EconPapers    
Economics at your fingertips  
 

A soft photopolymer cuboid that computes with binary strings of white light

Alexander D. Hudson, Matthew R. Ponte, Fariha Mahmood, Thomas Pena Ventura and Kalaichelvi Saravanamuttu ()
Additional contact information
Alexander D. Hudson: McMaster University
Matthew R. Ponte: McMaster University
Fariha Mahmood: McMaster University
Thomas Pena Ventura: McMaster University
Kalaichelvi Saravanamuttu: McMaster University

Nature Communications, 2019, vol. 10, issue 1, 1-9

Abstract: Abstract Next-generation stimuli–responsive materials must be configured with local computational ability so that instead of a discrete on-off responsiveness, they sense, process and interact reciprocally with environmental stimuli. Because of their varied architectures and tunable responsiveness to a range of physical and chemical stimuli, polymers hold particular promise in the generation of such “materials that compute”. Here, we present a photopolymer cuboid that autonomously performs pattern recognition and transfer, volumetric encoding and binary arithmetic with incandescent beams. The material’s nonlinear response to incident beams generates one, two or three mutually orthogonal ensembles of white-light filaments, which respectively self-organize into disordered, 1-D and 2-D periodic geometries. Data input as binary (dark-bright) strings generate a unique distribution of filament geometries, which corresponds to the result of a specific operation. The working principles of this material that computes with light is transferrable to other nonlinear systems and incoherent sources including light emitting diodes.

Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-019-10166-4 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10166-4

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-019-10166-4

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-19
Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10166-4