Funded OA publications

Blooming and pruning is one of the most important developmental mechanisms of the biological brain in the first years of life, enabling it to adapt its network structure to the demands of the environment. The mechanism is thought to be fundamental for the development of cognitive skills. Inspired by this, Chialvo and Bak proposed in 1999 a learning scheme that learns from mistakes by eliminating from the initial surplus of synaptic connections those that lead to an undesirable outcome. Here, this idea is implemented in a neuromorphic circuit scheme using CMOS integrated HfO2-based memristive devices. The implemented two-layer neural network learns in a self-organized manner without positive reinforcement and exploits the inherent variability of the memristive devices. This approach provides hardware, local, and energy-efficient learning. A combined experimental and simulation-based parameter study is presented to find the relevant system and device parameters leading to a compact and robust memristive neuromorphic circuit that can handle association tasks.

Artificial leaves could be the breakthrough technology to overcome the limitations of storage and mobility through the synthesis of chemical fuels from sunlight, which will be an essential component of a sustainable future energy system. However, the realization of efficient solar-driven artificial leaf structures requires integrated specialized materials such as semiconductor absorbers, catalysts, interfacial passivation, and contact layers. To date, no competitive system has emerged due to a lack of scientific understanding, knowledge-based design rules, and scalable engineering strategies. Here, we will discuss competitive artificial leaf devices for water splitting, focusing on multi-absorber structures to achieve solar-to-hydrogen conversion efficiencies exceeding 15%. A key challenge is integrating photovoltaic and electrochemical functionalities in a single device. Additionally, optimal electrocatalysts for intermittent operation at photocurrent densities of 10-20 mA cm^-2 must be immobilized on the absorbers with specifically designed interfacial passivation and contact layers, so-called buried junctions. This minimizes voltage and current losses and prevents corrosive side reactions. Key challenges include understanding elementary steps, identifying suitable materials, and developing synthesis and processing techniques for all integrated components. This is crucial for efficient, robust, and scalable devices. Here, we discuss and report on corresponding research efforts to produce green hydrogen with unassisted solar-driven (photo-)electrochemical devices. This article is protected by copyright. All rights reserved.

Research on innovation failure has proliferated lately but with little theoretical attention given to the diversity of the concept. Using process theorizing, we present a model and propositions to understand how a firm's anticipation and value toward failure depends on the type of failure (task versus outcome) and the phase (divergent versus convergent) and point (early versus later) ‘within’ the process that the failure occurs. Using the anticipation-value stances, we then present a typology of four modes of innovation failure that can arise ‘from’ task and outcomes failure in the innovation process. The four modes (and associated learning response) are unsolicited failures (prevent-alert-eliminate); hazardous failures (predict-modify-mitigate); fortuitous failures (probe-expose-extrapolate); and excursive failures (facilitate-analyze-harness). To help explain the ideas in our process model and typology, we use the well-known IDEO shopping cart innovation project as an illustrative example. Together, these contributions provide contingency oriented insights on how failure varies and journeys within and from the innovation process, which helps researchers and managers to better understand the related causes, effects and learning responses.

Elastic, bendable and stretchable electronics establish a new and promising area of multi-physics engineering for a variety of applications, e.g. on wearables or in complex-shaped machine parts. While the area of metamorphic electronics has been investigated comprehensively, the behavior at radio frequencies (RFs), especially in the GHz range, is much less well studied. The mechanical deformation of the soft substrates, for instance, due to stretching, changes the geometrical dimensions and the electrical properties of RF transmission lines. This effect could be desirable in some cases, e.g. for smart devices with shape-dependent transmission or radiation characteristics, or undesirable in other cases, e.g. in feed and distribution networks due to the variable electrical lengths and thus phase variations. This contribution describes the results of a systematic study of the broadband RF properties of coplanar transmission lines on Ecoflex® substrates, based on numerical simulations and experimental data. Two types of stretchable transmission line structures were studied: Meander- and circular ring-segmented lines. Modeling and simulation were performed combining a 2D circuit simulation software with electromagnetic full-wave simulations. The experimental part of the work included the fabrication of metamorphic substrates metallized with thin copper layers and systematic measurements of the electrical lengths and phase constants of coplanar waveguides in the frequency range from 1 to 5 GHz based on vector network analysis for different stretching levels. With the given substrate technology, we succeeded in demonstrating stretchability up to a level of 21%, while the theoretical limit is expected at 57%. The meander- and circular-shaped line structures revealed markedly different sensitivities to the stretching level, which was lower for circular structures compared to the meander structures by approximately a factor of three.

Artificial intelligence (AI) has become a part of the mainstream public discourse beyond expert communities about its risks, benefits, and need for regulation. In particular, since 2014, the news media have intensified their coverage of this emerging technology and its potential impact on most domains of society. Although many studies have analyzed traditional media coverage of AI, analyses of social media, especially video-sharing platforms, are rare. In addition, research from a risk communication perspective remains scarce, despite the widely recognized potential threats to society from many AI applications. This study aims to detect recurring patterns of societal threat/efficacy in YouTube videos, analyze their main sources, and compare detected frames in terms of reach and response. Using a theoretical framework combining framing and risk communication, the study analyzed the societal threat/efficacy attributed to AI in easily accessible YouTube videos published in a year when public attention to AI temporarily peaked (2018). Four dominant AI frames were identified: the balanced frame, the high-efficacy frame, the high-threat frame, and the no-threat frame. The balanced and no-threat frames were the most prevalent, with predominantly positive and neutral AI narratives that neither adequately address the risks nor the necessary societal response from a normative risk communication perspective. The results revealed the specific risks and benefits of AI that are most frequently addressed. Video views and user engagement with AI videos were analyzed. Recommendations for effective AI risk communication and implications for risk governance were derived from the results.