Results 211 to 220 of about 295,670 (287)

Electroactive Metal–Organic Frameworks for Electrocatalysis

Advanced Functional Materials, EarlyView.
Electrocatalysis is crucial in sustainable energy conversion as it enables efficient chemical transformations. The review discusses how metal–organic frameworks can revolutionize this field by offering tailorable structures and active site tunability, enabling efficient and selective electrocatalytic processes.
Irena Senkovska, Yutong Luo, Ruyan Zhao, Jianjun Zhang, Mingchao Wang, Xinliang Feng, Eike Brunner, Stefan Kaskel   +7 more
wiley   +1 more source

Exact analytical Taub-NUT like solution in f(T) gravity. [PDF]

Eur Phys J C Part Fields
Fenwick JG, Ghezelbash M.
europepmc   +1 more source

On‐Surface Indigo‐Based Bimolecular Coordination Networks with Programmable Regular or Vitreous Structure

Advanced Functional Materials, EarlyView.
A previously unreported coordination motif stabilising single Fe atoms by indigo chelation and pyridyl coordination on Au(111) has been revealed. By using planar tritopic pyridyl linkers (TPyB), extended 2D porous networks of indigo3(TPyB)2Fe6 form. These networks can be crystalline or vitreous and offer an environment where individual coordination ...
Hongxiang Xu, Ritam Chakraborty, Biao Yang, Dennis Meier, Joachim Reichert, Svetlana Klyatskaya, Mario Ruben, Shobhana Narasimhan, Johannes V. Barth, Anthoula C. Papageorgiou   +9 more
wiley   +1 more source

Development mechanism and parameter control of jet impingement based on chaotic modulation. [PDF]

Sci Rep
Zhang X, Li X, Wang H, Zhang G.
europepmc   +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

Tutorial on quantifying and sampling biomolecular ensembles with ShapeGMM. [PDF]

J Chem Phys
Sasmal S, McCullagh M, Hocky GM.
europepmc   +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

Analogue simulation of quantum gravity black hole models in a dc-SQUID array. [PDF]

Sci Rep
Maceda MD, Sabín C.
europepmc   +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

Scalable and programmable topological transitions in plasmonic Moiré superlattices. [PDF]

Nat Commun
Tian B, Zhang X, Wu R, Zhang Y, Du L, Yuan X.   +5 more
europepmc   +1 more source