Results 221 to 230 of about 4,000,570 (355)

Atomically Revealing Bulk Point Defect Dynamics in Hydrogen‐Driven γ‐Fe2O3 → Fe3O4 → FeO Transformation

Advanced Functional Materials, EarlyView.
In situ TEM uncovers the atomic‐scale mechanisms underlying hydrogen‐driven γ‐Fe2O3→Fe3O4→FeO reduction. In γ‐Fe2O3, oxygen vacancies cluster around intrinsic Fe vacancies, leading to nanopore formation, whereas in Fe3O4, vacancy aggregation is suppressed, preserving a dense structure.
Yupeng Wu, Dongxiang Wu, Wenhui Zhu, Xiaobo Chen, Zhikang Zhou, Zhilu Liang, Shuonan Ye, Meng Li, Xiao Tong, Kim Kisslinger, Sooyeon Hwang, Dmitri Zakharov, Renu Sharma, Judith C. Yang, Guangwen Zhou   +14 more
wiley   +1 more source

Comments on the International Federation of Clinical Chemistry and Laboratory Medicine Continuous Glucose Monitoring Accuracy Requirements. [PDF]

J Diabetes Sci Technol
Krouwer J.
europepmc   +1 more source

PV-Based EV Charging with Grid Integration in MATLAB Simulink

K, Premkumar
openalex   +1 more source

Distributed Computing – Grid Computing: Self-Organising Grid Resource Management [PDF]

, 2006
Lazarevic, A.
core  

DNA‐Templated 2D Heterostructures as Phototriggered Dynamic Nanohybrids: From Releasing Molecular Loads to Controlling Enzyme Biocatalytic Function

Advanced Functional Materials, EarlyView.
DNA strands are employed both as dynamic linkers and nanoscale templates for the integration of Ag2S nanoparticles on MoS2, which in turn imparted photothermal responsiveness; this feature permits the selective cargo (fluorophore, quantum dots or an enzyme) release from the MoS2 surface in response to local heat induced by light irradiation.
Kai Chen, Haosen Miao, Weiying Hong, Matteo Palma   +3 more
wiley   +1 more source

Research on the design and simulation of grid-connected system of vertical gravity energy storage. [PDF]

Sci Rep
Ji Y, Lu Y, Hong J, Zhou R, Cao J, Yuan W, Huai Q.   +6 more
europepmc   +1 more source

Smarter Sensors Through Machine Learning: Historical Insights and Emerging Trends across Sensor Technologies

Advanced Functional Materials, EarlyView.
This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee, Osman Gul, Yeongjae Kwon, Jaeseok Jeong, Seokjoo Cho, Jihyeon Ahn, Jehee Yu, Cheolmin Kim, Donho Lee, Hyeonseok Han, Byeongju Lee, Jungrak Choi, Ji‐Hwan Ha, Yongrok Jeong, Kyungnam Kang, Ali Javey, Junseong Ahn, Inkyu Park   +17 more
wiley   +1 more source

Evaluation of application effect of full⁃process grid management model of key diseases in emergency department

Huli yanjiu, 2021
CHU Zhe, ZHANG Chunyan, XIONG Wenyan, WANG Jing, JU Guiping   +4 more
doaj  

Integrating Population Approaches With Physiologically Based Pharmacokinetic Models: A Novel Framework for Parameter Estimation. [PDF]

CPT Pharmacometrics Syst Pharmacol
Teutonico D, Marchionni D, Lavielle M, Nguyen L.   +3 more
europepmc   +1 more source

Fault Detection in Smart Grids Using Deep Learning-Based Phasor Measurement Unit Data Analysis

Depandi Enda
openalex   +2 more sources