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At least 487 records · Page 27

Magnesium Oxide Reduces Anxiety-like Behavior in Mice by Inhibiting Sulfate-Reducing Bacteria

The gut microbiota–brain axis allows for bidirectional communication between the microbes in our gastrointestinal (GI) tract and the central nervous system. Psychological stress has been known to disrupt the gut microbiome (dysbiosis) leading to anxiety-like behavior. Pathogens administered into the gut have been reported to cause anxiety. Whether commensal bacteria affect the gut–brain axis is not well understood. In this study, we examined the impact of a commensal sulfate-reducing bacteria (SRB) and its metabolite, hydrogen sulfide (H2S), on anxiety-like behavior. We found that mice gavaged with SRB had increased anxiety-like behavior as measured by the open field test. We also tested the effects of magnesium oxide (MgO) on SRB growth both in vitro and in vivo using a water avoidance stress (WAS) model. We found that MgO inhibited SRB growth and H2S production in a dose-dependent fashion. Mice that underwent psychological stress using the WAS model were observed to have an overgrowth (bloom) of SRB (Deferribacterota) and increased anxiety-like behavior. However, WAS-induced overgrowth of SRB and anxiety-like behavioral effects were attenuated in animals fed a MgO-enriched diet. These findings supported a potential MgO-reversible relationship between WAS-induced SRB blooms and anxiety-like behavior.

60 APPLIED LIFE SCIENCES↗

Quantifying Behavior Driven Energy Savings for Hotels

Hotel facilities present abundant opportunities for energy savings. In the United States, there are around 25,000 hotels that spend on an average of $2,196 on energy costs per room each year. This amounts to about 6% of the total annual hotel operating cost. However, unlike offices, there are limited studies on establishing appropriate baselines and quantifying hotel energy savings given the variety of services and amenities, unpredictable customer behaviors, and the around-the-clock operation hours. In this study, we investigate behavior driven energy savings for three medium-size (around 90,000 sf2) hotels that offer similar services in different climate zones. We first used Department of Energy Asset Scoring Tool to establish baseline models. We then conducted energy saving analysis in EnergyPlus based on a behavior model that defines the upper bound and lower bound of customer and hotel staff behavior. Lastly, we presented a probabilistic energy savings outlook for each hotel. The analysis shows behavior driven energy savings up to 25%. We believe this is the first study to incorporate behavioral factors into energy analysis for hotels. It also demonstrates a procedure to quickly create tailored baselines and identify improvement opportunities for hotels.

Dong, Bing↗

On-the fly scheduling of execution of dynamic hardware behaviors

Methods for dynamically executing computer code across multiple disparate processing unit architectures are disclosed. During execution of a first portion of computer code on a first processing unit, it is determined that a first dynamic hardware behavior of a plurality of dynamic hardware behaviors will occur at a subsequent point in time, based on a second dynamic hardware behavior that is occurring. The methods include determining to execute code corresponding to the first dynamic hardware behavior on a second processing unit, rather than the first processing unit, and scheduling computer program code corresponding to the first dynamic hardware behavior to execute on the second processing unit rather than the first processing unit. Upon completion of execution of the computer code corresponding to the first dynamic hardware behavior, a remaining portion of the computer code is scheduled to execute on the first processing unit.

97 MATHEMATICS AND COMPUTING↗

Data-driven modeling of dynamic occupant thermostat override behavior for demand response applications

Buildings consume nearly 40% of global energy and produce similar emissions. Whiletechnological advances address efficiency, occupant behavior causes energy use variations up to 300% between identical buildings. This gap between predicted and actual building performance impacts building design, operations, and grid demand management programs. Through analyses of smart thermostat data from 1,400 single-occupant homes, the researchdemonstrates that occupants respond to 8°F thermostat setpoint changes within a median of 15 minutes, while 2°F changes trigger responses within a median of 30 minutes. This highlights an understudied temporal relationship between thermostat setbacks and response time of occupant behaviors. Models of such behavior dynamics are required to incorporate occupant impacts into building performance simulation. A key contribution of this dissertation is the Thermal Frustration Theory (TFT), which positsthat thermal discomfort driven behaviors are caused by the time-accumulation of discomfort, not simply a temperature deviation threshold or a delay from an initiating event. Using a dataset of 634 thermostats, each with 25+ manual setpoint changes, a comparative analysis of TFT and comfort zone and a delayed response theories demonstrated that personalized TFT models better predict when manual setpoint change occur. This was measured by the area under the curve statistical measure (AUC); all three models perform similarly by a Matthews Correlation Coefficient measure. Higher AUC performance is especially important for modeling occupant behavior in demand response programs where false negatives of rare occupant interactions could adversely affect grid stability. EnergyPlus based simulations were conducted with TFT-derived occupant models, demonstrating the ability to identify parameters of known TFT models from only data observable with smart thermostats, even under the presence of noise from routine overrides. Overall, the dissertation highlights that thermostat interactions are neither static,instantaneous, nor driven solely by the environment. Instead, temporal accumulation of discomfort and routine-based behavior play important roles. The methodology and results offer a pathway towards more accurate modeling of human-building interactions for policy assessment, building design, and demand response programs.

Sharma, Kunind [Northeastern University] (ORCID:00↗

Evaluation of the critical behavior near ferromagnetic to paramagnetic phase transition in CrTe 1-x Se x alloys: An experimental study

For this study, we used the conventional solid-state reaction method to prepare stoichiometric samples of CrTe 1-x Se x , where 0 ≤ x ≤ 0.10, and investigated the structural and critical behavior of the prepared samples. Room temperature powder X-ray diffraction, along with Rietveld refinement, revealed the emergence of the NiAs structure with P6 3 /mmc (194) space group with increasing Se substitution. The high-temperature linear fit to inverse of the dc-susceptibility versus temperature for the mother sample resulted in an effective moment of 3.65μ B Cr with Curie-Weiss temperature near 335K, which is slightly higher than the Tc of ~332K obtained from the inflection point of magnetization versus temperature. Magnetization isotherms were employed to investigate the critical behavior of ferromagnetic CrTe 1-x Se x with 0.0 ≤ x ≤ 0.10 near their Curie temperatures (Tc). The magnetic behavior near Tc was found to follow 3D mean-field critical exponents with a second-order phase transition in all samples investigated. We fine-tuned the critical exponents (β, γ, and δ) using a combination of an iteration technique, the Kouvel-Fisher method, and modified Arrott plots. All samples follow a mean field behavior, with Tc ranging from 298 to 340K. The acquired values exhibit self-consistency, as indicated by the results from the Widom scaling relation. Furthermore, the magnetization isotherms exhibit a universal scaling behavior, providing additional credence to the calculated critical exponents.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Microstructure-sensitive mechanical behavior of an additively manufactured psuedoelastic shape memory alloy

The additive manufacturing of shape memory alloys into complex geometries enables fabrication of advanced functional systems across a variety of fields and domains. This work presents results focused on the mechanical behavior of additively manufactured shape memory pseudoelastic NiTi. The deformation induced solid state phase transformation from austenite to martensite allows this system to accommodate large recoverable strains. This deformation behavior is fundamentally driven by crystal-scale transformation physics. Laser powder bed fusion processing reveals that the resulting microstructure, both grain morphology and crystallographic texture, is strongly dependent on the manufacturing processing history. Exhaustive mechanical testing demonstrates that these microstructural factors strongly impact both tensile and cyclic stress–strain behavior. Cyclic dissipative behavior, however, is similar across all tested microstructures following an initial transient period. Remarkably, analysis of spatial strain fields during tensile loading reveals two distinctly different localization “modes”. The first is initiation of localized deformation bands which continuously propagate through the tensile bar during loading. In the second mode localization is observed but lacks propagation; instead additional localization cites nucleate during subsequent loading. The latter phenomena is suspected to be driven by grain-scale deformation physics as the localized band morphologies coincide with grain morphologies. These phenomena strongly impact the resulting aggregate stress–strain behavior. Hence, manufacturers and designers of psuedoelastic functional components must at the very least consider the potential variability in properties when considering additive manufacturing processing. More ideally the process–structure–property relations can be used to further tailor and optimize final functional performance.

Additive manufacturing↗

Residential Demand Flexibility: Modeling Occupant Behavior using Sociodemographic Predictors

Demand flexibility (DF) has the potential to increase the saturation of renewables in the grid and reduce operating costs for both utilities and customers. However, less than 8% of U.S. residential electric customers are enrolled in DF programs. A major research gap on this topic is an uneven understanding of behavioral drivers of electricity use and DF program participation at the household level. In this study, we employ machine learning models to predict residential occupant behavior in activities relevant to DF. We model occupants' extensive decisions (i.e., choice of action) and intensive behaviors (i.e., amount of time spent) during peak and off-peak time periods using the publicly available American Time Use Survey, which includes activities data for approximately 200,000 respondents. In our machine learning models, predictions for both extensive and intensive behavior fell within a +/-20% error margin at the aggregate level. We identify 13 key sociodemographic predictors of DF-related intensive behavior using LASSO inference and beta coefficient ranking. However, these top predictors differ by activity, suggesting potential scope for differential user targeting for DF events and technologies during program design. This work also contributes to understanding when and who might adopt these DF technologies based on their daily routine activities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Bridging adsorption behavior of confined CH 4 -CO 2 binary mixtures across scales

An accurate understanding of the competitive adsorption of CH 4 -CO 2 binary mixtures in nano-confined systems is critical for engineering CO 2 storage in shale gas reservoirs. Due to difficulties in making reliable experimental observations in nano-scale, atomistic simulations (ASs), such as the Grand Canonical Monte Carlo (GCMC) method, provide a viable approach to studying the adsorption behavior of confined fluids. ASs are, however, limited in the size of the compositional domain due to the high computational cost. This work proposes a framework that combines AS and the lattice Boltzmann (LB) method to bridge the physics of confined fluids across scales. The Peng–Robinson equation of state (PR-EoS) produces fugacity coefficients, which serve as input for conducting multi-component GCMC simulations. These GCMC simulations explore the competitive adsorption behavior of CH 4 -CO 2 in nano-slits at various composition, pressure, and channel-width conditions. Both components generate adsorption layers with high densities near the walls with CO 2 preferentially adsorbing compared to CH 4 on the organic walls of carbon sheets. At the mesoscale, a pseudopotential model represents the intermolecular forces in multi-component, multiple-relaxation-time LB simulations. The LB simulations are in good agreement with the GCMC results, allowing us to obtain values for tunable LB parameters. We then extend the use of LB to simulate adsorption behavior in complex networks with nano-sized channels. The phase behavior and fluid properties in the complex geometries of nano-channels differ from nano-slits and bulk systems. Furthermore, the bridging of physics from GCMC (microscale) to LB (mesoscale) via the macroscale PR-EoS connects the adsorption behavior of binary systems across scales.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Emotional and Behavioral Functioning After Conformal Radiation Therapy for Pediatric Ependymoma

Purpose: The standard of care for pediatric patients with ependymoma involves postoperative radiation therapy. Prior research suggests that conformal radiation therapy (CRT) is associated with relative sparing of cognitive and academic functioning, but little is known about the effect of CRT on emotional and behavioral functioning. Methods and Materials: A total of 113 patients with pediatric ependymoma underwent CRT using photons as part of their enrollment on an institutional trial. Patients completed annual evaluations of neurocognitive functioning during the first 5 years after CRT. Emotional and behavioral functioning was assessed via the Child Behavior Checklist. Results: Before CRT, emotional and behavioral functioning were commensurate with those of the normative population and within normal limits. After 5 years, means remained within normal limits but were significantly below the normative mean. Linear mixed models revealed a significant increase in attention problems over time. These problems were associated with age at diagnosis/CRT, tumor location, and extent of resection. A higher-than-expected incidence of school problems was present at all assessment points after baseline. Conclusions: The use of photon CRT for ependymoma is associated with relatively stable emotional and behavioral functioning during the first 5 years after treatment. The exception is an increase in attention problems. Results suggest that intervening earlier in the survivorship period—during the first year posttreatment—may be beneficial.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Improving the behavioral realism of global integrated assessment models: An application to consumers’ vehicle choices

A large body of transport sector-focused research recognizes the complexity of human behavior in relation to mobility. Yet, global integrated assessment models (IAMs), which are widely used to evaluate the costs, potentials, and consequences of different greenhouse gas emission trajectories over the medium-to-long term, typically represent behavior and the end use of energy as a simple rational choice between available alternatives, even though abundant empirical evidence shows that real-world decision making is more complex and less routinely rational. This paper demonstrates the value of incorporating certain features of consumer behavior in IAMs, focusing on light-duty vehicle (LDV) purchase decisions. An innovative model formulation is developed to represent heterogeneous consumer groups with varying preferences for vehicle novelty, range, refueling/recharging availability, and variety. The formulation is then implemented in the transport module of MESSAGE-Transport, a global IAM, although it also has the generic flexibility to be applied in energy-economy models with varying set-ups. Comparison of conventional and behaviorally-realistic model runs with respect to vehicle purchase decisions shows that consumer preferences may slow down the transition to alternative fuel (low-carbon) vehicles. Consequently, stronger price-based incentives and/or non-price based measures may be needed to transform the global fleet of passenger vehicles, at least in the initial market phases of novel alternatives. Otherwise, the mitigation burden borne by other transport sub-sectors and other energy sectors could be higher than previously estimated. Moreover, capturing behavioral features of energy consumers in global IAMs increases their usefulness to policy makers by allowing a more realistic assessment of a more diverse suite of policies.

33 ADVANCED PROPULSION SYSTEMS↗

Past agricultural land use affects multiple facets of ungulate antipredator behavior

Antipredator behavior affects prey fitness, prey demography, and the strength of ecological interactions. Although predator-prey interactions increasingly occur in habitats that experience multiple forms of human-generated disturbance, it is unclear how different forms of disturbance might affect antipredator behavior. Fire is a contemporary disturbance that has dramatic effects on terrestrial habitats. Such habitats may have also experienced past disturbances, like agricultural land use, that leave lasting legacies on habitat structure (e.g., overstory and understory composition). It is unclear how these past and present disturbances affect the use of different antipredator behaviors, like temporal avoidance and vigilance. We examined whether variation in disturbance regimes generates differences in ungulate antipredator behavior by using cameras to measure white-tailed deer vigilance and activity time across 24 longleaf pine woodlands that vary in past land use and contemporary fire regime. Regardless of land-use history, woodlands with high fire frequencies had 4 times less vegetation cover than low-fire woodlands, generating riskier habitats for deer; however, deer responded to fire with different antipredator strategies depending on land-use history. In nonagricultural woodlands, fire affected deer activity time such that activity was nocturnal in low-fire woodlands and crepuscular in high-fire woodlands. In post-agricultural woodlands, fire affected vigilance and not activity time such that deer were more vigilant in high-fire woodlands than in low-fire woodlands. Furthermore, these results suggest that ungulate antipredator behavior may vary spatially depending on past land use and contemporary fire regime, and such disturbances may generate “landscapes of fear” that persist for decades after agricultural use.

60 APPLIED LIFE SCIENCES↗

Promoting Sustainable Transportation Modes: A Systematic Review of Behavior-Change Strategies

In previous studies, many travel-behavior-change strategies often relied on single behavior determinants or psychological theories, overlooking the incorporation of sociopsychological theories for guidance in their design. Integrating these theories could offer consistent guidance for program developers and enhance intervention effectiveness. This paper systematically reviews interventions targeting travel-behavior change, with a focus on self-determination theory and its principles of satisfying individuals’ competence, autonomy, and relatedness needs for enacting change. Additionally, experiment design methods, including randomized controlled trials and quasi-experimental designs, are reviewed and discussed. Key findings highlight the effectiveness of personalized interventions and integrating feedback with goal-setting strategies. Given the limited direct references to sociopsychological theories in existing studies, we explore relevant sociopsychological theories applicable to travel-behavior-change programs to provide examples of how strategies could be designed based on them. This review contributes valuable insights into the development of strategies for changing travel behavior, offering a theoretical framework for researchers and practitioners to guide intervention design, experimentation, and evaluation. In conclusion, leveraging these theories not only facilitates reproducibility but also provides a standardized approach for transportation demand management program developers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Preconception paternal bisphenol A exposure induces sex-specific anxiety and depression behaviors in adult rats

Bisphenol A (BPA), an environmental endocrine-disrupting compound, has drawn a great attention for its adverse effect on behavioral development. Maternal exposure to this compound has been reported to induce anxiety and depression in offspring, but the effect of its paternal exposure is rarely discussed. This study investigated whether preconception paternal BPA exposure can affect the emotions of male rats and their offspring. Eighteen adult male rats (F0) received either a vehicle or 50 μg/kg/day BPA diet for 21 weeks and were then mated with non-exposed females to produce offspring (F1). The affective behaviors of F0 and F1 rats were evaluated in the open-field test, the elevated-plus maze and the forced swimming test, and their serum corticosterone were then examined. BPA exposure induced increased anxiety behaviors along with increased serum corticosterone in F0 rats. This paternal exposure also led to increased anxiety behaviors in F1 females and aggravated depression behaviors in both sexes of F1 rats. Furthermore, only F1 females exhibited increased serum corticosterone. Overall, these data indicate that preconception paternal exposure to a low dose of BPA may induce transgenerational sex-specific impairments in the affection of adult rats.

59 BASIC BIOLOGICAL SCIENCES↗

Acoustic Imaging Evaluation of Juvenile Salmonid Behavior in the Immediate Forebay of the Water Temperature Control Tower at Cougar Dam, 2010

This report presents the results of an evaluation of juvenile Chinook salmon (Oncorhynchus tshawytscha) behavior at Cougar Dam on the south fork of the McKenzie River in Oregon in 2010. The study was conducted by the Pacific Northwest National Laboratory for the U.S. Army Corps of Engineers (USACE). The overall goal of the study was to characterize juvenile salmonid behavior and movement patterns in the immediate forebay of the Water Temperature Control (WTC) tower of the dam for USACE and fisheries resource managers use in making decisions about bioengineering designs for long-term structures and/or operations to facilitate safe downstream passage for juvenile salmonids. We collected acoustic imaging (Dual-Frequency Identification Sonar; DIDSON) data from March 1, 2010, through January 31, 2011. Juvenile salmonids (hereafter, called 'fish') were present in the immediate forebay of the WTC tower throughout the study. Fish abundance index was low in early spring (<200 fish per sample-day), increased in late April, and peaked on May 19 (6,039 fish). A second peak was observed on June 6 (2904 fish). Fish abundance index decreased in early June and remained low in the summer months (<100 fish per sample-day). During the fall and winter, fish numbers varied with a peak on November 10 (1881 fish) and a minimum on December 7 (12 fish). A second, smaller, peak occurred on December 22 (607 fish). A univariate statistical analysis indicated fish abundance index (log10-transformed) was significantly (P<0.05) positively correlated with forebay elevation, velocity over the WTC tower intake gate weirs, and river flows into the reservoir. A subsequent multiple regression analysis resulted in a model (R2=0.70) predicting fish abundance (log-transformed index values) using two independent variables of mean forebay elevation and the log10 of the forebay elevation range. From the approximate fish length measurements made using the DIDSON imaging software, the average fish length during early spring 2010 was 214 ± 86 mm (standard deviation). From May through early November, the average fish length remained relatively consistent (132 ± 54 mm), after which average lengths increased to 295 ± 148 mm for mid-November though early December. From mid-December through January the average fish length decreased to 151 76 mm. Milling in front of the WTC tower was the most common fish behavior observed throughout the study period. Traversing along the front of the tower, east-to-west and west-to-east, was the next common behavior. The percentage of fish events showing movement from the forebay to the tower or from the tower to the forebay was generally low throughout the spring, summer, and early fall (0 to 30% for both directions combined, March through early November). From mid-November 2010 through the end of the study (January 31, 2011), the combined percentages of fish moving into and out of the tower were higher (25 to 70%) than during previous months of the study. Schooling behavior was most distinct in the spring. Schooling events were present in 30 to 96% of the fish events during that period, with a peak on May 19. Schooling events were also present in the summer, but at lower numbers. With the exception of some schooling in mid-December, few to no schooling events were observed in the fall and winter months. Diel distributions for schooling fish during spring and fall months indicate schooling was concentrated during daylight hours and no schooling was observed at night. However, in December, schooling occurred at night, after midnight, and during daylight hours. Predator activity, most likely bull trout or rainbow trout according to a USACE biologist, was observed during late spring, when fish abundance index and schooling were highest for the year, and again in the fall months when fish events increased from a summer low. No predator activity was observed in the summer, and little activity occurred during the winter months.

54 ENVIRONMENTAL SCIENCES↗

Empirical Study of Effect of Dynamic Travel Time Information on Driver Route Choice Behavior

The objective of this paper is to study the effect of travel time information on day-to-day driver route choice behavior. A real-world experimental study is designed to have participants repeatedly choose between two alternative routes for five origin-destination pairs over multiple days after providing them with dynamically updated travel time information (average travel time and travel time variability). The results demonstrate that historical travel time information enhances behavioral rationality by 10% on average and reduces inertial tendencies to increase risk seeking in the gain domain. Furthermore, expected travel time information is demonstrated to be more effective than travel time variability information in enhancing rational behavior when drivers have limited experiences. After drivers gain sufficient knowledge of routes, however, the difference in behavior associated with the two information types becomes insignificant. The results also demonstrate that, when drivers lack experience, the faster less reliable route is more attractive than the slower more reliable route. However, with cumulative experiences, drivers become more willing to take the more reliable route given that they are reluctant to become risk seekers once experience is gained. Furthermore, the effect of information on driver behavior differs significantly by participant and trip, which is, to a large extent, dependent on personal traits and trip characteristics.

99 GENERAL AND MISCELLANEOUS↗

Implementing Sustainability: The Behavioral-Institutional Dimension

Organizations, both public and private sector, are increasingly pursuing strategies to reduce their energy use and increase sustainability. Whether these efforts are based on economic rationale, community spirit, environmental ethics, federal mandates, or other reasons, they predominantly feature strategies that rely on new technologies. If there is any focus on behavior change, it is typically addressed to changing individual behavior. While we recognize the importance and limitations of the role of individual behavior in promoting sustainability goals, we are more interested in the role of institutional behavior. We have reviewed the small but growing literature on institutional behavior change, and have identified eight evidence-based principles as a guide for federal agencies to take action. This article presents the principles and illustrates them with examples to suggest ways that they can serve as models for others.

Sustain↗

OpenPATH - Leveraging Technology to Measure Travel Behavior

Shifting transportation to more sustainable modes is a key piece of the decarbonization puzzle. However, mobility behavior and travel patterns are difficult to influence because they are difficult to measure. OpenPATH provides a tool to capture longitudinal behaviors through a smartphone application. Agencies interested in gathering data about a population's travel behavior can set up a deployment of the app customized to the needs of their community. Partners can choose between simple mode and purpose labels or surveys for each trip to balance the level of user engagement with the associated burden. The labels, trip surveys, and an initial demographic survey can all be tailored to the specific context of the deployment. The OpenPATH tool is unique in its open-source nature, ability to gather detailed longitudinal travel data, and design allowing direct engagement with travelers. A valuable technological advancement, this tool enables partners to measure the way changes in the transportation landscape impact their community. The suite of tools includes both public and administrator dashboards. The public dashboard supports continuous data analysis through charts presenting trip information updated daily. The administrator dashboard displays geospatial data and supports data export. Example applications have included e-bike programs; gathering valuable metrics on increased access to opportunities and reduction in VMT, and studies aimed at understanding existing mobility behavior to see where advancements such as electric vehicles could fit into these habits. OpenPATH collects travel data in association with an initial demographic survey, enabling detailed insight into the behavior patterns or impact of a certain program on different populations.

ADVANCED PROPULSION SYSTEMS↗

Engineering Relaxor Behavior in (BaTiO 3 ) n /(SrTiO 3 ) n Superlattices

Complex-oxide superlattices provide a pathway to numerous emergent phenomena because of the juxtaposition of disparate properties and the strong interfacial interactions in these unit-cell-precise structures. This is particularly true in superlattices of ferroelectric and dielectric materials, wherein new forms of ferroelectricity, exotic dipolar textures, and distinctive domain structures can be produced. Here, in this study, relaxor-like behavior, typically associated with the chemical inhomogeneity and complexity of solid solutions, is observed in (BaTiO 3 ) n /(SrTiO 3 ) n (n = 4–20 unit cells) superlattices. Dielectric studies and subsequent Vogel–Fulcher analysis show significant frequency dispersion of the dielectric maximum across a range of periodicities, with enhanced dielectric constant and more robust relaxor behavior for smaller period n. Bond-valence molecular-dynamics simulations predict the relaxor-like behavior observed experimentally, and interpretations of the polar patterns via 2D discrete-wavelet transforms in shorter-period superlattices suggest that the relaxor behavior arises from shape variations of the dipolar configurations, in contrast to frozen antipolar stripe domains in longer-period superlattices (n = 16). Moreover, the size and shape of the dipolar configurations are tuned by superlattice periodicity, thus providing a definitive design strategy to use superlattice layering to create relaxor-like behavior which may expand the ability to control desired properties in these complex systems.

36 MATERIALS SCIENCE↗