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Watts, Jeremy

Publications and source records attributed to Watts, Jeremy.

6 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Metal-to-Ceramic Joining Methods to Support Development of Advanced Ceramic-Based CSP Components

The National Renewable Energy Laboratory, Missouri University of Science and Technology (MS&T), Massachusetts Institute of Technology (MIT), and Colorado School of Mines (CSM) collaborated to design, develop, and test a material concept at bench scale which will be used to achieve a ceramic-to-metal (C2M) joint between a selected metal HTF loop material and a selected ceramic material used by the Gen3 CSP technology pathway. The final joint assembly will need to possess sufficient mechanical properties to withstand static high-temperature (650 degrees-700 degrees C) and high-pressure (20 MPa) and thermal cycling (between 650 degrees C and 100 degrees C) conditions. The material concept consists of three key components: (1) a ceramic matrix composite (CMC) that serves as a compliant transition material aiming to mitigate the stresses due to the mismatch of coefficient of thermal expansion (CTE) from a direct ceramic-to-metal joint, (2) a metal-end joint utilizing a multi-principal element alloy (MPEA) with changing percentage of particle loading to bond the candidate metal to the CMC, and (3) a ceramic-end joint utilizing a glass ceramic to bond the candidate ceramic to the CMC.

14 SOLAR ENERGY↗
DOE OSTI2022

Particle Migration in Large Cross-Section Ceramic On-Demand Extrusion Components

Ceramic On-Demand Extrusion (CODE) is a direct ink writing process which allows for the creation of near theoretically dense ceramic components with large cross-sections due to oil-assisted drying. Here, Yttria-stabilized zirconia (YSZ) colloidal pastes were used in CODE to produce dense (multi-road infill and ≳ 98% relative density), large continuous volume (> 1 cm 3 ), and high fidelity (nozzle diameters ≲ 1 mm) structural ceramic components with nanoparticle feedstocks (~d 50 ≲ 1 µm). However, many of these printed components underwent significant particle migration after forming. The reason for this particle migration defect was investigated using the coffee-ring effect for dilute solutions and rheological methods for dense suspensions. Modifications to the colloidal paste, such as changes in solids loading, pH, or surfactant concentration were explored as to their effectiveness to mitigate the defect. Ultimately, paste formulation and printing trade-offs are discussed with respect to the post-printing defect and as to general direct-write patterning.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Thermal properties of HfN-MoW surrogate cermet fuel for nuclear thermal propulsion

Here, the thermal properties of MoW-HfN, a surrogate cermet for MoW-UN nuclear thermal propulsion fuel, were characterized over a wide range of elevated temperatures. Thermal diffusivity, coefficient of thermal expansion (CTE), and heat capacity were measured. Optical and scanning electron microscopy were performed to characterize the microstructure and draw structure–property correlations. The thermal diffusivity was obtained using the laser flash method. Diffusivity values ranged from about 0.18 cm 2 /s at 200°C to 0.15 cm 2 /s at 1800°C. The CTE was measured using push-rod dilatometry up to 1600°C, giving values between 6.0 and 9.0 μm/m. A scientific rationalization of the effective material properties is made using the rule-of-mixtures and other effective properties models.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Improving Medication Regimen Recommendation for Parkinson’s Disease Using Sensor Technology

Parkinson’s disease medication treatment planning is generally based on subjective data obtained through clinical, physician-patient interactions. The Personal KinetiGraph™ (PKG) and similar wearable sensors have shown promise in enabling objective, continuous remote health monitoring for Parkinson’s patients. In this proof-of-concept study, we propose to use objective sensor data from the PKG and apply machine learning to cluster patients based on levodopa regimens and response. The resulting clusters are then used to enhance treatment planning by providing improved initial treatment estimates to supplement a physician’s initial assessment. We apply k-means clustering to a dataset of within-subject Parkinson’s medication changes—clinically assessed by the MDS-Unified Parkinson’s Disease Rating Scale-III (MDS-UPDRS-III) and the PKG sensor for movement staging. A random forest classification model was then used to predict patients’ cluster allocation based on their respective demographic information, MDS-UPDRS-III scores, and PKG time-series data. Clinically relevant clusters were partitioned by levodopa dose, medication administration frequency, and total levodopa equivalent daily dose—with the PKG providing similar symptomatic assessments to physician MDS-UPDRS-III scores. A random forest classifier trained on demographic information, MDS-UPDRS-III scores, and PKG time-series data was able to accurately classify subjects of the two most demographically similar clusters with an accuracy of 86.9%, an F1 score of 90.7%, and an AUC of 0.871. A model that relied solely on demographic information and PKG time-series data provided the next best performance with an accuracy of 83.8%, an F1 score of 88.5%, and an AUC of 0.831, hence further enabling fully remote assessments. These computational methods demonstrate the feasibility of using sensor-based data to cluster patients based on their medication responses with further potential to assist with medication recommendations.

59 BASIC BIOLOGICAL SCIENCES↗