Statistics, damned statistics and nanoscience – using data science to meet the challenge of nanomaterial complexity-Reference-Cited by-同舟云学术

Statistics, damned statistics and nanoscience – using data science to meet the challenge of nanomaterial complexity

Published:2016 Issue:2 Volume:1 Page:89-95
ISSN:2055-6756
Container-title:Nanoscale Horizons
language:en
Short-container-title:Nanoscale Horiz.

Author:

Sun Baichuan¹²³,Fernandez Michael¹²³,Barnard Amanda S.¹²³

Affiliation:

1. CSIRO Virtual Nanoscience Laboratory

2. Parkville

3. Australia

Abstract

Combining advances in digital technology and modern methods in statistics with a detailed understanding of nano-structure/property relationships can pave the way for more realistic predictions of nanomaterials performance.

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science

Link

http://pubs.rsc.org/en/content/articlepdf/2016/NH/C5NH00126A

Reference79 articles.

1. The Effect of Size-Dependent Nanoparticle Energetics on Catalyst Sintering

2. Synthesis and size-dependent properties of zinc-blende semiconductor quantum rods

3. Size-dependent structure of MoS2 nanocrystals

4. Nanoparticle-mediated cellular response is size-dependent

5. Size-Dependent Endocytosis of Nanoparticles

Cited by 35 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Bioinspired designer surface nanostructures for structural color;Nanotechnology for Environmental Engineering;2024-06-18

2. Simultaneous Prediction and Optimization of Charge Transfer Properties of Graphene and Graphene Oxide Nanoflakes from Multitarget Machine Learning;The Journal of Physical Chemistry C;2023-10-18

3. Federated data processing and learning for collaboration in the physical sciences;Machine Learning: Science and Technology;2022-12-01

4. Quasi-SMILES for predicting toxicity of Nano-mixtures to Daphnia Magna;NanoImpact;2022-10

5. Safety-by-design using forward and inverse multi-target machine learning;Chemosphere;2022-09