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  <doc>
    <id>49802</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>115</pageFirst>
    <pageLast>128</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>47</volume>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation>Gesellschaft für Umweltsimulation e.V.</creatingCorporation>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Bewitterungsszenarien im Vergleich – Veränderungen in der Oberflächenmorphologie von Polypropylen (PP) und Polystyrol (PS) unter dem Aspekt des Austrags von polybromierten Flammschutzmitteln</title>
    <abstract language="deu">Gegenstand der vorzustellenden Arbeiten ist die Prüfung der Umwelt-beständigkeit und -verträglichkeit von Materialien und Produkten hinsichtlich der Emission von potenziellen Schadstoffen in die Umwelt. Hierzu werden chemisch-physikalische Einflüsse (Bewitterung) und mikrobielle Beanspruchungen an Modellmaterialien evaluiert. So werden die Freisetzungsraten von Schadstoffen in Abhängigkeit der Beanspruchung beschrieben. Als Modellmaterialien kommen die Polymere Polystyrol (PS) und Polypropylen (PP) zum Einsatz. Synergistische Effekte der Bewitterungsparameter und der mikrobiologischen Beanspruchung sollen dabei ebenso betrachtet werden, wie die gezielte Alterung. Auch findet eine Beschreibung des Verhaltens der ausgetragenen Schadstoffe in den Umweltkompartimenten Boden oder Wasser statt. Hier sind mit Hilfe der zu entwickelnden Screening- und non-Target-Analyseverfahren die Transformation und der Metabolismus durch Mikroorganismen zu beschreiben. Aus den Ergebnissen sollen Korrelationen zwischen den künstlichen Alterungsverfahren und realen Szenarien abgeleitet werden.</abstract>
    <parentTitle language="deu">Umwelteinflüsse erfassen, simulieren, bewerten</parentTitle>
    <identifier type="isbn">978-981-18507-2-7</identifier>
    <enrichment key="eventName">47. Jahrestagung der GUS</enrichment>
    <enrichment key="eventPlace">Blankenloch-Stutensee, Germany</enrichment>
    <enrichment key="eventStart">21.03.2018</enrichment>
    <enrichment key="eventEnd">23.03.2018</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
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    <author>Christian Piechotta</author>
    <author>Hassan Iznaguen</author>
    <author>Heike Traub</author>
    <author>Ines Feldmann</author>
    <author>Robert Köppen</author>
    <author>Angelika Witt</author>
    <author>Christian Jung</author>
    <author>Roland Becker</author>
    <author>Katrin Oleszak</author>
    <author>Michael Bücker</author>
    <author>Klaus Urban</author>
    <author>Christian Reger</author>
    <author>Markus Ostermann</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Schadstoffaustrag</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Umweltsimulation</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Bewitterung</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="ddc" number="628">Sanitär- und Kommunaltechnik; Umwelttechnik</collection>
    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="literaturgattung" number="">Graue Literatur</collection>
  </doc>
  <doc>
    <id>50895</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>169</pageFirst>
    <pageLast>170</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
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    <publisherName/>
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    <completedDate>--</completedDate>
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    <title language="eng">New techniques in super resolution photothermal imaging for nondestructive testing</title>
    <abstract language="eng">In this work we focus on our most recent studies to super resolution (SR) laser thermography. The goal of SR nondestructive testing methods is to facilitate the separation of closely spaced defects. We explain how to combine laser scanning with SR techniques. It can be shown that stepwise as well as continuous scanning techniques are applicable. Finally, we discuss the effect of experimental parameters and im-age processing techniques to find the optimal SR technique which leads to the highest reconstruction quality within laser thermography.</abstract>
    <parentTitle language="eng">SMSI 2020 Proceedings</parentTitle>
    <identifier type="doi">10.5162/SMSI2020/C4.1</identifier>
    <enrichment key="eventName">SMSI 2020 Conference</enrichment>
    <enrichment key="eventPlace">Online meeting</enrichment>
    <enrichment key="eventStart">22.06.2020</enrichment>
    <enrichment key="eventEnd">25.06.2020</enrichment>
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    <author>Samim Ahmadi</author>
    <author>Philipp Daniel Hirsch</author>
    <author>Julien Lecompagnon</author>
    <author>Christian Hassenstein</author>
    <author>P. Jung</author>
    <author>G. Caire</author>
    <author>Mathias Ziegler</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Super resolution</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Laser thermography</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Nondestructive testing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Laser scanning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Photothermal imaging</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
    <collection role="institutes" number="">8.3 Thermografische Verfahren</collection>
    <collection role="themenfelder" number="">Chemie und Prozesstechnik</collection>
    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="literaturgattung" number="">Graue Literatur</collection>
  </doc>
  <doc>
    <id>52536</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>11</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>preprint</type>
    <publisherName>Cornell University</publisherName>
    <publisherPlace>Ithaca, NY</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Learned block iterative shrinkage thresholding algorithm for photothermal super resolution imaging</title>
    <abstract language="eng">Block-sparse regularization is already well-known in active thermal imaging and is used for multiple measurement based inverse problems. The main bottleneck of this method is the choice of regularization parameters which differs for each experiment. To avoid time-consuming manually selected regularization parameters, we propose a learned block-sparse optimization approach using an iterative algorithm unfolded into a deep neural network. More precisely, we show the benefits of using a learned block iterative shrinkage thresholding algorithm that is able to learn the choice of regularization parameters. In addition, this algorithm enables the determination of a suitable weight matrix to solve the underlying inverse problem. Therefore, in this paper we present the algorithm and compare it with state of the art block iterative shrinkage thresholding using synthetically generated test data and experimental test data from active thermography for defect reconstruction. Our results show that the use of the learned block-sparse optimization approach provides smaller normalized mean square errors for a small fixed number of iterations than without learning. Thus, this new approach allows to improve the convergence speed and only needs a few iterations to generate accurate defect reconstruction in photothermal super resolution imaging.</abstract>
    <parentTitle language="eng">arXiv.org</parentTitle>
    <identifier type="urn">urn:nbn:de:kobv:b43-525364</identifier>
    <identifier type="issn">2331-8422</identifier>
    <identifier type="doi">10.48550/arXiv.2012.03547</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="RelatedIdentifier">https://nbn-resolving.org/urn:nbn:de:kobv:b43-554598</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Samim Ahmadi</author>
    <author>Jan Christian Hauffen</author>
    <author>L. Kästner</author>
    <author>P. Jung</author>
    <author>G. Caire</author>
    <author>Mathias Ziegler</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Iterative shrinkage thresholding algorithm</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Neural network</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Active thermography</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Photothermal super resolution</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
    <collection role="institutes" number="">8.3 Thermografische Verfahren</collection>
    <collection role="themenfelder" number="">Chemie und Prozesstechnik</collection>
    <collection role="fulltextaccess" number="">Datei für die Öffentlichkeit verfügbar ("Open Access")</collection>
    <collection role="literaturgattung" number="">Graue Literatur</collection>
    <collection role="unnumberedseries" number="">Preprints der BAM</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/52536/2012.03547.pdf</file>
  </doc>
  <doc>
    <id>52537</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>10</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>preprint</type>
    <publisherName>Cornell University</publisherName>
    <publisherPlace>Ithaca, NY</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Photothermal-SR-Net: A customized deep unfolding neural network for photothermal super resolution imaging</title>
    <abstract language="eng">This paper presents deep unfolding neural networks to handle inverse problems in photothermal radiometry enabling super resolution (SR) imaging. Photothermal imaging is a well-known technique in active thermography for nondestructive inspection of defects in materials such as metals or composites. A grand challenge of active thermography is to overcome the spatial resolution limitation imposed by heat diffusion in order to accurately resolve each defect. The photothermal SR approach enables to extract high-frequency spatial components based on the deconvolution with the thermal point spread function. However, stable deconvolution can only be achieved by using the sparse structure of defect patterns, which often requires tedious, hand-crafted tuning of hyperparameters and results in computationally intensive algorithms. On this account, Photothermal-SR-Net is proposed in this paper, which performs deconvolution by deep unfolding considering the underlying physics. This enables to super resolve 2D thermal images for nondestructive testing with a substantially improved convergence rate. Since defects appear sparsely in materials, Photothermal-SR-Net applies trained blocksparsity thresholding to the acquired thermal images in each convolutional layer. The performance of the proposed approach is evaluated and discussed using various deep unfolding and thresholding approaches applied to 2D thermal images. Subsequently, studies are conducted on how to increase the reconstruction quality and the computational performance of Photothermal-SR-Net is evaluated.&#13;
Thereby, it was found that the computing time for creating high-resolution images could be significantly reduced without decreasing the reconstruction quality by using pixel binning as a preprocessing step.</abstract>
    <parentTitle language="eng">arxiv.org</parentTitle>
    <identifier type="urn">urn:nbn:de:kobv:b43-525371</identifier>
    <identifier type="issn">2331-8422</identifier>
    <identifier type="doi">10.48550/arXiv.2104.10563</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="RelatedIdentifier">https://opus4.kobv.de/opus4-bam/frontdoor/index/index/docId/54678</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Samim Ahmadi</author>
    <author>L. Kästner</author>
    <author>Jan Christian Hauffen</author>
    <author>P. Jung</author>
    <author>Mathias Ziegler</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Photothermal super resolution</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Nondestructive testing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep unfolding</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Physics-based deep learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Laser thermography</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Elastic net</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Iterative shrinkage thresholding algorithm</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
    <collection role="institutes" number="">8.3 Thermografische Verfahren</collection>
    <collection role="themenfelder" number="">Chemie und Prozesstechnik</collection>
    <collection role="fulltextaccess" number="">Datei für die Öffentlichkeit verfügbar ("Open Access")</collection>
    <collection role="literaturgattung" number="">Graue Literatur</collection>
    <collection role="unnumberedseries" number="">Preprints der BAM</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/52537/2104.10563v1.pdf</file>
  </doc>
  <doc>
    <id>23167</id>
    <completedYear/>
    <publishedYear>2010</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>255</pageFirst>
    <pageLast>258</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>72</volume>
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    <publisherName/>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
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    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Time courses of HBCD levels and enantiomeric signatures in herring gull eggs from the German coast</title>
    <parentTitle language="eng">Dioxin 2010 - 30th International symposium on halogenated persistent organic pollutants (Proceedings)</parentTitle>
    <identifier type="old">25738</identifier>
    <identifier type="url">http://www.dioxin20xx.org/pdfs/2010/10-1112.pdf</identifier>
    <note>Serientitel: Organohalogen Compounds – Series title: Organohalogen Compounds</note>
    <enrichment key="bibliotheksstandort">Sonderstandort: Publica-Schrank</enrichment>
    <enrichment key="eventName">Dioxin 2010 - 30th International symposium on halogenated persistent organic pollutants</enrichment>
    <enrichment key="eventPlace">San Antonio, TX, USA</enrichment>
    <enrichment key="eventStart">2010-09-12</enrichment>
    <enrichment key="eventEnd">2010-09-17</enrichment>
    <author>Susanne Esslinger</author>
    <author>Roland Becker</author>
    <author>Christian Jung</author>
    <author>C. Schröter-Kermani</author>
    <author>Irene Nehls</author>
    <collection role="fulltextaccess" number="">Physisches Exemplar in der Bibliothek der BAM vorhanden ("Hardcopy Access")</collection>
    <collection role="literaturgattung" number="">Graue Literatur</collection>
  </doc>
  <doc>
    <id>58488</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
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    <issue/>
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    <type>researchdata</type>
    <publisherName>Zenodo</publisherName>
    <publisherPlace>Geneva</publisherPlace>
    <creatingCorporation>Bundesanstalt für Materialforschung und -prüfung (BAM)</creatingCorporation>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
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    <title language="eng">CRM BAM-A001 eCerto data file</title>
    <abstract language="eng">Data file (RData) containing measurement data recorded during the production process of the Certified Reference Material BAM-A001 containing Polycyclic Aromatic Hydrocarbons (PAH) in olive oil. The data can be most conveniently openend using the Shiny-App eCerto which is accessible at https://www.bam.de/eCerto.</abstract>
    <identifier type="doi">10.5281/zenodo.8380869</identifier>
    <identifier type="url">https://doi.org/10.5281/zenodo.8380870</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="ScientificResourceTypeGeneral">Datensatz</enrichment>
    <enrichment key="ScientificGeolocation">Berlin, Germany</enrichment>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Thomas Sommerfeld</author>
    <author>Matthias Koch</author>
    <author>Tatjana Mauch</author>
    <author>Christian Jung</author>
    <author>Juliane Riedel</author>
    <author>Jan Lisec</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Certified reference material</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mass spectrometry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Olive oil</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Aromatic hydrocarbons</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Reference data</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="institutes" number="">1 Analytische Chemie; Referenzmaterialien</collection>
    <collection role="institutes" number="">1.7 Organische Spuren- und Lebensmittelanalytik</collection>
    <collection role="themenfelder" number="">Chemie und Prozesstechnik</collection>
    <collection role="fulltextaccess" number="">Datei für die Öffentlichkeit verfügbar ("Open Access")</collection>
    <collection role="literaturgattung" number="">Graue Literatur</collection>
    <collection role="unnumberedseries" number="">Forschungsdatensätze der BAM</collection>
  </doc>
  <doc>
    <id>64854</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
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    <pageNumber/>
    <edition/>
    <issue/>
    <volume>11</volume>
    <type>article</type>
    <publisherName>Pensoft Publishers</publisherName>
    <publisherPlace/>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
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    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Proposal NFDI4Chem 2025-2030 In the National Research Data Infrastructure (NFDI) — Our Vision: All Chemists Publish FAIR Data</title>
    <abstract language="eng">The first funding period of NFDI4Chem established a robust foundation for research data management (RDM) in chemistry by promoting FAIR data principles and creating a cohesive infrastructure to capture well-annotated data early in the lab through electronic lab notebooks (ELNs) and making this data available in public repositories. Key achievements include standardised data formats and metadata, a federated repository environment, and improved data visibility and accessibility. Training programs and outreach have significantly increased awareness and adoption of best RDM practices. In the second funding period, the consortium aims to advance these achievements by consolidating this infrastructure, developing a model for its sustainable maintenance and operation, and fostering cultural change for its widespread adoption. Goals include ensuring seamless data workflows from laboratories to open repositories, enhancing interoperability, and supporting innovative research through AI-ready data. The work plan is organised into six task areas (TAs). TA1 (Management) provides leadership and supports all other TAs in achieving their objectives. TA2 (Smart Lab) aims to develop a fully digital research environment, including an ELN as a modular platform. This environment will support data collection, management, storage, analysis, and sharing. Integrating devices and external resources will enable seamless data transfer to repositories. TA3 (Repositories) will consolidate the repository ecosystem. The goal is to integrate repositories into a federated system for better accessibility and interoperability, ensuring long-term data availability and sustainability. TA4 (Metadata, Data Standards, and Publication Standards) focuses on developing and promoting new data and metadata standards in an international community process. This includes applying ontologies to create a semantic foundation for linking research data, making it machine-readable and enabling knowledge graphs. TA5 (Community and Training) is dedicated to fostering a cultural shift towards digital chemistry through continuous engagement, collecting requirements, and providing extensive training and support through workshops and open education resources. It will promote FAIR-compliant machine learning applications, embedding RDM into academic curricula to ensure future scientists are well-versed in these practices. TA6 (Synergies and Cross-Cutting Topics) aims to enhance collaboration across NFDI consortia and beyond. This includes developing ontologies, terminology services, the search service, and other cross-cutting solutions, integrating these developments into existing infrastructure, enabling interdisciplinary data harmonisation and fostering machine learning applications.</abstract>
    <parentTitle language="eng">Research Ideas and Outcomes</parentTitle>
    <identifier type="issn">2367-7163</identifier>
    <identifier type="doi">10.3897/rio.11.e177037</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-648540</identifier>
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