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• Electrochemical impedimetric biosensor for umami substance detection was developed. • Electrochemical impedance spectroscopy enhanced T1R1 biosensor performance. • T1R1 biosensor had low detection limits for monosodium glutamate and inosine-5′-monophosphate. • T1R1 biosensor had wide linear ranges for monosodium glutamate and inosine-5′-monophosphate. Umami substances can provide a palatable flavour for food. In this study, an electrochemical impedimetric biosensor was developed for detecting umami substances. This biosensor was fabricated by immobilising T1R1 onto AuNPs/reduced graphene oxide/chitosan which was in advance electro-deposited onto a glassy carbon electrode. The evaluation by the electrochemical impedance spectrum method showed that the T1R1 biosensor performed well with low detection limits and wide linear ranges. Under the optimised incubation time (60 s), the electrochemical response was linearly related to the concentrations of the detected targets monosodium glutamate and inosine-5′-monophosphate within their respective linear range of 10-14 to 10-9 M and 10-16 to 10-13 M. The low detection limit of monosodium glutamate and inosine-5′-monophosphate was 10−15 M and 10−16 M, respectively. Moreover, the T1R1 biosensor exhibited high specificity to umami substances even in the real food sample. The developed biosensor still retained 89.24% signal intensity after 6-day storage, exhibiting a desirable storability. [ABSTRACT FROM AUTHOR]

Achieving the economical and stable operation of Multi-microgrids (MMG) systems is vital. However, there are still some challenging problems to be solved. Firstly, from the perspective of stable operation, it is necessary to minimize the energy fluctuation of the main grid. Secondly, the characteristics of energy conversion equipment need to be considered. Finally, privacy protection while reducing the operating cost of an MMG system is crucial. To address these challenges, a Data-driven strategy for MMG systems with Shared Energy Storage (SES) is proposed. In this paper, the Mixed-Attention is applied to fit the conditions of the equipment, and Multi-Agent Soft Actor-Critic(MA-SAC) , Multi-Agent Win or Learn Fast Policy Hill-Climbing (MA-WoLF-PHC) are proposed to solve the partially observable dynamic stochastic game problem. By testing the operation data of the MMG system in Northwest China, following conclusions are drawn: the R-Square (R2) values of results reach 0.999, indicating the neural network effectively models the nonlinear conditions. The proposed MMG system framework can reduce energy fluctuations in the main grid by 1746.5 kW in 24 h and achieve a cost reduction of 16.21% in the test. Finally, the superiority of the proposed algorithms is verified through their fast convergence speed and excellent optimization performance. • A data-driven method to describe the nonlinear conditions of energy conversion equipment was proposed. • A novel energy management-energy auction strategy based on MMG system framework with SES was designed. • SAC algorithm and WoLF-PHC algorithm were improved. • Privacy protection of MGs by nonlinear and non-convex constraints is realized. • The effect of different hyperparameters on the MA-SAC algorithm was analyzed. [ABSTRACT FROM AUTHOR]

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]

The mirror was one of the most important components of aviation remote sensors. Due to its high specific stiffness and good environmental adaptability, metal material mirror was suitable for application in airborne photoelectric load as the main optical system. In this paper, through the research on the kinematic support form of metal mirror, the flexibility of flexible joint form was optimized, and the design of aperture 190 mirror was carried out, so that the shape accuracy under the action of gravity reaches 1 / 50λ, which better meets the design requirements of aviation photoelectric load. [ABSTRACT FROM AUTHOR]

Proton ceramic fuel cells (PCFCs) are gaining attention due to their high energy conversion efficiency. However, the development of PCFCs is hindered by the lack of efficient and robust cathode materials. This article proposes a solution by introducing certain cationic deficiency into the Ba 0.9 La 0.1 Co 0.7 Fe 0.2 Nb 0.1 O 3-δ (BLCFN) cathode matrix, which results in abundance of oxygen vacancies as well as enhanced proton defects. Consequently, the catalytic activity and hydration activity of the BLCFN oxygen electrode are improved during the reduction reaction process. Specifically, the polarization impedance (R p) of the (Ba 0.9 La 0.1) 0.95 Co 0.7 Fe 0.2 Nb 0.1 O 3-δ (BL95CFN) decreases by 20% compared to the pristine BLCFN, reaching 0.12 Ω·cm2 at 700 °C in air. The R p of BL95CFN decreases by 12% in a steam environment of 20 vol%. This enhancement is attributed to the higher concentration of oxygen vacancies, which leads to an improved proton diffusion rate and hydration performance. The peak power density of the anode-supported BL95CFN | BZCYYb | BZCYYb-NiO single cell reaches 641 mW cm−2 at 700 °C, and the performance of this single cell remains stable for nearly 100 h. These findings highlight the vital role of adjusting oxygen vacancy and proton diffusion, providing a feasible strategy for improving the performance and stability of PCFC cathode materials. • An A-site deficiency engineered Ba 0.9 La 0.1 Co 0.7 Fe 0.2 Nb 0.1 O 3-δ cathode is proposed. • A-site deficiency greatly enhanced ORR activity of Ba 0.9 La 0.1 Co 0.7 Fe 0.2 Nb 0.1 O 3-δ. • Oxygen vacancy by introducing cationic defects, enhances catalytic activity and hydration reaction activity. • (Ba 0.9 La 0.1) 0.95 Co 0.7 Fe 0.2 Nb 0.1 O 3-δ cathode showed good long-term stability. [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]

Capillary adhesion contributes to friction of sliding multi-asperity contact in humid environment by increasing the solid-solid contact area; whereas the increased adhesion from capillary meniscus is hard to measure due to elastic recovery of contacting asperities. Here, we report the first direct measurement of increased interfacial adhesion from mesoscopic menisci in a macroscale multi-asperity contact comprising soft elastomer and glass surfaces with a diminishing water film. Results also found capillary adhesion and its contribution to a high transient friction coefficient decrease with increased interfacial roughness, which is consistent with prior literature hypotheses. [ABSTRACT FROM AUTHOR]

In this study, a progress damage model that integrates the Maximum Entropy Fracture Model (MEFM) with the A-F kinematic hardening model is proposed to characterize the low-cycle fatigue damage behavior of metals. The MEFM establishes a correlation between the damage value and cumulative dissipation at the integration point through a damage accumulation parameter. Concurrently, the constitutive relationship of the material is delineated by the A-F kinematic hardening model. To shift enhance both efficiency and accuracy of obtaining model parameters, this study employs the inverse analysis techniques to derive damage accumulation parameter and material properties, departing from the conventional approach of experimental data fitting. Uniaxial tensile fatigue experiments were conducted on copper to validate the efficacy of the progressive damage model, which is also a key step in the process of inverse analysis. Digital Image Correlation (DIC) was employed to rectify the post-test displacement field, with the corrected displacement serving as a boundary condition in simulations. Comparisons between simulation results and experimental data highlight the capability of the developed progressive damage model. This model demonstrates effectiveness in predicting the formation and propagation of low-cycle fatigue damage in ductile metal materials, leading to ultimate failure. • A damage model used for predicting low-cycle fatigue failure was developed. • The Kalman filtering algorithm was used to inversely identify damage parameters. • The Nelder–Mead algorithm was used to inversely identify material parameters. • The DIC technique was used to correct boundary conditions in fatigue simulation. [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]

Abstract: A sensitive and specific method for the determination of bifendate in human plasma was developed, based on high-performance liquid chromatography (HPLC)–mass spectrometry (MS). The samples were extracted from plasma with diethyl ether, followed by separation and evaporation after addition of internal standard diazepam. The residue was reconstituted in methanol and injected into the HPLC–MS. Chromatography was performed on an Inertsil ODS column with a mobile phase consisting of methanol–distilled water (70/30, v/v) at a flow rate of 0.3mL/min. Quantitative analysis was achieved by MS detection, using a mass spectrometer equipped with an electrospray ionization interface (ESI) and operated in selected ion monitoring (SIM) and positive-ionization mode using target ions at m/z 419 for bifendate and m/z 285 for internal standard, respectively. The linearity was confirmed in the concentration range of 2–200ng/mL in human plasma and the precision of this assay was not more than 6.79% over the entire concentration range. The method was sensitive and repeatable enough to be used in pharmacokinetic and bioavailability studies. [Copyright &y& Elsevier]

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]

Magnetic levitation (Maglev) train's position and speed detection system facilitates the control of the Maglev train. Yet nearly all previous schemes are unsuitable for the high temperature superconducting (HTS) Maglev train. New sensors and signal sources are expected to improve the system. Here the tunnel magneto resistance (TMR) sensor and the magnetic arrays are used to realize the speed and position detection and the improved automatic multiscale-based peak detection (AMPD) algorithm is applied. Furthermore, the algorithm's adaptability to various operating conditions is verified and the system can be applied in different working conditions. Finally, a preliminary experimental test is carried out. Such a system, accompanied by the equipment and the algorithm, offers results of accuracies of 95%–97% for speed measurement and 1 cm–3 cm for positioning, providing an avenue to improve the performance of HTS Maglev train's operating control system. • A novel HTS Maglev train's speed and position detection method is proposed. • The magnet arrays based method adapts to unique trackside environments. • The improved AMPD algorithm can be effectively used in various working conditions. • Simulations and experiments validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]