Results 231 to 240 of about 1,166,203 (287)

Image analysis-based recognition and quantification of grain number per panicle in rice. [PDF]

Plant Methods, 2019
Wu W, Liu T, Zhou P, Yang T, Li C, Zhong X, Sun C, Liu S, Guo W.   +8 more
europepmc   +1 more source

An Experimental High‐Throughput Approach for the Screening of Hard Magnet Materials

Advanced Engineering Materials, EarlyView.
An entire workflow for the high‐throughput characterization and analysis of compositionally graded magnetic films is presented. Characterization protocols, data management tools and data analysis approaches are illustrated with test case Sm(Fe, V)12 based films.
William Rigaut, Pierre Le Berre, Hamdi Jaballah, Lukas Fink, Victor Poline, Richard Haettel, Stéphane Grenier, Gilbert Chahine, Ewen Bellec, Guillaume Buatier, Wilfried Hortschitz, Santa Pile, Thomas Schrefl, Nils Blanc, Yuan Hong, Thibaut Devillers, Nora M. Dempsey   +16 more
wiley   +1 more source

Exploring Dipolar Dynamics and Ionic Transport in Metal‐Organic Frameworks: Experimental and Theoretical Insights

Advanced Functional Materials, EarlyView.
In this study, the interplay of dipolar dynamics and ionic charge transport in MOF compounds is investigated. Synthesizing the novel structure CFA‐25 with integrated freely rotating dipolar groups, local and macroscopic effects, including interactions with Cs cations are explored.
Ralph Freund, Arthur Schulz, Peter Lunkenheimer, Maryana Kraft, Thomas Bergler, Harald Oberhofer, Dirk Volkmer   +6 more
wiley   +1 more source

Ideal Molecular Sieving with a Dense MOF for Helium Upgrading with Highly Diffusion Selective Mixed Matrix Membranes

Advanced Functional Materials, EarlyView.
The separation of Helium gas from natural gas is challenging but highly important. MIL‐116(Ga), a “non‐porous” metal–organic framework is used as a molecular sieve to separate He from CH4. Druse‐like MIL‐116(Ga) particles are integrated into polysulfone mixed matrix membranes.
Ayisha Komal, Laura Calderón‐Rodríguez, Oksana Smirnova, Eren Grossmann, Aparna Binu Varghese, Karen Marlenne Garcia Alvarez, Andreas Schneemann, Thomas Hoyer, Ralf Wyrwa, Felix Helmut Schacher, Alexander Knebel   +10 more
wiley   +1 more source

Laser‐Induced Graphene from Waste Almond Shells

Advanced Functional Materials, EarlyView.
Almond shells, an abundant agricultural by‐product, are repurposed to create a fully bioderived almond shell/chitosan composite (ASC) degradable in soil. ASC is converted into laser‐induced graphene (LIG) by laser scribing and proposed as a substrate for transient electronics.
Yulia Steksova, Anna Chiara Bressi, Marina Galliani, Attilio Marino, Gianni Ciofani, Eduardo Machado‐Charry, Hilda Gomez Bernal, Alessandra Francini, Luca Sebastiani, Francesco Greco   +9 more
wiley   +1 more source

Near‐Infrared Emitting Lanthanide Catecholate Giant Single Crystals – Morphology Control and Photon Down‐Conversion

Advanced Functional Materials, EarlyView.
Controlled syntheses of lanthanide coordination polymers based on the dihydroxybenzoquinone (DHBQ) organic linker afforded large single crystals of Ln‐DHBQ CPs (Ln = Yb, Nd). A novel structural variant of Yb‐DHBQ is identified by means of single crystal diffraction analysis.
Marina I. Schönherr, Alexander Biewald, Andre Mähringer, Thaddäus J. Koller, Peter Mayer, Markus Döblinger, Achim Hartschuh, Dana D. Medina   +7 more
wiley   +1 more source

MOFs and COFs in Electronics: Bridging the Gap between Intrinsic Properties and Measured Performance

Advanced Functional Materials, EarlyView.
Metal‐organic frameworks (MOFs) and covalent organic frameworks (COFs) hold promise for advanced electronics. However, discrepancies in reported electrical conductivities highlight the importance of measurement methodologies. This review explores intrinsic charge transport mechanisms and extrinsic factors influencing performance, and critically ...
Jonas F. Pöhls, R. Thomas Weitz
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

Emergent Spin‐Glass Behavior in an Iron(II)‐Based Metal–Organic Framework Glass

Advanced Functional Materials, EarlyView.
A one‐pot, solvent‐free synthesis yields an Fe2+‐based metal‐organic framework (MOF) glass featuring a continuous random network structure. The material exhibits spin‐glass freezing at 14 K, driven by topological‐disorder and short‐range magnetic frustration, showcasing the potential of MOF glasses as a plattform for cooperative magnetic phenomena in ...
Chinmoy Das, Soma Salamon, Joachim Landers, Jan‐Benedikt Weiß, Wen‐Long Xue, Pascal Kolodzeiski, Roman Pallach, Heiko Wende, Sebastian Henke   +8 more
wiley   +1 more source

Sorghum MSD3 Encodes an ω-3 Fatty Acid Desaturase that Increases Grain Number by Reducing Jasmonic Acid Levels. [PDF]

Int J Mol Sci, 2019
Dampanaboina L, Jiao Y, Chen J, Gladman N, Chopra R, Burow G, Hayes C, Christensen SA, Burke J, Ware D, Xin Z.   +10 more
europepmc   +1 more source