Results 141 to 150 of about 883,011 (303)

Unlocking NIR‐II Photoluminescence in 2D Copper Tetrasilicate Nanosheets through Flame Spray Synthesis

open access: yesAdvanced Materials, EarlyView.
A flame‐spray‐pyrolysis method is presented to synthesize ultra‐bright CTS nanosheets with tunable NIR‐II emission. Achieving quantum yields up to 34%, these materials support high‐speed imaging and enable super‐resolution in vivo applications such as transcranial microcirculation mapping and macrophage tracking.
Robert Nißler   +12 more
wiley   +1 more source

Decision trees to evaluate the risk of developing multiple sclerosis. [PDF]

open access: yesFront Neuroinform, 2023
Pasella M   +9 more
europepmc   +1 more source

Spin Engineering of Dual‐Atom Site Catalysts for Efficient Electrochemical Energy Conversion

open access: yesAdvanced Materials, EarlyView.
This review highlights recent progress in spin engineering of dual‐atom site catalysts (DASCs), emphasizing how spin‐related properties enhance electrocatalytic activity, selectivity, and stability. It summarizes cutting‐edge developments in dual‐atom catalysis, discusses the underlying spin‐catalysis mechanisms and structure–performance relationships,
Dongping Xue   +5 more
wiley   +1 more source

The Role of Artificial Intelligence in Advancing Biosensor Technology: Past, Present, and Future Perspectives

open access: yesAdvanced Materials, EarlyView.
This review explores the transformative role of AI in biosensor technology and provides a holistic interdisciplinary perspective that covers a broader scope of AI‐enabled biosensor technologies across various sectors including healthcare, environmental monitoring, food safety, and agriculture. It also highlights the important role of novel materials in
Tuğba Akkaş   +4 more
wiley   +1 more source

A comparative analysis of methods for pruning decision trees [PDF]

open access: green, 1997
Floriana Esposito   +3 more
openalex   +1 more source

AI‐Driven Defect Engineering for Advanced Thermoelectric Materials

open access: yesAdvanced Materials, EarlyView.
This review presents how AI accelerates the design of defect‐tuned thermoelectric materials. By integrating machine learning with high‐throughput data and physics‐informed representations, it enables efficient prediction of thermoelectric performance from complex defect landscapes.
Chu‐Liang Fu   +9 more
wiley   +1 more source

Home - About - Disclaimer - Privacy