Search for: All records

Ferroelectric materials, which exhibit switchable polarization, are potential candidates for photovoltaic applications owing to their intriguing charge carrier separation mechanism associated with polarization and breaking of inversion symmetry. To overcome the low photocurrent of ferroelectrics, extensive efforts have focused on reducing their bandgaps to increase the optical absorption of the solar spectrum and thus the power conversion efficiency. Here, a new avenue of enhancing photovoltaic performance via engineering the polarization across a morphotropic phase boundary (MPB) is reported. Tetragonal compositions in the vicinity of the MPB in a PbTiO₃‐Bi(Ni_1/2Ti_1/2)O₃solid solution are shown to generate up to 3.6 kV cm⁻¹photoinduced electric field and 6.2 µA cm⁻²short‐circuit photocurrent, multiple times higher than its pseudocubic counterpart under the same illumination conditions with excellent polarization retention. This enhancement allows the investigation of the correlation between the polarization switching and photovoltaic switching, which enables a controllable multistate photocurrent. Combined with a bandgap of 2.2 eV, this material exhibits a sizable photoresponse over a broad spectral range. These findings provide a new approach to improve the photovoltaic performance of ferroelectric materials and can expand their potential applications in optoelectronic devices.

In the Alzheimer’s disease (AD) continuum, the prodromal state of mild cognitive impairment (MCI) precedes AD dementia and identifying MCI individuals at risk of progression is important for clinical management. Our goal was to develop generalizable multivariate models that integrate high-dimensional data (multimodal neuroimaging and cerebrospinal fluid biomarkers, genetic factors, and measures of cognitive resilience) for identification of MCI individuals who progress to AD within 3 years. Our main findings were i) we were able to build generalizable models with clinically relevant accuracy (~93%) for identifying MCI individuals who progress to AD within 3 years; ii) markers of AD pathophysiology (amyloid, tau, neuronal injury) accounted for large shares of the variance in predicting progression; iii) our methodology allowed us to discover that expression ofCR1(complement receptor 1), an AD susceptibility gene involved in immune pathways, uniquely added independent predictive value. This work highlights the value of optimized machine learning approaches for analyzing multimodal patient information for making predictive assessments.