accelerated to increase the overall benefits to the academic and industrial communities, requiring a paradigm shift beyond traditional single-shot, sophisticated experiments. Here a multi-scale and multi-technique integrated workflow is presented to enhance bulk characterization, based on standardized and automated data acquisition and analysis for high-throughput and high-fidelity experiments, the optimization of versatile and tunable cells, as well as multi-modal correlative characterization. Furthermore, new mechanisms, methods and organizations such as artificial intelligence-aided modeling-driven strategies, coordinated beamtime allocations, and community-unified infrastructures are discussed in order to highlight perspectives in battery research at large scale facilities.

Here we introduce PRISMA, an open-source tool to rapidly analyse large numbers of spectra into meaningful spectroscopic trends. PRISMA follows a human-in-the-loop workflow, where the user interacts with an intuitive graphical user interface to control multiple steps in the spectrum analysis process: trimming, baseline correction, and peak fitting. The tool outputs the results in an easy-to-read csv format within seconds. We describe the functionalities implemented in PRISMA and test its capabilities with three experimental cases relevant to the study of Electrochemical energy storage and conversion devices: a temperature-dependent Raman measurement of phase transitions, a linear Raman mapping of a graphite composite electrode, and an operando X-ray diffraction experiment of lithium nickel oxide composite battery electrode. The case studies demonstrate the robustness of the app and its ability to unearth insightful trends in peak parameters, which are easier to represent, interpret and further analyse with more advanced techniques.

Aluminum doped ZnO is one of the main candidates to replace indium tin oxide used as a transparent conducting oxide. Despite the intensive research, the mechanism behind the poor thin-film-resistivity uniformity over the sample deposited by magnetron sputtering is not completely understood. Several independent reports correlate the mirroring of an erosion track on the substrate with energetic negative oxygen ions. However, their role on assisting the thin film growth is not known. In this work, the physical and chemical properties of aluminum doped ZnO are measured with a high spatial resolution at different thin film deposition pressures. The results show that Zn depletion by energetic negative oxygen ion re-sputtering is the main factor correlating with the resistivity and the band gap energy profiles of the deposited films.

This roadmap aims to define the guidelines to maximise the impact of the 3D printing revolution on the next generation of devices for the energy transition. It also outlines the current status, challenges and required advances in Science and Technology for a series of power generation technologies (fuel cells, solar cells, thermoelectric generators and turbomachinery) and energy storage technologies (electrolysers, batteries and supercapacitors). Finally, the roadmap discusses the role of 3D printing in improving the mass and heat transfer to improve the energy efficiency of chemical reactors (CO2 conversion) and novel cooling systems. With this document, the authors intend to provide a valuable tool for researchers, technology developers, and policymakers when defining their strategies for the energy sector's future.