TY - JOUR A1 - Ortel, Erik A1 - Häusler, Ines A1 - Österle, Werner A1 - Narbey, S. A1 - Oswald, F. A1 - Andersen, I. H. A1 - Holzweber, Markus A1 - Unger, Wolfgang A1 - Hodoroaba, Vasile-Dan T1 - In-depth structural and chemical characterization of engineered TiO2 films N2 - Analytical routines for a comprehensive in-depth morphological, structural, and chemical characterization of functionalized TiO2 films by using different state-of-the-art analytical techniques are presented and discussed with the main objective to identify potential reference TiO2 coating parameters able to be certified at a later stage. TiO2 films fabricated by two different synthetic procedures as representative for two main large-scale applications were selected: (i) pulsed d.c. magnetron sputtering for photocatalytic applications and (ii) screen printing from preformed anatase nanoparticles. The screen-printed films were further loaded with a sensitizing dye for application as a dye-sensitized solar cell. Film properties such as microstructure and crystallographic texture of pulsed d.c. magnetron sputtering synthesized films were systematically studied by means of scanning nanobeam electron diffraction in a transmission electron microscope and the surface and inner morphology by scanning electron microscopy. The dye distribution over the depth of screen-printed TiO2 layers was analyzed before and after dye-loading by means of energy dispersive X-ray spectroscopy at scanning electronmicroscope, Auger electron spectroscopy and time-of-flight secondary ion mass spectrometry. The long-term goal of the present study is the improvement of quality of the TiO2 film parameters as measured by using different types of reference TiO2 coatings having specific parameters certified. T2 - 16th European Conference on Applications of Surface and Interface Analysis ECASIA'15 CY - Granada, Spain DA - 28.09.2015 KW - Mapping KW - Line scan KW - Depth profiling KW - TiO2 films KW - Crystallinity KW - Ru dye sensitizer PY - 2016 DO - https://doi.org/10.1002/sia.5966 SN - 0142-2421 SN - 1096-9918 VL - 48 SP - 664 EP - 669 PB - John Wiley & Sons, Ltd. AN - OPUS4-36791 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Heinrich, Thomas A1 - Darlatt, Erik A1 - Lippitz, Andreas A1 - Müller, S. A1 - Schalley, C. A1 - Unger, Wolfgang T1 - Systematic XP and NEXAFS spectroscopy studies of (ter-)pyridineterminated self-assembled monolayers and their addressability for functional molecules N2 - The chemical composition of surfaces functionalized with self-assembled monolayers (SAMs) is an important parameter that determines their performance in a broad range of applications, from immobilizing molecular machines to initiation and growth control of MOFs (Metal-Organic Frameworks). In this article, a critical Survey of XPS (X-ray photoelectron spectroscopy) and NEXAFS (near edge X-ray absorption fine structure) spectroscopy data for pyridine-functionalized monolayers on gold surfaces is presented to compile correlations that have been indiscernible before. Monolayers with aromatic backbones are compared to monolayers with aliphatic backbones. Monolayers with pyridine end-groups are compared to mixed monolayers formed terpyridine-functionalized by molecules end-groups and non-functionalized molecules. Thiol-oxidation during ageing of the SAMs in air is addressed. And finally, the addressability of the SAMs for the deposition of metal-ions and organic molecules is investigated and compared. This work consequently delivers a comprehensive set of spectroscopic data of (ter-)pyridine-terminated SAMs and their performance as template for the preparation of functional multilayers of macrocycles exhibiting a preferential orientation. KW - (ter-)pyridineterminated self-assembled monolayer KW - X-ray Photoelectron Spectroscopy (XPS) KW - NEXAFS PY - 2019 DO - https://doi.org/10.1016/j.elspec.2019.03.011 VL - 233 SP - 28 EP - 37 PB - Elsevier B.V. AN - OPUS4-48018 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Daneshnia, S. A1 - Adeli, M. A1 - Yari, A. A1 - Shams, A. A1 - Donskyi, Ievgen A1 - Unger, Wolfgang T1 - Low temperature functionalization of two-dimensional boron nitride for electrochemical sensing N2 - Two-dimensional hexagonal boron nitride(h-BN)as an emerging nanomaterial exhibits uniquephysicochemical properties, making it suitable candidate for a wide spectrum of applications.However, due to its poor functionality, the processability of this nanomaterial is low. In this work, wereport on a straightforward and scalable approach for the functionalization of h-BN by nitrene[2+1]cycloaddition at room temperature. The triazine-functionalized h-BN(Trz-BNs)showed ahigh reactivity toward nucleophiles, through which post-modifications are performable. The post-modification of Trz-BNs by L-cysteine was studied using cyclic voltammetry and differential pulsevoltammetry. Taking advantage of the scalable and straightforward functionalization as well as abilityof triazine functional groups for the controlled post-modifications, Trz-BNs is a promisingnanoplatform for a wide range of future applications. KW - Two-dimensional hexagonal boron nitride(h-BN) KW - Nitrene[2+1]cycloaddition KW - Post-modification by L-cysteine KW - Electrochemical sensing KW - XPS PY - 2019 DO - https://doi.org/10.1088/2053-1591/ab317b SN - 2053-1591 VL - 6 IS - 9 SP - 095076, 1 EP - 11 PB - IOP Publishing Ltd AN - OPUS4-48635 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kim, K.J. A1 - Kim, J.W. A1 - Moon, D.W. A1 - Wirth, Thomas A1 - Hodoroaba, Vasile-Dan A1 - Gross, Thomas A1 - Unger, Wolfgang A1 - Jordaan, W. A1 - Staden, M.v. A1 - Prins, S. A1 - Wang, H. A1 - Song, X. A1 - Zhang, L. A1 - Fujimoto, T. A1 - Kojima, I. T1 - Final report on key comparison K67 and parallel pilot study P108: measurement of composition of a thin Fe-Ni alloy film N2 - The Key Comparison K67 and the parallel Pilot Study P108 on quantitative analysis of thin alloy films have been completed in the Surface Analysis Working Group (SAWG) of the Consultative Committee for Amount of Substance (CCQM). The aim of these inter-laboratory comparisons is to determine the degree of equivalence in the measurement capability of national metrology institutes (NMIs) and designated institutes (DIs) for the determination of the composition of thin alloy films. The measurand is expressed in atomic percent. A Fe-Ni alloy film with a certified composition was available for the participants of the inter-laboratory comparison. It has been used as a reference specimen to determine the relative sensitivity factors (RSF) of Fe and Ni for the different analytical methods used by the participants to determine the composition of the test sample. As was shown in the preceding Pilot Study P98, the degrees of equivalence in the measurement capabilities of the participants can be improved in that way. The composition of the reference specimen was certified by inductively coupled plasma mass spectrometry (ICP-MS) using the isotope dilution method. The in-depth and lateral homogeneity, determined in terms of elemental composition, of the certified reference sample and the unknown test sample were confirmed by secondary ion mass spectrometry (SIMS) using C60 primary ions by the leading laboratory. Five laboratories participated in the key comparison. Four of them used x-ray photoelectron spectroscopy (XPS) and one Auger electron spectroscopy (AES). One laboratory participated in the parallel P108 pilot study using electron probe micro analysis with an energy-dispersive spectrometer (ED EPMA) and XPS. KW - XPS KW - AES KW - EDX KW - Fe-Ni alloy film KW - Key comparison KW - CCQM PY - 2010 DO - https://doi.org/10.1088/0026-1394/47/1A/08011 SN - 0026-1394 SN - 1681-7575 VL - 47 IS - 1A SP - 08011-1 - 08011-15 PB - Inst. of Physics Publ. CY - Bristol AN - OPUS4-21045 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kim, K.J. A1 - Unger, Wolfgang A1 - Kim, J.W. A1 - Moon, D.W. A1 - Gross, Thomas A1 - Hodoroaba, Vasile-Dan A1 - Schmidt, Dieter A1 - Wirth, Thomas A1 - Jordaan, W. A1 - van Staden, M. A1 - Prins, S. A1 - Zhang, L. A1 - Fujimoto, T. A1 - Song, X.P. A1 - Wang, H. T1 - Inter-laboratory comparison: quantitative surface analysis of thin Fe-Ni alloy films N2 - An international interlaboratory comparison of the measurement capabilities of four National Metrology Institutes (NMIs) and one Designated Institute (DI) in the determination of the chemical composition of thin Fe-Ni alloy films was conducted via a key comparison (K-67) of the Surface Analysis Working Group of the Consultative Committee for Amount of Substance. This comparison was made using XPS (four laboratories) and AES (one laboratory) measurements. The uncertainty budget of the measured chemical composition of a thin alloy film was dominated by the uncertainty of the certified composition of a reference specimen which had been determined by inductively coupled plasma mass spectrometry using the isotope dilution method. Pilot study P-98 showed that the quantification using relative sensitivity factors (RSFs) of Fe and Ni derived from an alloy reference sample results in much more accurate result in comparison to an approach using RSFs derived from pure Fe and Ni films. The individual expanded uncertainties of the participants in the K-67 comparison were found to be between 2.88 and 3.40 atomic %. The uncertainty of the key comparison reference value (KCRV) calculated from individual standard deviations and a coverage factor (k) of 2 was 1.23 atomic %. KW - Quantification KW - Fe-Ni alloy KW - Uncertainty KW - Key comparison KW - Traceability PY - 2012 DO - https://doi.org/10.1002/sia.3795 SN - 0142-2421 SN - 1096-9918 VL - 44 IS - 2 SP - 192 EP - 199 PB - Wiley CY - Chichester AN - OPUS4-24505 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Hodoroaba, Vasile-Dan A1 - Unger, Wolfgang A1 - Jenett, H. A1 - Hoffmann, V. A1 - Hagenhoff, B. A1 - Kayser, S. A1 - Wetzig, K. T1 - Depth profiling of electrically non-conductive layered samples by RF-GDOES and HFM plasma SNMS N2 - The work is intended to compare the capabilities of two similar depth profiling techniques to analyse electrically non-conductive samples. In order to get a better evaluation of the depth resolution, various multilayer sandwiches, such as SiO2/TiO2 and Si3N4/SiO2 deposited on glass substrates have been investigated. Optimised depth profiles are presented for both methods, glow discharge optical emission spectrometry (GDOES) and radiofrequency mode (known as “HFM” in the SNMS literature) of plasma secondary neutral mass spectrometry (SNMS). The optimisation procedure, necessary to get the best set of plasma parameters, which result in the optimal depth resolution, is also described for one selected sample. Additionally, sputtering crater profilometry was carried out in order to check out the flatness of the sputtered crater. The influence of the thickness of the sample substrate on the sputtering rate is discussed. Finally, advantages and disadvantages of the use of these two depth profiling methods, especially for the non-conductive samples, are concluded from this comparative study. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) analysis of a cross-sectioned sample was carried out in order to get supplementary information. KW - rf-Glow discharge KW - HFM plasma SNMS KW - Non-conductive layered samples KW - Depth resolution KW - Crater shape KW - ToF-SIMS PY - 2001 DO - https://doi.org/10.1016/S0169-4332(01)00259-8 SN - 0169-4332 SN - 1873-5584 VL - 179 IS - 1-4 SP - 30 EP - 37 PB - North-Holland CY - Amsterdam AN - OPUS4-6882 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Tan, K. H. A1 - Sattari, S. A1 - Donskyi, Ievgen A1 - Cuellar-Camacho, J. L. A1 - Cheng, C. A1 - Schwibbert, Karin A1 - Lippitz, Andreas A1 - Unger, Wolfgang A1 - Gorbushina, Anna A1 - Adeli, M. A1 - Haag, R. T1 - Functionalized 2D nanomaterials with switchable binding to investigate graphene–bacteria interactions N2 - Graphene and its derivatives have recently attracted much attention for sensing and deactivating pathogens. However, the mechanism of multivalent interactions at the graphene–pathogen interface is not fully understood. Since different physicochemical parameters of graphene play a role at this interface, control over graphene’s structure is necessary to study the mechanism of these interactions. In this work, different graphene derivatives and also zwitterionic graphene nanomaterials (ZGNMs) were synthesized with defined exposure, in terms of polymer coverage and functionality, and isoelectric points. Then, the switchable interactions of these nanomaterials with E. coli and Bacillus cereus were investigated to study the validity of the generally proposed “trapping” and “nano-knives” mechanisms for inactivating bacteria by graphene derivatives. It was found that the antibacterial activity of graphene derivatives strongly depends on the accessible area, i.e. edges and basal plane of sheets and tightness of their agglomerations. Our data clearly confirm the authenticity of “trapping” and “nano-knives” mechanisms for the antibacterial activity of graphene sheets. KW - XPS KW - Graphene KW - Graphene–bacteria interaction PY - 2018 DO - https://doi.org/10.1039/c8nr01347k SN - 2040-3364 SN - 2040-3372 VL - 10 IS - 20 SP - 9525 EP - 9537 PB - RSC CY - London AN - OPUS4-45084 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Agrawal, A. A1 - Tamsen, E. A1 - Unger, Jörg F. A1 - Koutsourelakis, P-S T1 - From concrete mixture to structural design—a holistic optimization procedure in the presence of uncertainties N2 - We propose a systematic design approach for the precast concrete industry to promote sustainable construction practices. By employing a holistic optimization procedure, we combine the concrete mixture design and structural simulations in a joint, forward workflow that we ultimately seek to invert. In this manner, new mixtures beyond standard ranges can be considered. Any design effort should account for the presence of uncertainties which can be aleatoric or epistemic as when data are used to calibrate physical models or identify models that fill missing links in the workflow. Inverting the causal relations established poses several challenges especially when these involve physicsbased models which more often than not, do not provide derivatives/sensitivities or when design constraints are present. To this end, we advocate Variational Optimization, with proposed extensions and appropriately chosen heuristics to overcome the aforementioned challenges. The proposed approach to treat the design process as a workflow, learn the missing links from data/models, and finally perform global optimization using the workflow is transferable to several other materials, structural, and mechanical problems. In the present work, the efficacy of the method is exemplarily illustrated using the design of a precast concrete beam with the objective to minimize the global warming potential while satisfying a number of constraints associated with its load-bearing capacity after 28 days according to the Eurocode, the demolding time as computed by a complex nonlinear finite element model, and the maximum temperature during the hydration. KW - Black-box optimization under uncertainty KW - Mix design KW - Performance oriented design KW - Precast concrete KW - Probabilistic machine learning KW - Sustainable material design PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-615443 DO - https://doi.org/10.1017/dce.2024.18 VL - 5 IS - e20 SP - 1 EP - 32 PB - Cambridge University Press CY - England AN - OPUS4-61544 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Andrés Arcones, Daniel A1 - Weise, M. A1 - Koutsourelakis, P-S. A1 - Unger, Jörg F. T1 - Bias Identification Approaches for Model Updating of Simulation-based Digital Twins of Bridges N2 - Simulation-based digital twins of bridges have the potential not only to serve as monitoring devices of the current state of the structure but also to generate new knowledge through physical predictions that allow for better-informed decision-making. For an accurate representation of the bridge, the underlying models must be tuned to reproduce the real system. Nevertheless, the necessary assumptions and simplifications in these models irremediably introduce discrepancies between measurements and model response. We will show that quantifying the extent of the uncertainties introduced through the models that lead to such discrepancies provides a better understanding of the real system, enhances the model updating process, and creates more robust and trustworthy digital twins. The inclusion of an explicit bias term will be applied to a representative demonstrator case based on the thermal response of the Nibelungenbrücke of Worms. The findings from this work are englobed in the initiative SPP 100+, whose main aim is the extension of the service life of structures, especially through the implementation of digital twins. T2 - EWSHM 2024 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Digital Twins KW - Model Bias KW - SPP100+ KW - Bridge Monitoring PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-622522 DO - https://doi.org/10.58286/30524 SN - 2941-4989 IS - 12 SP - 1 EP - 10 PB - NDT.net GmbH & Co. KG CY - Mayen, Germany AN - OPUS4-62252 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Unger, Jörg F. A1 - Tamsen, Erik A1 - Agrawal, A. A1 - Koutsourelakis, P.-S. ED - Rogge, Andreas ED - Meng, Birgit T1 - Von Messdaten zum optimierten Bauteil durch Kombination von Material- und Strukturdesign N2 - Die Entwicklung eines optimierten Designs für Bauwerke erfordert die Berücksichtigung sowohl des Materials als auch des Strukturdesigns. Ziel des Beitrages ist die Vorstellung eines Designprinzips basierend auf automatisierten Workflows, das am Beispiel eines Biegebalkendesign zur Reduzierung des Treibhauspotenzials vorgestellt wird. Eine ganzheitliche Optimierung berücksichtigt Material- und Strukturdesign. Es werden physikalische Modelle mit Ansätzen aus dem maschinellen Lernen kombiniert, die mit experimentellen Daten kalibriert bzw. trainiert werden. Eine besondere Bedeutung hat dabei die Berücksichtigung von Unsicherheiten. T2 - 11. Jahrestagung des DAfStb mit 63. Forschungskolloquium der BAM Green Intelligent Building CY - Berlin, Germany DA - 16.10.2024 KW - Materialdesign KW - Strukturdesign PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-613003 SN - 978-3-9818564-7-7 SP - 209 EP - 217 PB - Bundesanstalt für Materialforschung und -prüfung (BAM) CY - Berlin AN - OPUS4-61300 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Coelho Lima, Isabela A1 - Robens-Radermacher, Annika A1 - Titscher, Thomas A1 - Kadoke, Daniel A1 - Koutsourelakis, P.-S. A1 - Unger, Jörg F. T1 - Bayesian inference for random field parameters with a goal-oriented quality control of the PGD forwardmodel's accuracy N2 - Numerical models built as virtual-twins of a real structure (digital-twins) are considered the future ofmonitoring systems. Their setup requires the estimation of unknown parameters, which are not directly measurable. Stochastic model identification is then essential, which can be computationally costly and even unfeasible when it comes to real applications. Efficient surrogate models, such as reduced-order method, can be used to overcome this limitation and provide real time model identification. Since their numerical accuracy influences the identification process, the optimal surrogate not only has to be computationally efficient, but also accurate with respect to the identified parameters. This work aims at automatically controlling the Proper Generalized Decomposition (PGD) surrogate’s numerical accuracy for parameter identification. For this purpose, a sequence of Bayesian model identification problems, in which the surrogate’s accuracy is iteratively increased, is solved with a variational Bayesian inference procedure. The effect of the numerical accuracy on the resulting posteriors probability density functions is analyzed through two metrics, the Bayes Factor (BF) and a criterion based on the Kullback-Leibler (KL) divergence. The approach is demonstrated by a simple test example and by two structural problems. The latter aims to identify spatially distributed damage, modeled with a PGD surrogate extended for log-normal random fields, in two different structures: a truss with synthetic data and a small, reinforced bridge with real measurement data. For all examples, the evolution of the KL-based and BF criteria for increased accuracy is shown and their convergence indicates when model refinement no longer affects the identification results. KW - Variational inference KW - Proper generalized decomposition KW - Goal-oriented KW - Digital twin KW - Random field PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-555755 DO - https://doi.org/10.1007/s00466-022-02214-6 SN - 1432-0924 SP - 1 EP - 22 PB - Springer CY - Berlin AN - OPUS4-55575 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Andrés Arcones, Daniel A1 - Weiser, M. A1 - Koutsourelakis, P.-S. A1 - Unger, Jörg F. T1 - Model bias identification for Bayesian calibration of stochastic digital twins of bridges N2 - Simulation-based digital twins must provide accurate, robust, and reliable digital representations of their physical counterparts. Therefore, quantifying the uncertainty in their predictions plays a key role in making better-informed decisions that impact the actual system. The update of the simulation model based on data must then be carefully implemented. When applied to complex structures such as bridges, discrepancies between the computational model and the real system appear as model bias, which hinders the trustworthiness of the digital twin and increases its uncertainty. Classical Bayesian updating approaches aimed at inferring the model parameters often fail to compensate for such model bias, leading to overconfident and unreliable predictions. In this paper, two alternative model bias identification approaches are evaluated in the context of their applicability to digital twins of bridges. A modularized version of Kennedy and O'Hagan's approach and another one based on Orthogonal Gaussian Processes are compared with the classical Bayesian inference framework in a set of representative benchmarks. Additionally, two novel extensions are proposed for these models: the inclusion of noise-aware kernels and the introduction of additional variables not present in the computational model through the bias term. The integration of these approaches into the digital twin corrects the predictions, quantifies their uncertainty, estimates noise from unknown physical sources of error, and provides further insight into the system by including additional pre-existing information without modifying the computational model. KW - Gaussian process KW - KOH KW - Bayesian updating KW - Digital twins KW - Uncertainty quantification PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-615519 DO - https://doi.org/10.1002/asmb.2897 SN - 1526-4025 N1 - This work was supported by “C07 - Data driven model adaptation for identifying stochastic digital twins of bridges” from the Priority Program (SPP) 2388/1 “Hundred plus” of the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) - Project number 501811638. VL - 417 IS - 3 SP - 1 EP - 26 PB - Wiley CY - Chichester AN - OPUS4-61551 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Andrés Arcones, Daniel A1 - Weiser, M. A1 - Koutsourelakis, F.-S. A1 - Unger, Jörg F. T1 - Evaluation of Model Bias Identification Approaches Based on Bayesian Inference and Applications to Digital Twins N2 - In recent years, the use of simulation-based digital twins for monitoring and assessment of complex mechanical systems has greatly expanded. Their potential to increase the information obtained from limited data makes them an invaluable tool for a broad range of real-world applications. Nonetheless, there usually exists a discrepancy between the predicted response and the measurements of the system once built. One of the main contributors to this difference in addition to miscalibrated model parameters is the model error. Quantifying this socalled model bias (as well as proper values for the model parameters) is critical for the reliable performance of digital twins. Model bias identification is ultimately an inverse problem where information from measurements is used to update the original model. Bayesian formulations can tackle this task. Including the model bias as a parameter to be inferred enables the use of a Bayesian framework to obtain a probability distribution that represents the uncertainty between the measurements and the model. Simultaneously, this procedure can be combined with a classic parameter updating scheme to account for the trainable parameters in the original model. This study evaluates the effectiveness of different model bias identification approaches based on Bayesian inference methods. This includes more classical approaches such as direct parameter estimation using MCMC in a Bayesian setup, as well as more recent proposals such as stat-FEM or orthogonal Gaussian Processes. Their potential use in digital twins, generalization capabilities, and computational cost is extensively analyzed. T2 - 5th ECCOMAS Thematic Conference on Uncertainty Quantificationin Computational Sciences and Engineering CY - Athen, Greece DA - 12.06.2023 KW - Model bias KW - Bayesian Uncertainty Quantification KW - Digital Twins KW - Gaussian Processes KW - Statistical Finite Element Method PY - 2023 UR - https://2023.uncecomp.org/ SP - 1 EP - 15 AN - OPUS4-58227 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Giebler, Rainer A1 - Unger, Wolfgang A1 - Schulz, B. A1 - Reiche, J. A1 - Brehmer, L. A1 - Wühn, M. A1 - Wöll, Ch. A1 - Smith, A.P. A1 - Urquhart, S.G. T1 - Near-Edge X-ray Absorption Fine Structure Spectroscopy on Ordered Films of an Amphiphilic Derivate of 2,5-Diphenyl-1,3,4-Oxadiazole KW - NEXAFS KW - OMBD KW - X-ray Absorption Spectroscopy PY - 1999 SN - 0743-7463 SN - 1520-5827 VL - 15 IS - 4 SP - 1291 EP - 1298 PB - American Chemical Society CY - Washington, DC AN - OPUS4-828 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Starkholm, A. A1 - Al-Sabbagh, Dominik A1 - Sarisozen, S A1 - von Reppert, A A1 - Rössle, M A1 - Ostermann, Markus A1 - Unger, E A1 - Emmerling, Franziska A1 - Kloo, L A1 - Svensson, P A1 - Lang, F A1 - Maslyanchuk, O. T1 - Green Fabrication of Sulfonium-Containing Bismuth Materials for High-Sensitivity X-Ray Detection N2 - Organic–inorganic hybrid materials based on lead and bismuth have recently been proposed as novel X- and gamma-ray detectors for medical imaging, non-destructive testing, and security, due to their high atomic numbers and facile preparation compared to traditional materials like amorphous selenium and Cd(Zn)Te. However, challenges related to device operation, excessively high dark currents, and long-term stability have delayed commercialization. Here, two novel semiconductors incorporating stable sulfonium cations are presented, [(CH3CH2)3S]6Bi8I30 and [(CH3CH2)3S]AgBiI5, synthesized via solvent-free ball milling and fabricated into dense polycrystalline pellets using cold isostatic compression, two techniques that can easily be upscaled, for X-ray detection application. The fabricated detectors exhibit exceptional sensitivities (14 100–15 190 µC Gyair−1 cm−2) and low detection limits (90 nGyair s−1 for [(CH3CH2)3S]6Bi8I30 and 78 nGyair s−1 for [(CH3CH2)3S]AgBiI5), far surpassing current commercial detectors. Notably, they maintain performance after 9 months of ambient storage. The findings highlight [(CH3CH2)3S]6Bi8I30 and [(CH3CH2)3S]AgBiI5 as scalable, cost-effective and highly stable alternatives to traditional semiconductor materials, offering great potential as X-ray detectors in medical and security applications. KW - Mechanochemistry KW - X-ray detectors PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-630306 DO - https://doi.org/10.1002/adma.202418626 SP - 1 EP - 10 PB - Wiley VHC-Verlag AN - OPUS4-63030 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Andrés Arcones, Daniel A1 - Weiser, M. A1 - Koutsourelakis, P-S. A1 - Unger, Jörg F. T1 - Quantifying the uncertainty of predictive simulations in digital twins through the identification of model bias N2 - This work presents a novel approach to quantifying uncertainty in digital twin simulations by addressing model bias through embedded parameter distributions. Traditional Bayesian methods often underestimate uncertainty due to assumptions of model correctness. We propose a hierarchical Bayesian framework combined with Polynomial Chaos Expansion to better capture and propagate uncertainty. The methodology is validated on an analytical example and a real-world case involving thermal deformation predictions of the Nibelungen Bridge, demonstrating improved predictive accuracy and reliability. T2 - fib Symposium 2025 CY - Antibes, France DA - 16.06.2025 KW - Digital Twins KW - Model Bias KW - Predictive simulations KW - Quantifying the uncertainty PY - 2025 SP - 2867 EP - 2873 PB - The fib, Fédération international du béton CY - Antibes, France AN - OPUS4-63629 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - INPR A1 - Andrés Arcones, Daniel A1 - Weiser, M. A1 - Koutsourelakis, P-S. A1 - Unger, Jörg F. T1 - Embedded Model Bias Quantification with Measurement Noise for Bayesian Model Calibration N2 - A key factor in ensuring the accuracy of computer simulations that model physical systems is the proper calibration of their parameters based on real-world observations or experimental data. Inevitably, uncertainties arise, and Bayesian methods provide a robust framework for quantifying and propagating these uncertainties to model predictions. Nevertheless, Bayesian methods paired with inexact models usually produce predictions unable to represent the observed datapoints. Additionally, the quantified uncertainties of these overconfident models cannot be propagated to other Quantities of Interest (QoIs) reliably. A promising solution involves embedding a model inadequacy term in the inference parameters, allowing the quantified model form uncertainty to influence non-observed QoIs. This paper introduces a more interpretable framework for embedding the model inadequacy compared to existing methods. To overcome the limitations of current approaches, we adapt the existing likelihood models to properly account for noise in the measurements and propose two new formulations designed to address their shortcomings. Moreover, we evaluate the performance of this inadequacy-embedding approach in the presence of discrepancies between measurements and model predictions, including noise and outliers. Particular attention is given to how the uncertainty associated with the model inadequacy term propagates to the QoIs, enabling a more comprehensive statistical analysis of prediction’s reliability. Finally, the proposed approach is applied to estimate the uncertainty in the predicted heat flux from a transient thermal simulation using temperature observations. KW - Model bias KW - Bayesian inference KW - Noise KW - Model updating KW - Quantity of Interest PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-652720 DO - https://doi.org/10.48550/arXiv.2410.12037 SP - 1 EP - 37 PB - arXiv.org AN - OPUS4-65272 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -