Results 81 to 90 of about 109,853 (338)
Advanced Science, EarlyView.Mid‐infrared photothermal imaging enables multidimensional profiling of micro‐ and nanoplastics in bottled water. A total of 9.9 × 104 particles L−1 is detected, with 64% in the nanoscale regime. Spectral evolution, including peak narrowing and band shifts, reveals local chain reorganization in polyethylene terephthalate (PET), highlighting intrinsic ...
Xinyu Deng +4 more
wiley +1 more source
Advanced Science, EarlyView.Operando real‐time current density–voltage absorption spectroscopy visualizes spatial phase evolution in mixed‐halide perovskites from device to module scale. Phase instability preferentially initiates in regions with insufficient carrier extraction, revealing electrical boundary conditions as governing factors.
Hangyeol Choi +8 more
wiley +1 more source
Advanced Science, EarlyView.Silicon hot‐carrier photodetectors offer a CMOS‐compatible pathway for SWIR detection but suffer from intrinsically low quantum efficiency. Here, we introduce a quasi‐generalized antireflection coating (QARC) that universally enhances optical absorption and quantum efficiency, enabling the first CMOS‐compatible SWIR imaging with silicon hot‐carrier ...
Eui‐Hyoun Ryu +11 more
wiley +1 more source
Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories
Advanced Energy Materials, EarlyView.In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen +4 more
wiley +1 more source
Generalized linear mixing model accounting for endmember variability
, 2017 Endmember variability is an important factor for accurately unveiling vital information relating the pure materials and their distribution in hyperspectral images.
Bermudez, José Carlos Moreira +2 more
core +1 more source
Sparse Unmixing of Hyperspectral Data [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2011 Linear spectral unmixing is a popular tool in remotely sensed hyperspectral data interpretation. It aims at estimating the fractional abundances of pure spectral signatures (also called as endmembers) in each mixed pixel collected by an imaging spectrometer.
Marian-Daniel Iordache +2 more
openaire +1 more source
Remote SensingPlanetary exploration missions have acquired a growing amount of remote sensing data, offering a reliable basis for studying the geological evolution of planetary bodies such as Mars.
Tejay Lovelock, Rohitash Chandra
doaj +1 more source
HYPERSPECTRAL TRANSFORMATION FROM EO-1 ALI IMAGERY USING PSEUDO-HYPERSPECTRAL IMAGE SYNTHESIS ALGORITHM [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016 Hyperspectral remote sensing is more effective than multispectral remote sensing in many application fields because of having hundreds of observation bands with high spectral resolution.
N. T. Hoang, N. T. Hoang, K. Koike
doaj +1 more source
Distributed Unmixing of Hyperspectral Data With Sparsity Constraint
, 2017 Spectral unmixing (SU) is a data processing problem in hyperspectral remote sensing. The significant challenge in the SU problem is how to identify endmembers and their weights, accurately. For estimation of signature and fractional abundance matrices in
Khoshsokhan, Sara +2 more
core +2 more sources
Advanced Intelligent Discovery, EarlyView.A novel machine learning approach classifies macrophage phenotypes with up to 98% accuracy using only nuclear morphology from DAPI‐stained images. Bypassing traditional surface markers, the method proves robust even on complex textured biomaterial surfaces. It offers a simpler, faster alternative for studying macrophage behavior in various experimental
Oleh Mezhenskyi +5 more
wiley +1 more source