Quantum dots (QDs) with an alloy shell (CdSe@ZnS/ZnS) are one of the most promising emitters for optoelectronic devices for their superior properties such as narrow full width at half maximum (FWHM), ...high color purity and tunable wavelength. However, the presence of lattice stresses during the internal growth of quantum dots can cause severe exciton bursts. Optimizing the ramp-up process during the core growth of quantum dots and rising the temperature when cladding with ZnS shells is an effective strategy for obtaining high-quality quantum dots. Besides, the stability of quantum dots is further modified by using 1-Octanethiol (OT) as ligands. Detailed analyses of the morphology, colloidal stability, and luminescence performance of QDs were represented. Finally, green CdSe@ZnS/ZnS QLEDs with prolonged carrier lifetimes and less defect state densities achieved peak luminescence at 361850 cd/m2 with a current efficiency (CE) of 33 cd/A. This work provides a better understanding of the relationship between defect state density and the performance of quantum dots for designing and fabricating high-performance light-emitting diodes.
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•The density of defect states in CdSe@ZnS/ZnS can be divided into core and shell defects, and quantum dots with low trap state density are obtained by adjusting the synthesis strategy by controlling the temperature to reduce the core and shell defects.•The cooling down during the thermal injection process will lead to an increase in defect states, which can be reduced by maintaining the reaction temperature at a higher temperature, followed by slow cladding of the shell at a higher temperature to further reduce the defect density.•After a proper synthesis strategy to reduce the defect states of the quantum dots, the QLEDs had achieved a peak brightness of 361,850 cd/m2 and a current efficiency of 33 cd/m2.
The stabilizability of a linear dynamical system (LDS) refers to the existence of control inputs that drive the system state to zero. In this article, we analyze both the theoretical and algorithmic ...aspects of the stabilizability of an LDS using sparse control inputs with potentially time-varying supports. We show that an LDS is stabilizable using sparse control inputs if and only if it is stabilizable (using unconstrained inputs). For a stabilizable LDS, we present an algorithm to determine the sparse control inputs that steer the system state to zero. We show that all stabilizable LDSs are also sparse mean square stabilizable when the process noise has zero mean and bounded second moment. For such an LDS, we devise a method to sequentially estimate the sparse control inputs to stabilize the LDS in the mean square sense. We prove that a detectable and stabilizable LDS is sparse stabilizable through output feedback and develop an algorithm for finding the corresponding sparse control inputs. Finally, we analyze the stabilizability of an LDS using sparse control inputs with common support. Our results shed light on the conditions under which a given LDS is stabilizable using sparse control inputs and the design of the corresponding control inputs.
This paper investigates exponential stability of fractional order memristive discontinuous neural networks (FMDNNs). Under the framework of the fractional order Filippov solution and differential ...inclusion theory, the global existence of the solution for the FMDNNs is studied by a given growth conditions. Based on fractional stability theoryand the properties of the Mittag Leffler function, some new criteria for the stability of FMDNNs are obtained by using effective partial state impulsive control, which only needs to control a small fraction of the states. At every impulsive moment, these states of the trajectory far away from the desired trajectory will be firstly controlled. The relations between stable region and fractional order α, control parameters and control rate are discussed. Finally, a numerical simulation is given to verify the effectiveness of the theoretical analysis.
•The global existence of the solution for FMDNNs is studied by a given growth conditions.•The stabilization of FMDNNs are obtained by using partial state impulsive control.
Abstract We report a combined experimental and theoretical study on the influence of microwave pulse durations on enantiomer-specific state transfer. Two triads of rotational states within a chiral ...molecule (1-indanol) are selected to address the possible scenarios. In the triad connected to the absolute ground state, the simplest triad that exists for all chiral molecules, the enantiomer-specific state transfer process simplifies into a sequence of two-level transitions. The second triad, including higher rotational states, represents a more generic scenario that involves multiple Rabi frequencies for each transition. Our study reveals that the conventional π 2 − π − π 2 pulse sequence is not the optimal choice, except for the ideal case when in the simplest triad only the lowest state is initially populated. We find that employing a shorter duration for the first and last pulse of the sequence leads to significantly higher state-specific enantiomeric enrichment, albeit at the expense of overall population in the target state. Our experimental results are in very good agreement with theory, substantiating the quantitative understanding of enantiomer-specific state transfer.
This paper contributes insights on differences in observed rates of substitution for fossil fuels across countries as measured by changes in the energy mix. If some countries are relatively slower in ...substituting for fossil fuels, climate change will be addressed less successfully. This paper specifically investigates the role of industrialisation and state-related institutions in the speed of energy substitution in 19 European countries from years 2011–2018 using panel data econometric methods, and has three main results. This paper finds evidence that (1) higher industrialisation levels in the economy increase the speed of energy substitution; (2) that changes in industrialisation levels, not the levels themselves, decrease the speed of energy substitution, but state-related institutions in support of green energy policy weaken this effect; and that (3) higher degrees of state control of the electricity sector slows down energy substitution, but state-related institutions in support of green energy policy likewise weaken this effect. These findings imply that higher industrialisation levels require more energy in absolute terms, implying greater energy portfolio diversification in order to satisfy such energy demand, and that a faster energy substitution can result from a more liberalised electricity sector.
•A study on 19 European countries from years 2011–2018 using panel data.•Changes in industrialisation levels decelerates energy substitution.•More state control of the electricity sector decelerates energy substitution.•State-related institutions supporting green energy policies weaken these effects.•Higher industrialisation levels themselves however accelerate energy substitution.
Port state control (PSC) serves as the final defense against substandard ships in maritime transportation. The port state control officer (PSCO) routing problem involves selecting ships for ...inspection and determining the inspection sequence for available PSCOs, aiming to identify the highest number of deficiencies. Port authorities face this problem daily, making decisions without prior knowledge of ship conditions. Traditionally, a predict-then-optimize framework is employed, but its machine learning (ML) models' loss function fails to account for the impact of predictions on the downstream optimization problem, potentially resulting in suboptimal decisions. We adopt a decision-focused learning framework, integrating the PSCO routing problem into the ML models' training process. However, as the PSCO routing problem is NP-hard and plugging it into the training process of ML models requires that it be solved numerous times, computational complexity and scalability present significant challenges. To address these issues, we first convert the PSCO routing problem into a compact model using undominated inspection templates, enhancing the model's solution efficiency. Next, we employ a family of surrogate loss functions based on noise-contrastive estimation (NCE) for the ML model, requiring a solution pool treating suboptimal solutions as noise samples. This pool represents a convex hull of feasible solutions, avoiding frequent reoptimizations during the ML model's training process. Through computational experiments, we compare the predictive and prescriptive qualities of both the two-stage framework and the decision-focused learning framework under varying instance sizes. Our findings suggest that accurate predictions do not guarantee good decisions; the decision-focused learning framework's performance may depend on the optimization problem size and the training dataset size; and using a solution pool containing noise samples strikes a balance between training efficiency and decision performance.
•Transformation techniques for the NP-hard PSCO routing problem.•Two prescriptive analytics frameworks to solve the PSCO routing problem.•Multiple surrogate loss functions for the decision-focused learning framework.•Gradient-descent decision-focused learning algorithm with noise samples.
