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Epitaxial strain has been shown to produce dramatic changes to the orbital structure in transition metal perovskite oxides and, in turn, the rate of oxygen electrocatalysis therein. Here, epitaxial strain is used to investigate the relationship between surface electronic structure and oxygen electrocatalysis in prototypical fuel cell cathode systems. Combining high-temperature electrical-conductivity-relaxation studies and synchrotron-based X-ray absorption spectroscopy studies of La0.5Sr0.5CoO3 and La0.8Sr0.2Co0.2Fe0.8O3 thin films under varying degrees of epitaxial strain reveals a strong correlation between orbital structure and catalysis rates. In both systems, films under biaxial tensile strain simultaneously exhibit the fastest reaction kinetics and lowest electron occupation in the dz2 orbitals. These results are discussed in the context of broader chemical trends and electronic descriptors are proposed for oxygen electrocatalysis in transition metal perovskite oxides.

Solid-gas interactions at electrode surfaces determine the efficiency of solid-oxide fuel cells and electrolyzers. Here, the correlation between surface-gas kinetics and the crystal orientation of perovskite electrodes is studied in the model system La0.8 Sr0.2 Co0.2 Fe0.8 O3 . The gas-exchange kinetics are characterized by synthesizing epitaxial half-cell geometries where three single-variant surfaces are produced [i.e., La0.8 Sr0.2 Co0.2 Fe0.8 O3 /La0.9 Sr0.1 Ga0.95 Mg0.05 O3-δ /SrRuO3 /SrTiO3 (001), (110), and (111)]. Electrochemical impedance spectroscopy and electrical conductivity relaxation measurements reveal a strong surface-orientation dependency of the gas-exchange kinetics, wherein (111)-oriented surfaces exhibit an activity >3-times higher as compared to (001)-oriented surfaces. Oxygen partial pressure ( pO2 )-dependent electrochemical impedance spectroscopy studies reveal that while the three surfaces have different gas-exchange kinetics, the reaction mechanisms and rate-limiting steps are the same (i.e., charge-transfer to the diatomic oxygen species). First-principles calculations suggest that the formation energy of vacancies and adsorption at the various surfaces is different and influenced by the surface polarity. Finally, synchrotron-based, ambient-pressure X-ray spectroscopies reveal distinct electronic changes and surface chemistry among the different surface orientations. Taken together, thin-film epitaxy provides an efficient approach to control and understand the electrode reactivity ultimately demonstrating that the (111)-surface exhibits a high density of active surface sites which leads to higher activity.

Cyberbullying is a major issue during adolescence; however, little is known about trends in its prevalence, or how the early stages of the COVID-19 pandemic has impacted these trends. This study investigated how the prevalence of cyberbullying and associated factors (depression, social anxiety, and coping self-efficacy) have changed from 2015 to 2020. The study used self-report data from Australian high-school students (n = 1418) aged 11-16 years, collected yearly from 2015 to 2020. Findings showed that while prevalence of cyber victimization increased over time, prevalence of cyber perpetration did not. However, sharp rises of both cyber victimization and cyber perpetration were noted during the early stages of the COVID-19 pandemic in 2020. While no increases in mental health difficulties were reported in 2020, increases in cyberbullying and decreases in coping self-efficacy are concerning.

Solid-oxide fuel/electrolyzer cells are limited by a dearth of electrolyte materials with low ohmic loss and an incomplete understanding of the structure-property relationships that would enable the rational design of better materials. Here, using epitaxial thin-film growth, synchrotron radiation, impedance spectroscopy, and density-functional theory, the impact of structural parameters (i.e., unit-cell volume and octahedral rotations) on ionic conductivity is delineated in La0.9 Sr0.1 Ga0.95 Mg0.05 O3- δ . As compared to the zero-strain state, compressive strain reduces the unit-cell volume while maintaining large octahedral rotations, resulting in a strong reduction of ionic conductivity, while tensile strain increases the unit-cell volume while quenching octahedral rotations, resulting in a negligible effect on the ionic conductivity. Calculations reveal that larger unit-cell volumes and octahedral rotations decrease migration barriers and create low-energy migration pathways, respectively. The desired combination of large unit-cell volume and octahedral rotations is normally contraindicated, but through the creation of superlattice structures both expanded unit-cell volume and large octahedral rotations are experimentally realized, which result in an enhancement of the ionic conductivity. All told, the potential to tune ionic conductivity with structure alone by a factor of ≈2.5 at around 600 °C is observed, which sheds new light on the rational design of ion-conducting perovskite electrolytes.

Heterointerfaces introduce unique localized defects into ionic conductors. This study explores the nanoionic characteristics exhibited by the proton-conducting oxides SrZr 0.9 Y 0.1 O 3-δ and SrCe 0.95 Yb 0.05 O 3-δ including finely dispersed precipitated platinum nanoparticles. The electrical conductivity of both the platinum-doped oxides revealed reversible nanoionic phenomena caused by the exsolution of the platinum in the form of platinum nanoparticles, at 0.5 vol% relative to the metal oxides, and dissolution in response to a change in gas atmosphere. In comparison with the original conductivity of SrZr 0.9 Y 0.1 O 3-δ and SrCe 0.95 Yb 0.05 O 3-δ , the conductivity of platinum-doped SrZr 0.9 Y 0.1 O 3-δ decreased significantly in a wet hydrogen atmosphere, whereas platinum-doped SrCe 0.95 Yb 0.05 O 3-δ showed almost no decrease in conductivity in the same atmosphere. The different responses of the two materials to the change in gas atmosphere are discussed in relation to the precipitation of platinum nanoparticles.

Moral disengagement, a term coined by Bandura, explains how people can hurt others and behave immorally, contradicting their beliefs and values and yet live peaceably with themselves. He developed a conceptual model of moral disengagement based on the Social Cognitive Theory of moral agency. By activating moral disengagement mechanisms, people can disregard their moral standards and behave immorally without any self‐censure. This paper synthesises the extensive research on moral disengagement in a youth bullying context to demonstrate the conceptual strength of moral disengagement theory for research and interventions on bullying. It advances the understanding of the contextual basis of moral disengagement by examining its diverse role in bullying episodes that includes bullying perpetration, bullying victimisation and bullying bystanders. The analysis of the different components of moral disengagement representing the loci of the disengagement mechanisms sheds light on how the disengagement processes function in the youth bullying context. Its role as both a moderator and mediator further advances its divergent functioning in the youth bullying context. Finally, implications of the review for advancing knowledge on the moral basis of youth bullying are discussed with suggestions for the development of interventions to attenuate the use of moral disengagement. [ABSTRACT FROM AUTHOR]

We report the first experimental demonstration of ferroelectric field-effect transistor (FEFET) based spiking neurons. A unique feature of the ferroelectric (FE) neuron demonstrated herein is the availability of both excitatory and inhibitory input connections in the compact 1T-1FEFET structure, which is also reported for the first time for any neuron implementations. Such dual neuron functionality is a key requirement for bio-mimetic neural networks and represents a breakthrough for implementation of the third generation spiking neural networks (SNNs) - also reported herein for unsupervised learning and clustering on real world data for the first time. The key to our demonstration is the careful design of two important device level features: (1) abrupt hysteretic transitions of the FEFET with no stable states therein, and (2) the dynamic tunability of the FEFET hysteresis by bias conditions which allows for the inhibition functionality. Experimentally calibrated, multi-domain Preisach based FEFET models were used to accurately simulate the FE neurons and project their performance at scaled nodes. We also implement an SNN for unsupervised clustering and benchmark the network performance across analog CMOS and emerging technologies and observe (1) unification of excitatory and inhibitory neural connections, (2) STDP based learning, (3) lowest reported power (3.6nW) during classification, and (4) a classification accuracy of 93%.

Many energy conversion, sensing, and microelectronic applications based on ferroic materials are determined by the domain structure evolution under applied stimuli. New hyperspectral, multidimensional spectroscopic techniques now probe dynamic responses at relevant length and time scales to provide an understanding of how these nanoscale domain structures impact macroscopic properties. Such approaches, however, remain limited in use because of the difficulties that exist in extracting and visualizing scientific insights from these complex datasets. Using multidimensional band-excitation scanning probe spectroscopy and adapting tools from both computer vision and machine learning, an automated workflow is developed to featurize, detect, and classify signatures of ferroelectric/ferroelastic switching processes in complex ferroelectric domain structures. This approach enables the identification and nanoscale visualization of varied modes of response and a pathway to statistically meaningful quantification of the differences between those modes. Among other things, the importance of domain geometry is spatially visualized for enhancing nanoscale electromechanical energy conversion.

Many energy conversion, sensing, and microelectronic applications based on ferroic materials are determined by the domain structure evolution under applied stimuli. New hyperspectral, multidimensional spectroscopic techniques now probe dynamic responses at relevant length and time scales to provide an understanding of how these nanoscale domain structures impact macroscopic properties. Such approaches, however, remain limited in use because of the difficulties that exist in extracting and visualizing scientific insights from these complex datasets. Using multidimensional band-excitation scanning probe spectroscopy and adapting tools from both computer vision and machine learning, an automated workflow is developed to featurize, detect, and classify signatures of ferroelectric/ferroelastic switching processes in complex ferroelectric domain structures. This approach enables the identification and nanoscale visualization of varied modes of response and a pathway to statistically meaningful quantification of the differences between those modes. Among other things, the importance of domain geometry is spatially visualized for enhancing nanoscale electromechanical energy conversion.

ObjectiveMethodResultsConclusions\nKey PointsCognitive behaviour therapy (CBT) for social anxiety disorder (SAD) can be delivered through several modalities, including individually-administered CBT (ICBT), group-based CBT (GCBT), and CBT delivered remotely (RCBT). We synthesised the current literature on ICBT, GCBT, and RCBT approaches in adults with SAD, and compared their relative effectiveness using a meta-analytic approach.This review included randomised controlled trials comparing a disorder specific CBT monotherapy (ICBT, GCBT, or RCBT) to a non-active control group in adults with diagnosed SAD. Eligible studies were searched through PsycINFO, Scopus, and EMBASE databases to April 2023. A total of 37 studies met the inclusion criteria (with 55 between-group comparisons; N = 3234). Between-group effect sizes were conducted using random effects models.Analyses indicated that RCBT (k = 23; g = 0.90; 95% CI = 0.74–1.06) and ICBT (k = 17; g = 0.95; 95% CI = 0.66–1.23) demonstrated large effects, while GCBT demonstrated medium effects (k = 15; g = 0.71; 95% CI = 0.49–0.94). The groups, however, did not differ significantly (Q2 = 2.17, p > .05).This study builds on the existing literature demonstrating the efficacy of these treatment approaches.What is already known about this topic: Cognitive behaviour therapy (CBT) is an effective treatment for adults with social anxiety disorder (SAD).CBT for SAD can be delivered using a variety of treatment approaches.Individually-administered CBT, group-based CBT and remotely-delivered CBT are the primary treatment modalities.Cognitive behaviour therapy (CBT) is an effective treatment for adults with social anxiety disorder (SAD).CBT for SAD can be delivered using a variety of treatment approaches.Individually-administered CBT, group-based CBT and remotely-delivered CBT are the primary treatment modalities.What this topic adds: This study compared the effectiveness of different CBT delivery methods.Individually-administered CBT and remotely delivered CBT result in large effect sizes, while group-based CBT results in medium effect sizes.Individually-administered CBT, group-based CBT and remotely-delivered CBT can be delivered with equal confidence based on client preference.This study compared the effectiveness of different CBT delivery methods.Individually-administered CBT and remotely delivered CBT result in large effect sizes, while group-based CBT results in medium effect sizes.Individually-administered CBT, group-based CBT and remotely-delivered CBT can be delivered with equal confidence based on client preference. [ABSTRACT FROM AUTHOR]

Introduction: Meta‐analytic reviews suggest similar outcomes across trauma‐focused psychotherapies for adults with post‐traumatic stress disorder (PTSD). However, this conclusion may be premature due to suboptimal statistical‐review methodologies. Network meta‐analysis (NMA) allows a detailed rank‐ordering of the efficacy of established psychotherapy interventions derived from indirect evidence as well as results from direct head‐to‐head comparisons. Objective: We sought to determine the efficacy and attrition rates of psychotherapy interventions for PTSD by applying NMA. Methods: We searched EMBASE, PsychINFO, PTSDPubs and PubMed for randomised controlled trials that compared psychotherapies either head‐to‐head or against controls for adults with PTSD. A frequentist NMA was used to compare direct and indirect effects to determine the efficacy and attrition rates of psychotherapy interventions. Results: Of the 5649 papers identified, 82 trials comprising of 5838 patients were included. The network comprised 17 psychotherapies and four control conditions. Network estimates indicated superior efficacy of meta‐cognitive therapy and cognitive processing therapy over other psychotherapies (ESs between = 0.26 and 2.32). Written exposure therapy and narrative exposure therapy were associated with lower risk of drop out when considered alongside other psychotherapies. Confidence in the network meta‐analytic estimates was considered moderate for both outcomes. Conclusions: In broad terms, therapeutic commensurability was evident. Nevertheless, with additional studies and larger sample sizes, meta‐cognitive and written exposure therapies could indeed differentiate themselves from other approaches as having favourable efficacy and acceptability respectively. These findings may inform clinical decision‐making, as well as guide future research for PTSD. [ABSTRACT FROM AUTHOR]

Cognitive‐behavioral therapy (CBT) is regarded as an effective treatment for anxiety disorders in childhood. Researchers have begun to investigate potential mechanisms of change that drive these positive outcomes, including shifts in cognitions, behavior, and affect. However, few studies have established the mediational effects of these factors as a proxy for establishing mechanistic change. This meta‐analysis attempts to synthesize the literature on potential mechanisms of change in CBT for childhood anxiety and investigates the mediational effects of these factors on treatment outcomes. Seventeen studies met the inclusion criteria. Across studies, five potential mediators were identified: externalizing difficulties, negative self‐talk, coping, fear, and depression. Results indicated that CBT was effective in improving outcomes on all potential mediators, except for fear. Mediational analyses showed that externalizing difficulties, negative self‐talk, coping, and depression mediated anxiety following treatment. Fear did not mediate the relationship. Implications for future mechanisms of change research are proposed. [ABSTRACT FROM AUTHOR]

Many energy conversion, sensing, and microelectronic applications based on ferroic materials are determined by the domain structure evolution under applied stimuli. New hyperspectral, multidimensional spectroscopic techniques now probe dynamic responses at relevant length and time scales to provide an understanding of how these nanoscale domain structures impact macroscopic properties. Such approaches, however, remain limited in use because of the difficulties that exist in extracting and visualizing scientific insights from these complex datasets. Using multidimensional band-excitation scanning probe spectroscopy and adapting tools from both computer vision and machine learning, an automated workflow is developed to featurize, detect, and classify signatures of ferroelectric/ferroelastic switching processes in complex ferroelectric domain structures. This approach enables the identification and nanoscale visualization of varied modes of response and a pathway to statistically meaningful quantification of the differences between those modes. Among other things, the importance of domain geometry is spatially visualized for enhancing nanoscale electromechanical energy conversion., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)