Electrocaloric (EC) materials show promise in eco-friendly solid-state refrigeration and integrable on-chip thermal management. While direct measurement of EC thin-films still remains challenging, a generic theoretical framework for quantifying the cooling properties of rich EC materials including normal-, relaxor-, organic- and anti-ferroelectrics is imperative for exploiting new flexible and room-temperature cooling alternatives. Here, we present a versatile theory that combines Master equation with Maxwell relations and analytically relates the macroscopic cooling responses in EC materials with the intrinsic diffuseness of phase transitions and correlation characteristics. Under increased electric fields, both EC entropy and adiabatic temperature changes increase quadratically initially, followed by further linear growth and eventual gradual saturation. The upper bound of entropy change (∆S_max) is limited by distinct correlation volumes (V _cr ) and transition diffuseness. The linearity between V _cr and the transition diffuseness is emphasized, while ∆S_max = 300 kJ/(K.m³) is obtained for Pb_0.8Ba_0.2ZrO₃. The ∆S_max in antiferroelectric Pb_0.95Zr_0.05TiO₃, Pb_0.8Ba_0.2ZrO₃ and polymeric ferroelectrics scales proportionally with V _cr^-2.2, owing to the one-dimensional structural constraint on lattice-scale depolarization dynamics; whereas ∆S_max in relaxor and normal ferroelectrics scales as ∆S_max ~ V _cr^-0.37, which tallies with a dipolar interaction exponent of 2/3 in EC materials and the well-proven fractional dimensionality of 2.5 for ferroelectric domain walls.

The Metacapacitors project aims to improve efficiency, functionality and form factor of offline power converters suitable for LED solid-state lighting, with a view to developing an attractive technology platform for load management and power conversion across a broad range of applications. Based on integrated switched-capacitor (SC) topologies, the project adopts an integrated approach from materials to devices to circuits. We designed capacitors based on high-κ dielectric nanocrystals, that can be prepared using high-throughput microfabrication/ nanotechnology techniques, ink deposition and multilayering. The capacitor dielectric, a nanocomposite composed of (Ba, Sr)TiO3 nanocrystals in polyfurfuryl alcohol (BST/PFA, κ > 20, 100Hz-1 MHz, loss < 0.01, 20 kHz), targets a high volumetric capacitance density and ripple current capability. The Dielectric is demonstrated to function in a finished capacitor >1000 h at 125°C. The capacitors were board integrated with a custom hybrid-switched-capacitor-resonant dc-dc converter IC. The converter integrates a balanced SC front-end with a series resonant tank, enabling nearly lossless current regulation and tranformerless galvanic isolation. The converter IC can be stacked in the voltage domain to interface a range of inputs. The tested driver delivers about 15 Wat 470 mA to a string of 12 LEDs with 90% peak efficiency.

MOTIVATION: Pediatric kidney disease is a widespread, progressive condition that severely impacts growth and development of children. Chronic kidney disease is often more insidious in children than in adults, usually requiring a renal biopsy for diagnosis. Biopsy evaluation requires copious examination by trained pathologists, which can be tedious and prone to human error. In this study, we propose an artificial intelligence (AI) method to assist pathologists in accurate segmentation and classification of pediatric kidney structures, named as AI-based Pediatric Kidney Diagnosis (APKD). RESULTS: We collected 2935 pediatric patients diagnosed with kidney disease for the development of APKD. The dataset comprised 93 932 histological structures annotated manually by three skilled nephropathologists. APKD scored an average accuracy of 94% for each kidney structure category, including 99% in the glomerulus. We found strong correlation between the model and manual detection in detected glomeruli (Spearman correlation coefficient r = 0.98, P < .001; intraclass correlation coefficient ICC = 0.98, 95% CI = 0.96-0.98). Compared to manual detection, APKD was approximately 5.5 times faster in segmenting glomeruli. Finally, we show how the pathological features extracted by APKD can identify focal abnormalities of the glomerular capillary wall to aid in the early diagnosis of pediatric kidney disease. AVAILABILITY AND IMPLEMENTATION: https://github.com/ChunyueFeng/Kidney-DataSet.