Herein, two bis­(dicyanomethylene)-substituted quinoidal molecules QBDT and QTBDT-3H were designed and synthesized to explore the open-shell effect on tuning the charge transport behavior of organic π-functional materials. The biradical character of QTBDT-3H was confirmed by DFT calculation, variable-temperature NMR, electron spin resonance (ESR), and superconducting quantum-interfering device (SQUID). The open-shell character enables QTBDT-3H an ambipolar characteristic under ambient conditions with highly balanced electron and hole mobilities of 0.32 and 0.16 cm2 V–1 s–1, respectively.

A helical perylene diimide oligomer (PDI2) is gradually emerging as a promising building block for the construction of organic optoelectronic materials.

As a fundamental task in power system operations, transmission-constrained unit commitment (TCUC) decides ON/OFF state (i.e., commitment) and scheduled generation for each unit. Generally, TCUC is formulated as a mixed-integer linear programming (MILP) and must be resolved within a limited time window. However, due to the NP-hard property of MILP and the increasing complexity of power systems, solving the TCUC within a limited time is computationally challenging. Regarding the computation challenge, the availability of historical TCUC data and the development of the machine learning (ML) community are potentially helpful. To this end, this paper designs an ML-aided framework that can leverage historical data in enabling computation improvement of TCUC. In the offline stage, ML models are trained to predict the commitments based on historical TCUC data. In the online stage, the commitments are quickly predicted using the well-trained ML. Furthermore, a feasibility checking process is conducted to ensure the commitment feasibility. As a result, only a reduced TCUC with fewer binary variables needs to be solved, leading to computation acceleration. Case studies on an IEEE 24-bus and a practical 5655-bus system show the effectiveness of the presented framework.

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