Organic donor-acceptor cocrystals of π-conjugated molecues have shown diverse applications in materials science. However,most cocrystals exist in neutral forms dominated by π-π interactions, while the ionic congene...
Organic donor-acceptor cocrystals of π-conjugated molecues have shown diverse applications in materials science. However,most cocrystals exist in neutral forms dominated by π-π interactions, while the ionic congeners and corresponding properties are rarely studied due to difficulties in harnessing the degree of charge transfer. Herein, we report cocrystals of axially N-embedded quasi-carbon nanohoops(DPP-D and DPP-T) with electron deficient guests. By modulating the electron affinity of the acceptor guests, the electronic structures of the complexes can be tuned from neutral to ionic states. Specifically, DPP-D interacts with TCNB molecules to form neutral superstructures via intermolecular π-π interactions, giving rise to a deep-red emission in the solid state. In contrast, an ionic complex showing near-infrared region absorptions and paramagnetic character on account of strong charge-transfer interactions is generated when DDQ molecules are involved. Their unique properties can be explained by different degrees of charge transfer and assembly modes, which have clearly been manifested by crystal structures and theoretical calculations. Our studies provide rare examples of π-conjugated macrocycle-based donor-acceptor cocrystals in both neutral and ionic forms, and give insight to the design of multicomponent carbon nanomaterials.
The governance of service ecosystem needs to balance efficiency and fairness to promote the sustainable development of the system, making it an important topic. However, service entities in the service ecosystem have ...
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ISBN:
(数字)9798350368550
ISBN:
(纸本)9798350368567
The governance of service ecosystem needs to balance efficiency and fairness to promote the sustainable development of the system, making it an important topic. However, service entities in the service ecosystem have autonomy and engage in dynamic game with governance strategies, leading to governance challenges. To address this issue, we model the governance process as a repeated sequential game between the governance algorithm and service entities, and propose a novel two-level learning algorithm. This algorithm considers the learning evolution of service entities and the co-evolutionary of the governance algorithm, using reinforcement learning to learn effective governance strategies while also considering the response function of service entities to balance efficiency and fairness. Combining the ideas of bilevel optimization and online learning, this algorithm effectively balances exploration (understanding the service entities’ responses function) and exploitation (choosing efficient actions). We apply the algorithm to a classic service ecosystem, the ride-hailing service system, and empirically demonstrate its effectiveness in the governance task of order dispatching.
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