Premium
A Single Magnetic Particle with Nearly Unlimited Encoding Options
Author(s) -
Müssig Stephan,
Reichstein Jakob,
Prieschl Johannes,
Wintzheimer Susanne,
Mandel Karl
Publication year - 2021
Publication title -
small
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.785
H-Index - 236
eISSN - 1613-6829
pISSN - 1613-6810
DOI - 10.1002/smll.202101588
Subject(s) - magnetic nanoparticles , particle (ecology) , computer science , toolbox , magnetic particle inspection , encoding (memory) , key (lock) , smart material , magnetic separation , identification (biology) , materials science , nanotechnology , artificial intelligence , nanoparticle , oceanography , botany , computer security , metallurgy , biology , programming language , geology
Communicating objects are demanded for product security and the concepts of a circular economy or the Internet of Nano Things. Smart additives in the form of particles can be the key to equip objects with the desired materials intelligence as their miniaturized size improves applicability and security. Beyond their proposed identification by optical signals, magnetic signals deriving from magnetic particles can hypothetically be used for identification but are to date only resolved roughly. Herein, a magnetic particle‐based toolbox is reported, that provides more than 77 billion (77 × 10 9 ) different magnetic codes, adjustable in one single particle, that can be read out unambiguously, easily, and quickly. The key towards achieving the vast code variety is a hierarchical supraparticle design that is inspired by music: similarly to how the line‐up variation of a musical ensemble yields distinguishable overtones, the variation of the supraparticle composition alters their magnetic overtones. By minimizing magnetic interactions, customizable signals are spectrally decoded by the simple method of magnetic particle spectroscopy. A large number of chemically adjustable magnetic codes and the possibility of their remote, contactless detection from within materials is a breakthrough for unexploited labeling applications and pave the way towards materials intelligence.