Results 211 to 220 of about 1,220,933 (279)

‘Oxygen Bound to Magnesium’ as High Voltage Redox Center Causes Sloping of the Potential Profile in Mg‐Doped Layered Oxides for Na‐Ion Batteries

open access: yesAdvanced Functional Materials, EarlyView.
Na‐ion batteries ‐ Impact of doping on the oxygen redox: The sloping potential of NaMg0.1Ni0.4Mn0.5O2 above 4.0 V is caused by a new redox center (arising from the ‘O bound to Mg’), having a higher potential but being more irreversible compared to the ‘O bound to Ni’.
Yongchun Li   +12 more
wiley   +1 more source

Tuning the Electronic Structure and Spin State of Fe─N─C Catalysts Using an Axial Oxygen Ligand and Fe Clusters for High‐Efficiency Rechargeable Zinc–Air Batteries

open access: yesAdvanced Functional Materials, EarlyView.
A FeN4─O/Clu@NC‐0.1Ac catalyst containing atomically‐dispersed FeN4─O sites (medium‐spin Fe2+) and Fe clusters delivered a half‐wave potential of 0.89 V for ORR and an overpotential of 330 mV at 10 mA cm−2 for OER in 0.1 m KOH. When the catalyst was used in a rechargeable Zn–air battery, a power density of 284.5 mW cm−2 was achieved with excellent ...
Yongfang Zhou   +8 more
wiley   +1 more source

Single Solid‐State Ion Channels as Potentiometric Nanosensors

open access: yesAdvanced Functional Materials, EarlyView.
Single gold nanopores functionalized with mixed self‐assembled monolayers act as solid‐state ion channels for direct, selective potentiometric sensing of inorganic ions (Ag⁺). The design overcomes key miniaturization barriers of conventional ion‐selective electrodes by combining low resistivity with suppressed loss of active components, enabling robust
Gergely T. Solymosi   +4 more
wiley   +1 more source

Spectrally Tunable 2D Material‐Based Infrared Photodetectors for Intelligent Optoelectronics

open access: yesAdvanced Functional Materials, EarlyView.
Intelligent optoelectronics through spectral engineering of 2D material‐based infrared photodetectors. Abstract The evolution of intelligent optoelectronic systems is driven by artificial intelligence (AI). However, their practical realization hinges on the ability to dynamically capture and process optical signals across a broad infrared (IR) spectrum.
Junheon Ha   +18 more
wiley   +1 more source

Digital Discovery of Synthesizable Metal−Organic Frameworks via Molecular Dynamics‑Informed, High‑Fidelity Deep Learning

open access: yesAdvanced Functional Materials, EarlyView.
Tabular foundation model interrogates the synthetic likelihood of metal−organic frameworks. Abstract Metal–organic frameworks (MOFs) are celebrated for their chemical and structural versatility, and in‑silico screening has significantly accelerated their discovery; yet most hypothetical MOFs (hMOFs) never reach the bench because their synthetic ...
Xiaoyu Wu   +3 more
wiley   +1 more source

A Tracer Diffusion Study of Diverse Photo‐Ionic Phenomena in Strontium Titanate

open access: yesAdvanced Functional Materials, EarlyView.
Two strong interfacial photo‐ionic effects are demonstrated for the model system SrTiO3 through the application of isotope exchange experiments: UV illumination is found to enhance the oxygen surface exchange coefficient by several orders of magnitude and to depress the surface space‐charge potential substantially.
David M. Schwenkel   +3 more
wiley   +1 more source

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

open access: yesAdvanced 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   +3 more
wiley   +1 more source

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

open access: yesAdvanced 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   +17 more
wiley   +1 more source

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