Colloidal robotics.


Journal

Nature materials
ISSN: 1476-4660
Titre abrégé: Nat Mater
Pays: England
ID NLM: 101155473

Informations de publication

Date de publication:
Dec 2023
Historique:
received: 29 02 2020
accepted: 30 03 2023
medline: 25 8 2023
pubmed: 25 8 2023
entrez: 24 8 2023
Statut: ppublish

Résumé

Robots have components that work together to accomplish a task. Colloids are particles, usually less than 100 µm, that are small enough that they do not settle out of solution. Colloidal robots are particles capable of functions such as sensing, computation, communication, locomotion and energy management that are all controlled by the particle itself. Their design and synthesis is an emerging area of interdisciplinary research drawing from materials science, colloid science, self-assembly, robophysics and control theory. Many colloidal robot systems approach synthetic versions of biological cells in autonomy and may find ultimate utility in bringing these specialized functions to previously inaccessible locations. This Perspective examines the emerging literature and highlights certain design principles and strategies towards the realization of colloidal robots.

Identifiants

pubmed: 37620646
doi: 10.1038/s41563-023-01589-y
pii: 10.1038/s41563-023-01589-y
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

1453-1462

Subventions

Organisme : United States Department of Defense | United States Air Force | AFMC | Air Force Office of Scientific Research (AF Office of Scientific Research)
ID : FA9550-15-1-0514
Organisme : United States Department of Defense | United States Army | U.S. Army Research, Development and Engineering Command | Army Research Office (ARO)
ID : W911NF-19-1-0233
Organisme : United States Department of Defense | United States Army | U.S. Army Research, Development and Engineering Command | Army Research Office (ARO)
ID : W911NF-19-10372

Informations de copyright

© 2023. Springer Nature Limited.

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Auteurs

Albert Tianxiang Liu (AT)

Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA.

Marek Hempel (M)

Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.

Jing Fan Yang (JF)

Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.

Allan M Brooks (AM)

Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.

Ana Pervan (A)

Department of Mechanical Engineering, Northwestern University, Evanston, IL, USA.

Volodymyr B Koman (VB)

Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.

Ge Zhang (G)

Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.

Daichi Kozawa (D)

Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.

Sungyun Yang (S)

Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.

Daniel I Goldman (DI)

School of Physics, Georgia Institute of Technology, Atlanta, GA, USA.

Marc Z Miskin (MZ)

Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA.

Andréa W Richa (AW)

School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA.

Dana Randall (D)

School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA.

Todd D Murphey (TD)

Department of Mechanical Engineering, Northwestern University, Evanston, IL, USA.

Tomás Palacios (T)

Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA. tpalacios@mit.edu.

Michael S Strano (MS)

Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. strano@mit.edu.

Classifications MeSH