Publication:
Statistical reprogramming of macroscopic self-assembly with dynamic boundaries

dc.contributor.coauthorCulha, U.
dc.contributor.coauthorDavidson, Z.S.
dc.contributor.coauthorMastrangeli, M.
dc.contributor.departmentDepartment of Mechanical Engineering
dc.contributor.departmentSchool of Medicine
dc.contributor.kuauthorSitti, Metin
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.schoolcollegeinstituteSCHOOL OF MEDICINE
dc.date.accessioned2024-11-09T11:53:37Z
dc.date.issued2020
dc.description.abstractSelf-assembly is a ubiquitous process that can generate complex and functional structures via local interactions among a large set of simpler components. The ability to program the self-assembly pathway of component sets elucidates fundamental physics and enables alternative competitive fabrication technologies. Reprogrammability offers further opportunities for tuning structural and material properties but requires reversible selection from multistable self-assembling patterns, which remains a challenge. Here, we show statistical reprogramming of two-dimensional (2D), noncompact self-assembled structures by the dynamic confinement of orbitally shaken and magnetically repulsive millimeter-scale particles. Under a constant shaking regime, we control the rate of radius change of an assembly arena via moving hard boundaries and select among a finite set of self-assembled patterns repeatably and reversibly. By temporarily trapping particles in topologically identified stable states, we also demonstrate 2D reprogrammable stiffness and three-dimensional (3D) magnetic clutching of the self-assembled structures. Our reprogrammable system has prospective implications for the design of granular materials in a multitude of physical scales where out-of-equilibrium self-assembly can be realized with different numbers or types of particles. Our dynamic boundary regulation may also enable robust bottom-up control strategies for novel robotic assembly applications by designing more complex spatiotemporal interactions using mobile robots.
dc.description.fulltextYES
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.indexedbyPubMed
dc.description.issue21
dc.description.openaccessYES
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.description.sponsorshipAlexander von Humboldt Foundation, Humboldt Postdoctoral Research Fellowship
dc.description.sponsorshipFederal Ministry for Education and Research
dc.description.sponsorshipMax Planck Society
dc.description.versionPublisher version
dc.description.volume117
dc.identifier.doi10.1073/pnas.2001272117
dc.identifier.embargoNO
dc.identifier.filenameinventorynoIR02250
dc.identifier.issn0027-8424
dc.identifier.quartileQ1
dc.identifier.scopus2-s2.0-85085504227
dc.identifier.urihttps://doi.org/10.1073/pnas.2001272117
dc.identifier.wos536797100023
dc.keywordsDynamic confinement control
dc.keywordsMechanism design
dc.keywordsProgrammable self-assembly
dc.language.isoeng
dc.publisherNational Academy of Sciences
dc.relation.grantnoNA
dc.relation.ispartofProceedings of the National Academy of Sciences of the United States of America
dc.relation.urihttp://cdm21054.contentdm.oclc.org/cdm/ref/collection/IR/id/8888
dc.subjectScience and technology
dc.titleStatistical reprogramming of macroscopic self-assembly with dynamic boundaries
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.kuauthorSitti, Metin
local.publication.orgunit1College of Engineering
local.publication.orgunit1SCHOOL OF MEDICINE
local.publication.orgunit2Department of Mechanical Engineering
local.publication.orgunit2School of Medicine
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