Results 211 to 220 of about 167,567 (320)
This review synthesizes the evolution of radiative heat transfer, emphasizing the transition from far‐field to near‐field regimes. Traditional frameworks, such as Planck's law, are revisited alongside modern innovations like fluctuational electrodynamics. Applications span nanoscale thermal management, energy harvesting, and thermophotovoltaic systems.
Ambali Alade Odebowale+6 more
wiley +1 more source
Establishing a periodic SM model with Fourier analysis for enhancing global soil moisture forecasting. [PDF]
Zhu J, Wang S, Li Q.
europepmc +1 more source
Making Photoresponsive Metal–Organic Frameworks an Effective Class of Heterogeneous Photocatalyst
This review summarizes photoresponsive MOFs for photocatalytic applications, focusing on their capacity to enhance light harvesting, charge transfer, and surface reactions. While existing studies provide foundational insights, emerging characterization techniques enable a deeper understanding of photoresponsive MOFs.
Rui Liu+3 more
wiley +1 more source
Spontaneous symmetry breaking and panic escape. [PDF]
Sun Kim C, Dib C, Oh S.
europepmc +1 more source
Carboxyl‐functionalized graphene quantum dots (cGQDs) exhibit high singlet oxygen quantum yield due to strong spin–orbit coupling. cGQDs achieve minimum bactericidal concentration of only 0.4 µg mL−1 against S. aureus under low‐intensity illumination.
Muhammad Hassnain+10 more
wiley +1 more source
A fault diagnosis method for rolling bearings in open-set domain adaptation with adversarial learning. [PDF]
Lei T, Pan F, Hu J, He X, Li B.
europepmc +1 more source
Bi‐directionally assembled BN µ‐platelets in micropatterns formed by a micro‐molding method for thermal interface materials are demonstrated. The BN µ‐platelets are vertically aligned selectively, while compressed regions without patterns accommodate horizontally assembled BN µ‐platelets. Through anisotropic orientation, high thermal conductivities for
Young Gil Kim+12 more
wiley +1 more source
ICRL: independent causality representation learning for domain generalization. [PDF]
Xu L, Shao Y.
europepmc +1 more source