Search for: All records

We have revisited the Kittel model that describes antiferroelectricity (AFE) in terms of two sublattices of spontaneous polarization with antiparallel couplings. By constructing a comprehensive phase diagram including the antiferroelectric, ferroelectric, and paraelectric phases in the parameter space, we have identified an AFE phase with stable antipolar states and metastable polar states (SAMP) when the coupling between the two sublattices is weak. We find that the metastability of the polar state in the SAMP AFE phase can lead to apparent ferroelectric behavior, depending on the measurement timescale—for example, the frequency of the applied electric field. This explains the observed ferroelectric behavior of orthorhombic hafnia, which is predicted to be antipolar from density functional theory calculations. 

The atomic structures at epitaxial film–substrate interfaces determine the scalability of thin films and can result in new phenomena. However, it is challenging to control the structure of the interface. In this work, we report the strong tunability of the epitaxial interface of improper ferroelectric hexagonal ferrites deposited on spinel ferrites, achieving the artificial selection of two types of interfaces that are related by a 90° rotation of in-plane epitaxial relations and feature either disordered or hybrid reconstruction. The hybrid-type interface exhibits characteristic structures of both hexagonal ferrites and spinel ferrites, which remove the critical thickness for improper ferroelectricity. This tunable interfacial structure provides critical insight into controlling interfacial clamping to maintain robust improper ferroelectricity at the two-dimensional limit. 

Abstract Study Objectives The usage of wrist-worn wearables to detect sleep–wake states remains a formidable challenge, particularly among individuals with disordered sleep. We developed a novel and unbiased data-driven method for the detection of sleep–wake and compared its performance with the well-established Oakley algorithm (OA) relative to polysomnography (PSG) in elderly men with disordered sleep. Methods Overnight in-lab PSG from 102 participants was compared with accelerometry and photoplethysmography simultaneously collected with a wearable device (Empatica E4). A binary segmentation algorithm was used to detect change points in these signals. A model that estimates sleep or wake states given the changes in these signals was established (change point decoder, CPD). The CPD’s performance was compared with the performance of the OA in relation to PSG. Results On the testing set, OA provided sleep accuracy of 0.85, wake accuracy of 0.54, AUC of 0.67, and Kappa of 0.39. Comparable values for CPD were 0.70, 0.74, 0.78, and 0.40. The CPD method had sleep onset latency error of −22.9 min, sleep efficiency error of 2.09%, and underestimated the number of sleep–wake transitions with an error of 64.4. The OA method’s performance was 28.6 min, −0.03%, and −17.2, respectively. Conclusions The CPD aggregates information from both cardiac and motion signals for state determination as well as the cross-dimensional influences from these domains. Therefore, CPD classification achieved balanced performance and higher AUC, despite underestimating sleep–wake transitions. The CPD could be used as an alternate framework to investigate sleep–wake dynamics within the conventional time frame of 30-s epochs.