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Umami substances are nutrients to humans, and their synergistic effect is associated with food acceptance. In this study, a new biosensor was developed to detect umami substances, their synergistic effect, and detection kinetics. Porcine taste-bud tissues were used as the sensing element, and the umami substance signals were characterized using an electrochemical workstation. The responses of taste-bud tissue sensors to monosodium L-glutamate (MSG) were compared based on different tongue sites. The interaction law between MSG and receptors in the taste-bud tissues of the three sensors conforms to enzymatic-reaction kinetics, where rectangular hyperbola curves in the Michaelis–Menten equation were followed with fitting coefficients (>0.91). However, the taste-bud sensors respond differently to MSG stimuli, with those based on a tip and mediolateral tongue, producing the lowest detection limit of 10−16 mol/L. The number of receptors required for a single cell to achieve maximum output signal is 3.68, 30.42, and 7.27, respectively. Moreover, the taste-bud tissue sensors identified the synergistic effect of umami substances. In addition, they were sensitive to umami variations in soy sauce and mandarin fish. The developed porcine taste-bud tissue biosensor revealed the interaction law between umami substances and receptors, providing a new idea for umami evaluation. • Porcine taste bud tissue biosensors were developed to detect umami substances and their synergistic effects. • Kinetics for umami substance detection was investigated. • Tongue tip tissues showed the highest sensitivity to umami substances. • Higher umami detection sensitivity was achieved. [ABSTRACT FROM AUTHOR]

• Peer-to-peer energy trading problem of multi-energy microgrids are investigated. • The concept of energy trading consistency is firstly proposed. • The off-design performance model of the energy conversion device is considered. • The decision-making problem is solved by multi-agent soft actor-critic approach. Multi-energy microgrid technology is an essential for addressing the diversification of energy demand and local consumption of renewable energy sources. Peer-to-peer energy trading has emerged as a promising paradigm for the design of a decentralized trading framework. Therefore, this paper investigated the external peer-to-peer energy trading problem and internal energy conversion problem of interconnected multi-energy microgrids. The concept of energy trading consistency to avoid unreasonable energy trading behavior is first proposed and an off-design performance model of the energy conversion device is considered to more accurately reflect the operating status of the device. The complex decision-making problem with significantly large high-dimensional data is formulated as a partially observable Markov decision process and solved using the proposed multi-agent deep reinforcement learning approach combining the centralized training decentralized execution framework and soft actor-critic algorithm. Finally, the effectiveness of the proposed method was verified through a case simulation. The simulation results showed that the proposed method can reduce the total cost compared with the rule-based method. [ABSTRACT FROM AUTHOR]

Protonic ceramic fuel cells (PCFCs) have been the focus of research due to their advantageous properties compared to oxygen ion-conducting solid oxide fuel cells (SOFCs). However, the biggest obstacles to the development of PCFC are the lower proton conductivity and the poor stability in high concentration steam environment. Herein, we report a novel composite cathode with surface reconfiguration, comprised of BaCoO 3-δ (BCO) nanoparticles by water-induced precipitation and Ba 0.9 Pr 0.1 Co 0.7 Fe 0.2 Y 0.1 O 3-δ (BPCFY) substrate with e−/O2−/H+ triple conducting behavior, exhibiting high catalytic activity towards oxygen reduction reaction (ORR) and stability in high steam concentration. With wet hydrogen (3 vol% H 2 O) as fuel and wet air (3 vol% H 2 O) as oxidant, the peak power density of NiO-BZCYYb|BZCYYb|BPCFY single cell reaches 750 mW•cm−2 at 700 °C, approximately 25 % higher than that using dry air at cathode side. Such improvement performance can be attributed to the precipitation of BaCoO 3-δ nanoparticles that increase the active adsorption sites as well as the increased oxygen vacancy concentration by Pr doping in BaCo 0.7 Fe 0.2 Y 0.1 O 3-δ , which synergistically expand the triple phase boundary and facilitate ions migration. This work provided a promising approach to enhance the catalytic activity of PCFC at lower temperatures while also improving its tolerance to steam. • Pr-doping promotes BaCoO 3 nanoparticles precipitation after steam treatment. • Pr-doping improves the ions diffusion process. • The exsolved BaCoO 3 nanoparticles facilitate the gas exchange process. • The composite cathode showed desirable tolerance for steam and carbon dioxide. [ABSTRACT FROM AUTHOR]

To address the challenge of global warming stemming from excessive fossil fuel combustion, the exploration of low-carbon technologies and improvements to carbon trading mechanisms stand out as vital strategies. An effective conduit for these strategies is the integrated energy system (IES), which has garnered increasing attention for its potential. To realize low-carbon advancements within energy systems, this study proposes a low-carbon economic scheduling framework for the IES that integrates the coupled operation of carbon-capture power plants, power-to-gas (CCPP–P2G) unit and the price-based demand response (PDR) considering dynamic carbon trading mechanisms. The proposed approach firstly concurrently considers source-side CCPP-P2G and load-side PDR to achieve coordinated low-carbon operation, elucidating the underlying mechanisms. Subsequently, a dynamic carbon trading model for the IES is formulated, flexibly capturing the ebb and flow of supply and demand in the carbon trading market. Amidst the uncertainty of the system, a bi-level optimization model is constructed to minimize the operational cost. Finally the effectiveness of the proposed method is demonstrated on an IES consisting of the IEEE-30 bus power system, 6-bus natural gas system and 6-bus district heating system. The findings indicate that the proposed dynamic carbon trading mechanism is able to effectively provide time-varying carbon trading price signals to fully reflect the supply and demand relationship of carbon emission allowance. Furthermore, the synchronized operation of CCPP-P2G and PDR yields substantial benefits, compared to the benchmark. Specifically for the test case, the proposed framework reduces system operational costs, wind power curtailment, and carbon emissions by $87,160, 1,410 MWh, and 4,849t, respectively. [ABSTRACT FROM AUTHOR]