Engineering topics
Olszta, Matthew J.
Publications and source records attributed to Olszta, Matthew J..
Computer vision models and advanced TEM imaging for microstructures of irradiated AM316 stainless steels
Advancements were made in automating microscopy-based material characterization, particularly in studying irradiation effects on additively manufactured (AM) materials using machine learning (ML) and computer vision (CV). These automation efforts address the challenges of analyzing complex microstructures, accelerating the detection of irradiation-induced defects. Two CV models were developed at Argonne National Laboratory (ANL) to enhance transmission electron microscopy (TEM) analysis of irradiated AM 316 stainless steel. The first model focused on the detection of irradiation-induced dislocation loops, which contribute to material hardening and embrittlement. These loops, categorized as faulted or perfect, were automatically detected and classified using a Mask R-CNN model trained on TEM images from both in-situ and ex-situ ion irradiation experiments. The model achieved high accuracy, with precision, recall, and F1 scores of 0.839, 0.734, and 0.776, respectively, demonstrating its effectiveness in analyzing dislocation loops in irradiated AM materials. The second CV model was developed to analyze the size and wall thickness of dislocation cells in laser powder bed fusion (LPBF) 316 stainless steel. Using a U-Net++ architecture with EfficientNet as the encoder, the model was trained on TEM images to segment and measure cell size and wall thickness.
Preliminary Characterization and Evaluation on ShAPE Manufactured 316H and ODS Steels
This study provides the first- of- a- kind results of direct tube formation through shear assisted processing and extrusion (ShAPE) for oxide dispersion strengthened (ODS) steel material; previously only bar was successfully made. The Advanced Materials and Manufacturing Technology (AMMT) program develops cross-cutting technologies in support of a broad range of nuclear reactor technologies and maintains U.S. leadership in materials and manufacturing technologies for nuclear energy applications. The overarching vision of AMMT is to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy. Solid-state advanced manufacturing techniques can overcome some of the challenges in liquid-based additive manufacturing processes and should therefore be considered in material design and manufacturing as well. The work presented in this report forms part of a study on solid-state additive manufacturing techniques of 316 stainless steels and ODS steel components and supports the vision and goals of the AMMT program relevant to accelerate the development and deployment of advanced manufacturing processes. Achieving this can provide a safety improvement through larger safety margins, economic benefit for higher efficiency during operation, and a cost reduction through more effective manufacturing processes and less waste.
Materials Characterization, Prediction, and Control Project: Summary Report on Material Characterization, Part 4
The Pacific Northwest National Laboratory (PNNL) undertook the Materials Characterization, Prediction, and Control (MCPC) Laboratory Directed Research and Development Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems in national security and advanced energy applications (Smith 2021). The MCPC Project executed research across three scientific vertices—material characterization, predictive modeling, and data analytics—with extensive support by a data curation and management team. The central technical objective in the MCPC Project was to improve the prediction and characterization of the process-structure-property relationships within the microstructurally refined region of stainless-steel samples prepared utilizing friction stir processing (FSP). Application of the FSP technique is well established at PNNL within the Solid Phase Processing capability through many years of investment across a range of materials and applications (PNNL 2024).
Materials Characterization, Prediction, and Control Project: Summary Report on Material Characterization, Part 1
The Pacific Northwest National Laboratory (PNNL) undertook the Materials Characterization, Prediction, and Control (MCPC) Laboratory Directed Research and Development Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems in national security and advanced energy applications (Smith 2021). The MCPC Project executed research across three scientific vertices—material characterization, predictive modeling, and data analytics—with extensive support by a data curation and management team. The central technical objective in the MCPC Project was to improve the prediction and characterization of the process-structure-property relationships within the microstructurally refined region of stainless-steel samples prepared utilizing friction stir processing (FSP). Application of the FSP technique is well established at PNNL within the Solid Phase Processing capability through many years of investment across a range of materials and applications (PNNL 2024). Three distinct rounds of FSP experiments were performed by the experimental team, producing replicate samples utilizing across different nominal processing conditions (Condition IDs) listed in Table 1. The starting material on which FSP was applied was commercially available unprocessed stainless-steel type 316L material. Chosen processing conditions were very diverse, and some were intentionally chosen to produce defects. Several samples experienced tool breakage during experimentation, so a full set of three replicates was not produced for every nominal processing condition.
Materials Characterization, Prediction, and Control Project: Summary Report on Material Characterization, Part 2
The Pacific Northwest National Laboratory (PNNL) undertook the Materials Characterization, Prediction, and Control (MCPC) Laboratory Directed Research and Development Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems in national security and advanced energy applications (Smith 2021). The MCPC Project executed research across three scientific vertices—material characterization, predictive modeling, and data analytics—with extensive support by a data curation and management team. The central technical objective in the MCPC Project was to improve the prediction and characterization of the process-structure-property relationships within the microstructurally refined region of stainless-steel samples prepared utilizing friction stir processing (FSP). Application of the FSP technique is well established at PNNL within the Solid Phase Processing capability through many years of investment across a range of materials and applications (PNNL 2024).
Materials Characterization, Prediction, and Control Project: Summary Report on Material Characterization, Part 3
The Pacific Northwest National Laboratory (PNNL) undertook the Materials Characterization, Prediction, and Control (MCPC) Laboratory Directed Research and Development Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems in national security and advanced energy applications (Smith 2021). The MCPC Project executed research across three scientific vertices—material characterization, predictive modeling, and data analytics—with extensive support by a data curation and management team. The central technical objective in the MCPC Project was to improve the prediction and characterization of the process-structure-property relationships within the microstructurally refined region of stainless-steel samples prepared utilizing friction stir processing (FSP). Application of the FSP technique is well established at PNNL within the Solid Phase Processing capability through many years of investment across a range of materials and applications (PNNL 2024).
Impact of environmental oxygen on nanoparticle formation and agglomeration in aluminum laser ablation plumes
Here, the role of ambient oxygen gas (O 2 ) on molecular and nanoparticle formation and agglomeration was studied in laser ablation plumes. As a lab-scale surrogate to a high explosion detonation event, nanosecond laser ablation of an aluminum alloy (AA6061) target was performed in atmospheric pressure conditions. Optical emission spectroscopy and two mass spectrometry techniques were used to monitor the early to late stages of plasma generation to track the evolution of atoms, molecules, clusters, nanoparticles, and agglomerates. The experiments were performed under atmospheric pressure air, atmospheric pressure nitrogen, and 20% and 5% O 2 (balance N 2 ), the latter specifically with in situ mass spectrometry. Electron microscopy was performed ex situ to identify crystal structure and elemental distributions in individual nanoparticles. We find that the presence of ≈20% O 2 leads to strong AlO emission, whereas in a flowing N 2 environment (with trace O 2 ), AlN and strong, unreacted Al emissions are present. In situ mass spectrometry reveals that as O 2 availability increases, Al oxide cluster size increases. Nanoparticle agglomerates formed in air are found to be larger than those formed under N 2 gas. High-resolution transmission electron microscopy demonstrates that Al 2 O 3 and AlN nanoparticle agglomerates are formed in both environments; indicating that the presence of trace O 2 can lead to Al 2 O 3 nanoparticle formation. The present results highlight that the availability of O 2 in the ambient gas significantly impacts spectral signatures, cluster size, and nanoparticle agglomeration behavior. These results are relevant to understanding debris formation in an explosion event, and interpreting data from forensic investigations.
Role of phosphorus impurities in decomposition of La 2 NiO 4 –La 0.5 Ce 0.5 O 2-δ oxygen electrode in a solid oxide electrolysis cell
This study explored the decomposition mechanism of a La 2 NiO 4 (LNO) phase in the La 2 NiO 4 –La 0.5 Ce 0.5 O 2-δ (LNO-LDC) oxygen electrode in a solid oxide electrolysis cell (SOEC) after testing at 800 °C. Scanning electron microscopy and scanning transmission electron microscopy examinations of the LNO-LDC oxygen electrode before and after testing were undertaken. Other than phosphorus contamination in the form of P-rich grains and P-rich deposits along all grain boundaries (GBs), LNO and LDC phases were intact without degradation in the as-fabricated electrode. However, mild to aggressive LNO phase decomposition triggered by the phosphorus poisoned GBs was observed after testing at 800 °C for 900 h. The evolution of the LNO phase decomposition was noted beginning with the exsolution of Ni into the surrounding LNO matrix and GBs, forming La-rich and Ni-rich phases correspondingly, in the LNO. Importantly, this study illustrates a detailed decomposition progress of the LNO phase at the atomic level under an SOEC operation condition, and sheds light on how to ameliorate the fabrication process of SOECs to enhance their performance and durability.
Revealing the Latent Atomic World Through Data-Driven Microscopy
Many emerging technologies depend on the precise design of materials structure, chemistry, and defects. As devices shrink, manufacturing tolerances tighten, and performance envelopes improve, we must increasingly measure and manipulate materials at or near the single atom level. Here we describe how transmission electron microscopy (TEM) underpins our ability to see and direct the latent atomic world. We review a selection of our recent high-resolution TEM studies of the synthesis of oxide-based nanomaterials and their evolution in extreme environments. We then discuss powerful new artificial intelligence (AI) and machine learning (ML) approaches we have developed for rich, reproducible, and scalable experimentation. Furthermore, we conclude by discussing future developments that will enable new materials for breakthrough technologies.
Initial demonstration of computer vision models for defect quantification of TEM data
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Visualizing oxygen transport pathways during intergranular oxidation in Ni-Cr
The transport paths of O during intergranular oxidation in binary Ni-Cr were investigated. To isolate the selective oxidation of Cr, oxidation was performed with a CO/CO 2 gas mixture in which the oxygen partial pressure was kept under the NiO dissociation pressure. A combination of electron microscopy and atom probe tomography (APT) was used to study the nanometer-scale details of the passivation and penetrative intergranular oxidation processes at high-energy grain boundaries. Oxygen transport towards the terminating oxidation front is elucidated with dedicated usage of oxygen tracer exchange experiments. Secondary ion mass spectroscopy and APT support classical theories of internal oxidation, revealing preferred transport paths at the oxide/alloy interface with sub-nanometer resolution.
TiltEM User Manual
The ability to automate the scanning transmission electron microscope (S/TEM) is tantamount to addressing next generation artificial intelligence, machine learning, and materials modeling capabilities. Constant utilization of these high-end capital equipment purchases also serves the requirements of the Department of Energy (DOE) to be fiscally responsible to the public. The development of an automated multi-modal tilt series algorithm for dark field/bright field S/TEM imaging plus chemical identification with energy dispersive x-ray spectroscopy has been achieved at PNNL. A tilt series workflow has now been generated which allows for autonomous collection of imaging and compositional information simultaneously. This advancement is due in large part to a transition from the rigid and microscope specific JEOL hardware environment, to the more adaptable GMS environment. This has thereby led to a greater degree of flexibility in the application of this method across platforms in addition to substantial time savings to the user.
Characterization of boundary precipitation in a heavy ion irradiated tungsten heavy alloy under the simulated fusion environment
In the concerted effort to identify materials capable of surviving the adverse environment of a fusion reactor interior, tungsten heavy alloys have been put forth as leading candidates. Experimental trials and behavioral studies have yielded positive results for their adoption by taking advantage of the alloy’s unique balance of inherently high fracture toughness and low sputter yields; yet due to their relative novelty in the fusion community, there remains a lack of understanding on the response of these materials to the extended high temperature irradiation environment of the reactor interior. Further, to alleviate this issue and provide the necessary data on the behavior of tungsten heavy alloys to the simulated fusion environment, a 90W-7Ni-3Fe alloy has been subjected to elevated temperature sequential Ni + and He + ion irradiations to mimic the expected displacement damage and gas production expected after five years of service as a plasma facing material component. Atomic-scale structural analyses and nanoscale chemical mapping have identified the formation of two distinct precipitation structures, a surface localized η-carbide and an a-W 2 C type tungsten carbide, both of which appear to originate at the bi-phase interface between W and the ductile phase. This irradiation assisted and induced precipitate formation respectively is anticipated to adversely affect these materials by selective embrittlement at bi-phase interfaces leading to a reduction in the material’s overall fracture toughness during prolonged high temperature irradiation. It is asserted that any potential exposure to C during fusion reactor operational service should be minimized as to prevent the formation of these phases.