The electric field-induced phase transitions in Li-modified Na_0.5K_0.5NbO₃ at the polymorphic phase boundary (PPB) were observed using in situ X-ray diffraction. The ratio of monoclinic to tetragonal phase fraction was used as an indicator of the extent and reversibility of the phase transitions. The reversibility of the phase transition was greater in compositions further from the PPB. These results demonstrate that the field-induced phase transition is one of the origins of high piezoelectric properties in lead-free ferroelectric materials.

Diabetes is a significant global health issue impacting millions. Approximately 26 million diabetics experience foot ulcers, with 20% ending up with amputations, resulting in high morbidity, mortality, and costs. Plantar pressure screening shows potential for early detection of Diabetic Foot Ulcers (DFUs). Although foot ulcers often occur due to excessive pressure on the soles during dynamic activities, most studies focus on static pressure measurements. This study’s primary objective is to apply wireless plantar pressure sensor-embedded insoles to classify and detect diabetic feet from healthy ones based on dynamic plantar pressure. The secondary objective is to compare statistical-based and Machine Learning (ML) classification methods. Data from 150 subjects were collected from the insoles during walking, revealing that diabetic feet have higher plantar pressure than healthy feet, which is consistent with prior research. The Adaptive Boosting (AdaBoost) ML model achieved the highest accuracy of 0.85, outperforming the statistical method, which had an accuracy of 0.67. These findings suggest that ML models, combined with pressure sensor-embedded insoles, can effectively classify healthy and diabetic feet using plantar pressure features. Future research will focus on using these insoles with ML to classify various stages of diabetic neuropathy, aiming for early prediction of foot ulcers in home settings.

Recent advancements in electrochemical biosensors for cancer diagnostics, with a focus on the incorporation of nanomaterials and cutting-edge sensing strategies.