Aliovalent Dopants in ZnO Nanocrystals: Synthesis to Electronic Structure

Semiconductor nanocrystal doping has stimulated broad interest for many applications including solar energy conversion, nanospintronics, and phosphors or optical labels. The study of the chemistry and physics of doped colloidal semiconductor nanocrystals has been dominated in the literature by isovalent dopants such as Mn2+ and Co2+ ions in II-VI semiconductors, in which the dopant oxidation state is the same as the cation ions. Until recently, aliovalent dopants has received much attention due to the plasmonic properties. Aliovalent is when the oxidation states of the dopant in the lattice differs from the cation ions. In the plasmonic semiconductor nanocrystals, the dopants are noted as the shallow donor and can donate electrons to the conduction band, resulting in the collectively resonate of the delocalized electrons under certain electromagnetic radiation, i.e. localized surface plasmon resonances (LSPR). However, only small amount of the dopants can donate delocalized electrons. The amount of ‘activated’ dopant is restricted by the synthetic methods and the defect chemistry related to the plasmonic property is still under debate. In this report, we are using Al3+ doped ZnO nanocrystals as an example. We have established a synthesis method to bring more dopant incorporation and less charge compensation defect, so higher electronically activated dopant is achieved. These results provide a synthetic strategy as well as the electronic structure understanding for the aliovalent dopants in semiconductor nanocrystals. On the other hand, less attention has been focused on the deep donor dopant such as Fe3+ in II-VI semiconductors. The deep donor, due to the oxidation redox potential lower than the conduction band potential (deep), instead of donating electrons in conduction bands, will donate electrons in trap states or defect states. For the deep donor, less is known for the driving force to determine the oxidation state of the n-type dopant. Here, we have developed a dopant-specific spectroscopy for estimation the deep donor Fe3+ speciation, i.e. substitutional, interstitial, or surface Fe3+ in ZnO nanocrystals and conducted photo-charging experiment to study the redox potential of the deep donor dopant. These studies could help to identify the dopant speciation and understand the interactions between the conduction band electrons and the deep dopants

Chemical Stabilization of Perovskite Solar Cells with Functional Fulleropyrrolidines.

While perovskite solar cells have invigorated the photovoltaic research community due to their excellent power conversion efficiencies (PCEs), these devices notably suffer from poor stability. To address this crucial issue, a solution-processable organic chemical inhibition layer (OCIL) was integrated into perovskite solar cells, resulting in improved device stability and a maximum PCE of 16.3%. Photoenhanced self-doping of the fulleropyrrolidine mixture in the interlayers afforded devices that were advantageously insensitive to OCIL thickness, ranging from 4 to 190 nm. X-ray photoelectron spectroscopy (XPS) indicated that the fulleropyrrolidine mixture improved device stability by stabilizing the metal electrode and trapping ionic defects (i.e., I-) that originate from the perovskite active layer. Moreover, degraded devices were rejuvenated by repeatedly peeling away and replacing the OCIL/Ag electrode, and this repeel and replace process resulted in further improvement to device stability with minimal variation of device efficiency

IEEE Access Special Section Editorial: Secure Modulations for Future Wireless Communications and Mobile Networks

Security has become an extremely important research topic in wireless networks over the last decade, as it is intimately related to both individual privacy and national security. Directional modulation, as a conventional type of secure modulations, transmits confidential information along the desired directions of legitimate receivers, and artificial noise in other directions, to deliberately confuse eavesdroppers in line-of-sight channels. Recently, artificial noise is also introduced into spatial modulation, leading to a secure spatial modulation strategy. In this Special Section in IEEE A CCESS, secure modulation is defined broadly as any secure modulation method, which includes, but is not limited to, secure directional modulation, secure spatial modulation, and secure index modulation

Aligning Recommendation and Conversation via Dual Imitation

Human conversations of recommendation naturally involve the shift of interests which can align the recommendation actions and conversation process to make accurate recommendations with rich explanations. However, existing conversational recommendation systems (CRS) ignore the advantage of user interest shift in connecting recommendation and conversation, which leads to an ineffective loose coupling structure of CRS. To address this issue, by modeling the recommendation actions as recommendation paths in a knowledge graph (KG), we propose DICR (Dual Imitation for Conversational Recommendation), which designs a dual imitation to explicitly align the recommendation paths and user interest shift paths in a recommendation module and a conversation module, respectively. By exchanging alignment signals, DICR achieves bidirectional promotion between recommendation and conversation modules and generates high-quality responses with accurate recommendations and coherent explanations. Experiments demonstrate that DICR outperforms the state-of-the-art models on recommendation and conversation performance with automatic, human, and novel explainability metrics.Comment: EMNLP 202

Integrating Stock Features and Global Information via Large Language Models for Enhanced Stock Return Prediction

The remarkable achievements and rapid advancements of Large Language Models (LLMs) such as ChatGPT and GPT-4 have showcased their immense potential in quantitative investment. Traders can effectively leverage these LLMs to analyze financial news and predict stock returns accurately. However, integrating LLMs into existing quantitative models presents two primary challenges: the insufficient utilization of semantic information embedded within LLMs and the difficulties in aligning the latent information within LLMs with pre-existing quantitative stock features. We propose a novel framework consisting of two components to surmount these challenges. The first component, the Local-Global (LG) model, introduces three distinct strategies for modeling global information. These approaches are grounded respectively on stock features, the capabilities of LLMs, and a hybrid method combining the two paradigms. The second component, Self-Correlated Reinforcement Learning (SCRL), focuses on aligning the embeddings of financial news generated by LLMs with stock features within the same semantic space. By implementing our framework, we have demonstrated superior performance in Rank Information Coefficient and returns, particularly compared to models relying only on stock features in the China A-share market.Comment: 8 pages, International Joint Conferences on Artificial Intelligenc

Persistent radical anion polymers based on naphthalenediimide and a vinylene spacer

Persistent n-doped conjugated polymers were achieved by doping the electron accepting PDNDIV and PFNDIVpolymers with ionic (TBACN) or neutral (TDAE) dopants. The great electron affinities, as indicated by the low LUMO levels of PDNDIV (−4.09 eV) and PFNDIV (−4.27 eV), facilitated the chemical reduction from either TBACN or TDAE. The low-lying LUMOs of the neutral polymers PDNDIV and PFNDIV were achieved by incorporation of vinylene spacers between the electron poor NDI units to increase the conjugation length without the use of an electron donor, and this was lowered further by an electron-withdrawing fluorinated N-substituent on the NDI moiety. The polymer radical anions were found to persist for several days under ambient conditions by EPR spectroscopy. A distinguishing and noteworthy feature of these polymers is that they can be consecutively reduced by up to four electrons in acetonitrile. Conductivity measurements demonstrate the prospective impact of PDNDIV and PFNDIV for organic electronics

Autopilot Design for Unmanned Surface Vehicle based on CNN and ACO

There is a growing concern to design intelligent controllers for autopiloting unmanned surface vehicles as solution for many naval and civilian requirements. Traditional autopilot’s performance declines due to the uncertainties in hydrodynamics as a result of harsh sailing conditions and sea states. This paper reports the design of a novel nonlinear model predictive controller (NMPC) based on convolutional neural network (CNN) and ant colony optimizer (ACO) which is superior to a linear proportional integral-derivative counterpart. This combination helps the control system to deal with model uncertainties with robustness. The results of simulation and experiment demonstrate the proposed method is more efficient and more capable to guide the vehicle through LOS waypoints particularly in the presence of large disturbances

Sensitivity Study for Improved Magnetic Induction Tomography (MIT) Coil System

Abstract-The improved magnetic induction tomography (MIT) coil system which consists of the two-arm Archimedean spiral coil (TAASC) as excitation coil and the solenoid as receiver coil has much better performance in coil system sensitivity than the conventional MIT coil system which uses the solenoids as excitation coil and receiver coil. In this paper the theoretical sensitivity property for improved MIT coil system are studied fully