Results 11 to 20 of about 177 (170)
A New Family of Ternary Intermetallic Compounds with Dualistic Atomic Ordering – The ZIP Phases
Advanced Materials, EarlyView.The ZIP phases are ternary intermetallic compounds with dualistic atomic ordering, i.e., they exhibit one face‐centered cubic (fcc; space group Fd3¯$\bar 3$m) variant and one hexagonal (space group P63/mmc) variant. The ZIP phases in the Nb‐Si‐Ni system are the Nb3SiNi2 (fcc) and Ni3SiNb2 (hexagonal) ternary IMCs, crystal structure schematics of which ...
Matheus A. Tunes +24 more
wiley +1 more source
Opportunities of Semiconducting Oxide Nanostructures as Advanced Luminescent Materials in Photonics
Advanced Materials, EarlyView.The review discusses the challenges of wide and ultrawide bandgap semiconducting oxides as a suitable material platform for photonics. They offer great versatility in terms of tuning microstructure, native defects, doping, anisotropy, and micro‐ and nano‐structuring. The review focuses on their light emission, light‐confinement in optical cavities, and
Ana Cremades +7 more
wiley +1 more source
Terahertz Volume Plasmon‐Polariton Modulation in All‐Dielectric Hyperbolic Metamaterials
Advanced Optical Materials, EarlyView.THz volume plasmon‐polariton (VPP) propagation through plasmon‐based hyperbolic metamaterials, made of alternating layers of doped and undoped III‐V semiconductors. Abstract The development of plasmonics and related applications in the terahertz range faces limitations due to the intrinsic high electron density of the standard metals.
Stefano Campanaro +3 more
wiley +1 more source
Data‐Driven Multi‐Objective Optimization of Large‐Diameter Si Floating‐Zone Crystal Growth
Advanced Theory and Simulations, EarlyView.This study presents a surrogate‐based Multi‐Objective Optimization framework for Floating Zone silicon crystal growth. An ensemble of Neural Networks is trained on simulation data and combined with Genetic Algorithms to explore trade‐offs in process parameters.
Lucas Vieira +3 more
wiley +1 more source
Single‐Target Pairing System (StarPair) for Large‐Scale Interrogation of Cell–Cell Interactions
Advanced Science, EarlyView.This work presents a single‐target pairing system, StarPair, enabling cell–cell interaction studies by target combination in droplets. StarPair offers superior pairing efficiencies over 95% and operation frequencies of 105 pairs per 9.5 h for two‐target pairing, allowing large‐scale interrogation of immune cell‐cancer cell interactions and precise ...
Tianjiao Mao +6 more
wiley +1 more source
Advanced Science, EarlyView.RegGAIN is a novel and powerful deep learning framework for inferring gene regulatory networks (GRNs) from single‐cell RNA sequencing data. By integrating self‐supervised contrastive learning with dual‐role gene representations, it consistently outperforms existing methods in both accuracy and robustness.
Qiyuan Guan +9 more
wiley +1 more source
Nanozymes Integrated Biochips Toward Smart Detection System
Advanced Science, EarlyView.This review systematically outlines the integration of nanozymes, biochips, and artificial intelligence (AI) for intelligent biosensing. It details how their convergence enhances signal amplification, enables portable detection, and improves data interpretation.
Dongyu Chen +10 more
wiley +1 more source
Advanced Science, EarlyView.SAGE is a unified framework for spatial domain identification in spatial transcriptomics that jointly models tissue architecture and gene programs. Topic‐driven gene selection (NMF plus classifier‐based scoring) highlights spatially informative genes, while dual‐view graph embedding fuses local expression and non‐local functional relations.
Yi He +5 more
wiley +1 more source
High‐Conductivity Electrolytes Screened Using Fragment‐ and Composition‐Aware Deep Learning
Advanced Science, EarlyView.We present a new deep learning framework that hierarchically links molecular and functional unit attributions to predict electrolyte conductivity. By integrating molecular composition, ratios, and physicochemical descriptors, it achieves accurate, interpretable predictions and large‐scale virtual screening, offering chemically meaningful insights for ...
Xiangwen Wang +6 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.Machine learning predicts activation energies for key steps in the water‐gas shift reaction on 92 MXenes. Random Forest is identified as the most accurate model. Reaction energy and reactant LogP emerge as key descriptors. The approach provides a predictive framework for catalyst design, grounded in density functional theory data and validated through ...
Kais Iben Nassar +3 more
wiley +1 more source