Results 271 to 280 of about 1,178,363 (355)

Comments on Global Symmetries and Anomalies of 5d SCFTs. [PDF]

open access: yesCommun Math Phys
Benetti Genolini P, Tizzano L.
europepmc   +1 more source

Electronic Structure of Kramers Nodal‐Line Semimetal YAuGe and Anomalous Hall Effect Induced by Magnetic Rare‐Earth Substitution

open access: yesAdvanced Science, EarlyView.
The rare‐earth intermetallics YAuGe is identified as a Kramers nodal‐line semimetal, a topological state inherent to non‐centrosymmetric achiral lattices. Magnetic rare‐earth substitution induces an anomalous Hall effect, highlighting the impact of time‐reversal symmetry breaking on topological electronic states and offering insights into designing the
Takashi Kurumaji   +13 more
wiley   +1 more source

Unravelling the influence of mixed layer depth on chlorophyll-a dynamics in the Red Sea. [PDF]

open access: yesPLoS One
Pateraki M   +4 more
europepmc   +1 more source

Direction‐Dependent Conduction Polarity in Altermagnetic CrSb

open access: yesAdvanced Science, EarlyView.
A recently identified altermagnetic candidate, CrSb, has been found to exhibit direction‐dependent conduction polarity (DDCP). Conduction is dominated by electrons in the ab$\textit{ab}$‐plane and by holes along the c$c$‐axis of the hexagonal unit cell of CrSb.
Banik Rai   +7 more
wiley   +1 more source

Two‐Step Tandem Catalysis for High‐Efficiency Ammonia Synthesis Via Nitrate Reduction on Anion‐Intercalated CoNi LDH and Cu/Cu2O

open access: yesAdvanced Science, EarlyView.
By integrating Cu/Cu2O and CoNi LDH, tandem kinetic descriptors, including a volcano curve, are employed to predict rate constants, facilitating ideal kinetic matching for efficient ammonia synthesis. The highest ammonia yield (1.12 mmol cm−2 h−1) and Faraday efficiency (99.78%) are achieved by rate matching between Cu/Cu2O and MoO4‐CoNi LDH.
Changzheng Lin   +11 more
wiley   +1 more source

Capacity Estimation and Knee Point Prediction Using Electrochemical Impedance Spectroscopy for Lithium Metal Battery Degradation via Machine Learning

open access: yesAdvanced Science, EarlyView.
This work explores a machine learning (ML) model with electrochemical impedance spectroscopy (EIS) data to address two critical challenges in Lithium metal batteries (LMBs): capacity degradation trajectory and knee point (KP) estimation, for the first time.
Qianli Si   +4 more
wiley   +1 more source

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