nanoparticle transport
Recently Published Documents


TOTAL DOCUMENTS

188
(FIVE YEARS 62)

H-INDEX

27
(FIVE YEARS 7)

2021 ◽  
Vol 7 (49) ◽  
Author(s):  
Sungsu Kang ◽  
Ji-Hyun Kim ◽  
Minyoung Lee ◽  
Ji Woong Yu ◽  
Joodeok Kim ◽  
...  

2021 ◽  
Author(s):  
Satish K. Nune ◽  
Quin R.S. Miller ◽  
H. Todd Schaef ◽  
Tengyue Jian ◽  
Miao Song ◽  
...  

Abstract Injecting fluids into deep underground geologic structures is a critical component to development of long-term strategies for managing greenhouse gas emissions and facilitating energy extraction operations. Recently, we reported that metal-organic frameworks are low-frequency absorptive acoustic metamaterial that may be injected into the subsurface enhancing geophysical monitoring tools used to track fluids and map complex structures. A key requirement for this nanotechnology deployment is transportability through porous geologic media without being retained by mineral-fluid interfaces. Flow-through column studies were used to estimate transport and retention properties of five different polymer-coated MIL-101(Cr) nanoparticles in siliceous porous media. Nanoparticle transport experiments revealed that nanoparticle surface characteristics play a critical role in nanoparticle colloidal stability and as well the transport.


2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Namid R. Stillman ◽  
Igor Balaz ◽  
Michail-Antisthenis Tsompanas ◽  
Marina Kovacevic ◽  
Sepinoud Azimi ◽  
...  

AbstractWe present the EVONANO platform for the evolution of nanomedicines with application to anti-cancer treatments. Our work aims to decrease both the time and cost required to develop nanoparticle designs. EVONANO includes a simulator to grow tumours, extract representative scenarios, and simulate nanoparticle transport through these scenarios in order to predict nanoparticle distribution. The nanoparticle designs are optimised using machine learning to efficiently find the most effective anti-cancer treatments. We demonstrate EVONANO with two examples optimising the properties of nanoparticles and treatment to selectively kill cancer cells over a range of tumour environments. Our platform shows how in silico models that capture both tumour and tissue-scale dynamics can be combined with machine learning to optimise nanomedicine.


2021 ◽  
Vol 350 ◽  
pp. S63
Author(s):  
L.A. Furer ◽  
A. Díaz Abad ◽  
G. Fortunato ◽  
S. Schürle-Finke ◽  
T Buerki-Thurnherr

2021 ◽  
Author(s):  
Darren Yohan

Gold nanoparticles (GNPs) possess a number of useful characteristics that have catapulted them into the mainstream of cancer research. Their optical properties enable them to be used in photodynamic and photothermal therapy as well as contrast agents in photoacoustic imaging. In addition, the ability to bind ligands to the GNP surface has made them valuable bio-markeraware drug carriers. But the effectiveness of any cancer fighting tool relies on homogenous distribution and penetration throughout the tumor, and the uptake and transport dynamics of GNPs has previously been held to monolayer cell models. In this work, multicellular layers (MCLs) are used as a solid tumor model to measure the penetration and uptake of GNPs in tumor tissue. MCLs offer a unique way to bridge the gap between in vitro single-layer cell models and the in vivo tumor. The effects of increased cell-to-cell connections, extracellular matrix and tumor characteristics are investigated to deliver new insights into the transport of GNPs in tissue.


2021 ◽  
Author(s):  
Darren Yohan

Gold nanoparticles (GNPs) possess a number of useful characteristics that have catapulted them into the mainstream of cancer research. Their optical properties enable them to be used in photodynamic and photothermal therapy as well as contrast agents in photoacoustic imaging. In addition, the ability to bind ligands to the GNP surface has made them valuable bio-markeraware drug carriers. But the effectiveness of any cancer fighting tool relies on homogenous distribution and penetration throughout the tumor, and the uptake and transport dynamics of GNPs has previously been held to monolayer cell models. In this work, multicellular layers (MCLs) are used as a solid tumor model to measure the penetration and uptake of GNPs in tumor tissue. MCLs offer a unique way to bridge the gap between in vitro single-layer cell models and the in vivo tumor. The effects of increased cell-to-cell connections, extracellular matrix and tumor characteristics are investigated to deliver new insights into the transport of GNPs in tissue.


2021 ◽  
Vol 103 (5) ◽  
Author(s):  
Xue-Zheng Cao ◽  
Holger Merlitz ◽  
Chen-Xu Wu ◽  
M. Gregory Forest

Sign in / Sign up

Export Citation Format

Share Document