Dipole−Dipole Interactions in Spin-Labeled Au Nanoparticles as a Measure of Interspin Distances

2005 ◽  
Vol 109 (9) ◽  
pp. 3734-3742 ◽  
Author(s):  
Petre Ionita ◽  
Agneta Caragheorgheopol ◽  
Bruce C. Gilbert ◽  
Victor Chechik
Keyword(s):  
Au Nanoparticles ◽  
Dipole Interactions ◽  
Interspin Distances

Tuning the Magnetic Vortex State in Magnetite Nanodiscs for Remote Control of Biological Signalling

2019 ◽  
Author(s):  
Danijela Gregurec ◽  
Alexander W. Senko ◽  
Andrey Chuvilin ◽  
Pooja Reddy ◽  
Ashwin Sankararaman ◽  
...  
Keyword(s):  
Remote Control ◽  
Ion Channels ◽  
Ground State ◽  
Magnetic Particles ◽  
Colloidal Stability ◽  
Vortex State ◽  
Magnetic Vortex ◽  
Electron Holography ◽  
Dipole Interactions ◽  
Magnetite Particles

In this work, we demonstrate the application of anisotropic magnetite nanodiscs (MNDs) as transducers of torque to mechanosensory cells under weak, slowly varying magnetic fields (MFs). These MNDs possess a ground state vortex configuration of magnetic spins which affords greater colloidal stability due to eliminated dipole-dipole interactions characteristic of isotropic magnetic particles of similar size. We first predict vortex magnetization using micromagnetic stimulations in sub-micron anisotropic magnetite particles and then use electron holography to experimentally investigate the magnetization of MNDs 98–226 nm in diameter. When MNDs are coupled to MFs, they transition between vortex and in-plane magnetization allowing for the exertion of the torque on the pN scale, which is sufficient to activate mechanosensitive ion channels in cell membranes.<br>

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

Synergistic effects of SnO2 and Au nanoparticles decorated on WS2 nanosheets for flexible, room-temperature CO gas sensing

2021 ◽  
Vol 332 ◽  
pp. 129493
Author(s):  
Jae-Hun Kim ◽  
Jin-Young Kim ◽  
Ali Mirzaei ◽  
Hyoun Woo Kim ◽  
Sang Sub Kim
Keyword(s):  
Room Temperature ◽  
Au Nanoparticles ◽  
Gas Sensing ◽  
Synergistic Effects ◽  
Ws2 Nanosheets ◽  
Co Gas

scholarly journals DEER and RIDME Measurements of the Nitroxide-Spin Labelled Copper-Bound Amine Oxidase Homodimer from Arthrobacter Globiformis

2021 ◽  
Author(s):  
Hannah Russell ◽  
Rachel Stewart ◽  
Christopher Prior ◽  
Vasily S. Oganesyan ◽  
Thembaninkosi G. Gaule ◽  
...  
Keyword(s):  
Dipolar Coupling ◽  
Amine Oxidase ◽  
Spin Labels ◽  
Arthrobacter Globiformis ◽  
Copper Amine Oxidase ◽  
Dipole Interactions ◽  
Nitroxide Spin Labels ◽  
Double Electron Electron Resonance ◽  
Dipolar Modulation ◽  
Paramagnetic Centres

AbstractIn the study of biological structures, pulse dipolar spectroscopy (PDS) is used to elucidate spin–spin distances at nanometre-scale by measuring dipole–dipole interactions between paramagnetic centres. The PDS methods of Double Electron Electron Resonance (DEER) and Relaxation Induced Dipolar Modulation Enhancement (RIDME) are employed, and their results compared, for the measurement of the dipolar coupling between nitroxide spin labels and copper-II (Cu(II)) paramagnetic centres within the copper amine oxidase from Arthrobacter globiformis (AGAO). The distance distribution results obtained indicate that two distinct distances can be measured, with the longer of these at c.a. 5 nm. Conditions for optimising the RIDME experiment such that it may outperform DEER for these long distances are discussed. Modelling methods are used to show that the distances obtained after data analysis are consistent with the structure of AGAO.

scholarly journals Designing the Hotspots Distribution by Anisotropic Growth

Molecules ◽  
2021 ◽  
Vol 26 (1) ◽  
pp. 187
Author(s):  
Tianshun Li ◽  
Renxian Gao ◽  
Xiaolong Zhang ◽  
Yongjun Zhang
Keyword(s):  
Au Nanoparticles ◽  
Fdtd Method ◽  
Polarization Dependence ◽  
Noble Metal Nanoparticles ◽  
Oblique Angle Deposition ◽  
Matlab Simulation ◽  
Different Shapes ◽  
Plasma Enhancement ◽  
Difference Time ◽  
Good Agreement

Changing the morphology of noble metal nanoparticles and polarization dependence of nanoparticles with different morphologies is an important part of further research on surface plasma enhancement. Therefore, we used the method based on Matlab simulation to provide a simple and effective method for preparing the morphologies of Au nanoparticles with different morphologies, and prepared the structure of Au nanoparticles with good uniformity and different morphologies by oblique angle deposition (OAD) technology. The change of the surface morphology of nanoparticles from spherical to square to diamond can be effectively controlled by changing the deposition angle. The finite difference time domain (FDTD) method was used to simulate the electromagnetic fields of Au nanoparticles with different morphologies to explore the polarization dependence of nanoparticles with different shapes, which was in good agreement with Raman spectrum.

Sign in / Sign up

Export Citation Format

Share Document