Results 21 to 30 of about 8,613 (235)

Reconfigurable Three‐Dimensional Superconducting Nanoarchitectures

Advanced Functional Materials, EarlyView.
3D superconducting nanostructures offer new possibilities for emergent physical phenomena. However, fabricating complex geometries remains challenging. Here 3D nanoprinting of complex 3D superconducting nanoarchitectures is established. As well as propagating superconducting vortices in 3D, anisotropic superconducting properties with geometric ...
Elina Zhakina, Luke Alexander Turnbull, Weijie Xu, Markus König, Paul Simon, Wilder Carrillo‐Cabrera, Amalio Fernández‐Pacheco, Uri Vool, Dieter Suess, Claas Abert, Vladimir M. Fomin, Claire Donnelly   +11 more
wiley   +1 more source

Composition and Resulting Band Alignment at the TiO2/InP Heterointerface: A Fundamental Study Combining Photoemission Spectroscopy and Theory

Advanced Functional Materials, EarlyView.
The chemical composition and band alignment are systematically investigated at the TiO2/InP heterointerface. Thin TiO2 films are deposited by ALD on atomically ordered, P‐terminated p‐InP(100). By combining UPS, XPS, and ab initio molecular dynamics, the atomistic structure and electronic alignment are revealed.
Mohammad Amin Zare Pour, Muhammad Nawaz Qaisrani, Christian Höhn, Johannes Laurenz Wolf, Negin Mogharehabed, Jennifer Velazquez Rojas, Wolfram Jaegermann, Erich Runge, Roel van de Krol, Thomas Hannappel, Christian Dreßler, Agnieszka Paszuk   +11 more
wiley   +1 more source

Biodegradable and Recyclable Luminescent Mixed‐Matrix‐Membranes, Hydrogels, and Cryogels based on Nanoscale Metal‐Organic Frameworks and Biopolymers

Advanced Functional Materials, EarlyView.
The study presents biodegradable and recyclable mixed‐matrix membranes (MMMs), hydrogels, and cryogels using luminescent nanoscale metal‐organic frameworks (nMOFs) and biopolymers. These bio‐nMOF‐MMMs combine europium‐based nMOFs as probes for the status of the materials with the biopolymers agar and gelatine and present alternatives to conventional ...
Moritz Maxeiner, Julian Burkhart, Kateryna Borysova, Chiara Mazzariol, Klaus Müller‐Buschbaum   +4 more
wiley   +1 more source

The Synergy of Artificial Intelligence and 3D Bioprinting: Unlocking New Frontiers in Precision and Tissue Fabrication

Advanced Functional Materials, EarlyView.
Advances in integrating artificial intelligence into 3D bioprinting are systematically reviewed here. Machine learning, computer vision, robotics, natural language processing, and expert systems are examined for their roles in optimizing bioprinting parameters, real‐time monitoring, quality control, and predictive maintenance.
Joao Vitor Silva Robazzi, Irem Deniz Derman, Deepak Gupta, Logan Haugh, Yogendra Pratap Singh, Vaibhav Pal, Yasar Ozer Yilmaz, Suihong Liu, Andre Luis Dias, Rogerio Andrade Flauzino, Ibrahim Tarik Ozbolat   +10 more
wiley   +1 more source

CO2 Reduction on Copper‐Nitrogen‐Doped Carbon Catalysts Tuned by Pulsed Potential Electrolysis: Effect of Pulse Potential

Advanced Functional Materials, EarlyView.
This study demonstrates that pulsed potential electrolysis significantly improves CO2 reduction performance on copper‐nitrogen doped carbon electrodes. The formation of cationic copper sites and metallic clusters as a function of applied intermittent potential leads to notable selectivity changes compared to potentiostatic reduction.
Dorottya Hursán, Janis Timoshenko, Andrea Martini, Hyo Sang Jeon, Eduardo Ortega, Martina Rüscher, Arno Bergmann, Aram Yoon, Uta Hejral, Antonia Herzog, Clara Rettenmaier, Felix T. Haase, Philipp Grosse, Beatriz Roldan Cuenya   +13 more
wiley   +1 more source

SMaRT Stacking: A Methodology to Produce Optimally Layered EMI Shields with Maximal Green Index Using Fused Deposition Modeling

Advanced Functional Materials, EarlyView.
Electromagnetic interference (EMI) shields consisting of polylactic acid (PLA) in layers with different concentrations of multiwalled carbon nanotubes (MWCNT) are produced using additive manufacturing. The permittivity function of layers with different filler concentrations is learned using data of homogeneous and randomly ordered shields.
Stijn De Smedt, Brecht Evens, Pramod Ravichandran, Panagiotis Patrinos, Frederik Van Loock, Ruth Cardinaels   +5 more
wiley   +1 more source

An Ultra‐Robust Memristor Based on Vertically Aligned Nanocomposite with Highly Defective Vertical Channels for Neuromorphic Computing

Advanced Functional Materials, EarlyView.
An ultra‐robust memristor based on SrTiO3‐CeO2 (S‐C) vertically aligned nanocomposite (VAN) achieving exceptional endurance of 1012 switching cycles via interface engineering. Artificial neural networks (ANNs) integrated with S‐C VAN memristors exhibit high training accuracy across multiple datasets.
Zedong Hu, Hongyi Dou, Jeremy B. Gan, Cun Li, Xuanyu Sheng, Juanjuan Lu, Abhijeet Choudhury, Jialong Huang, Yizhi Zhang, Zhongxia Shang, Ye Cao, Xinghang Zhang, Haiyan Wang   +12 more
wiley   +1 more source

Design and Applications of Multi‐Frequency Programmable Metamaterials for Adaptive Stealth

Advanced Functional Materials, EarlyView.
This article provides a comprehensive overview of metamaterials, including their fundamental principles, properties, synthesis techniques, and applications in stealth, as well as their challenges and future prospects. It covers topics that are more advanced than those typically discussed in existing review articles, while still being closely connected ...
Jonathan Tersur Orasugh, Ankita Mohanty, Amit Malakar, Suryasarathi Bose, Suprakas Sinha Ray   +4 more
wiley   +1 more source

Amorphous High Entropy Alloy Nanosheets Enabling Robust Li–S Batteries

Advanced Functional Materials, EarlyView.
Amorphous ultrathin FeCoNiMoW high entropy alloy nanosheets are incorporated into the polypropylene separator of lithium‐sulfur batteries, enhancing their capacity, rate performance, and cycling stability. Abstract High‐entropy alloys (HEAs) show great potential for catalyzing complex multi‐step reactions, but optimizing their parameters, i.e ...
Ren He, Seungho Lee, Yang Ding, Chen Huang, Xuan Lu, Lirong Zheng, Ao Yu, Chaoyue Zhang, Canhuang Li, Xiaoyu Bi, Yaqiang Li, Yaqi Liao, Junshan Li, Ahmad Ostovari Moghaddam, Salimov Yernar, Ying Xu, Maria Ibáñez, Chaoqi Zhang, Linlin Yang, Yingtang Zhou, Andreu Cabot   +20 more
wiley   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source