Tootfinder

GAMORA: A Gesture Articulated Meta Operative Robotic Arm for Hazardous Material Handling in Containment-Level Environments
Farha Abdul Wasay, Mohammed Abdul Rahman, Hania Ghouse
https://arxiv.org/abs/2506.14513

GAMORA: A Gesture Articulated Meta Operative Robotic Arm for Hazardous Material Handling in Containment-Level Environments
The convergence of robotics and virtual reality (VR) has enabled safer and more efficient workflows in high-risk laboratory settings, particularly virology labs. As biohazard complexity increases, minimizing direct human exposure while maintaining precision becomes essential. We propose GAMORA (Gesture Articulated Meta Operative Robotic Arm), a novel VR-guided robotic system that enables remote execution of hazardous tasks using natural hand gestures. Unlike existing scripted automation or trad…

Quantum algorithm for solving generalized eigenvalue problems with application to the Schr\"odinger equation
Grzegorz Rajchel-Mieldzio\'c, Szymon Pli\'s, Emil Zak
https://arxiv.org/abs/2506.13534

Quantum algorithm for solving generalized eigenvalue problems with application to the Schrödinger equation
Accurate computation of multiple eigenvalues of quantum Hamiltonians is essential in quantum chemistry, materials science, and molecular spectroscopy. Estimating excited-state energies is challenging for classical algorithms due to exponential scaling with system size, posing an even harder problem than ground-state calculations. We present a quantum algorithm for estimating eigenvalues and singular values of parameterized matrix families, including solving generalized eigenvalue problems that …

A case study: the savings potential thanks to FAIR data in one Materials Science PhD project
Michael Seitz, Nick Garabedian, Ilia Bagov, Christian Greiner
https://arxiv.org/abs/2506.12043

A case study: the savings potential thanks to FAIR data in one Materials Science PhD project
The FAIR (Findable, Accessible, Interoperable, and Reusable) data principles have gained significant attention as a means to enhance data sharing, collaboration, and reuse across various domains. Here, we explore the potential benefits of implementing FAIR data practices within engineering projects, with a monetary focus in the German context, but by considering aspects which are relatively universal. By examining the FAIR-data aspect of a Materials Science and Engineering PhD project, it becom…

Replaced article(s) found for cond-mat.mtrl-sci. https://arxiv.org/list/cond-mat.mtrl-sci/new
[1/1]:
Accelerating Discovery of Metal-Insulator Transition Compounds Using Physics-Informed Machine Lea...

Plug-and-Play with 2.5D Artifact Reduction Prior for Fast and Accurate Industrial Computed Tomography Reconstruction
Haley Duba-Sullivan, Aniket Pramanik, Venkatakrishnan Singanallur, Amirkoushyar Ziabari
https://arxiv.org/abs/2506.14719

Plug-and-Play with 2.5D Artifact Reduction Prior for Fast and Accurate Industrial Computed Tomography Reconstruction
Cone-beam X-ray computed tomography (XCT) is an essential imaging technique for generating 3D reconstructions of internal structures, with applications ranging from medical to industrial imaging. Producing high-quality reconstructions typically requires many X-ray measurements; this process can be slow and expensive, especially for dense materials. Recent work incorporating artifact reduction priors within a plug-and-play (PnP) reconstruction framework has shown promising results in improving i…

Thermal Fisher information for a rotating BTZ black hole
Everett A. Patterson, Robert B. Mann
https://arxiv.org/abs/2506.12656 https://

Thermal Fisher information for a rotating BTZ black hole
Relativistic quantum metrology provides a framework within which we can quantify the quality of measurement and estimation procedures while accounting for both quantum and relativistic effects. The chief measure for describing such procedures is the Fisher information, which quantifies how sensitive a given estimation is to a variance of some underlying parameter. Recently, the Fisher information has been used to quantify the spacetime information accessible to two-level quantum particle detect…

Focusing on Students, not Machines: Grounded Question Generation and Automated Answer Grading
G\'er\^ome Meyer, Philip Breuer
https://arxiv.org/abs/2506.12066

Focusing on Students, not Machines: Grounded Question Generation and Automated Answer Grading
Digital technologies are increasingly used in education to reduce the workload of teachers and students. However, creating open-ended study or examination questions and grading their answers is still a tedious task. This thesis presents the foundation for a system that generates questions grounded in class materials and automatically grades student answers. It introduces a sophisticated method for chunking documents with a visual layout, specifically targeting PDF documents. This method enhance…

Simulation of Shattered Pellet Injections with Plasmoid Drifts in ASDEX Upgrade and ITER
O. Vallhagen (Department of Physics, Chalmers University of Technology, Gothenburg, Sweden), L. Antonsson (Department of Physics, Chalmers University of Technology, Gothenburg, Sweden), P. Halldestam (Max Planck Institute for Plasma Physics, Garching, Germany), G. Papp (Max Planck Institute for Plasma Physics, Garching, Germany), P. Heinrich (Max Planck Institute for Plasma Physics, Garching, Germa…

Simulation of Shattered Pellet Injections with Plasmoid Drifts in ASDEX Upgrade and ITER
Pellet injection is an important means to fuel and control discharges and mitigate disruptions in reactor-scale fusion devices. To accurately assess the efficiency of these applications, it is necessary to account for the drift of the ablated material toward the low-field side. In this study, we have implemented a semi-analytical model for ablation cloud drifts in the numerical disruption modelling tool DREAM. We show that this model is capable of reproducing the density evolution in shattered …

Replaced article(s) found for cond-mat.mtrl-sci. https://arxiv.org/list/cond-mat.mtrl-sci/new
[1/1]:
Origin of unexpected weak Gilbert damping in the LSMO/Pt bilayer system

Inferring Material Parameters from Current-Voltage Curves in Organic Solar Cells via Neural-Network-Based Surrogate Models
Eunchi Kim, Paula Hartnagel, Barbara Urbano, Leonard Christen, Thomas Kirchartz
https://arxiv.org/abs/2506.13308

Inferring Material Parameters from Current-Voltage Curves in Organic Solar Cells via Neural-Network-Based Surrogate Models
Machine learning has emerged as a promising approach for estimating material parameters in solar cells. Traditional methods for parameter extraction often rely on time-consuming numerical simulations that fail to capture the full complexity of the parameter space and discard valuable information from suboptimal simulations. In this study, we introduce a novel workflow for parameter estimation in organic solar cells based on a combination of numerical simulations and neural networks. The workflo…