Results 151 to 160 of about 419,205 (270)

Systolic blood pressure estimation method using electrocardiogram RRI data. [PDF]

Sci Rep
Fujita E, Uchikawa R, Ogura Y, Nobuhiro Y, Maeda S, Kojima S, Maeno K, Igarashi S, Kitahara T, Tsujimura H, Taoda K, Kisaka T, Murata K, Yoshizumi M.   +13 more
europepmc   +1 more source

Defect Analysis of the β– to γ–Ga2O3 Phase Transition

Advanced Functional Materials, EarlyView.
The role of defects at all the relevant stages of the β$\beta$‐ to γ$\gamma$‐Ga2O3 polymorph transition is investigated using a multi method approach. The positron annihilation spectroscopy based results show that the defect density decreases after the transition, and that changes in defect configuration within the γ phase occur with increasing ...
Umutcan Bektas, Maciej O. Liedke, Huan Liu, Fabian Ganss, Maik Butterling, Nico Klingner, René Hübner, Ilja Makkonen, Andreas Wagner, Gregor Hlawacek   +9 more
wiley   +1 more source

A least squares finite element method to multi-species modeling of negative DC corona discharges in point-to-plane configurations. [PDF]

Sci Rep
Majazi S, Sobhanian S, Ahmadi AR, Fard MM.   +3 more
europepmc   +1 more source

Modulating Electrochemical CO2 Reduction Pathways via Interfacial Electric Field

Advanced Functional Materials, EarlyView.
Engineering interfacial electric fields in Cu/ITO electrodes enables precise control of CO2 reduction pathways. Charge transfer from Cu to ITO generates positively charged Cu species that steer selectivity from ethylene toward methane. This work demonstrates how interfacial electric‐field modulation can direct reaction intermediates and transform ...
Mahdi Salehi, Zhengyuan Gao, Sai Phani Kumar Vangala, Amirhossein Farzi, Hamed Heidarpour, Morgan McKee, Nikolay Kornienko, Ali Seifitokaldani   +7 more
wiley   +1 more source

Mechanical Properties of Architected Polymer Lattice Materials: A Comparative Study of Additive Manufacturing and CAD Using FEM and µ‐CT

Advanced Functional Materials, EarlyView.
This study examines how pore shape and manufacturing‐induced deviations affect the mechanical properties of 3D‐printed lattice materials with constant porosity. Combining µ‐CT analysis, FEM, and compression testing, the authors show that structural imperfections reduce stiffness and strength, while bulk material inhomogeneities probably enhance ...
Oliver Walker, Thierry Roland, Damien Favier, Antoine Egele, Michael Heymann, Cosima Stubenrauch   +5 more
wiley   +1 more source

Reevaluating the Activity of ZIF‐8 Based FeNCs for Electrochemical Ammonia Production

Advanced Functional Materials, EarlyView.
Though receiving much attention, the field of electrochemical nitrogen reduction reaction (eNRR) to ammonia is marked by doubts about whether this reaction is possible in aqueous media. This work sheds light on this question for iron single‐atom on N‐doped carbon (FeNC) catalysts—a class of well‐known catalysts that is also worth testing for the sister
Caroline Schneider, S. Esmael Balaghi, Niklas Ortlieb, Patrick Elsaesser, Sima Heidari, Christopher S. Allen, Anna Fischer   +6 more
wiley   +1 more source

Enhanced optical visible light positioning via RSS-based evolutionary optimization under LOS and NLOS conditions. [PDF]

Sci Rep
Pathak P, Sharavanan S, Karthikeyan M, Mubarakali A, Arun Kumar U.   +4 more
europepmc   +1 more source

Implementation of Drug‐Induced Rhabdomyolysis and Acute Kidney Injury in Microphysiological System

Advanced Functional Materials, EarlyView.
A modular Muscle–Kidney proximal tubule‐on‐a‐chip integrates 3D skeletal muscle and renal proximal tubule tissues to model drug‐induced rhabdomyolysis and acute kidney injury. The coculture system enables dynamic tissue interaction, functional contraction monitoring, and quantification of nephrotoxicity, revealing drug side effect‐induced metabolic ...
Jaesang Kim, Zhuo Feng Lee, Gi‐Dong Sim, Sejoong Kim, Jessie S. Jeon   +4 more
wiley   +1 more source

Multi-marker discovery for mild cognitive impairment in metabolomics using machine learning with a global surrogate model via partial least squares. [PDF]

Metabolomics
Tatara Y, Yamamoto H, Terai K, Matsuta R, Kasai S, Tamada Y, Mikami T, Murashita K, Itoh K.   +8 more
europepmc   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source