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Towards an automated workflow in materials science for combining multi-modal simulation and experimental information using data mining and large language models
No full textTo retrieve and compare scientific data of simulations and experiments in materials science, data needs to be easily accessible and machine readable to qualify and quantify various materials science phenomena. The recent progress in open science leverages the accessibility to data. However, a majority of information is encoded within scientific documents limiting the capability of finding suitable literature as well as material properties. This manuscript showcases an automated workflow, which unravels the encoded information from scientific literature to a machine readable data structure of texts, figures, tables, equations and meta-data, using
natural language processing and language as well as vision transformer models to generate a machine-readable database. The machine-readable database can be enriched with local data, as e.g. unpublished or private material data, leading to knowledge synthesis. The study shows that such an automated workflow accelerates
information retrieval, proximate context detection and material property extraction from multi-modal input data exemplarily shown for the research field of microstructural analyses of face-centered cubic single crystals. Ultimately, a Retrieval-Augmented Generation (RAG) based Large Language Model (LLM) enables a fast and efficient question answering chat bot
Evaluation of a Newly Developed Analytic Model for Predicting Drag Torque in Wet Clutches under Single- and Two-Phase Conditions Using Computational Fluid
No full textExploring The Role of Technology for Accessible Stadium Experiences for People With Limited Mobility
Get PDFNatural abundance sediment δ15N as a proxy for long-term gross N-turnover processes, GHG emissions, and denitrification hotspots within fluvial ecosystems
Get PDFNitrogen-rich agricultural headwater streams are known hotspots for fluvial
greenhouse gas (GHG) emissions and denitrification, yet the underlying processes driving
these elevated rates are not fully understood. In this study, we examined these mechanisms by
combining measurements of gross nitrogen turnover processes, open-channel GHG and N2
saturation (%) and fluxes, and δ15N isotopic analysis of stream sediment and water at nine
headwater stream sites with varying levels of agricultural land use. To assess seasonal
patterns, data were collected across two transitional periods: spring–summer and winter–
spring. Catchment land use emerged as an important environmental driver of variability, as
open channel GHG emissions and denitrification rates were up to 11 times higher in fertilized
grasslands and croplands compared to those in forested areas. In-vitro nitrogen turnover rates
followed a similar trend and were mainly positively related to both GHG and N2
oversaturation. This finding suggests that the excess nitrogen inputs in agricultural streams
promote enhanced nitrogen turnover and gaseous carbon and nitrogen losses. We also
observed a proportional increase in CO2, CH4, and N2 saturation in the water column with
sediment δ15N enrichment, a known indicator of long-term nitrogen turnover processes.
Because the highest GHG emissions and denitrification N2 losses occurred within streams in
fertilized areas, our findings highlight the potential of using sediment δ15N as an indicator of
long-term anthropogenic hotspots of fluvial GHG emissions and denitrification rates
Emissions Accounting for Germany’s Construction and Real Estate Industry: Basics, Current Status, and Needs
Get PDFA global carbon budget, as defined by the IPCC, is derived by subtracting the emissions released since 1850 from total permissible emissions, based on the Paris Agreement’s 1.5°C target. The IPCC’s global reduction pathway defines the required emissions reductions and their timeline. However, translating this budget into national and sectoral targets is challenging due to country-specific differences and methodological uncertainties. For example, most studies and statistics in Germany are based on the Climate Protection Act (KSG), which only accounts for direct emissions in relation to specific sectors. As a result, there is no uniform and cross-sectoral system boundary for the German construction and real estate industry, which includes direct and indirect emissions as well as embodied emissions.
This contribution develops an integrative approach combining top-down and bottom-up methods to identify gaps in existing statistics and studies that primarily focus on the KSG-defined building sector. The top-down approach applies macroeconomic data and multiregional input-output (MRIO) models to quantify consumption-based emissions and cross-sectoral linkages. The bottom-up approach combines disaggregated life cycle data from environmental product declarations (EPDs) for construction products and processes and national production statistics to assess material- and activity-specific emissions along the entire building life cycle. Simultaneously, identified gaps in the opening balance and emissions trajectories are addressed by integrating disaggregated bottom-up data into the overarching top-down structure. The objective is to develop a hybrid conceptual model that bridges both approaches and enables a harmonized, phase-specific attribution of greenhouse gas emissions. This paper focuses on a conceptual approach rather than presenting quantitative results. It supports German authorities in closing gaps in national statistics and regulation. The outcome demonstrates how emissions should be quantified to define a reduction pathway for the construction and real estate industry in accordance with the requirements of the European Energy Performance of Buildings Directive (EPBD) and guiding future integration into dynamic, scenario-based modelling to support compliance with carbon budgets
Online Motion Planning for Robot Manipulators in Dynamic Environments
Get PDFThis thesis focuses on the development of motion planning algorithms for dynamic environments. A dynamic environment is defined as one that evolves over time, necessitating that planning algorithms be efficient in terms of planning and replanning and capable of adapting to the robot\u27s current state and to potential changes in the environment.
To address these challenges, this thesis proposes three contributions, each targeting a different aspect of motion planning in dynamic settings.
In order to achieve efficient planning and replanning with short planning time and small variations, the first research question is formulated to address the bottleneck of speeding up motion planning methods. Since collision checking is a major bottleneck for rapid motion planning, a motion planning method is proposed to identify and eliminate unnecessary collision checks during planning.
This method uses a precomputed deterministic roadmap to capture the collision-free space of the static environment. It then performs a heuristic-informed search on the roadmap and introduces a novel safe zones concept for edge examination to find a feasible solution.
When exploring the precomputed roadmap for a solution, the heuristic-informed search minimizes the number of edge examinations by prioritizing edges based on heuristics derived from path costs in the roadmap. The concept of safe zones utilizes the spatial relation between the robot and the environment to identify regions that do not require collision checks. As a result, the method achieves both a reduced number of edge examinations and a decrease in collision checks for each examination.
Despite the significant speedup by reducing the number of collision checks, the planning time naturally increases with problem complexity and remains unbounded. The second contribution is a method to generate subgoals for decomposing complex motion planning problems into small, easily solvable subproblems. Iteratively planning the subproblems ensures short planning time in dynamic environments. This method begins with a pipeline to collect a dataset of suitable subgoals, which can be planned within a specific bounded time. Using this dataset, a conditional generative model captures the distribution of subgoals based on the given planning problems. During inference, a time estimator acts as a critic to evaluate whether the generated subgoals can meet the desired time constraints and lead the robot toward the final goal. Based on this evaluation, the proposed method makes informed decisions to select a suitable subgoal for planning.
The contributions above solely consider the current geometric state of dynamic environments and overlook the temporal aspect. This short-sightedness over the time horizon can result in frequent replanning and local-minima issues. The third contribution addresses this issue by using episodic reinforcement learning to implicitly account for potential changes in the environment over time. The learned policy refines reference trajectories based on the dynamic environment.
Three trajectory refinement strategies based on B-spline movement primitives are introduced to modify the reference trajectories while ensuring smooth trajectory transitions. The planning results of the contributions above can be used as reference trajectories. This spatiotemporally aware method achieves superior task performance compared to methods that solely depend on geometric information and other spatiotemporal planners.
Comprehensive evaluations and ablation studies in simulation and real robot experiments are conducted to demonstrate the effectiveness and limitations of the proposed methods
Local electronic structure and magnetic properties of double-perovskite LaCo₀.₅Ni₀.₅O₃ and PrCo₀.₅Ni₀.₅O₃
Get PDFCathode chemistry innovations in anode-free aqueous zinc metal batteries
Get PDFThe emergence of anode-free aqueous zinc metal batteries (AF-ZMBs) represents a transformative approach that combines intrinsic safety and low cost with maximized energy density. While significant research has focused on electrolyte optimization and interface engineering to enhance zinc reversibility, comprehensive analysis of cathode chemistry specifically tailored for anode-free configurations remains limited. This review systematically examines recent advancements in innovative cathode design strategies, spanning intercalation, hybrid-ion, dual-ion, and conversion mechanisms, and analyzes their respective capabilities in maintaining zinc inventory and structural stability. By critically assessing the current landscape and future potential of these cathode systems, this work aims to establish fundamental design principles for developing practical anode-free zinc battery technologies