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Latent and controllable doping of stimuli-activated molecular dopants for flexible and printable organic thermoelectric generators
No full textConjugated polymers (CPs) are a promising class of materials for organic thermoelectric generators (OTEGs); however, achieving high electrical conductivity through molecular doping while maintaining compatibility with thin-film printing processes remains a huge challenge. In this paper, we present a novel doping strategy using stimuli-activated molecular dopants (SAMDs) based on photoacid generators (PAGs) that can be activated by light of a specific wavelength. We demonstrate that this approach can effectively control the doping efficiency and optoelectronic properties of CP-PAG-blended thin films, resulting in a wide range of electrical conductivities. Our selected PAG molecules enabled efficient printing of the CP-PAG mixed solution and yielded a high thermoelectric figure of merit. To elucidate the mechanism behind this high thermoelectric performance, we systematically investigated the morphologies, microstructures, and energy structures of the PAG-doped CP thin films and performed various comparative tests. We also demonstrate the feasibility of using SAMDs to print flexible OTEG modules on thin polyimide substrates. We believe that our work represents a significant step toward the development of efficient, scalable, and sustainable thermoelectric devices for power generation and waste heat recovery, and highlights the advantages of PAG-based SAMDs for printable organic thermoelectrics
A versatile hybrid catalyst platform of Na/ZnFe2O4 and zeolite for selective hydrocarbon production from CO2 hydrogenation
No full textCatalytic CO2 hydrogenation faces great challenges in both reaction rates and selectivity to desired high-value products. Herein, we present a one-pot reaction platform that converts CO2 into various long-chain hydrocarbons selectively by combining a Na/ZnFe2O4-based catalyst for CO2 activation and carbon???carbon coupling, and a zeolite for fine-tuning the selectivity of desired products by exploiting its shape selectivity. Thus, the Na/ZnFe2O4 catalyst without zeolite produces highly olefinic diesel range hydrocarbons, and a hybrid catalyst with ZSM-5 produces highly aromatic gasoline range hydrocarbons, that with ZSM-11 produces branched kerosene range hydrocarbons, and that with SSZ-13 produces hydrocarbons rich in C2-C4 olefins. In all cases, high CO2 conversions of over 35% and low CO selectivity of near 10% are maintained. Therefore, the hybrid catalyst platform proposed here demonstrates that an elaborate catalyst design enables fine-tuning of the reaction pathway of CO2 hydrogenation to produce selectively versatile value-added hydrocarbons
Computational Prediction of Stacking Mode in Conductive Two-Dimensional Metal-Organic Frameworks: An Exploration of Chemical and Electrical Property Changes
No full textConductive two-dimensional metal-organic frameworks(2DMOFs) have attracted interest as they induce strong charge delocalizationand improve charge carrier mobility and concentration. However, characterizingtheir stacking mode depends on expensive and time-consuming experimentalmeasurements. Here, we construct a potential energy surface (PES)map database for 36 2D MOFs using density functional theory (DFT)for the experimentally synthesized and non-synthesized 2D MOFs topredict their stacking mode. The DFT PES results successfully predictthe experimentally synthesized stacking mode with an accuracy of 92.9%and explain the coexistence mechanism of dual stacking modes in asingle compound. Furthermore, we analyze the chemical (i.e., host-guestinteraction) and electrical (i.e., electronic structure) propertychanges affected by stacking mode. The DFT results show that the host-guestinteraction can be enhanced by the transition from AA to AB stacking,taking H2S gas as a case study. The electronic band structurecalculation confirms that as AB stacking displacement increases, thein-plane charge transport pathway is reduced while the out-of-planecharge transport pathway is maintained or even increased. These resultsindicate that there is a trade-off between chemical and electricalproperties in accordance with the stacking mode
Large-Area Printed Oxide Film Sensors Enabling Ultrasensitive and Dual Electrical/Colorimetric Detection of Hydrogen at Room Temperature
No full textCommercialhydrogen (H-2) sensors operate at high temperatures,which increases power consumption and poses a safety risk owing tothe flammable nature of H-2. Here, a polymer-noblemetal-metal oxide film is fabricated using the spin-coatingand printing methods to realize a highly sensitive, low-voltage operation,wide-operating-concentration, and near-monoselective H-2 sensor at room temperature. The H-2 sensors with an optimizedthickness of Pd nanoparticles and SnO2 showed an extremelyhigh response of 16,623 with a response time of 6 s and a recoverytime of 5 s at room temperature and 2% H-2. At the sametime, printed flexible sensors demonstrate excellent sensitivity,with a response of 2300 at 2% H-2. The excellent sensingperformance at room temperature is due to the optimal SnO2 thickness, corresponding to the Debye length and the oxygen andH(2) spillover caused by the optimized coverage of the Pdcatalyst. Furthermore, multistructures of WO3 and SnO2 films are used to fabricate a new type of dual-signal sensor,which demonstrated simultaneous conductance and transmittance, i.e.,color change. This work provides an effective strategy to developrobust, flexible, transparent, and long-lasting H-2 sensorsthrough large-area printing processes based on polymer-metal-metaloxide nanostructures
A device for the verification of reliability and accuracy of post-stroke upper limb mechanical impedance estimation
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No full textDeep reinforcement learning for a multi-objective operation in a nuclear power plant
No full textNuclear power plant (NPP) operations with multiple objectives and devices are still performed manually by operators despite the potential for human error. These operations could be automated to reduce the burden on operators; however, classical approaches may not be suitable for these multi-objective tasks. An alternative approach is deep reinforcement learning (DRL), which has been successful in automating various complex tasks and has been applied in automation of certain operations in NPPs. But despite the recent progress, previous studies using DRL for NPP operations have limitations to handle complex multi-objective operations with multiple devices efficiently. This study proposes a novel DRL-based approach that addresses these limitations by employing a continuous action space and straightforward binary rewards supported by the adoption of a soft actor-critic and hindsight experience replay. The feasibility of the proposed approach was evaluated for controlling the pressure and volume of the reactor coolant while heating the coolant during NPP startup. The results show that the proposed approach can train the agent with a proper strategy for effectively achieving multiple objectives through the control of multiple devices. Moreover, hands-on testing results demonstrate that the trained agent is capable of handling untrained objectives, such as cooldown, with substantial success
A Hexacyanomanganate Negolyte for Aqueous Redox Flow Batteries
No full textAqueous redox flow batteries (RFBs) have emerged as promisinglarge-scaleenergy storage devices due to their high scalability, safety, andflexibility. Manganese-based redox materials are promising sourcesfor use in RFBs owing to their earth abundance, affordability, andvariety of oxidation states. However, the instability of Mn redoxcouples, attributed to the unstable d-orbital configuration of Mn3+(d(4)) known to involve strong Jahn-Tellereffects, has hindered their practical use. Here, we discover thatthe [Mn(CN)(6)](5-/4-/3-) negolyteoffers advantages in terms of reversibility, stability, and reactionkinetics owing to the addition of NaCN supporting electrolyte, whichinhibits ligand exchange reactions, resulting in high performance.[Mn(CN)(6)](5-/4-/3-) negolytepossesses stable multielectron reactions from Mn(I) to Mn(III), leadingto a high capacity of 133.7 mAh after 100 cycles. We provide chemicalevidence obtained from in situ Raman analysis for unprecedented Mn(I) stability during electrochemical cycling, openingup new avenues for the design of low-cost Mn-based redox systems
Brain-Inspired Mutual Synchronization in Cross-Coupled NbOx Oscillation Neurons for Oscillatory Neural Network Applications
No full textThe brain performs cognitive functions through rhythmic communications of neural oscillations across numerous spatially distributed neurons. This process is known as "binding by synchrony". Herein, we demonstrate oscillatory neural networks (ONNs) based on a nanoscale NbOx device for compact oscillation neurons (ONs). When a voltage (V-DD) is applied to the NbOx-based device, a high resistance state is temporarily changed to a low resistance state due to the formation of a conducting path. Owing to the volatile switching characteristics, the VDD across the NbOx device, serially connected with an additional load resistor (R-L), is repeatedly increased and decreased, generating oscillations at the intermediate node. We experimentally investigated the impact of R-L and V-DD on the oscillation behavior of the single ON circuit. Thereafter, through simulations, we analyzed the interactions between the voltage oscillations when two NbOx-based ONs were connected by a coupling element (e.g., variable resistor or capacitor). The results showed that the oscillations were either in- or out-of-phase synchronized owing to the coupling strength. These two distinguishable synchronizations can be used to encode binary information in the phase domain, resulting in energy-efficient computing. This study proves that by building ONNs comprising multiple ONs, both sharp edges and pretrained patterns can be detected from images
Guide for Processing of Textured Piezoelectric Ceramics Through the Template Grain Growth Method
No full textThe templated grain growth (TGG) method has gained significant attention for its ability to produce highly textured piezoelectric ceramics with significantly enhanced performance, making it a promising method for transducer and actuator applications. However, the texturing process using the TGG method requires the optimization of multiple steps, which can be challenging for beginners in this field. Therefore, in this tutorial, we provide an overview of the TGG method mainly based on our previous published works, including its various processing steps such as synthesizing anisotropic-shaped templates with size and size distribution control using the molten salt synthesis technique, tape casting, and identifying key factors for proper alignment of the templates in the target matrix system. Our goal is to provide a resource that can serve as a basic reference for researchers and engineers looking to improve their understanding and utilization of the TGG method for producing textured piezoelectric ceramics