Results 221 to 230 of about 5,097,999 (301)
Understanding Mass Spectrometry: From Ion Generation to Spectral Interpretation. [PDF]
Steckel A, Papp D, Schlosser G.
europepmc +1 more source
Researchers developed and applied a generative artificial intelligence (AI) model to optimize epsilon‐near‐zero thin film stacks for ultra‐broadband perfect absorption of light. The fabricated AI‐designed absorber, with a total thickness of only 160 nm, experimentally demonstrated an absorption bandwidth exceeding 1000 nm while maintaining greater than
David Dang +10 more
wiley +1 more source
A Hybrid Ridge Regression-Convolutional Bidirectional Long Short-Term Memory Framework with Dual-Level Transfer Learning for State-of-Health Estimation of Lithium-Ion Batteries Under High Temperatures. [PDF]
Ge C +6 more
europepmc +1 more source
PrOMet: Accurate and Robust Metal‐Binding Site and Type Prediction [PDF]
PrOMet is a two‐stage deep learning framework for jointly predicting metal‐binding sites and ion identities from protein structures. Surface candidate sites are first pruned, followed by refinement of the most probable sites and metal types. This hierarchical strategy enables robust and accurate metal prediction for cryo‐EM and AlphaFold‐derived ...
Park G, Seok C, Park H.
europepmc +2 more sources
Osmium nanoparticles anchored on CoFe layered double hydroxide (Os/CoFe LDH) stabilize high‐valent Co/Fe species and modulate the interfacial water configuration via surface engineering. This work presents a facile surface engineering strategy for constructing efficient and durable OER electrocatalysts for alkaline seawater, and expands the ...
Xindong She +8 more
wiley +1 more source
Reproducible Ethanol-Induced Precipitation and Systematic Characterization of an α-D-Glucopyranose-Rich Solid from Khalas Date Fruit (<i>Phoenix dactylifera</i> L.). [PDF]
Al Alawi RA, Baqi Y.
europepmc +1 more source
Review of Solar, Thermal, and Electromagnetic Energy Harvesting for Satellites. [PDF]
Lu Y, Gao R, Chen X, Wang L.
europepmc +1 more source
Q‐LEAP: Millisecond Hyperdimensional Optimization for Full‐Spectrum Optical Metamaterials
Q‐LEAP integrates physics‐informed residual machine learning with factorization‐machine‐encoded quantum annealing to design full‐spectrum optical metamaterials. It explores a 2108 design space and, in a single 2.56 ms annealing step, reaches 85.83% of the theoretical FoM limit, enabling selective 5‐8 µm emission with 3–5 and 8–14 µm suppression and ∼40×
Zikang Guo +3 more
wiley +1 more source

