Construction and function of a highly efficient supramolecular luminescent system

2017 ◽  
Vol 196 ◽  
pp. 219-229 ◽  
Author(s):  
Yingjie Liu ◽  
Suqian Ma ◽  
Bin Xu ◽  
Wenjing Tian

Aggregation-induced emission (AIE) provides a new way of achieving highly efficient luminescent materials. In this contribution, the self-assembly behavior, molecular stacking structure and photophysical properties of two polymorphs of a supramolecular co-crystal (C1 and C2) are investigated. The block-like crystal C1, packed in segregated stacking with strong π–π interactions between the H and G molecules, shows weak green emission with a low efficiency (ΦF) of 2%. In comparison, the needle-like crystal C2, packed in segregated stacking with no obviously strong intermolecular interactions, shows bright yellow emission. More importantly, C1 exhibits mechanochromic behavior.

2017 ◽  
Vol 8 (23) ◽  
pp. 3647-3656 ◽  
Author(s):  
Ryoto Tanaka ◽  
Kodai Watanabe ◽  
Takuya Yamamoto ◽  
Kenji Tajima ◽  
Takuya Isono ◽  
...  

The effect of intramolecular cross-linking on aqueous self-assembly behavior was systematically investigated based on an amphiphilic block copolymer system.


2005 ◽  
Vol 11 (23) ◽  
pp. 6833-6845 ◽  
Author(s):  
Chunyan Chi ◽  
Chan Im ◽  
Volker Enkelmann ◽  
Andreas Ziegler ◽  
Günter Lieser ◽  
...  

RSC Advances ◽  
2016 ◽  
Vol 6 (11) ◽  
pp. 9186-9193 ◽  
Author(s):  
Han Zhang ◽  
Menghong Yu ◽  
Aixin Song ◽  
Yawen Song ◽  
Xia Xin ◽  
...  

The self-assembly behavior of a nonionic surfactant (n-dodecyl tetraethylene monoether, C12E4) and a peptide amphiphile (PA, C16-GK-3) mixed system was investigated using a combination of microscopic, scattering and spectroscopic techniques.


Materials ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 310
Author(s):  
Hao Kong ◽  
Bin Liu ◽  
Guozheng Yang ◽  
Yun Chen ◽  
Gang Wei

Studying the interactions between biomolecules and material interfaces play a crucial role in the designing and synthesizing of functional bionanomaterials with tailored structure and function. Previously, a lot of studies were performed on the self-assembly of peptides in solution through internal and external stimulations, which mediated the creation of peptide nanostructures from zero-dimension to three-dimension. In this study, we demonstrate the self-assembly behavior of the GNNQQNY peptide on the surface of mica and highly oriented pyrolytic graphite through tailoring the self-assembly conditions. Various factors, such as the type of dissolvent, peptide concentration, pH value, and evaporation period on the formation of peptide nanofibers and nanoribbons with single- and bi-directional arrays are investigated. It is found that the creation of peptide nanoribbons on both mica and HOPG can be achieved effectively through adjusting and optimizing the experimental parameters. Based on the obtained results, the self-assembly and formation mechanisms of peptide nanoribbons on both material interfaces are discussed. It is expected that the findings obtained in this study will inspire the design of motif-specific peptides with high binding affinity towards materials and mediate the green synthesis of peptide-based bionanomaterials with unique function and application potential.


2012 ◽  
Vol 116 (20) ◽  
pp. 11401-11407 ◽  
Author(s):  
Sadananda Mandal ◽  
Santanu Bhattacharyya ◽  
Victor Borovkov ◽  
Amitava Patra

Author(s):  
Chelsea Barabas

This chapter discusses contemporary debates regarding the use of artificial intelligence as a vehicle for criminal justice reform. It closely examines two general approaches to what has been widely branded as “algorithmic fairness” in criminal law: the development of formal fairness criteria and accuracy measures that illustrate the trade-offs of different algorithmic interventions; and the development of “best practices” and managerialist standards for maintaining a baseline of accuracy, transparency, and validity in these systems. Attempts to render AI-branded tools more accurate by addressing narrow notions of bias miss the deeper methodological and epistemological issues regarding the fairness of these tools. The key question is whether predictive tools reflect and reinforce punitive practices that drive disparate outcomes, and how data regimes interact with the penal ideology to naturalize these practices. The chapter then calls for a radically different understanding of the role and function of the carceral state, as a starting place for re-imagining the role of “AI” as a transformative force in the criminal legal system.


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