Technical University of Berlin

About: Technical University of Berlin is a education organization based out in Berlin, Germany. It is known for research contribution in the topics: Laser & Catalysis. The organization has 27292 authors who have published 59342 publications receiving 1414623 citations. The organization is also known as: Technische Universität Berlin & TU Berlin.

Combining Brain-Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges.

Abstract: In recent years, new research has brought the field of electroencephalogram (EEG)-based brain–computer interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely, “Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user–machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human–computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices.

Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation

Abstract: A situation where training and test samples follow different input distributions is called covariate shift. Under covariate shift, standard learning methods such as maximum likelihood estimation are no longer consistent—weighted variants according to the ratio of test and training input densities are consistent. Therefore, accurately estimating the density ratio, called the importance, is one of the key issues in covariate shift adaptation. A naive approach to this task is to first estimate training and test input densities separately and then estimate the importance by taking the ratio of the estimated densities. However, this naive approach tends to perform poorly since density estimation is a hard task particularly in high dimensional cases. In this paper, we propose a direct importance estimation method that does not involve density estimation. Our method is equipped with a natural cross validation procedure and hence tuning parameters such as the kernel width can be objectively optimized. Simulations illustrate the usefulness of our approach.

Understanding activity and selectivity of metal-nitrogen-doped carbon catalysts for electrochemical reduction of CO 2

Abstract: Direct electrochemical reduction of CO2 to fuels and chemicals using renewable electricity has attracted significant attention partly due to the fundamental challenges related to reactivity and selectivity, and partly due to its importance for industrial CO2-consuming gas diffusion cathodes. Here, we present advances in the understanding of trends in the CO2 to CO electrocatalysis of metal- and nitrogen-doped porous carbons containing catalytically active M–N x moieties (M = Mn, Fe, Co, Ni, Cu). We investigate their intrinsic catalytic reactivity, CO turnover frequencies, CO faradaic efficiencies and demonstrate that Fe–N–C and especially Ni–N–C catalysts rival Au- and Ag-based catalysts. We model the catalytically active M–N x moieties using density functional theory and correlate the theoretical binding energies with the experiments to give reactivity-selectivity descriptors. This gives an atomic-scale mechanistic understanding of potential-dependent CO and hydrocarbon selectivity from the M–N x moieties and it provides predictive guidelines for the rational design of selective carbon-based CO2 reduction catalysts. Inexpensive and selective electrocatalysts for CO2 reduction hold promise for sustainable fuel production. Here, the authors report N-coordinated, non-noble metal-doped porous carbons as efficient and selective electrocatalysts for CO2 to CO conversion.

Negative emissions-Part 2 : Costs, potentials and side effects

Abstract: The most recent IPCC assessment has shown an important role for negative emissions technologies (NETs) in limiting global warming to 2 °C cost-effectively. However, a bottom-up, systematic, reproducible, and transparent literature assessment of the different options to remove CO2 from the atmosphere is currently missing. In part 1 of this three-part review on NETs, we assemble a comprehensive set of the relevant literature so far published, focusing on seven technologies: bioenergy with carbon capture and storage (BECCS), afforestation and reforestation, direct air carbon capture and storage (DACCS), enhanced weathering, ocean fertilisation, biochar, and soil carbon sequestration. In this part, part 2 of the review, we present estimates of costs, potentials, and side-effects for these technologies, and qualify them with the authors' assessment. Part 3 reviews the innovation and scaling challenges that must be addressed to realise NETs deployment as a viable climate mitigation strategy. Based on a systematic review of the literature, our best estimates for sustainable global NET potentials in 2050 are 0.5–3.6 GtCO₂ yr⁻¹ for afforestation and reforestation, 0.5–5 GtCO₂ yr⁻¹ for BECCS, 0.5–2 GtCO₂ yr⁻¹ for biochar, 2–4 GtCO₂ yr⁻¹ for enhanced weathering, 0.5–5 GtCO₂ yr⁻¹ for DACCS, and up to 5 GtCO2 yr⁻¹ for soil carbon sequestration. Costs vary widely across the technologies, as do their permanency and cumulative potentials beyond 2050. It is unlikely that a single NET will be able to sustainably meet the rates of carbon uptake described in integrated assessment pathways consistent with 1.5 °C of global warming.