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Biobased diglycidyl ether diphenolates: effect of the ester moiety on fragrance oil microencapsulation by interfacial polymerization
No full textColloids and Surfaces A: Physicochemical and Engineering Aspects, in pressNote : if this item contains full text it may be a preprint, author manuscript, or a Gold OA copy that permits redistribution with a license such as CC BY. The final version is available through the publisher’s platform.Health risks are associated with capsule-forming synthetic chemicals such as formaldehyde, isocyanates, and bisphenol A (BPA). This work is focused on using safe biobased molecules to build capsule walls for oil encapsulation. Cellulose-derived diphenolic acid was converted into a series of diglycidyl ether n-alkyl diphenolates (DGEDP-esters). Interfacial polymerization of oil-soluble DGEDP-esters and water-soluble amine hardeners, hexamethylenediamine (HMDA) and chitosan oligosaccharide (COS), were used to build capsule walls. DGEDP-esters with small methyl and polar monomethyl ethylene glycol (DGEDP-ME and DGEDP-MG, respectively) are most reactive forming compact crosslinked capsules with high microencapsulation efficiency (EE of 97% and 94.3%) and good stability (oil leaking was ≤ 15.7% after acidification, and ≤ 64.2% after sonication). With increased hydrophobicity of the DGEDP ester moiety, deformed capsules with low EE (63 – 87%) and poor stability (oil leaking was ≥ 18.4% after acidification, and ≥ 81.8% after sonication) were obtained. Oil encapsulated with DGEDP-esters exhibits oil release of 0.5 – 8% at day 30 (45 °C and 11 kPa). The use of COS as the hardener yields capsules with EE (88.3 – 94%) and higher oil release (9–15% at day 30) compared to HMDA, but the acid stability was remarkably improved (oil leaking ≤ 3.7%). Cytotoxicity tests suggest that capsules formed with DGEDP-ME and HMDA or COS show very low cytotoxicity against human breast cancer cells and human fibroblasts cells (cell viability of 87–102% after 24 h exposure at capsule concentration of 2 mg/mL). These observations demonstrate that the structures of DGEDP epoxy monomers and amine hardeners have significant effects. Preferred DGEDP ester groups were methyl and mono-methoxy, and effective capsule formation was achieved using either HMDA or COS, the latter providing the advantage of being a readily renewable biobased multifunctional amine.https://login.libproxy.rpi.edu/login?url=https://doi.org/10.1016/j.colsurfa.2022.12924
Phase transformations of spherical block copolymer micelles
No full text2019 MaySchool of EngineeringPolymorphism is ubiquitous in nearly all crystalline materials, and control of polymorphism is important to obtain material properties for target applications. Therefore, understanding how polymorphs of materials change and how to access target polymorph by controlling thermodynamic and kinetic factors is the fundamental task in materials research. Block copolymer surfactants consist of covalently connected chemically distinct polymer blocks and aggregate into micellar structures in selective solvents. Advances in the polymer chemistry enables fine-tuning of the size and chemical properties of block copolymer surfactants, which leads to fabrication of various block copolymer micelles with properties and shapes based on solid design principles. The rich structural and property variabilities of block copolymer micelles make this material class as one of the most important model systems for exploring and understanding self-assembly of nanoscale particles. Packing structures of spherical micelles prepared with poly(1,2-butadiene-b-ethylene oxide) (PB-PEO) diblock copolymers in aqueous solutions were investigated using small angle X-ray scattering (SAXS) technique. Depending on the processing conditions, the PB-PEO spherical micelles were observed self-assembling into different close-packed structures, i.e., polytypes made by stacking two-dimensional hexagonal close-packed (2D-HCP) layers of block copolymer micelles in different stacking orders. In a 12.7 wt % solution of the PB-PEO diblock copolymer (Mn = 6.8 kg/mol and the weight fraction of the PEO block wPEO = 0.71), direct dissolution of the PB-PEO diblock copolymer produced face-centered cubic (FCC) crystals of the PB-PEO micelles. The micellar FCC structures become disordered by heating to 90 °C, and rapid temperature quenching of the disordered micelle solution to three different temperature, 40 °C, 25 °C, and 0 °C, produced FCC, randomly stacked hexagonal close packing (RHCP), and hexagonal close-packing (HCP) structures, respectively. The micellar HCP and RHCP structures are stable for at least a few weeks when maintained at the quenched temperature, but heating or cooling transformed these HCP and RHCP to FCC by grain-coarsening. Careful examination of the 2D SAXS patterns reveals that the formation of HCP and RHCP structures is related to the size of crystallites. This suggests the Laplace pressure from the finite size of crystal domains is likely the origin of the formation of the non-cubic close-packed structures of block copolymer micelles. As the crystal grains grow, the Laplace pressure becomes weak, and the micelles on close-packed lattices transform into the most stable close-packed structures: FCC. In a 23 wt % PB-PEO solution, careful thermal treatments lead to discover martensitic transformations of shear-aligned FCC crystallites of block copolymer micelles to HCP structures. It occurs by selectively sliding one specific set of 2D-HCP layers in the shear-aligned FCC crystallites among other equally possible layers. Consideration of the morphology of the shear-aligned FCC crystallites suggests that the selective martensitic shear transformation originates from different areas of available 2D-HCP layers for the martensitic shear transformation: the transformation chooses the 2D-HCP layers with the lowest sliding area, i.e., lowest frictional kinetic energy barrier. Both thermally induced diffusive and diffusionless transformations of model block copolymer micelles suggest that the size of crystal domains is a critical factor in the polymorphism of crystalline materials. In the diffusive nucleation and growth process, the size of crystal domains determines the type of crystal structures. In the diffusionless transformation, the size of crystal domains regulates the initiation and kinetics of diffusionless transformations. This finding stimulates further investigations of the effects of polymer concentration and cooling rates to the crystal structures of block copolymer micelles. Non-close packed structures of PB-PEO micelles are observed from the solutions with the tetrahydrofuran co-solvent, which is a non-selective solvent for both PB and PEO blocks. This thesis work reveals the importance of the size of crystal domains to the crystal structures and transformation kinetics and provides new understanding.Ph
Intro to Web Science (Oct 2022)
No full text"What is Web Science?" Guest lecture for ITWS 1100 (Fall 2022), Rensselaer Polytechnic Institute, Troy, N
Mycoscaffold : a biodiversity framework for soil mycoremediation
No full textAugust 2021School of ArchitectureAs the span of our urban environments continue to grow, and industries supporting life in our cities proliferate, a key dilemma one can observe is the contamination of soil and a detrimental loss of biodiversity. This prominent threat to the fundamental elements of our environment has captured the attention of designers where multidisciplinary approaches are imperative in combating the severe damage inflicted by anthropogenic activity. A prominent discourse of action against soil pollution evident in landscape design is phytoremediation. Within this field of naturally based remediation efforts, the strategy known as mycoremediation, which utilizes mushroom root networks known as mycelia to immobilize toxic chemicals embedded in soil systems. While this method of landscape remediation is extremely effective against a multitude of chemicals, it is evident that these efforts have yet to be a seamlessly integrated aspect of the urban fabric. The proposal will then focus upon the development of a framework for biodiverse mycelium-based soil remediation deployment strategy that integrates three integral elements: biodiversity, material design, and data visualization. This study will include, (i) a comprehensive literature review to observe the advancements in each of these fields, (ii) a design proposal for the biodiverse mycoremediation deployment system within a case study environment, (iii) material scale experiments to test the remediation efficacy of mycelium along with two species of native flora to the case study site against lead, and (iv) a material thermal and moisture simulation using Energyplus software to test the material component’s capabilities of supporting mycelium growth within the structure. The interdisciplinary design research is significant to investigate prospective approaches utilizing ecologically conscious mycoremediation to protect vulnerable urban communities against detrimental toxins.M
Tackling health inequity using machine learning fairness, AI, and optimization
No full textAugust 2022School of ScienceHealth inequity, which leads to unfair and preventable disparities across individuals in opportunities to achieve optimal health, has been brought back into the national spotlight by global COVID-19 pandemic. As artificial intelligence (AI) is increasingly being applied within the health domain, this work aims to develop a new fairness-aware framework, based on machine learning (ML) fairness metrics, AI technologies, and optimization, to help clinical researchers, healthcare providers, and policy makers identify, quantify, reduce, and eventually eliminate potential biases in data-based decision making and implement evidence-based practice to improve patient outcomes. The ultimate goal is to enhance diversity, equity, and inclusion (DEI) in population health in support of better health outcomes for all. We developed a set of health equity metrics to identify and quantify disparities between research sample learnt by AI models and the real-world population that eventual research findings will be applied to. These health equity metrics were derived from existing fairness metrics applied in other areas such as machine learning. Unlike reference-group based metrics measuring bias against a golden truth defined by researchers, these scalable metrics quantify bias against target populations who should have equal opportunity for selection. This research proves that equity metrics could be effectively applied to multiple health domains and shed light on clinical and policy implications. We applied our novel health equity assessment framework, embedded with the proposed equity metrics, to three use cases in population health: randomized clinical trials (RCTs) in Chapter 2, clinical trial recruitment planning in Chapter 3, and healthcare utilization including prescription drugs and vaccines in Chapter 4. To turn health data into usable information that can be understood by observers, we present key equity evaluation results both analytically and visually. In RCTs (Chapter 2), equity metrics, which act as representativeness metrics, enable users to determine overrepresentation, underrepresentation, or exclusion of subgroups with respect to a target population indicating potential limitations of RCTs. Additional statistical tests quantify the significance of observed subgroup inequities with consideration of study sizes and estimation errors of ideal rates. These metrics can measure the level of inequity for all possible protected subgroups of patients defined using multiple protected attributes and provide a single visualization that incorporates and compares these subgroup measures. For clinical trials recruitment planning (Chapter 3), a goal-programming-based multi-objective optimization approach, integrating quantitatively defined enrollment goals, was designed to make equitable enrollment plans for RCTs. The method can prospectively produce equitable enrollment plans in the experiment design stage and retrospectively evaluate inequities in clinical trial enrollment during and after the experiment. It provides opportunities for researchers to demonstrate validity of investigation and to examine disparities across subgroups defined over subjects' characteristics of interest. Furthermore, equity metrics can be used as measures of effects of demographic and socioeconomic determinants on healthcare access and utilization (Chapter 4). They enable users to find differences in healthcare services associated with vulnerable subpopulations such as overprescription and underprescription to medications and insufficient accessibility and utilization of healthcare services. The findings suggest that different determinants exist regarding to the resources/service of different health needs. This method can be valuable assistance in decisions regarding healthcare and provides an opportunity to promote equitable access to healthcare and improved health outcomes. Finally, we developed an interactive web-based R-Shiny prototype toolkit called TrialEquity to address the equity problem of supporting health using our fairness-aware approaches described in the above cases (Chapter 5). To move toward greater health equity, we expect our AI-empowered health equity evaluation framework can be an important and fundamental tool to guide the way.Ph
Data-driven control of laser powder bed fusion
No full textAugust 2021School of EngineeringThere is growing attention to additive manufacturing (AM) processes in industry and academia. Specifically, one metal AM process - laser powder bed fusion (LPBF) - is particularly attractive as it can directly produce functional metal parts with fine feature resolution. However, LPBF suffers from quality control issues that hinder its wider adoption. These issues are not easily addressed, as the process is challenging to monitor and computationally expensive to model. Significant research efforts have been devoted to the improvement of LPBF quality outcomes: real-time process monitoring, control, and computational modeling have all seen progress in recent years. This thesis contributes to efforts in empirical modeling and real-time control of LPBF, approaching them from a mechatronics perspective. It first asks: what should be measured, what should be controlled, and how those things are empirically related. Then, given those empirically-modeled relations, the laser power is controlled in both a feedback and a feedforward manner to regulate the melt pool emission. But first, to make these studies possible, systems for melt pool monitoring and control were designed and implemented on a laboratory testbed. The designed control system allows for laser power modulation based on either the melt pool images or a pre-defined input profile, at 2 kHz. With the described system in place, both feedback and feedforward control schemes were experimentally demonstrated. With respect to feedback control, a melt pool signal reference was tracked on a part scale. First, transfer function models were identified from the experimental data. Then, feedback controller design and tuning were performed, accounting for process-related plant model variation, as well as modelling uncertainties. The feedback controller corrected in-layer and inter-layer melt pool signal drifts present during open-loop operation. With respect to feedforward control, this work makes two contributions. First, iterative learning control was shown to correct for repeating layer-to-layer disturbances in LPBF, but only if the geometry is repeating exactly. Second, certain geometric features, such as corners and narrow sections, were shown to create a disturbance that is observable in coaxial melt pool images. Further, the geometry-dependent model of this behavior was constructed, validated experimentally, and used for model-based feedforward control. The designed model-based feedforward controller reduced geometry-related signal deviations by 50%.Ph
Terahertz-wave absorption gas sensing for dimethyl sulfoxide
No full textMay 2022School of EngineeringRotational absorption spectroscopy for dimethyl sulfoxide (DMSO) is characterized in the
220-330 GHz frequency range using a robust electronic THz-wave spectrometer, for the
development of THz gas sensing for this compound of commercial relevance. DMSO is a common
solvent used in many food, pharmaceutical, and manufacturing applications, and can present
danger to human health and the work environment; hence, remote gas sensors for DMSO
environmental and process monitoring are desired. Absorption measurements were carried out for
pure DMSO at 297 K and 0.4 Torr. DMSO was shown to have a unique rotational fingerprint with
series of repeating absorption features. The frequencies of transitions observed in the present study
are found to be in good agreement with prior experimental work and spectral simulations based on
rotational parameters. The sensor developed here exhibits a detection limit of 1.3-2.6 x 1015 DMSO
molecules/cm3 per meter of absorption pathlength, with the potential for greater sensitivity with
signal-to-noise improvements. The study illustrates the potential of all electronic THz-wave
systems for miniaturized remote gas sensorsM
Dielectric permittivity of interfaces in polymer nanocomposites from electrostatic force microscopy
No full textMay 2022School of EngineeringThe addition of nanofillers can lead to a significant change in the dielectric properties of polymer nanocomposites. The cause for the change has been identified as the presence of an interfacial layer of polymer surrounding the nanoparticle, which has properties different from the bulk matrix polymer. Controlling the properties of the nanocomposite requires an understanding of the interfacial region between the nanoparticle and the matrix polymer. The nanometer-scale dimension of the interfacial region falls below the spatial resolution of many experimental measurement techniques. This makes the quantitative characterization of their properties a challenge. Electrostatic Force Microscopy (EFM), an AFM-derived method, is a promising technique to characterize interfacial regions owing to its ability to probe the local capacitive response of the sample. However, due to the probe geometry and the long-range nature of electrostatic forces, the actual probed region of the specimen becomes too complex to be defined, and EFM signals get easily misinterpreted. This thesis work presents a methodology to reliably extract interfacial permittivity combining machine learning, numerical simulations, and experimental EFM measurements. We first demonstrate the efficacy of machine learning (ML) models to extract interface permittivity using a data set of synthetic EFM force gradient scans generated by finite element simulations. We show that both support vector regression (SVR) and random forest (RF) algorithms can ‘invert’ the force gradient scan to predict the permittivity with high accuracy. We investigate a two-unknown case where particle depth inside the surface and interphase dielectric constant are unknown, but the interphase thickness is assumed to be known. From a modest database of 200 finite-element simulations, we show that ML models can predict interphase permittivity with a typical accuracy of 0.24 (mean absolute error). We then investigate a case where interphase thickness is also assumed unknown and demonstrate that the models continue to achieve an impressive accuracy of 0.45 for the extracted interphase permittivity. Feature reduction by principal component analysis (PCA) improves the model’s performance and reveals force gradient contrast to be the most important feature in permittivity detection. These ML models perform better than analytical approaches by capturing significant geometric complexity of EFM measurements. We then performed EFM measurements on the tailored nanocomposite systems to test ML performance on the experimental data. Interfacial measurements were carried for two different grafted silica nanoparticles dispersed in PMMA. For a grafted brush of high dielectric constant, signal contrast at the interfacial region was observed in EFM images. The dielectric permittivity and thickness of the interfacial region were quantified using the ML model with high accuracy. The predicted interface parameters match with the parameters of grafted brush estimated from other experiments. For PMMA-grafted brushes, an intrinsic interfacial region of higher permittivity than the matrix was predicted. The results of interfacial permittivity obtained from the EFM measurements were also verified by referring to bulk material characterization. It is anticipated that the present method, opening new possibilities in understanding the matrix/particle interfacial region, may help with the judicious design and engineering of high-performance polymer nanodielectrics. In addition, nanocomposites samples with varying surface chemistry and filler loading were prepared and characterized by TEM, dielectric spectroscopy, and breakdown strength measurements. The impact of filler loading, surface chemistry, and dispersion on the dielectric property was quantified using tools available in Nanomine (a polymer nanocomposite repository). Our collaborators used the experimental data to validate simulation models and build a design strategy for nanodielectrics.Ph
Optimizing personalized menus and incentives to increase driver autonomy in ridesharing and crowdsourced delivery platforms with stochastic driver behavior
No full textAugust 2021School of SciencePeer-to-peer logistics platforms have become increasingly popular in recent years for performing last mile delivery, ridesharing, and more. In general, current platforms have the suppliers i.e. the drivers either sift through and select from a large number of requests or are assigned a single request that they may or may not be able to reject. In this dissertation we offer an alternative framework, that provides drivers with a small but personalized menu of requests to choose from. This creates a Stackelberg game, in which the platform leads by deciding what menu of requests to send to each driver, and the drivers follow by selecting which request(s) to accept from their received menus. Determining optimal menus, menu size, and request overlaps is complex as the platform has limited knowledge of drivers' request preferences. Exploiting the problem structure when drivers signal willingness to fulfill each request, we reformulate our problem as an equivalent single-level Mixed Integer Linear Program (MILP) and apply the Sample Average Approximation (SAA) method. Computational tests recommend a training sample size for inputted SAA scenarios and a test sample size for completing performance analysis. Our stochastic optimization approach performs better than current approaches, as well as deterministic optimization alternatives. A simplified formulation ignoring `unhappy drivers' who accept requests but are not matched is shown to produce similar objective values with a fraction of the runtime. A ridesharing case study of the Chicago Regional transportation network provides insights for a platform wanting to provide driver autonomy via menu creation. The proposed methods achieved high demand performance as long as the drivers are well compensated (e.g., even when drivers are allowed to reject requests, on average over 90% of requests are fulfilled when 80% of the fare goes to drivers; this drops to below 60% when only 40% of the fare goes to drivers). Thus, neither the platform nor the drivers benefit from low driver compensation due to its resulting low driver participation and thus low request fulfillment. Finally, for the cases tested, a maximum menu size of 5 is recommended as it produces good quality platform solutions without requiring much driver selection time. Stochastic driver responses, independently accepting or rejecting each request in their menus, endogenously depend on the offered compensation for each request and the driver's effort required to fulfill the request (e.g. extra driving time). Therefore, in this dissertation, we also create and solve an optimization model to simultaneously determine personalized menus and incentives to offer drivers. We exploit variable properties to circumvent nonlinear variable relationships, formulating the model as a linear integer program. Stochastic driver responses are modeled as a sample of variable and fixed scenarios. An imposed premium counterbalances solution overfitting. Solution methods decompose and iterate, improving performance of computational experiments that use request/driver trip information from the Chicago Regional transportation network. Our approach outperforms alternative methods and our first approach that has no incentives by strategically using personalized incentives to prioritize promising matches and to increase drivers’ willingness to accept requests. This benefits both customers and drivers: the average driver income is increased by 4.1% compared to the menu-only model, and 96.6% of requests are matched (4.1% higher than the menu-only method). Higher incentives are offered when drivers are more likely to accept, while fewer incentives and menu slots are reserved for driver-request pairs less likely to be accepted. We design a third approach to examine the tradeoffs between the potential performance gains with the inclusion of incentives, and ensuring a fair experience for drivers. We use the same methodology and experiment data as our second framework producing menus and incentives, with added constraints that enforce one of three fairness types. These constraints require that nearby drivers receive the same compensation for the same request (driver proximity fairness), that drivers closer to the request receive higher compensation as they incur a shorter customer wait time (closer higher fairness), or that all compensation offers for a request are the same (all equal fairness). Computational experiments illustrate that personalized incentives even under such fairness constraints can still benefit the platform. Several of the properties of the fairness-constrained solutions, namely, match rate, profit, and driver income, are in between those of the two extremes found in the no-incentives solutions and the incentives-without-fairness-constraints solutions, while also providing a certain level of incentive fairness. Compared to the remaining fairness settings, solutions from driver proximity fairness with a low distance threshold value (the method with the fewest fairness constraints) and from our method with no fairness constraints have fewer compensation offers that have a nonzero incentive, but have the highest incentive offer average.Ph
Benchmark development of temperature dependent critical experiments at the Walthousen reactor critical facility
No full textDecember 2021School of EngineeringCritical reactor experiments have been a crucial part of the development of reactor science and technologies in the past 60 years. These experiments serve for the greater understanding of nuclear physics with a reactor and the prediction of reactor operation behavior. More recently, critical experiments have served to validate reactor simulation tools such as MCNP and Serpent 2. There is a continuing need for newer and more accurate critical reactor experiments for the purpose of validation of novel modeling techniques and simulation toolkits; while many critical experiments already exist, many of them have large uncertainties in output criticality, do not have highly accurate system parameters which allow for users to model these experiments, or they don’t provide precise temperature measurements in the system. The Walthousen Reactor Critical Facility (RCF) is a low-power open pool research reactor with the unique capability of performing a wide range of critical experiments. Reactivity experiments of the RCF have been performed and reported using two standard configurations utilizing 332 and 333 fuel pins as well as a non-standard configuration. This non-standard configuration includes a large pipe which was inserted in the center of the reactor core such that heated or cooled water could be pumped inward and outward from the reactor core which served as a coupled physics experiment configuration. However, for this work the heated or cooled water injection experiments are not considered, but the isothermal reactivity experiments which were taken of this configuration are studied. These experiments were performed by carefully raising the reactor tank moderator – light water – using immersed electric heaters, raising the reactor control rods, and measuring the reactivity of the system as a function of temperature. Since the temperatures were slowly raised over the course of multiple hours at a rate of less than 5 degrees Celsius per hour and the moderator was thoroughly mixed using a tank agitator, the thermocouple measurements in the reactor core revealed that the temperature was to within 0.33 degrees Celsius throughout the core. Thus, these experiments are considered to be isothermal. This slow and methodical measurement of reactivity as a function of isothermal temperature is suitable for the development of a set of benchmarks which is the purpose of this work. Contained in this work is the description of the computational models created in Serpent 2 and MCNP and the accompanying sensitivity analysis using these models for the purpose of the development of benchmarks based on the RCF critical experiments. The sensitivity analysis for the standard core configuration revealed there is a ± 166 pcm (per cent mille) benchmark model uncertainty due to input parameters (geometry, material specifications, and temperature measurements) and a ± 191 pcm benchmark model uncertainty for the coupled physics experiment core configuration. Using these computational models, the simulated criticality of the experiments as a function of temperature are compared to experimental measurements and agree to within these benchmark model uncertainties. Since the uncertainty of the criticality as determined in this sensitivity analysis is far less than 1% of the measured values (being within 0.2% of the measured values), and the computational models are within this benchmark uncertainty, this work demonstrates that this dataset is valuable and recommended for the use of validation of reactor simulation tools. Comparing the final benchmark uncertainties of the RCF temperature-dependent critical experiments to other benchmark experiments included in the International Handbook of Evaluated Criticality Safety Benchmark Experiments (IHECSBE) [1] demonstrates the quality of the RCF benchmark dataset developed in this work. The SPERT-D aluminum-clad plate-type fuel experiments (IHECSBE identification numbers HEU-MET-THERM-006 and HEU-MISC-THERM-001) had benchmark uncertainties up to ± 610 pcm, and a set of experiments using SPERT III stainless-steel-clad plate-type fuel in water (IHECSBE ID number HEU-COMP-THERM-022) had a benchmark uncertainty of ± 810 pcm. By comparison, the RCF benchmark uncertainties of ± 166 pcm and ± 191 pcm are exceptional. This demonstrates that the RCF model can be created to a much higher level of accuracy based on the lower uncertainty on criticality due to uncertainty in the model input parameters. The temperature range over which the RCF critical experiments were performed is much wider than the other SPERT experiments mentioned; the SPERT-D aluminum-clad plate-type fuel experiments were measured at 22.2 °C, and the SPERT III stainless-steel-clad plate-type fuel experiments were measured between 55 and 60 °F (approximately between 13 and 16 °C). By comparison, the RCF 333-pin experiments ranged from 29 °C to 46 °C, the 332-pin experiments ranged from 16 °C to 36 °C, and the coupled physics experiment configuration measurements ranged from 11 °C to 41 °C. With such a wide range of temperatures for benchmark measurements, including those below room temperature, this data is highly valuable for validation of computational tool methods for accounting for change in reactor temperature as well as evaluating thermal scattering law/S(α,β) data libraries.Ph