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Abstract The unique semiconducting characteristics of 2D materials, such as transition metal dichalcogenides (TMDs), have drawn significant interest in the field of electronic devices. However, the dependence of the device performance on the electrical properties of electrochemically exfoliated TMD nanoflakes remains unclear. In this study, the intrinsic electrical properties of diverse electrochemically exfoliated TMD nanoflakes are investigated and their applications in fully solution‐processed 2D electronics are explored. The study reveals that MoSe2 and WS2 nanoflakes are suitable candidates with moderate electron concentrations of 5.7 × 1012 and 2.5 × 1012 cm−2, respectively. These moderate electron concentrations enable a low off‐state current and high on‐state current, leading to a large on–off ratio (> 106) for the fabricated transistors. Furthermore, moderate annealing can increase the mobility by one order of magnitude by reducing the contact resistance and improving charge transport. Additionally, the trap density is significantly reduced, leading to an improved stability. Finally, fully solution‐processed 2D complementary inverters with a high voltage gain of 60 are demonstrated. This study contributes to a growing understanding of the properties of 2D materials in terms of transistor performances and promotes the development of solution‐processed devices applicable in next‐generation electronics.

Abstract In this study, it is demonstrated that CsPbBr3 perovskite nanocrystals (NCs) can enhance the overall performances of photomultiplication‐type organic photodiodes (PM‐OPDs). The proposed approach enables the ionic‐polarizable CsPbBr3 NCs to be evenly distributed throughout the depletion region of Schottky junction interface, allowing the entire trapped electrons within the depletion region to be stabilized, in contrast to previously reported interface‐limited strategies. The optimized CsPbBr3‐NC‐embedded poly(3‐hexylthiophene‐diyl)‐based PM‐OPDs exhibit exceptionally high external quantum efficiency, specific detectivity, and gain–bandwidth product of 2,840,000%, 3.97 × 1015 Jones, and 2.14 × 107 Hz, respectively. 2D grazing‐incidence X–ray diffraction analyses and drift–diffusion simulations combined with temperature‐dependent J–V characteristic analyses are conducted to investigate the physics behind the success of CsPbBr3‐NC‐embedded PM‐OPDs. The results show that the electrostatic interactions generated by the ionic polarization of NCs effectively stabilize the trapped electrons throughout the entire volume of the photoactive layer, thereby successfully increasing the effective energy depth of the trap states and allowing efficient PM mechanisms. This study demonstrates how a hybrid‐photoactive‐layer approach can further enhance PM‐OPD when the functionality of inorganic inclusions meets the requirements of the target device.

Carbon capture and utilization (CCU), which emerged as a means to reduce anthropogenic carbon emissions, has been highlighted to close the carbon cycle and combat climate change. CCU involves utilizing or converting captured CO2 to create value-added products that can replace or supplement fossil fuel-derived products. In order to meet climate goals, commercial-scale CCU facilities need to be built and their capacities increased, but barriers to large-scale CCU deployment still exist, primarily caused by large uncertainties in product/energy prices, markets, technologies, and policies. Conventional techno-economic analysis (TEA) methods cannot appropriately assess viability of CCU deployment projects which include dynamic uncertainties and deployment strategies. More flexible methods that can account for both time-varying uncertainties and dynamic capacity building are needed. We propose a systematic framework for the evaluation of commercial CCU deployment using the theory of real options and reinforcement learning (RL). RL is needed as considering options of adding capacities or delaying/abandoning the project at multiple time points under dynamic uncertainties lead to a large-scale stochastic optimal control problem which cannot be solved using conventional optimization methods like stochastic programming. The framework consists of three steps: surrogate modeling, uncertainty modeling, and assessment modeling. The framework is dependent on the type of CCU technology, uncertainties, real options and RL algorithm. We demonstrate the application of the framework through a case study of CO2 hydrogenation-to-methanol process in Europe, which is a late-stage, i.e., high technology readiness level (TRL), technology.