Results 171 to 180 of about 502,111 (300)

Development of a Cost-Effective and Food-Grade Medium for Rice Cellular Agriculture. [PDF]

Foods
Matsumoto M, Igarashi K.
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

Characterization of Fused Mn and Cu Corroles and Their Potency as ORR Electrocatalysts. [PDF]

Inorg Chem
Kumar S, Kumar A, Mondal S, Raslin A, Rawat A, Fridman N, Mahammed A, Gross Z.   +7 more
europepmc   +1 more source

Highly Sensitive Electrochemical Biosensor Based on Hairy Particles with Controllable High Enzyme Loading and Activity

Advanced Functional Materials, EarlyView.
For the first time, a highly sensitive electrochemical biosensor based on SiO2‐based hairy particles with a grafted PDMAEMA polymer brush containing a quantifiable and large amount of immobilized Laccase is reported. The fabricated biosensor exhibits a sensitivity of 0.14 A·m⁻¹, a limit of detection (LOD) of 0.1 µm, and a detection range of 0.3–750 µm,
Pavel Milkin, Anila Antony, Hongtao Cai, Antonia Debevc, Ceyda Topal, Anne Linhardt, Alla Synytska, Leonid Ionov   +7 more
wiley   +1 more source

Cervicovaginal lavage fluid iron and manganese levels in women with and without vaginitis. [PDF]

BMC Womens Health
Kozma B, Ratonyi D, Kozma B, Takacs P, Sipos AG.   +4 more
europepmc   +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

Exposure to manganese during juvenile development increases microglial activation in the hippocampus following systemic infection with A/California/04/2009 Influenza A H1N1 virus. [PDF]

Front Toxicol
Hager MR, Schuller AJ, Yanouri OA, Bantle CM, Smeyne RJ, Tjalkens RB.   +5 more
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

Exploring the Potential Role of Manganese-Based Zeolitic Imidazolate Framework Nanoparticles in Cancer Therapy: <i>In vitro</i> Studies Using Lung Cancer Cells. [PDF]

Int J Nanomedicine
Ivasiv V, Neves IC, Baltazar F, Moreira JN, Bañobre López M, Larios J, Duruz J, Balog S, Vanhecke D, Lee WS, Keshavan S, Petri-Fink A, Rothen-Rutishauser B.   +12 more
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