Results 211 to 220 of about 184,271 (322)
A machine-learning Approach for Stress Detection Using Wearable Sensors in Free-living Environments [PDF]
Mohamed Abd Al-Alim +3 more
openalex +1 more source
Electrospun PEO/PEDOT:PSS nanofibers for wearable physiological flex sensors
Eve Verpoorten +2 more
openalex +2 more sources
A multilayer‐stackable carbon nanotuber (CNT) scaffold‐based piezoelectric nanogenerator (CPENG) with domino‐patterned CNT pillars presents high, stable output (12.3 V, size of 1 cm × 1 cm) over 2000 cycles, operates across a wide temperature range, and efficiently converts energy from real‐life stimuli through optimized CNT length, layer stacking, and
Kwangjun Kim +3 more
wiley +1 more source
A Systematic Review of Wearable Sensors in Rett Syndrome-What Physiological Markers Are Informative for Monitoring Disease States? [PDF]
Singh J +7 more
europepmc +1 more source
Development of substrate integrated waveguides with textile materials by manufacturing techniques [PDF]
Agneessens, Sam +5 more
core +1 more source
Real‐Time, Label‐Free Monitoring of Cell Behavior on a Bioelectronic Scaffold
A bioelectronic nanofibrous scaffold is introduced that supports cell growth while enabling real‐time, label‐free monitoring of cellular behavior through impedance measurements. The system correlates electrical signals with cell viability and surface coverage, offering an integrated platform for studying dynamic biological processes and advancing next ...
Dana Cohen‐Gerassi +10 more
wiley +1 more source
Geometrically‐Screened, Sterically‐Hindered Additive for Wide‐Temperature Aqueous Zinc‐Ion Batteries
A molecular‑engineering strategy combining steric hindrance tuning with geometric optimization identifies cellobiose as an ideal additive for aqueous zinc‑ion batteries, enabling stable Zn deposition across a wide temperature range from −30 to 50 °C. Abstract Aqueous zinc‐ion batteries (AZIBs) are emerging as a highly promising alternative to lithium ...
Sida Zhang +13 more
wiley +1 more source
Intelligent optimization of track and field teaching using machine learning and wearable sensors. [PDF]
Li Y +5 more
europepmc +1 more source

