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ANSTO Publications Online

Welcome to the ANSTO Institutional Repository known as APO.

The APO database has been migrated to version 8.3. The functionality has changed, but the content remains the same.

ANSTO Publications Online is a digital repository for publications authored by ANSTO staff since 2007. The Repository also contains ANSTO Publications, such as Reports and Promotional Material. ANSTO publications prior to 2007 continue to be added progressively as they are in identified in the library. ANSTO authors can be identified under a single point of entry within the database. The citation is as it appears on the item, even with incorrect spelling, which is marked by (sic) or with additional notes in the description field.

If items are only held in hardcopy in the ANSTO Library collection notes are being added to the item to identify the Dewey Call number: as DDC followed by the number.

APO will be integrated with the Research Information System which is currently being implemented at ANSTO. The flow on effect will be permission to publish, which should allow pre-prints and post prints to be added where content is locked behind a paywall. To determine which version can be added to APO authors should check Sherpa Romeo. ANSTO research is increasingly being published in open access due mainly to the Council of Australian University Librarians read and publish agreements, and some direct publisher agreements with our organisation. In addition, open access items are also facilitated through collaboration and open access agreements with overseas authors such as Plan S.

ANSTO authors are encouraged to use a CC-BY licence when publishing open access. Statistics have been returned to the database and are now visible to users to show item usage and where this usage is coming from.

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Recent Submissions

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    Archaeometric investigations on manufacturing processes in ancient cultures with the neutron imaging station DINGO at ANSTO
    (Elsevier, 2017) Salvemini, F; Luzin, V; Grazzi, F; Olsen, SR; Sheedy, K; Gatenby, S; Kim, MJ; Garbe, U
    This paper focuses on recent archaeometric investigations conducted with the neutron imaging station DINGO at ANSTO. The synergic application of non-invasive scientific analytical methods is becoming a common practice in archaeometry and conservation science. Neutron tomography is playing a significant role in expanding the technical limits and investigation capabilities of traditional analytical methods. We discuss advantages and limitations of the technique through the discussion of results obtained from the investigation of artefacts produced by different ancient cultures. © 2017 The Author(s) Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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    Modelling the creep behaviour of a reheat header longitudinal weld
    (Elsevier, 2000-02) Law, M; Payten, WM; Small, R
    A method is suggested for deriving creep data with only limited material testing by comparison with a data base of similar, well-characterised materials. The results are used within a finite element code to derive stresses and predict the life of the component. © 2000 Elsevier Science Ltd.
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    Attenuated total reflection FTIR microspectroscopy at the Australian Synchrotron
    (Optica Publishing Group, 2016-11-14) Tobin, MJ; Bambery, KR; Martin, DE; Puskar, L; Beattie, DA; Ivanova, EP; Nguyen, SH; Webb, HK; Vongsvivut, JP
    ATR FTIR microspectroscopy enables the analysis of samples with lateral resolution greater than is possible by transmission FTIR. ATR accessories have been developed for the study of materials with multilayer coatings and fine surface structures. © 2016 Optical Society of America
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    Characterization of the residual stresses introduced by a new joining method in diamond and tungsten carbide composites
    (Elsevier, 2019-12) Lavigne, O; Luzin, V; Mendez, M; Malik, AS; Carrasco, O; Salvemini, F
    In this work, a co-sintering method was used to attach diamond to cemented carbide composites. The joining method consists of sintering a green part (ring) of cemented carbide (CC) around a thermally stable diamond composite (TSDC) part (plug) to radially contain it. During the sintering step, the green body shrinks to a controlled level and therefore forms interference fit between the two parts (mismatch between the inner diameter (ID) of the CC ring and the outer diameter (OD) of the TSDC cylinder). The residual stresses induced by this process as well as the bond strength between the CC and the TSDC parts were quantitatively evaluated. It is shown that the interface pressure between the two parts, and the level of residual stresses, increased with increase in the designed interference fit, as well as with the increase of the ID/OD ratios of the CC ring. For the chosen material combination (cemented carbide ring comprised 90 vol% WC and 10 vol% Co with medium coarse WC grains; diamond composite plug comprised 84 vol% diamond and 16 vol% SiC), the values of the hoop stresses at the interface in the CC ring measured by neutron diffraction was determined to be between 150 MPa and 550 MPa, depending on the ID/OD ratio. It was also found that for a given ID/OD ratio, the increase of the designed interference fit had little effect on that attained due to the plastic deformation of the cemented carbide material at the interface during the sintering (dynamic) process. A mechanical bond around 60 MPa was nevertheless achieved. © 2019 Published by Elsevier Ltd.
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    A general framework to govern machine learning oriented materials data quality
    (Elsevier BV, 2025-09) Liu, Y; Yang, ZW; Zou, XX; Lin, YX; Ma, SC; Zuo, W; Zou, Z; Wang, H; Avdeev, M; Shi, SQ
    Machine learning (ML) is increasingly applied in materials discovery and property prediction, mainly due to its advantage of low-cost and efficient data analysis process. The materials data quality can heavily influence the performance of ML models. However, most current data quality improvement approaches are purely data-driven, neglecting materials domain knowledge and data quality issues latent in the entire process of ML modelling. Here, we address the definition of high-quality data and propose a general framework for ML-oriented MATerials Data Quality Governance incorporating domain knowledge (MAT-DQG), involving nine dimensions defining WHAT materials data quality should be evaluated, lifecycle models guiding WHEN to execute data governance activities in the entire process of ML modelling, and processing models guiding HOW to detect and address issues related to materials data quality. 60 datasets from materials ML studies are assembled to demonstrate potential utility and applications of MAT-DQG, including mining complicated structure-activity relationships in metals, inorganic non-metals, polymers, and composite materials. MAT-DQG identifies and resolves issues in 17 datasets and as a result prediction accuracy improvements of up to 49 % are achieved. Our work lays a foundation for governing ML-oriented materials data and ensuring its reusability and reliability, which advances the frontiers of materials discovery and design. © 2025 Elsevier B.V. All rights are reserved.