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At least 1,207 records · Page 67

Ka-Band MMIC Subarray Technology Program (Ka-Mist)

Ka-band monolithic microwave integrated circuit (MMIC) arrays have been considered as having high potential for increasing the capability of space, aircraft, and land mobile communication systems in terms of scan performance, data rate, link margin, and flexibility while offering a significant reduction in size, weight, and power consumption. Insertion of MMIC technology into antenna systems, particularly at millimeter wave frequencies using low power and low noise amplifiers in closed proximity to the radiating elements, offers a significant improvement in the array transmit efficiency, receive system noise figure, and overall array reliability. Application of active array technology also leads to the use of advanced beamforming techniques that can improve beam agility, diversity, and adaptivity to complex signal environments. The objective of this program was to demonstrate the technical feasibility of the 'tile' array packaging architecture at EHF via the insertion of 1990 MMIC technology into a functional tile array or subarray module. The means test of this objective was to demonstrate and deliver to NASA a minimum of two 4 x 4 (16 radiating element) subarray modules operating in a transmit mode at 29.6 GHz. Available (1990) MMIC technology was chosen to focus the program effort on the novel interconnect schemes and packaging requirements rather than focusing on MMIC development. Major technical achievements of this program include the successful integration of two 4 x 4 subarray modules into a single antenna array. This 32 element array demonstrates a transmit EIRP of over 300 watts yielding an effective directive power gain in excess of 55 dB at 29.63 GHz. The array has been actively used as the transmit link in airborne/terrestrial mobile communication experiments accomplished via the ACTS satellite launched in August 1993.

Pottinger, W.↗

Origins of Line Defects in Self-Reacting Friction Stir Welds and Their Impact on Weld Quality

Friction stir welding (FSWing) is a solid state joining technique which reduces the occurrence of typical defects formed in fusion welds, especially of highly alloyed metals. Although the process is robust for aluminum alloys, occasional reductions in the strength of FSWs have been observed. Shortly after the NASA-MSFC implemented a variation of FSW called self-reacting (SR), low strength properties were observed. At that time, this reduction in strength was attributed to a line defect. At that time, the limited data suggested that the line defect was related to the accumulation of native oxides that form on the weld lands and faying surfaces. Through a series of improved cleaning methods, tool redesign, and process parameter modifications, the reduction in the strength of the SR-FSWs was eliminated. As more data has been collected, the occasional reduction in the strength of SR-FSW still occurs. These occasional reductions indicate a need to reexamine the underlying causes. This study builds off a series of self reacting (SR)-FSWs that were made in 3 different thickness panels of AA2219 (0.95, 1.27 and 1.56 cm) at 2 different weld pitches. A bead on plate SR-FSW was also made in the 1.56 cm thick panel to understand the contribution of the former faying surfaces. Copper tracer studies were used to understand the flow lines associated with the weld tool used. The quality of the SR-FSWs was evaluated from tensile testing at room temperature. Reductions in the tensile strength were observed in some weldments, primarily at higher weld pitch or tool rotations. This study explores possible correlations between line defects and the reduction of strength in SR-FSWs. Results from this study will assist in a better understand of the mechanisms responsible for reduced tensile strength and provide methodology for minimizing their occurrence.

Schneider, Judy↗

Reduction of Wind Tunnel Contamination During Flow Visualization Experiments Using Polystyrene Microspheres

Evaluation of novel methods and materials for seeding tracer particles for particle image velocimetry (PIV) was carried out in the Basic Aerodynamic Research Tunnel (BART) at NASA’s Langley Research Center (LaRC). Seeding of polystyrene latex microspheres (PSLs) from ethanol/water suspensions and from the dry state was carried out using custom built seeders. PIV data generated using the novel methods were found to be in general agreement with data collected using the current seeding methods. Techniques for assessing PSL fouling of wind tunnel surfaces were identified and refined. Initial results suggest that dry seeding PSLs may allow comparable data quality to wet seeding while reducing wind tunnel screen fouling. Results also indicate that further developments to the dry seeding system should focus on increasing single particle flux into the wind tunnel. Modifications to PSLs and seeding equipment to achieve this have been identified and are discussed.

Schmitz, Russell C.↗

Wind tunnel test and calibration techniques for the measurement of damping and dynamic cross derivatives due to pitching and yawing

The paper shows how the oscillatory apparatus described previously by Orlik-Rueckemann et al. (1974) for determining cross and cross-coupling moment derivatives due to pitching and yawing oscillation is strengthened, extensively modified, and adapted to permit operation in a large continuous-flow wind tunnel. Particular attention is given to an electromagnetic three-degrees-of-freedom calibrating system developed to verify the validity of the experimental method and the various aspects of data reduction procedure. Typical results from calibration tests and from a series of wind tunnel experiments are presented. The only significant limitation is that the apparatus can only be used for models that are relatively small and have a conically-shaped longitudinal cavity.

Hanff, E. S.↗

Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters

The industrial sector consumes about one-third of global energy, making them a frequent target for energy use reduction. Variation in energy usage is observed with weather conditions, as space conditioning needs to change seasonally, and with production, energy-using equipment is directly tied to production rate. Previous models were based on engineering analyses of equipment and relied on site-specific details. Others consisted of single-variable regressors that did not capture all contributions to energy consumption. Further, new modeling techniques could be applied to rectify these weaknesses. Applying data from 45 different manufacturing plants obtained from industrial energy audits, a supervised machine-learning model is developed to create a general predictor for industrial building energy consumption. The model uses features of air enthalpy, solar radiation, and wind speed to predict weather-dependency; motor, steam, and compressed air system parameters to capture support equipment contributions; and operating schedule, production rate, number of employees, and floor area to determine production-dependency. Results showed that a model that used a linear regressor over a transformed feature space could outperform a support vector machine and utilize features more representative of physical systems. Using informed parameters to build a reliable predictor will more accurately characterize a manufacturing facility's energy savings opportunities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Improving CLM5.0 Biomass and Carbon Exchange Across the Western United States Using a Data Assimilation System

The Western United States is dominated by natural lands that play a critical role for carbon balance, water quality, and timber reserves. This region is also particularly vulnerable to forest mortality from drought, insect attack, and wildfires, thus requiring constant monitoring to assess ecosystem health. Carbon monitoring techniques are challenged by the complex mountainous terrain, thus there is an opportunity for data assimilation systems that combine land surface models and satellite-derived observations to provide improved carbon monitoring. Here, we use the Data Assimilation Research Testbed to adjust the Community Land Model (CLM5.0) with remotely sensed observations of leaf area and above-ground biomass. The adjusted simulation significantly reduced the above-ground biomass and leaf area, leading to a reduction in both photosynthesis and respiration fluxes. The reduction in the carbon fluxes mostly offset, thus both the adjusted and free simulation projected a weak carbon sink to the land. This result differed from a separate observation-constrained model (FLUXCOM) that projected strong carbon uptake to the land. Simulation diagnostics suggested water limitation had an important influence upon the magnitude and spatial pattern of carbon uptake through photosynthesis. We recommend that additional observations important for water cycling (e.g., snow water equivalent, land surface temperature) be included to improve the veracity of the spatial pattern in carbon uptake. Furthermore, the assimilation system should be enhanced to maximize the number of the simulated state variables that are adjusted, especially those related to the recommended observed quantities including water cycling and soil carbon.

54 ENVIRONMENTAL SCIENCES↗

AutoReP: Automatic ReLU Replacement for Fast Private Network Inference

The proliferation of the Machine-Learning-As-A-Service (MLaaS) market has brought to light a number of clients’ data privacy and security concerns. One promising solution is private inference (PI) techniques using cryptographic primitives. These techniques often come with high computation and communication overhead associated with the non-linear operator such as ReLU. Several approaches have been developed in reducing the number of ReLU operations, however, they either require a heuristic threshold selection or introduce significant accuracy drop. This work presents AutoReP, a gradient-based framework for non-linear operators reduction that aims to mitigate these concerns from a systematic perspective. AutoReP automates the process of discrete selection of ReLU and polynomial functions on neurons to accelerate PI applications. We also introduce distribution-aware polynomial approximation (DaPa) to accurately approximate ReLUs under given distribution, preserving model expressivity. Our experimental results demonstrate significant accuracy improvements of 6.12% (94.31%, 12.9K ReLU budget, CIFAR-10), 8.39% (74.92%, 12.9K ReLU budget, CIFAR-100), and 9.45% (63.69%, 55K ReLU budget, Tiny-ImageNet) over current state-of-the-art methods, e.g., SNL. Morever, AutoReP is applied to EfficientNet-B2 on ImageNet dataset, and achieved 75.55% accuracy with 176.1 × ReLU budget reduction.

Peng, Hongwu↗

Effect of fluctuating redox conditions on trace metal release from wetland soils from South Carolina and stream sediments from Tennessee

Natural aquatic systems undergo fluctuating redox conditions due to microbial activity, varying water saturation levels, and nutrients dynamics. With fluctuating oxic and anoxic conditions, trace metals can be mobilized or sequestered in response to changes in iron and sulfur speciation and the concentrations and lability of organic carbon. We conducted a systematic laboratory-based microcosm study to examine the effect of redox fluctuations on trace metal mobility in samples collected from two different natural aquatic systems: riparian wetlands and a stream. We incubated water-saturated soils under three cycles of anoxic-oxic conditions (τanoxic:τoxic = 3) spanning 24 days and monitored the change in dissolved and bioavailable metal (copper (Cu), nickel (Ni), zinc (Zn), cobalt (Co), iron (Fe), and manganese (Mn)) concentrations. The dataset includes the results of the variation in metal concentrations due to redox fluctuations in wetland soils and stream sediments. Additionally, the variation in the bioavailable concentration of metals obtained using passive samplers based on diffuse gradient in thin-films technique is included in the dataset. We have also reported the change in the pH values and dissolved oxygen concentrations in the microcosms during the entire study of 24 days. The dataset also contains the dissolved organic carbon and sulfate concentrations, and the data on fractions of metals bound to reducible and oxidizable phases. For materials from both the wetlands and the stream, anoxic conditions favored Co and Zn release, which corresponded with the reductive dissolution of iron oxides. In contrast, dissolved Cu concentrations increased under oxic conditions for both sites and correlated positively with the release of sulfate. In wetland soils, dissolution of Fe (hydr)oxides increased Ni solubility; however, in stream sediments, Ni release occurred when sulfides or organic matter were oxidized. For stream sediments, each subsequent redox cycle increased the bioavailability of trace metals. Upon redox fluctuations in wetland soils, the bioavailability of Zn and Cu increased, whereas bioavailable Ni and Co decreased. This study illustrates that different trace metals display distinct bioavailability patterns during oxic-anoxic fluctuations in natural environments. The biogeochemical cycling of carbon and nitrogen in systems with redox fluctuations may be influenced by these patterns in trace metal availability in addition to the availability of electron donors and acceptors.

54 ENVIRONMENTAL SCIENCES↗

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗

Effects of Daily Centrifugation on Segmental Fluid Distribution in Bed-rested Subjects

The effect of daily centrifugation on segmental fluid distribution have been studied during 21 days of 6 degree head down bedrest. One group (N=7) underwent no countermeasure while the other (N=8) received a daily, one hour, dose (2.5 gz at the foot, decreasing to 1.0 gz at the heart) of artificial gravity (AG) training on the Johnson Space Center short radius centrifuge. Fluid shifts of thoracic(VTO), abdominal (VAB), thigh (VTH), and calf (VCA) regions were measured by the tetrapolar segmental body impedance technique. Untrained subjects reduced their total volume from 18.9 plus or minus 0.5L to 17.9 plus or minus 0.9L (MN plus or minus SE, P less than 0.05) while trained subjects maintained their total volume. In untrained, control, subjects after bed rest, there was a trend toward reduced volume in all segments, with significant reductions in thigh and calf (fig, P less than 0.05). Trained subjects maintained volume in all segments. Our data indicate that artificial gravity treatment counteracts bed rest-induced hypovolemia.

Diedrich, Andre↗

Flight Test Results of a GPS-Based Pitot-Static Calibration Method Using Output-Error Optimization for a Light Twin-Engine Airplane

As part of the NASA Aviation Safety Program (AvSP), a novel pitot-static calibration method was developed to allow rapid in-flight calibration for subscale aircraft while flying within confined test areas. This approach uses Global Positioning System (GPS) technology coupled with modern system identification methods that rapidly computes optimal pressure error models over a range of airspeed with defined confidence bounds. This method has been demonstrated in subscale flight tests and has shown small 2- error bounds with significant reduction in test time compared to other methods. The current research was motivated by the desire to further evaluate and develop this method for full-scale aircraft. A goal of this research was to develop an accurate calibration method that enables reductions in test equipment and flight time, thus reducing costs. The approach involved analysis of data acquisition requirements, development of efficient flight patterns, and analysis of pressure error models based on system identification methods. Flight tests were conducted at The University of Tennessee Space Institute (UTSI) utilizing an instrumented Piper Navajo research aircraft. In addition, the UTSI engineering flight simulator was used to investigate test maneuver requirements and handling qualities issues associated with this technique. This paper provides a summary of piloted simulation and flight test results that illustrates the performance and capabilities of the NASA calibration method. Discussion of maneuver requirements and data analysis methods is included as well as recommendations for piloting technique.

Martos, Borja↗

NASA GRC ICME Schema for Materials Data Management: An Executive Summary

Integrated Computational Materials Engineering (ICME) has received a growing emphasis in attention due its potential impact on rapid material design, reduction in cost and time to market for new applications, and the promise of ‘fit-for-purpose’ materials coupled with recent advances in high performance computing and material characterization tools. However, for an organization to implement ICME practices for material discovery and design, a series of both technical and cultural challenges must be overcome to foster an environment that enables efficient, traceable, and predictive multiscale simulations of material behavior to enable virtual design of materials. In 2016, NASA sponsored a 2040 Vision study to define the potential 25-year future state required for integrated multiscale modeling of materials and systems to improve both the associated time and cost for aerospace and aeronautical innovation. The study envisions a cyber-physical-social ecosystem of experimentally validated computational models, tools, and techniques, along with the associated digital tapestry, that can enable rapid, optimized, ‘fit-for-purpose’ design of materials, components, and systems. A key requirement for such an ecosystem is the development of a robust information management system for materials across their full lifecycle, including material pedigree, experimental (real) and virtual (simulation) data, developed material models, and the implementation of models in engineering applications, such that process-structure-property-performance relationships can be established, thereby enabling the virtual design and optimization of materials. Such an information management system must be able to effectively capture: i) material information at each length scale; ii) test data and analysis; iii) associated material models; and iv) material and model deployment in engineering applications. These systems must also provide traceability between experimental and virtual representations of the material to ensure, when appropriate, the material digital twin is maintained. Additionally, this robust material information management system must be able to seamlessly connect with both commercial and an organization’s in-house software tools, be they analysis tools, other material databases, product lifecycle management (PLM) or simulation data management (SDM) tools, etc., such that automation of the design and analysis of a material across multiple length scales is possible. In this paper, an executive summary of the NASA GRC ICME Schema for materials information management is presented. The database best practices and schema design philosophy specifically for ICME materials data management and an overview description of each element in the schema is given, along with its associated role in an ICME workflow. Additionally, auxiliary tools that interact with the database and provide judicious automation with regards to importing, exporting, and analyzing materials data are presented. Such tools are critical to an ICME ecosystem, not only for their role in enabling optimization, but also in relieving users of tedious manual tasks, thus helping to promote adoption and combat the cultural challenges organizations face in enabling ICME.

Materials↗

TH-E-18A-01: Developments in Monte Carlo Methods for Medical Imaging

Monte Carlo simulation methods are widely used in medical physics research and are starting to be implemented in clinical applications such as radiation therapy planning systems. Monte Carlo simulations offer the capability to accurately estimate quantities of interest that are challenging to measure experimentally while taking into account the realistic anatomy of an individual patient. Traditionally, practical application of Monte Carlo simulation codes in diagnostic imaging was limited by the need for large computational resources or long execution times. However, recent advancements in high-performance computing hardware, combined with a new generation of Monte Carlo simulation algorithms and novel postprocessing methods, are allowing for the computation of relevant imaging parameters of interest such as patient organ doses and scatter-to-primaryratios in radiographic projections in just a few seconds using affordable computational resources. Programmable Graphics Processing Units (GPUs), for example, provide a convenient, affordable platform for parallelized Monte Carlo executions that yield simulation times on the order of 10{sup 7} xray/ s. Even with GPU acceleration, however, Monte Carlo simulation times can be prohibitive for routine clinical practice. To reduce simulation times further, variance reduction techniques can be used to alter the probabilistic models underlying the x-ray tracking process, resulting in lower variance in the results without biasing the estimates. Other complementary strategies for further reductions in computation time are denoising of the Monte Carlo estimates and estimating (scoring) the quantity of interest at a sparse set of sampling locations (e.g. at a small number of detector pixels in a scatter simulation) followed by interpolation. Beyond reduction of the computational resources required for performing Monte Carlo simulations in medical imaging, the use of accurate representations of patient anatomy is crucial to the virtual generation of medical images and accurate estimation of radiation dose and other imaging parameters. For this, detailed computational phantoms of the patient anatomy must be utilized and implemented within the radiation transport code. Computational phantoms presently come in one of three format types, and in one of four morphometric categories. Format types include stylized (mathematical equation-based), voxel (segmented CT/MR images), and hybrid (NURBS and polygon mesh surfaces). Morphometric categories include reference (small library of phantoms by age at 50th height/weight percentile), patient-dependent (larger library of phantoms at various combinations of height/weight percentiles), patient-sculpted (phantoms altered to match the patient's unique outer body contour), and finally, patient-specific (an exact representation of the patient with respect to both body contour and internal anatomy). The existence and availability of these phantoms represents a very important advance for the simulation of realistic medical imaging applications using Monte Carlo methods. New Monte Carlo simulation codes need to be thoroughly validated before they can be used to perform novel research. Ideally, the validation process would involve comparison of results with those of an experimental measurement, but accurate replication of experimental conditions can be very challenging. It is very common to validate new Monte Carlo simulations by replicating previously published simulation results of similar experiments. This process, however, is commonly problematic due to the lack of sufficient information in the published reports of previous work so as to be able to replicate the simulation in detail. To aid in this process, the AAPM Task Group 195 prepared a report in which six different imaging research experiments commonly performed using Monte Carlo simulations are described and their results provided. The simulation conditions of all six cases are provided in full detail, with all necessary data on material composition, source, geometry, scoring and other parameters provided. The results of these simulations when performed with the four most common publicly available Monte Carlo packages are also provided in tabular form. The Task Group 195 Report will be useful for researchers needing to validate their Monte Carlo work, and for trainees needing to learn Monte Carlo simulation methods. In this symposium we will review the recent advancements in highperformance computing hardware enabling the reduction in computational resources needed for Monte Carlo simulations in medical imaging. We will review variance reduction techniques commonly applied in Monte Carlo simulations of medical imaging systems and present implementation strategies for efficient combination of these techniques with GPU acceleration. Trade-offs involved in Monte Carlo acceleration by means of denoising and “sparse sampling” will be discussed. A method for rapid scatter correction in cone-beam CT (<5 min/scan) will be presented as an illustration of the simulation speeds achievable with optimized Monte Carlo simulations. We will also discuss the development, availability, and capability of the various combinations of computational phantoms for Monte Carlo simulation of medical imaging systems. Finally, we will review some examples of experimental validation of Monte Carlo simulations and will present the AAPM Task Group 195 Report. Learning Objectives: Describe the advances in hardware available for performing Monte Carlo simulations in high performance computing environments. Explain variance reduction, denoising and sparse sampling techniques available for reduction of computational time needed for Monte Carlo simulations of medical imaging. List and compare the computational anthropomorphic phantoms currently available for more accurate assessment of medical imaging parameters in Monte Carlo simulations. Describe experimental methods used for validation of Monte Carlo simulations in medical imaging. Describe the AAPM Task Group 195 Report and its use for validation and teaching of Monte Carlo simulations in medical imaging.

60 APPLIED LIFE SCIENCES↗

Detailed in situ ATR-FTIR spectroscopy study of the early stages of C-S-H formation during hydration of monoclinic C3S

The early stages of tricalcium silicate hydration has been studied by in situ ATR-FTIR spectroscopy. A method for data reduction based on background correction and subtraction of spectra of pure components is presented, which makes it possible to describe the kinetic evolution of the calcium silicate hydrate phase upon hydration with great detail. The results obtained are in full agreement with recently reported, detailed solid-state NMR spectroscopy measurements. As ATR-FTIR spectroscopy does not require any isotope enrichment in order to increase the signal intensities and is a widely available technique, we suggest that our study opens up an avenue towards detailed in situ characterization of hydrating cementitious systems and hydrating systems in general.

36 MATERIALS SCIENCE↗

An information theoretic approach to use high-fidelity codes to calibrate low-fidelity codes

For many simulation models, it can be prohibitively expensive or physically infeasible to obtain a complete set of experimental data to calibrate model parameters. In such cases, one can alternatively employ validated higher-fidelity codes to generate simulated data, which can be used to calibrate the lower-fidelity code. In this paper, we employ an information-theoretic framework to determine the reduction in parameter uncertainty that is obtained by evaluating the high-fidelity code at a specific set of design conditions. These conditions are chosen sequentially, based on the amount of information that they contribute to the low-fidelity model parameters. The goal is to employ Bayesian experimental design techniques to minimize the number of high-fidelity code evaluations required to accurately calibrate the low-fidelity model. We illustrate the performance of this framework using heat and diffusion examples, a 1-D kinetic neutron diffusion equation, and a particle transport model, and include initial results from the integration of the high-fidelity thermal-hydraulics code Hydra-TH with a low-fidelity exponential model for the friction correlation factor.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Origins of the two simultaneous mechanisms causing glass transition temperature reductions in high molecular weight freestanding polymer films

From ellipsometry measurements, Pye and Roth [Phys. Rev. Lett. 107, 235701 (2011)] presented evidence of the presence of two glass transitions originating from two distinctly different and simultaneous mechanisms to reduce the glass transition temperature within freestanding polystyrene films with thickness less than 70 nm. The upper transition temperature T{sub g}{sup u}(h) is higher than the lower transition temperature T{sub g}{sup l}(h) in the ultrathin films. After comparing their data with the findings of others, using the same or different techniques, they concluded that new theoretical interpretation is needed to explain the two transitions and the different dependences of T{sub g}{sup u}(h) and T{sub g}{sup l}(h) on film thickness and molecular weight. We address the problem based on advance in delineating the different viscoelastic mechanisms in the glass-rubber transition zone of polymers. Theoretical considerations as well as experiments have shown in time-scales immediately following the segmental α-relaxation are the sub-Rouse modes with longer length scale but shorter than that of the Rouse modes. The existence of the sub-Rouse modes in various polymers including polystyrene has been repeatedly confirmed by experiments. We show that the sub-Rouse modes can account for the upper transition and the properties observed. The segmental α-relaxation is responsible for the lower transition. This is supported by the fact that the segmental α-relaxation in ultrathin freestanding PS films had been observed by dielectric relaxation measurements and photon correlation spectroscopy. Utilizing the temperature dependence of the segmental relaxation times from these experiments, the glass transition temperature T{sub g}{sup α} associated with the segmental relaxation in the ultrathin film is determined. It turns out that T{sub g}{sup α} is nearly the same as T{sub g}{sup l}(h) of the lower transition, and hence definitely segmental α-relaxation is the mechanism for the lower transition. Since it is unlikely that the segmental α-relaxation can give rise to two very different transitions simultaneously, a new mechanism for the upper transition is needed, and the sub-Rouse modes provide the mechanism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Advancing Insights into Electrochemical Pre‐Treatments of Supported Nanoparticle Electrocatalysts by Combining a Design of Experiments Strategy with In Situ Characterization

Activation, break-in, and/or pre-treatment protocols are generally applied to energy conversion devices before regular operation to reach stable performance. There remains much to understand about the relationships among physical properties, performance, and electrochemical pre-treatments. Here, a design-of-experiments (DoE) strategy is employed to address this gap by demonstrating the influence of five pre-treatment parameters for carbon-supported Pt-nanoparticle catalysts on the electrocatalytic oxygen reduction reaction (ORR). A subset of pre-treatments, developed using a central composite design, are tested in a flow cell combined with an inductively-coupled plasma mass spectrometer (on-line ICP-MS). The DoE-based approach facilitates comprehensive insights from two orders of magnitude fewer experiments than a conventional grid search. The coupled on-line ICP-MS setup enables effective catalysis and real-time catalyst dissolution data. Leveraging insights from DoE for on-line ICP-MS and additional characterization, a model is built between the degradation of a multi-dimensional supported Pt surface, its performance, and applied electrochemical parameters. These investigations identify surface modifications, such as oxidation, and subsequent restructuring of Pt during pre-treatment as a primary cause of performance deterioration during ORR. By combining DoE with advanced characterization techniques, a powerful approach is demonstrated to gain a mechanistic understanding of pre-treatment protocols that can be broadly adapted to various reaction chemistries.

Platinum↗

Enhancing Sterile Neutrino Oscillation Sensitivities using SBND-PRISM at the Short-Baseline Neutrino Programme

The Short-Baseline Neutrino (SBN) Programme at Fermilab is comprised of two detectors, SBND and ICARUS, placed at $110$\,m and $600$\,m along the Booster Neutrino Beam at Fermilab. The key physics aim of the programme is to definitively test the sterile neutrino hypothesis, a proposed fourth flavour of neutrino that may explain certain experimental anomalies seen regarding standard model neutrino oscillations. To be able to detect the existence of sterile neutrinos, the uncertainties of the programme must be well constrained. To enable this, a robust analysis must be constructed that can consistently identify the correct values of systematic parameters and generate accurate predictions of what the neutrino energy spectrum at ICARUS should look like. SBND's design allows for the introduction of a technique called PRISM. In PRISM, the detector is divided into regions of different off axis angles from the beam, forming different samples where systematics impact each one in a distinct way. This allows any fits performed to obtain a better understanding of the correct value of the systematic parameters. The first analysis included in this thesis focuses on the improvements seen to the sensitivity of SBN to sterile oscillation parameters when using a PRISM configuration instead of treating SBND as a single whole. Focusing on the $5\sigma$ exclusion contour from the $\numu$ disappearance channel using three PRISM samples, an improvement on the order of $30$\,\% is seen, extending the parameter space for which the null hypothesis can be excluded. This thesis also presents a series of mock data studies comparing the abilities of SBND and PRISM analyses, concluding that in the case of simple changes between Monte Carlo (MC) and data, PRISM produces predictions of the ICARUS event rate spectrum that are more accurate and have smaller uncertainties. When moving to more realistic mock data, using different models to create the mock data than were used for the MC, the postfit predictions at ICARUS had a smaller difference between the postfit and mock data reconstructed energy spectra across all mock data samples tested. Finally, a covariance matrix defined by the maximum discrepancy between the postfit and mock data spectra at ICARUS from each of the SBND and PRISM fits across all the samples was constructed. The resultant $1\sigma$ fractional error induced by this bias systematic on the postfit spectrum has a smaller magnitude for PRISM than SBND, with the improvements ranging from $2.21$\,\% to $6.26$\,\% depending on the mock data samples. This summarises the reduction in systematic error when using PRISM instead of treating SBND as a single detector. *************************************** AUTHOR = Slater, Bethany University of Liverpool b.slater2@liverpool.ac.uk TITLE = Enhancing Sterile Neutrino Oscillation Sensitivities using SBND-PRISM at the Short-Baseline Neutrino Programme PAGES = 218 NOTE = Ph.D. University of Liverpool March 2026 ABSTRACT = The Short-Baseline Neutrino (SBN) Programme at Fermilab is comprised of two detectors, SBND and ICARUS, placed at $110$\,m and $600$\,m along the Booster Neutrino Beam at Fermilab. The key physics aim of the programme is to definitively test the sterile neutrino hypothesis, a proposed fourth flavour of neutrino that may explain certain experimental anomalies seen regarding standard model neutrino oscillations. To be able to detect the existence of sterile neutrinos, the uncertainties of the programme must be well constrained. To enable this, a robust analysis must be constructed that can consistently identify the correct values of systematic parameters and generate accurate predictions of what the neutrino energy spectrum at ICARUS should look like. SBND's design allows for the introduction of a technique called PRISM. In PRISM, the detector is divided into regions of different off axis angles from the beam, forming different samples where systematics impact each one in a distinct way. This allows any fits performed to obtain a better understanding of the correct value of the systematic parameters. The first analysis included in this thesis focuses on the improvements seen to the sensitivity of SBN to sterile oscillation parameters when using a PRISM configuration instead of treating SBND as a single whole. Focusing on the $5\sigma$ exclusion contour from the $\numu$ disappearance channel using three PRISM samples, an improvement on the order of $30$\,\% is seen, extending the parameter space for which the null hypothesis can be excluded. This thesis also presents a series of mock data studies comparing the abilities of SBND and PRISM analyses, concluding that in the case of simple changes between Monte Carlo (MC) and data, PRISM produces predictions of the ICARUS event rate spectrum that are more accurate and have smaller uncertainties. When moving to more realistic mock data, using different models to create the mock data than were used for the MC, the postfit predictions at ICARUS had a smaller difference between the postfit and mock data reconstructed energy spectra across all mock data samples tested. Finally, a covariance matrix defined by the maximum discrepancy between the postfit and mock data spectra at ICARUS from each of the SBND and PRISM fits across all the samples was constructed. The resultant $1\sigma$ fractional error induced by this bias systematic on the postfit spectrum has a smaller magnitude for PRISM than SBND, with the improvements ranging from $2.21$\,\% to $6.26$\,\% depending on the mock data samples. This summarises the reduction in systematic error when using PRISM instead of treating SBND as a single detector.

Slater, Bethany [Liverpool U.]↗