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Abstract This study presents findings that demonstrate the possibility of simplifying neural networks by inducing multifunctionality through separate manipulation within a single material. Herein, two‐terminal memristor W/ZnTe/W devices implemented a multifunctional memristor comprising a selector, synapse, and a neuron using an ovonic threshold switching material. By setting the low‐current level (µA) in the forming process, a stable memory‐switching operation is achieved, and the capacity to implement a synapse is demonstrated based on paired‐pulse facilitation/depression, potentiation/depression, spike‐amplitude‐dependent plasticity, and spike‐number‐dependent plasticity outcomes. Based on synaptic behavior, the Modified National Institute of Standards and Technology database image classification accuracy is up to 90%. Conversely, by setting the high‐current level (mA) in the forming process, the stable bipolar threshold switching operation and good selector characteristics (300 ns switching speed, free‐drift, recovery properties) are demonstrated. In addition, a stochastic neuron is implemented using the stochastic switching response in the positive voltage region. Utilizing stochastic neurons, it is possible to create a generative restricted Boltzmann machine model.

This study analyzes the potential for energy saving through the integration of a photovoltaic-thermal (PVT) system with a heat pump (HP) in a community with a small-scale thermal network. An empirical study was conducted in a community to analyze and compare the performance of a HP and small-scale thermal network with hub thermal energy storage system that utilizes the surplus thermal energy produced by a PVT system installed in a residential house from other buildings with that of an existing electric hot water boiler. The energy saving analysis of the proposed system revealed that the energy consumption could be reduced by more than 70 % compared to the existing system. The HP exhibited a COP ranging between 4.2 and 4.7. Throughout the experiment, the integrated system collected an average of 23.77 kWh of heat energy daily from the PVT units. The annual domestic hot water load coverage from PVT through the thermal network was found to be 50.4 %. These results indicate that PVT systems combined with thermal networks and HPs can effectively reduce energy consumption in individual buildings and enhance energy efficiency within communities.

As the demand for bio‐inspired neuromorphic systems grows, memristor has emerged as a pivotal component in artificial synaptic devices. This study delves into the advantages and limitations of the one‐transistor‐one‐resistor (1T‐1R) and 0T‐1R architectures for memory array configurations. A significant enhancement in the memristor's on/off ratio, surpassing 103, achieved by integrating both an overshoot suppression layer (OSL) and an ultra‐thin AlN oxygen barrier layer (OBL) is also reported. The concurrent insertion of an AlOx OSL is essential for manifesting the self‐compliance attribute in 4F2 memristor arrays. Evaluations of a 24 × 24 array embedded with OSL and OBL reveal a substantial reduction in retention variation. In terms of functionality, a 7.13‐fold decline in vector‐matrix multiplication error is observed, accentuating the potential of this approach for neural network synapse applications. The analog reset characteristics of the OBL memristor facilitate over 25 multilevel states. Furthermore, the adoption of nonnegative weights presents an avenue to potentially double synaptic integration density. Through simulation program with integrated circuit emphasis simulations, the necessity of nonnegative and 16‐level quantized conductance in balancing power consumption without accuracy loss at the image classification is validated.

The decarbonization of buildings and communities has become a requirement for sustainability. In this regard, South Korea has set a target of net-zero-energy buildings, wherein all public buildings with a floor area exceeding 500 m2 must cover 80% of the primary energy consumption using renewable energy systems by 2030. This study investigated the improvement and maximization of energy from renewable energy systems through energy sharing between buildings by retrofitting an existing community. The feasibility of achieving a positive energy balance was investigated via simulations. The existing community is comprised of two residential houses and two office buildings. Through the retrofit design planning of the existing community, the total capacity of renewable energy could be applied at 75.4 kW after renovation. Through simulation, power generation rates of the building integrated photovoltaic (BIPV) and photovoltaic and thermal (PVT) systems were analyzed. The renewable energy generation rates were 2.48 times higher when using BIPV and PVT systems. Therefore, the analysis of the annual energy balance of the community indicated that 46.2% of the surplus energy can be generated via renewable energy systems, compared with that of conventional community. Thus, the retrofit community can be renovated into a positive energy community.

In this work, we compare the resistive switching characteristics between Ti/ZrOX/TiN and Ti/ZrOX/HfAlOX/TiN. The bilayer structure of the ZrOX-based device enables power consumption reduction owing to a lower forming voltage and compliance current. Moreover, the on/off ratio of the Ti/ZrOX/HfAlOX/TiN device (>102) is higher than that of the Ti/ZrOX/TiN device (>10). We use the 1/f noise measurement technique to clarify the transport mechanism of the Ti/ZrOX/HfAlOX/TiN device; consequently, ohmic conduction and Schottky emission are confirmed in the low- and high-resistance states, respectively. In addition, the multilevel cell, potentiation, and depression characteristics of the Ti/ZrOX/HfOX/TiN device are considered to assess its suitability as a neuromorphic device. Accordingly, a modified National Institute of Standards and Technology database simulation is conducted using Python to test the pattern recognition accuracy.

Direct-expansion solar-assisted heat pumps (DX-SAHP) are increasingly used in heating applications owing to their high efficiency. In this study, the energy performance of a DX-SAHP for a distributed thermal prosumer in a thermal network was investigated. A thermal network for supplying hot water, established in the eco-friendly energy town of Jincheon, was selected as the test site, and a daycare center was selected as the pilot building. A control logic for serving produced thermal energy to the thermal network was developed as an operation of thermal prosumer. Experiments were conducted during the cooling and heating seasons, and the results of three operation cases showed that the proposed prosumer operation can transfer the thermal energy within the thermal network. The daily load of domestic hot water was approximately 50–100 kWh, and the daily coefficient of performance of the DX-SAHP ranged from 2.0 to 5.0. The coefficient of performance of the DX-SAHP was found to be strongly dependent on the outdoor air temperature, solar irradiation, and outlet water temperature. During the operation, the DX-SAHP contributed to achieving over 66% savings in the operating energy compared with a conventional electric heater.

Organic memristors are promising candidates for the flexible synaptic components of wearable intelligent systems. With heightened concerns for the environment, considerable effort has been made to develop organic transient memristors to realize eco‐friendly flexible neural networks. However, in the transient neural networks, achieving flexible memristors with biorealistic synaptic plasticity for energy efficient learning processes is still challenging. Herein, a biodegradable and flexible polymer‐based memristor, suitable for the spike‐dependent learning process, is demonstrated. An electrochemical metallization phenomenon for the conductive nanofilament growth in a polymer medium of poly (vinyl alcohol) (PVA) is analyzed and a PVA‐based transient and flexible artificial synapse is developed. The developed device exhibits superior biodegradability and stable mechanical flexibility due to the high water solubility and excellent tensile strength of the PVA film, respectively. In addition, the developed flexible memristor is operated as a reliable synaptic device with optimized synaptic plasticity, which is ideal for artificial neural networks with the spike‐dependent operations. The developed device is found to be effectively served as a reliable synaptic component with high energy efficiency in practical neural networks. This novel strategy for developing transient and flexible artificial synapses can be a fundamental platform for realizing eco‐friendly wearable intelligent systems. An interactive preprint version of the article can be found here: https://doi.org/10.22541/au.166603245.58711630/v1.

The utilization of renewable energy sources have gained significant attention in recent years for greenhouse that consumed lots of cooling and heating energy. This study investigated a solar thermal and seasonal thermal-based renewable energy convergence system. This system consists of a solar thermal system, tank- and borehole-type seasonal thermal energy storage, multi-source (i.e., air and water) heat pumps, and ground-source heat pumps. The detailed control logic of the proposed system was implemented, and the system was constructed in a test greenhouse at the Purme Yeoju Farm in South Korea. During the cooling season, the multi-source heat pumps were operated as air-source heat pumps, which showed a coefficient of performance (COP) only 0.5 less than that of the ground source heat pump. The efficiency of the solar thermal system was determined to be 36.6% throughout the investigation period, and the heat storage efficiency of the tank-type seasonal thermal energy storage was 75.7%. Consequently, the proposed system showed a COP of 2.9 during the heating season. The proposed system shows that over 70% of the operating energy can be saved, which is unachievable when using conventional electric heaters.

Neuromorphic electronics attract significant attention as a new computing architecture. Despite much effort for achieving practical neuromorphic systems, it is still challenging to construct a synapse array ideal for complex neural networks. Herein, a novel strategy for developing a highly integrated crossbar array of a one‐selector–one‐memory (1S–1R) synapse by systematically engineering ion injection is demonstrated. In the proposed synapse, an electrochemical metallization (ECM) memristor consisting of unstable filaments and a typical ECM device with stable filaments act as a selector with a low leakage current and a stable memory device, respectively. To overcome the voltage‐matching issues in constructing the 1S–1R synapse with high integration density, ion injection related with the electrical properties is optimized in the ECM devices via the distribution of active metal nanoparticles at the interface. The developed synapse possesses a high on/off ratio, superior selectivity, low operating current, and stable multilevel conductance, compared to the previously reported devices. High feasibility for complex neuromorphic systems is demonstrated, and the neural network based on the developed synapse array exhibits reliable parallel computation with high energy efficiency. This promising concept of realizing complex neuromorphic electronics is a fundamental building block for the practical artificial intelligence.

This study investigates the economic benefits of solar thermal and seasonal thermal energy storage based on a renewable energy conversion system for greenhouses. The proposed system consists of solar collectors, seasonal thermal energy storage, hybrid-source heat pumps, and ground-source heat pumps. The heat generated from the proposed system was stored in two types of seasonal thermal energy storage and supplied to the greenhouse using Purme Yeoju Farm in South Korea for experimental analysis. Based on the experimental data gathered over a heating system, the economic benefits of operating cost savings and carbon trading with the greenhouse gas emission reduction of the proposed system were investigated by comparing to a conventional heating season using oil and electric boilers. From October 2021 to March 2022, approximately 38.4% of the total 482 MWh of heat was supplied either directly or indirectly through the solar system. In addition, the coefficient of the performance of the entire proposed system was calculated to be 2.28. Both the operating cost savings and greenhouse gas emission reductions of the proposed system showed over 73% and 82% compared with those of conventional systems.

In this work, we compare the resistive switching characteristics between Ti/ZrOX/TiN and Ti/ZrOX/HfAlOX/TiN. The bilayer structure of the ZrOX-based device enables power consumption reduction owing to a lower forming voltage and compliance current. Moreover, the on/off ratio of the Ti/ZrOX/HfAlOX/TiN device (>10²) is higher than that of the Ti/ZrOX/TiN device (>10). We use the 1/f noise measurement technique to clarify the transport mechanism of the Ti/ZrOX/HfAlOX/TiN device; consequently, ohmic conduction and Schottky emission are confirmed in the low- and high-resistance states, respectively. In addition, the multilevel cell, potentiation, and depression characteristics of the Ti/ZrOX/HfOX/TiN device are considered to assess its suitability as a neuromorphic device. Accordingly, a modified National Institute of Standards and Technology database simulation is conducted using Python to test the pattern recognition accuracy. [ABSTRACT FROM AUTHOR]