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    <id>57550</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName>Wiley-VCH GmbH</publisherName>
    <publisherPlace>Weinheim</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
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    <title language="eng">The Intersection Between Semantic Web and Materials Science</title>
    <abstract language="eng">The application and benefits of Semantic Web Technologies (SWT) for managing, sharing, and (re-)using of research data are demonstrated in implementations in the field of Materials Science and Engineering (MSE). However, a compilation and classification are needed to fully recognize the scattered published works with its unique added values. Here, the primary use of SWT at the interface with MSE is identified using specifically created categories. This overview highlights promising opportunities for the application of SWT to MSE, such as enhancing the quality of experimental processes, enriching data with contextual information in knowledge graphs, or using ontologies to perform specific queries on semantically structured data. While interdisciplinary work between the two fields is still in its early stages, a great need is identified to facilitate access for nonexperts and develop and provide user-friendly tools and workflows. The full potential of SWT can best be achieved in the long term by the broad acceptance and active participation of the MSE community. In perspective, these technological solutions will advance the field of MSE by making data FAIR. Data-driven approaches will benefit from these data structures and their connections to catalyze knowledge generation in MSE.</abstract>
    <parentTitle language="eng">Advanced Intelligent Systems</parentTitle>
    <identifier type="doi">10.1002/aisy.202300051</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-575506</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">05.06.2023</enrichment>
    <enrichment key="PaperofMonth">1</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Andre Valdestilhas</author>
    <author>Bernd Bayerlein</author>
    <author>Benjami Moreno Torres</author>
    <author>Ghezal Ahmad Jan Zia</author>
    <author>Thilo Muth</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Linked open data</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Materials science</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Ontology</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Semantic web</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="ddc" number="620">Ingenieurwissenschaften und zugeordnete Tätigkeiten</collection>
    <collection role="institutes" number="">5 Werkstofftechnik</collection>
    <collection role="institutes" number="">5.2 Metallische Hochtemperaturwerkstoffe</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
    <collection role="themenfelder" number="">Material</collection>
    <collection role="themenfelder" number="">Chemie und Prozesstechnik</collection>
    <collection role="literaturgattung" number="">Verlagsliteratur</collection>
    <collection role="fulltextaccess" number="">Datei für die Öffentlichkeit verfügbar ("Open Access")</collection>
    <collection role="unnumberedseries" number="">Wissenschaftliche Artikel der BAM</collection>
    <collection role="institutes" number="">8.0 Abteilungsleitung und andere</collection>
    <collection role="themenfelder" number="">Materialdesign</collection>
    <collection role="institutes" number="">VP Vizepräsident</collection>
    <collection role="institutes" number="">VP.1 eScience</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/57550/Valdestilhas and Bayerlein - The Intersection Between SW and Materials Science.pdf</file>
  </doc>
  <doc>
    <id>58996</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1233</pageFirst>
    <pageLast>1250</pageLast>
    <pageNumber/>
    <edition/>
    <issue>5</issue>
    <volume>2</volume>
    <type>article</type>
    <publisherName>Royal Society of Chemistry (RSC)</publisherName>
    <publisherPlace/>
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    <title language="eng">14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon</title>
    <abstract language="eng">Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.</abstract>
    <parentTitle language="eng">Digital Discovery</parentTitle>
    <identifier type="doi">10.1039/d3dd00113j</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-589961</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Kevin Maik Jablonka</author>
    <author>Qianxiang Ai</author>
    <author>Alexander Al-Feghali</author>
    <author>Shruti Badhwar</author>
    <author>Joshua D. Bocarsly</author>
    <author>Andres M. Bran</author>
    <author>Stefan Bringuier</author>
    <author>L. Catherine Brinson</author>
    <author>Kamal Choudhary</author>
    <author>Defne Circi</author>
    <author>Sam Cox</author>
    <author>Wibe A. de Jong</author>
    <author>Matthew L. Evans</author>
    <author>Nicolas Gastellu</author>
    <author>Jerome Genzling</author>
    <author>María Victoria Gil</author>
    <author>Ankur K. Gupta</author>
    <author>Zhi Hong</author>
    <author>Alishba Imran</author>
    <author>Sabine Kruschwitz</author>
    <author>Anne Labarre</author>
    <author>Jakub Lála</author>
    <author>Tao Liu</author>
    <author>Steven Ma</author>
    <author>Sauradeep Majumdar</author>
    <author>Garrett W. Merz</author>
    <author>Nicolas Moitessier</author>
    <author>Elias Moubarak</author>
    <author>Beatriz Mouriño</author>
    <author>Brenden Pelkie</author>
    <author>Michael Pieler</author>
    <author>Mayk Caldas Ramos</author>
    <author>Bojana Ranković</author>
    <author>Samuel G. Rodriques</author>
    <author>Jacob N. Sanders</author>
    <author>Philippe Schwaller</author>
    <author>Marcus Schwarting</author>
    <author>Jiale Shi</author>
    <author>Berend Smit</author>
    <author>Ben E. Smith</author>
    <author>Joren Van Herck</author>
    <author>Christoph Völker</author>
    <author>Logan Ward</author>
    <author>Sean Warren</author>
    <author>Benjamin Weiser</author>
    <author>Sylvester Zhang</author>
    <author>Xiaoqi Zhang</author>
    <author>Ghezal Ahmad Jan Zia</author>
    <author>Aristana Scourtas</author>
    <author>K. J. Schmidt</author>
    <author>Ian Foster</author>
    <author>Andrew D. White</author>
    <author>Ben Blaiszik</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Large Language model</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Hackathon</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Concrete</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Prediction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Inverse Design</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Orchestration</value>
    </subject>
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  <doc>
    <id>58999</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>180</pageFirst>
    <pageLast>187</pageLast>
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    <edition/>
    <issue/>
    <volume>9</volume>
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    <title language="eng">Presenting SLAMD – A Sequential Learning Based Software for the Inverse Design of Sustainable Cementitious Materials</title>
    <abstract language="eng">In recent decades, the number of components in concrete has grown, particularly in formulations aimed at reducing carbon footprints. Innovations include diverse binders, supplementary cementitious materials, activators, concrete admixtures, and recycled aggregates. These developments target not only the enhancement of material properties but also the mitigation of the ecological and economic impacts of concrete — the most extensively used material by humankind. However, these advancements also introduce a greater variability in the composition of raw materials. The material’s behavior is significantly influenced by its nanoscale properties, which can pose challenges in accurate characterization. Consequently, there’s an increasing need for experimental tuning of formulations. This is accompanied by a more inconsistent composition of raw materials, which makes an experimental tuning of formulations more and more necessary. However, the increased complexity in composition presents a challenge in finding the ideal formulation through trial and error. Inverse design (ID) techniques offer a solution to this challenge by allowing for a comprehensive search of the entire design space to create new and improved concrete formulations. In this publication, we introduce the concept of ID and demonstrate how our open-source app “SLAMD” provides all necessary steps of the workflow to adapt it in the laboratory, lowering the application barriers. The intelligent screening process, guided by a predictive model, leads to a more efficient and effective data-driven material design process resulting in reduced carbon footprint and improved material quality while considering socio-economic factors in the materials design.</abstract>
    <parentTitle language="eng">NanoWorld Journal</parentTitle>
    <identifier type="doi">10.17756/nwj.2023-s2-032</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-589993</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Christoph Völker</author>
    <author>Benjami Moreno Torres</author>
    <author>Ghezal Ahmad Jan Zia</author>
    <author>Tehseen Rug</author>
    <author>Rafia Firdous</author>
    <author>Felix Böhmer</author>
    <author>Dietmar Stephan</author>
    <author>Sabine Kruschwitz</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sustainable concrete</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine learning optimization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Inverse design techniques</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Scientific software</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data-driven material design</value>
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    <file>https://opus4.kobv.de/opus4-bam/files/58999/Published_Version_Presenting_SLAMD_Voelker_2023.pdf</file>
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    <publishedYear>2023</publishedYear>
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    <language>eng</language>
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    <volume>418</volume>
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    <publisherName>Elsevier</publisherName>
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    <title language="eng">Data driven design of alkali-activated concrete using sequential learning</title>
    <abstract language="eng">This paper presents a novel approach for developing sustainable building materials through Sequential Learning. Data sets with a total of 1367 formulations of different types of alkali-activated building materials, including fly ash and blast furnace slag-based concrete and their respective compressive strength and CO2-footprint, were compiled from the literature to develop and evaluate this approach. Utilizing this data, a comprehensive computational study was undertaken to evaluate the efficacy of the proposed material design methodologies, simulating laboratory conditions reflective of real-world scenarios. The results indicate a significant reduction in development time and lower research costs enabled through predictions with machine learning. This work challenges common practices in data-driven materials development for building materials. Our results show, training data required for data-driven design may be much less than commonly suggested. Further, it is more important to establish a practical design framework than to choose more accurate models. This approach can be immediately implemented into practical applications and can be translated into significant advances in sustainable building materials development.</abstract>
    <parentTitle language="eng">Journal of Cleaner Production</parentTitle>
    <identifier type="url">https://www.sciencedirect.com/science/article/pii/S095965262302379X</identifier>
    <identifier type="doi">10.1016/j.jclepro.2023.138221</identifier>
    <identifier type="issn">0959-6526</identifier>
    <identifier type="issn">1879-1786</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-584376</identifier>
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    <enrichment key="date_peer_review">26.09.2023</enrichment>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Christoph Völker</author>
    <author>Benjami Moreno Torres</author>
    <author>Tehseen Rug</author>
    <author>Rafia Firdous</author>
    <author>Ghezal Ahmad Jan Zia</author>
    <author>Stefan Lüders</author>
    <author>Horacio Lisdero Scaffino</author>
    <author>Michael Höpler</author>
    <author>Felix Böhmer</author>
    <author>Matthias Pfaff</author>
    <author>Dietmar Stephan</author>
    <author>Sabine Kruschwitz</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sustainable building materials</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sequential learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data-driven materials design</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Alkali-activated building materials</value>
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    <file>https://opus4.kobv.de/opus4-bam/files/58437/data_driven_design.pdf</file>
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    <publishedYear>2024</publishedYear>
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    <title language="eng">Reincarnate: Shaping a sustainable future in construction through digital innovation</title>
    <abstract language="eng">We introduce the REINCARNATE project, funded by the European Union's Horizon Europe program, to boost circularity by merging digital innovations with practical applications and a focus on material reuse. The heart of REINCARNATE is the Circular Potential Information Model (CP-IM), a digital platform designed to assess and enhance the recyclability of construction materials, construction products, and buildings. The CP-IM integrates advanced technologies such as digital twins, AI, and robotics to revolutionize the handling of construction waste, turning it into valuable resources and cutting the environmental footprint of the sector. Among its features are digital tracing, material durability predictions, and CO2 reduction materials design. These are showcased in eleven European demonstration projects, highlighting the practical benefits of these technologies in reducing construction waste and CO2 emissions by up to 80% and 70% respectively. REINCARNATE aims to marry innovation with real-world application, providing the construction industry with strategies for sustainable and circular practices.</abstract>
    <enrichment key="eventName">Rilem Spring Convention</enrichment>
    <enrichment key="eventPlace">Milan, Italy</enrichment>
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    <author>Sabine Kruschwitz</author>
    <author>Christoph Völker</author>
    <author>Ghezal Ahmad Jan Zia</author>
    <author>Benjami Moreno Torres</author>
    <author>Timo Hartmann</author>
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      <language>eng</language>
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      <value>Construction sustainability</value>
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    <title language="eng">An Adaptive Upscaling Approach for Assessing Materials’ Circularity Potential with Non-destructive Testing (NDT)</title>
    <abstract language="eng">Advancing towards a circular economy necessitates the efficient reuse and maintenance of structural materials, which relies on accurate, non-damaging condition assessments. This paper introduces an innovative AI-driven adaptive sampling (AS) technique integrated with Non-Destructive Testing (NDT) to optimize this process. AS focuses on critical data points, reducing the amount of data needed for precise assessments—evidenced by our method requiring on average only 7 samples for Logistic Regression and 8 for Random Forest, contrasted with 29 for traditional sampling.&#13;
By reducing the necessity for extensive data collection, our method not only streamlines the assessment process but also significantly contributes to the sustainability goals of the circular economy. These goals include resource efficiency, waste reduction, and material reuse. Efficient condition assessments promote infrastructure longevity, reducing the need for new materials and the associated environmental impact.&#13;
The circular economy aims to create a sustainable system where resources are reused, and waste is minimized. This is achieved by extending the lifecycle of materials, reducing the environmental footprint, and promoting recycling and reuse. Longevity directly contributes to the circular economy by maximizing the utility and lifespan of existing materials and structures. Longer-lasting infrastructure means fewer resources are needed for repairs or replacements, leading to reduced material consumption and waste generation. This aligns with the circular economy's principles of sustainability and resource efficiency. This research not only advances the field of structural health monitoring but also aligns with the broader objective of enhancing sustainable construction practices within the circular economy framework.</abstract>
    <parentTitle language="eng">Proceedings of the RILEM Spring Convention and Conference 2024</parentTitle>
    <identifier type="issn">2211-0844</identifier>
    <identifier type="isbn">978-3-031-70277-8</identifier>
    <identifier type="doi">10.1007/978-3-031-70277-8_38</identifier>
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    <author>Ghezal Ahmad Jan Zia</author>
    <author>Christoph Völker</author>
    <author>Benjami Moreno Torres</author>
    <author>Sabine Kruschwitz</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Adaptive Sampling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Random Sampling</value>
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    <subject>
      <language>eng</language>
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      <value>Machine Learning</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Non-Destructive Testing</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Condition Assessment</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Circular Economy</value>
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    <title language="eng">REINCARNATE: Shaping a Sustainable Future in Construction Through Digital Innovation</title>
    <abstract language="eng">We introduce the REINCARNATE project, funded by the European Union’s Horizon Europe program, to boost circularity by merging digital innovations with practical applications and a focus on material reuse. The heart of REINCARNATE is the Circular Potential Information Model (CP-IM), a digital platform designed to assess and enhance the recyclability of construction materials, construction products, and buildings. The CP-IM integrates advanced technologies such as digital twins, AI, and robotics to revolutionize the handling of construction waste, turning it into valuable resources and cutting the environmental footprint of the sector. Among its features are digital tracing, material durability predictions, and CO2 reduction materials design. These are showcased in eleven European demonstration projects, highlighting the practical benefits of these technologies in reducing construction waste and CO2 emissions by up to 80% and 70%  respectively. REINCARNATE aims to marry innovation with real-world application, providing the construction industry with strategies for sustainable and circular practices.</abstract>
    <parentTitle language="eng">Proceedings of the RILEM Spring Convention and Conference 2024</parentTitle>
    <identifier type="issn">2211-0844</identifier>
    <identifier type="isbn">978-3-031-70280-8</identifier>
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    <author>Sabine Kruschwitz</author>
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      <language>eng</language>
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      <value>Llife cycle</value>
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      <language>eng</language>
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      <value>Recycled materials</value>
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    <title language="eng">Leveraging large language models for automated knowledge graphs generation in non-destructive testing</title>
    <abstract language="eng">This paper presents an innovative approach for the automatic generation of Knowledge Graphs (KGs) from heterogeneous scientific articles in the domain of Non-Destructive Testing (NDT) applied to building materials.&#13;
&#13;
Our methodology leverages large language models (LLMs) to extract and semantically relate concepts from diverse sources. We developed material-specific agents for concrete, wood, steel, and bricks, each equipped with a curated glossary of terms to ensure domain accuracy. These agents process PDF documents, extracting relevant information on deterioration mechanisms, physical changes, and applicable NDT methods. The extracted data is then normalized, validated, and structured into a Neo4j graph database, forming a comprehensive KG. Our results demonstrate the system’s ability to automatically discover and represent intricate relationships between materials, deterioration mechanisms, physical changes, and NDT techniques. The generated KG successfully captures complex interactions, such as the applicability of specific NDT methods to various materials under different deterioration conditions. This work not only highlights the potential of KGs in enhancing knowledge discovery and representation in NDT research but also provides a scalable framework for extending this approach to other scientific domains.</abstract>
    <parentTitle language="eng">SeMatS 2024 - Proceedings of the first international workshop on semantic materials science: Harnessing the power of semantic web technologies in materials science</parentTitle>
    <identifier type="issn">1613-0073</identifier>
    <identifier type="doi">10.5281/zenodo.13834164</identifier>
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    <author>Ghezal Ahmad Jan Zia</author>
    <author>Andre Valdestilhas</author>
    <author>Benjami Moreno Torres</author>
    <author>Sabine Kruschwitz</author>
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      <value>Data interoperability</value>
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    <subject>
      <language>eng</language>
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      <value>RDF</value>
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    <subject>
      <language>eng</language>
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      <value>Semantic web</value>
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    <title language="deu">KI und Robotik im Dienst der Nachhaltigkeit: Beschleunigung innovativer Lösungen im Bausektor</title>
    <abstract language="deu">Der Vortrag beschäftigt sich mit der Implementierung fortschrittlicher Technologien in neue Wertschöpfungsketten im Bausektor, insbesondere im Bereich Recycling, zirkuläres Produktdesign und Lebenszustandsanalyse. Im Zentrum stehen Industrie- und Grundlagenforschungsprojekte an der Schnittstelle zwischen Wissenschaft und praktischer Anwendung.</abstract>
    <enrichment key="eventName">DigiCon 2024</enrichment>
    <enrichment key="eventPlace">Munich, Germany</enrichment>
    <enrichment key="eventStart">21.11.2024</enrichment>
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    <author>Sarah Mandy Munsch</author>
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    <author>Ghezal Ahmad Jan Zia</author>
    <author>Katrin Schumacher</author>
    <author>Kaleb Yared</author>
    <author>Wolfram Schmidt</author>
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      <value>Baumaterial</value>
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    <publishedYear>2022</publishedYear>
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    <title language="eng">Quantitative Precipitate Analysis of an Age-Hardenable Aluminium Alloy Using a Deep Learning Approach</title>
    <abstract language="eng">Mechanical properties of metals and their alloys are strongly governed by their microstructure. The nanometer-sized precipitates in hardenable wrought aluminium alloys, which can be controlled by heat treatment, act as obstacles to dislocation movement within the material and are critical to the mechanical performance of the component, in this case a radial compressor wheel of a ships’ engine. TEM-based image analysis is essential for the study to investigate the microstructural changes (precipitation coarsening) that occur as a result of ageing at elevated temperatures.</abstract>
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    <author>Ghezal Ahmad Jan Zia</author>
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    <subject>
      <language>eng</language>
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      <value>Deep Learning</value>
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    <subject>
      <language>eng</language>
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    <title language="eng">Enhancing Reproducibility in Precipitate Analysis: A FAIR Approach with Automated Dark-Field Transmission Electron Microscope Image Processing</title>
    <abstract language="eng">AbstractHigh-strength aluminum alloys used in aerospace and automotive applications obtain their strength through precipitation hardening. Achieving the desired mechanical properties requires precise control over the nanometer-sized precipitates. However, the microstructure of these alloys changes over time due to aging, leading to a deterioration in strength. Typically, the size, number, and distribution of precipitates for a quantitative assessment of microstructural changes are determined by manual analysis, which is subjective and time-consuming. In our work, we introduce a progressive and automatable approach that enables a more efficient, objective, and reproducible analysis of precipitates. The method involves several sequential steps using an image repository containing dark-field transmission electron microscopy (DF-TEM) images depicting various aging states of an aluminum alloy. During the process, precipitation contours are generated and quantitatively evaluated, and the results are comprehensibly transferred into semantic data structures. The use and deployment of Jupyter Notebooks, along with the beneficial implementation of Semantic Web technologies, significantly enhances the reproducibility and comparability of the findings. This work serves as an exemplar of FAIR image and research data management.</abstract>
    <parentTitle language="eng">Integrating Materials and Manufacturing Innovation</parentTitle>
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    <author>Ghezal Ahmad Jan Zia</author>
    <author>Thomas Hanke</author>
    <author>Birgit Skrotzki</author>
    <author>Christoph Völker</author>
    <author>Bernd Bayerlein</author>
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      <type>uncontrolled</type>
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      <value>Automated image analysis</value>
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      <value>FAIR research data management</value>
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      <type>uncontrolled</type>
      <value>Reproducibility</value>
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      <language>eng</language>
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      <value>microstructural changes</value>
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