Photonic Microresonators from Charge Transfer in Polymer Particles: Toward Enhanced and Tunable Two-Photon Emission.
Radhika VattikuntaDasari VenkatakrishnaraoChakradhar SahooSri Ram Gopal NaraharisettyDesai Narayana RaoKlaus MüllenRajadurai ChandrasekarPublished in: ACS applied materials & interfaces (2018)
Novel photonic microresonators with enhanced nonlinear optical (NLO) intensity are fabricated from polymer particles. As an additional advantage, they offer band gap tunability from the visible to near-infrared regions. A special protocol including (i) copolymerization of 4-(1-pyrenyl)-styrene, styrene, and 1,4-divinylbenzene, (ii) extraction of a dispersible and partly dissolvable, lightly cross-linked polymer network (PN), and (iii) treatment of the blue-emitting PN with electron acceptor (A) molecules such as 1,2,4,5-tetracyanobenzene (TCNB) and 7,7,8,8-tetracyanoquinodimethane (TCNQ) furnishes orange- and red-emitting D-A charge-transfer (CT) complexes with the pendant pyrene units. These complexes, here named PN-TCNB and PN-TCNQ, respectively, precipitate as microparticles upon the addition of water and subsequent ultrasonication. Upon electronic excitation, these spherical microparticles act as whispering-gallery-mode resonators by displaying optical resonances in the photoluminescence (PL) spectra because of light confinement. Further, the trapped incident light increases the light-matter interaction and thereby enhances the PL intensity, including the two-photon luminescence. The described protocol for polymer-based CT microresonators with tunable NLO emissions holds promise for a myriad of photonic applications.
Keyphrases
- energy transfer
- light emitting
- high speed
- quantum dots
- randomized controlled trial
- computed tomography
- high resolution
- high intensity
- image quality
- dual energy
- living cells
- cardiovascular disease
- contrast enhanced
- fluorescent probe
- magnetic resonance imaging
- magnetic resonance
- molecular dynamics
- artificial intelligence
- mass spectrometry
- monte carlo
- deep learning