Results 131 to 140 of about 698,197 (236)

How Halide Segregation Governs the Ion Density Evolution and Ionic Performance Losses: From Degradation to Recovery

open access: yesAdvanced Energy Materials, EarlyView.
Halide segregation (HS) in mixed‐halide wide‐bandgap (WBG) perovskites is key to the stability of perovskite‐based tandem cells and a central focus of large‐scale research efforts. Here, we find that the underlying reason for the poor stability of cells prone to HS is enhanced ionic losses during operation. Furthermore, we identified irreversible ionic
Nikhil Kalasariya   +24 more
wiley   +1 more source

Astro2020 APC White Paper: Accessible Astronomy: Policies, Practices,\n and Strategies to Increase Participation of Astronomers with Disabilities [PDF]

open access: green, 2019
Alicia Aarnio   +16 more
openalex   +1 more source

Device Performance of Emerging Photovoltaic Materials (Version 6)

open access: yesAdvanced Energy Materials, EarlyView.
Efficiency of single junction perovskite (circles), organic (hexagons), dye sensitized (pentagons), kesterite (diamonds), Sb2Se3 (right triangle), and AgBiS2 (left triangles) solar cells at emerging‐pv.org over the last decade. ABSTRACT This 6th annual Emerging PV Report surveys peer‐reviewed advances since August 2024 across perovskite, organic ...
Osbel Almora   +29 more
wiley   +1 more source

COMETS IN AUSTRALIAN ABORIGINAL ASTRONOMY [PDF]

open access: bronze, 2011
Duane W. Hamacher, R. P. Norris
openalex   +1 more source

Influence of Metal Species and Content of Fe‐Ni‐Poly(heptazine imides) on their Properties as Electrocatalysts for Zinc‐Air Batteries

open access: yesAdvanced Energy Materials, EarlyView.
Iron/nickel‐modified poly(heptazine imides) are efficient, low‐cost bifunctional electrocatalysts for the oxygen reduction and evolution reactions in aqueous zinc–air batteries. Iron enhances oxygen reduction, while nickel single‐atoms deliver oxygen evolution activity comparable to RuO2.
Franz Jacobi   +10 more
wiley   +1 more source

Automatic Determination of Quasicrystalline Patterns from Microscopy Images

open access: yesAdvanced Intelligent Discovery, EarlyView.
This work introduces a user‐friendly machine learning tool to automatically extract and visualize quasicrystalline tiling patterns from atomically resolved microscopy images. It uses feature clustering, nearest‐neighbor analysis, and support vector machines. The method is broadly applicable to various quasicrystalline systems and is released as part of
Tano Kim Kender   +2 more
wiley   +1 more source

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