Results 161 to 170 of about 4,545,331 (281)

Combination Network with Multiaccess Caching. [PDF]

Entropy (Basel)
Zheng B, Huang Y, Wu D.
europepmc   +1 more source

MnI‐Functionalized Covalent Organic Framework as Efficient Electrocatalyst for CO2 Reduction in a Catholyte‐Free Zero‐Gap Electrolyzer

Advanced Functional Materials, EarlyView.
This work demonstrates the successful integration of a phenanthroline‐based 2D COF with MnI catalytic sites into a catholyte‐free membrane‐electrode‐assembly cell for CO2 electroreduction. The crystalline COF actively suppresses Mn⁰–Mn⁰ dimerization, achieving a turnover frequency of 617 h⁻¹ at 2.8 V (full‐cell potential), and enabling stable operation.
Laura Spies, Marcos Eduardo G. Carmo, Gonçalves J. Marrenjo, Stephan Reuther, Patrick Ganswindt, Antonio Otavio T. Patrocinio, Thomas Bein, Osmando F. Lopes, Jenny Schneider   +8 more
wiley   +1 more source

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

An adaptive extended radial basis function based interval analysis method for structural engineering solutions. [PDF]

PLoS One
Jitang X, Jinli H, Qiang C.
europepmc   +1 more source

Smart, Bio‐Inspired Polymers and Bio‐Based Molecules Modified by Zwitterionic Motifs to Design Next‐Generation Materials for Medical Applications

Advanced Functional Materials, EarlyView.
Bio‐based and (semi‐)synthetic zwitterion‐modified novel materials and fully synthetic next‐generation alternatives show the importance of material design for different biomedical applications. The zwitterionic character affects the physiochemical behavior of the material and deepens the understanding of chemical interaction mechanisms within the ...
Theresa M. Lutz, Jonas De Breuck, Sahar Salehi, Meike N. Leiske   +3 more
wiley   +1 more source

Improving Diagnosis: Evaluation of Diagnostic Experiences Classified as Problems, Mistakes, or Both by Patients and Care Partners From a Nationally Representative Survey. [PDF]

Med Care
Gleason KT, McDonald KM, Grob R, Yuan CT, Dhingra I, Warne E, Schlesinger M.   +6 more
europepmc   +1 more source

Inactivating SARS‐CoV‐2 Virus with MOF‐Composites as Smart Face Masks

Advanced Functional Materials, EarlyView.
In situ preparation and functionalization of MOF@Cotton fabrics as smart face masks for the immobilization of proteins and inactivation viruses, such as SARS‐CoV‐2. Abstract The significant impact of the SARS‐CoV‐2 (COVID‐19) pandemic outbreak on people's lives has highlighted the urgent need for effective personal protective equipment.
Romy Ettlinger, Aamod V. Desai, Hau Lam Jody Cheong, Louise L. Major, Terry K. Smith, Aaron B. Naden, Jane Hilton, Catherine S. Adamson, A. Robert Armstrong, Russell E. Morris   +9 more
wiley   +1 more source

The effect of the creative problem-solving approach on creative thinking skills. [PDF]

Front Psychol
Ersoy E, Kara DÖ.
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

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