Fast Fluctuations Research Articles

The Onset of Molecule-Spanning Dynamics in Heat Shock Protein Hsp90.

Protein dynamics have been investigated on a wide range of time scales. Nano- and picosecond dynamics have been assigned to local fluctuations, while slower dynamics have been attributed to larger conformational changes. However, it is largely unknown how fast (local) fluctuations can lead to slow global (allosteric) changes. Here, fast molecule-spanning dynamics on the 100to 200ns time scale in the heat shock protein 90 (Hsp90) are shown. Global real-space movements are assigned to dynamic modes on this time scale, which is possible by a combination of single-molecule fluorescence, quasi-elastic neutron scattering and all-atom molecular dynamics (MD) simulations. The time scale of these dynamic modes depends on the conformational state of the Hsp90 dimer. In addition, the dynamic modes are affected to various degrees by Sba1, a co-chaperone of Hsp90, depending on the location within Hsp90, which is in very good agreement with MD simulations. Altogether, this data is best described by fast molecule-spanning dynamics, which precede larger conformational changes in Hsp90 and might be the molecular basis for allostery. This integrative approach provides comprehensive insights into molecule-spanning dynamics on the nanosecond time scale for a multi-domain protein.

LSTM forecast of volatile national strategic food commodities

Using the Long Short-Term Memory (LSTM) forecast, this study suggested a short-term projection model for national critical food pricing commodities. The model was trained using historical time-series data from each commodity price over the previous three years. The results demonstrated that the proposed LSTM architecture model was generalizable to all commodities and performed well in the majority of cases. This result indicates that the model is resilient and can be used to forecast commodity prices and offer accurate forecasts for most of the ten volatile national strategic foods, with an error value of less than 0.01 and an accuracy value of >95%. The model, however, failed to recognize the pricing pattern in cooking oil and beef commodities, both of which had increasing trend patterns. This shows that the model may be unable to effectively estimate commodity prices in the face of fast price fluctuations. The magnitude and quality of the dataset hampered the investigation. The time period selected also influenced the study. Future research should employ a more extensive and diversified dataset to increase the model's performance, allow it to learn more patterns and make more accurate predictions, and could use a more extended lookup date to improve forecast accuracy. This would enable the model to account for more recent pricing changes. Despite the limitations, the results of this study are promising and could be used to develop a more accurate and reliable food price prediction model. Policymakers and stakeholders could use the model to make informed food prices and inflation decisions.

Operando investigation of deposition characteristics of submicron particles in electric-flow coupled fields combined with object detection

Submicron particle is a harmful air pollutant due to size effect, gathering characteristic and biological activity, which not only causes a serious threat to human health and public sanitary security, but also is difficult to capture with lowest removal efficiency. Here, Operando investigation system is constructed to explore deposition characteristics of submicron particles in electric-flow coupled fields through combining microfluidic technology for forming micro electric field on the lab-on-a-chip with micro-visualization technology for capturing dynamic deposition process. Besides, based on object detection and deep learning, an image processing method is developed which can realize automatic object detection and rapid extraction of required information. Results show that particle chain grows preferentially from the convex position to prove the influence of electrode surface roughness on the growth site. The global electric field has a positive effect on keeping particle chain growing up at upright direction, and the growth process of particle chain is proposed: forming growth site stage, grow fast stage and fluctuation stage. Particle velocity in the airflow direction presents a decrease trend during the process particle migrates towards to electrode surface, which proves the direct influence of boundary layer effect on the ultimate height of particle chain.

Introducing "MEW2" Software: A Tool to Analyze MQ-NMR Experiments for Elastomers.

Low-field time-domain proton Nuclear Magnetic Resonance (NMR) spectroscopy is an attractive and powerful tool for studying the structure and dynamics of elastomers. The existence of crosslinks and other topological constraints in rubber matrices (entanglements and filler-rubber interactions, among others) renders the fast segmental fluctuations of the polymeric chains non-isotropic, obtaining nonzero residual dipolar couplings, which is the main observable of MQ-NMR experiments. A new software, Multiple quantum nuclear magnetic resonance analyzer for Elastomeric Networks v2 (MEW2), provides a new tool to facilitate the study of the molecular structure of elastomeric materials. This program quantitatively analyzes two different sets of experimental data obtained in the same experiment, which are dominated by multiple-quantum coherence and polymer dynamics. The proper quantification of non-coupled network defects (dangling chain ends, loops, etc.) allows the analyzer to normalize the multiple quantum intensity, obtaining a build-up curve that contains the structural information without any influence from the rubber dynamics. Finally, it provides the spatial distribution of crosslinks using a fast Tikhonov regularization process based on a statistical criterion. As a general trend, this study provides an automatic solution to a tedious procedure of analysis, demonstrating a new tool that accelerates the calculations of network structure using 1H MQ-NMR low-field time-domain experiments for elastomeric compounds.

Reactive hypoglycemia in binge eating disorder, food addiction, and the comorbid phenotype: unravelling the metabolic drive to disordered eating behaviours

BackgroundImpaired metabolic response such as blood glucose fast fluctuations may be hypothesized in binge eating disorder (BED) and food addiction (FA) by virtue of the repetitive consumption of highly processed food. Conversely, rapid changes in plasma glucose (i.e., hypoglycemia) may trigger craving for the same food products. The investigation of early glycemic disturbances in BED and FA could enhance the understanding of the metabolic mechanisms involved in the maintenance of the disorders. Present study investigated hypoglycemia events during a 5-h-long oral glucose tolerance test (OGTT) in people with BED, FA, and the comorbid phenotype. Further, the association between the severity of eating psychopathology and the variability in hypoglycaemia events was explored.MethodsTwo-hundred participants with high weight and no diabetes completed the extended OGTT and were screened for BED, FA, BED-FA, or no-BED/FA. The four groups were compared in hypoglycemia events, OGTT-derived measures, and eating psychopathology. The association between predictors (eating psychopathology), confounders (demographics, metabolic features), and the outcomes (hypoglycemia, early/late hypoglycemia, severe hypoglycemia, reactive hypoglycemia) was examined through logistic regression.ResultsHypoglycemia in general, and reactive hypoglycemia were highly frequent (79% and 28% of the sample, respectively). Hypoglycemia events (< 70 mg/dL) were equally experienced among groups, whilst severe hypoglycemia (< 54 mg/dL) was more frequent in BED at the late stage of OGTT (5 h; χ2 = 1.120, p = .011). The FA and BED groups exhibited significantly higher number of reactive hypoglycemia (χ2 = 13.898, p = .003), in different times by diagnosis (FA: 210′–240′; BED: at the 270′). FA severity was the only predictor of early and reactive hypoglycemia.ConclusionsPeople with BED or FA are prone to experiencing reactive hypoglycemia; FA severity may predict early and symptomatic hypoglycemia events. This can further reinforce disordered eating behaviours by promoting addictive responses, both biologically and behaviourally. These results inform professionals dealing with eating disorders about the need to refer patients for metabolic evaluation. On the other hand, clinicians dealing with obesity should screen for and address BED and FA in patients seeking care for weight loss.

Improving Photovoltaic MPPT Performance through PSO Dynamic Swarm Size Reduction

Efficient energy extraction in photovoltaic (PV) systems relies on the effective implementation of Maximum Power Point Tracking (MPPT) techniques. Conventional MPPT techniques often suffer from slow convergence speeds and suboptimal tracking performance, particularly under dynamic variations of environmental conditions. Smart optimization algorithms (SOA) using metaheuristic optimization algorithms can avoid these limitations inherent in conventional MPPT methods. The problem of slow convergence of the SOA is avoided in this paper using a novel strategy called Swarm Size Reduction (SSR) utilized with a Particle Swarm Optimization (PSO) algorithm, specifically designed to achieve short convergence time (CT) while maintaining exceptional tracking accuracy. The novelty of the proposed MPPT method introduced in this paper is the dynamic reduction of the swarm size of the PSO for improved performance of the MPPT of the PV systems. This adaptive reduction approach allows the algorithm to efficiently explore the solution space of PV systems and rapidly exploit it to identify the global maximum power point (GMPP) even under fast fluctuations of uneven solar irradiance and temperature. This pioneering ultra-fast MPPT method represents a significant advancement in PV system efficiency and promotes the wider adoption of solar energy as a reliable and sustainable power source. The results obtained from this proposed strategy are compared with several optimization algorithms to validate its superiority. This study aimed to use SSR with different swarm sizes and then find the optimum swarm size for the MPPT system to find the lowest CT. The output accentuates the exceptional performance of this innovative method, achieving a time reduction of as much as 75% when compared with the conventional PSO technique, with the optimal swarm size determined to be six.

Distributed Online Voltage Control With Fast PV Power Fluctuations and Imperfect Communication

Coordinated Volt-Var control methods have demonstrated their techno-economic feasibility in voltage regulation of photovoltaic (PV) rich distribution systems. However, fast fluctuating PV power and imperfect communication networks may significantly challenge the effectiveness of these methods. In this paper, a revised dynamic consensus algorithm is proposed to coordinate distributed inverters for Volt-Var control in real time. With this proposed method, Var saturation and overvoltage issues which tend to occur at downstream buses of PV rich distribution systems are significantly mitigated. To quantitatively analyse the algorithm performance in imperfect communication environments, the information delivery between agents is modelled by stochastic state transition processes among finite numbers of virtual nodes so as to quantitatively depict the random time delay and packet dropout in a discrete way. On this basis, the state transition process of the whole system is further depicted by a series of row-stochastic matrices, and the ergodic theory is used to analytically derive the algorithm tracking error in an imperfect communication environment. Our proposed method can also be extended to more complex applications, where both Var compensation and PV curtailment (or EV dispatch) are available for system voltage control. Simulation results verify the superiority of our method over traditional ones.

A Bottom-up Transient Theory for Concentration Polarization with Applicability to over-Limiting Current Conditions

Renewable power sources have drawn interest due to scarcity of fossil fuels and associated pollution during their combustion-based power production. Renewables are projected to produce approximately 60% of US electricity generation by 2050, with an expected contribution of 2000 TW-h from wind power alone. To overcome the mismatch between electricity demand and intermittent supply, such power plants require large-scale energy-storage device integrated with them. In contrast to other technologies, redox flow batteries (RFBs) offer independently scalable power and energy capacity that is demanded for such applications. Despite significant achievement in their designs through experimentation and modeling, limited understanding has been established to date concerning their transient response while operating under irregular charge/discharge scenarios in renewable-powered grids and micro-grids. Under such conditions pore-scale mass transport physics and different mechanisms of concentration polarization can occur, but the conventional film law of mass transfer (FLoMT) using a constant Sherwood number cannot capture time-varying diffusion layer thickness near electrode surfaces. Further, the mean solute advection rate through porous RFB electrodes can deviate from the value expected from product of superficial velocity and average concentration inside the electrode if pore-scale concentration polarization is substantial. Consideration of these effects in the present bottom-up theory makes it applicable to over-limiting conditions, while predicting concentration polarization in agreement with experimental observations. We accomplish this by creating theory with similarity to the theory of Ralph White and co-workers for solid-state, particle-scale diffusion in Li-ion batteries [J. Power Sources, 2012, Vols. 214 and 218], while our theory is instead applied to an arbitrary representative volume element of flowing liquid electrolyte inside electrode pores.Here, we present a theory to incorporate these transient effects into a bottom-up transient model (B-UTM) by using frequency-dependent transfer functions (TFs) that are derived from the Fourier transformed pore-scale mass conservation equations (MCEs). These TFs preserve pore-scale mass transfer physics during their embedding into an up-scaled model. One of these TFs is the spectral Sherwood number that extends the film law of mass transfer to transient conditions and that correlates reactive flux with concentration polarization through an output-input relationship in the frequency domain. Another TF named the advective-flux transfer function captures the acceleration/suppression of solute advection rate that arises from the inhomogeneity in pore-scale concentration distributions. We first present validation of this theory using transient RFB experiments where the introduced B-UTM predicts the time evolution of concentration polarization in good agreement. Meanwhile the conventional FLoMT model that uses time-independent Sherwood number and neglects the solute acceleration/suppression effect shows systematically larger polarization (up to 400%), which restricts such models from simulating charging/discharging with applied current near and above the limiting current.The utility of the introduced bottom-up theory is demonstrated by incorporating it into a multi-scale RFB model in which redox-active electrolyte flows through a porous electrode comprised of an array of circular cylinders in crossflow that mimics commonly used carbon felt. The frequency dependence of the introduced TFs obtained from numerical solution of pore-scale MCEs are analyzed for different electrode porosity and Péclet number, where both the TFs reach a non-zero finite value in the limit of vanishing frequency. The non-zero value of the spectral Sherwood number in this limit is identical to the Sherwood number obtained in the pseudo-steady limit (PSL) where transient effects are negligible. With increasing frequency both the TFs show a transition from lagless response to semi-infinite Warburg response with orders higher values compared to that of the PSL. Finally, these TFs are embedded into an up-scaled model to obtain the time-domain response of RFBs operating under a step impulse applied current with different step durations. Numerically obtained concentration polarization and the average reactant concentration inside electrodes is used to construct non-dimensional regime maps showing a regime of over-limiting current. The results show that fast current fluctuations are sustained even when current exceeds the limiting current, which provides a means to increase the utilization of charge capacity of existing flow-based electrochemical devices that are operated under transient conditions. The theory introduced here can find applicability in other flow-based electrochemical devices including electrochemical separations and CO2 capture. Figure 1

GNSS-5G Hybrid Positioning Based on Joint Estimation of Multiple Signals in a Highly Dependable Spatio-Temporal Network

The Global Navigation Satellite System (GNSS) has been widely used in every area of our daily life to provide accurate Positioning, Navigation, and Timing (PNT) services. However, due to the multipatch effect and an obstructed view of the satellite, GNSS receivers are susceptible to large-ranging errors, which are particularly prevalent in urban areas where precise positioning is indispensable. The deployment of the high-spatial-density Fifth-Generation (5G) network makes it possible to provide a broad area with high-precision positioning service. Obviously, it promoting the deep integration of the GNSS system and the 5G mobile communication network and establishing a Highly Dependable Spatio-temporal Network (HDSN) have become an inevitable trend. The existing algorithm for the fusion of multiple signals has difficulty settling problems such as the fast fluctuation of available signal sources and the poor stability of multi-scale multi-type signal estimation in GNSS-5G hybrid networks. Here, we propose a Square Root Unscented Stable Filter (SRUSF) for GNSS and 5G joint positioning with a compact coupled filter group architecture in a highly dependable spatio-temporal network. A stabilized coefficient is added to guarantee positive covariance of the estimation error. The possibility of divergence of filtering results due to the variation in signal sources and the incomplete agreement between the system model and the actual situation are reduced. The simulation results show that the proposed SRUSF method substantially enhances the positioning accuracy and reliability compared with the other five joint estimation methods for multiple signals. This work will enable the terminal of mass users to provide timing and positioning services with unprecedented accuracy and dependability under the GNSS and 5 G-based spatio-temporal network’s architecture.

An effective SMES system control for enhancing the reliability of hybrid power generation systems

Recently, with the rising electrical energy demand, renewable energy resources (RERs) are considered an optimal solution for satisfying consumption needs. The power generated from RERs has an intermittent and unpredictable nature, given that it depends directly on weather conditions. Thus, the amount generated power from RERs will be taken in fluctuated curve form. This paper focuses on addressing the issue of fluctuation in the power output of a Solar-Wind hybrid power generation systems (HPGSs) resulting from variable weather conditions. Superconducting magnetic energy storage (SMES) system provide a viable solving to the issue of power output fluctuations in HPGSs due to their unique characteristics. To this aim, this paper proposes two robust controllers for SMES systems to smooth out the power provided by a HPGS. In order to evaluate the proposed SMES system efficiency's during significant and fast fluctuations (at level of up to ∼1 MW in the range and seconds in the duration), computer simulations were implemented using the MATLAB/Simulink software package. The results obtained from the simulations have confirmed that the controllers of the proposed SMES system could efficiently reduce power output fluctuations from HPGSs, minimize power losses on the transmission lines, and regulate the voltage at the point of common coupling (PCC) at an acceptable standard level.

Uncommon Magnetism in Rare-Earth Intermetallic Compounds with Strong Electronic Correlations

Rare-earth intermetallic compounds are characterised by the presence of a long-range magnetic order due to the interaction of local magnetic moments periodically located within the crystal lattice. This paper considers the possibility of forming an ordered state in cases where there is no opportunity to observe the local moment of the f-electronic shell in a traditional sense. These are, first of all, systems with a singlet ground state, as well as systems with fast spin fluctuations caused by a homogeneous intermediate-valence state of a rare-earth ion. Extensive experimental studies of these effects using neutron diffraction, neutron spectroscopy, and high-pressure studies of the magnetic phase diagram are presented and analysed, and the corresponding microscopic model representations are discussed. In particular, the possible origin of long-range magnetic order in mixed-valence compounds is analysed.

Detection of Rain in Tropical Cyclones by Underwater Ambient Sound

Abstract Rain in tropical cyclones is studied using eight time series of underwater ambient sound at 40–50 kHz with wind speeds up to 45 m s−1 beneath three tropical cyclones. At tropical cyclone wind speeds, rain- and wind-generated sound levels are comparable, and therefore rain cannot be detected by sound level alone. A rain detection algorithm that is based on the variations of 5–30-kHz sound levels with periods longer than 20 s and shorter than 30 min is proposed. Faster fluctuations (&lt;20 s) are primarily due to wave breaking, and slower ones (&gt;30 min) are due to overall wind variations. Higher-frequency sound (&gt;30 kHz) is strongly attenuated by bubble clouds. This approach is supported by observations that, for wind speeds &lt; 40 m s−1, the variation in sound level is much larger than that expected from observed wind variations and is roughly comparable to that expected from rain variations. The hydrophone results are consistent with rain estimates by the Tropical Rainfall Measuring Mission (TRMM) satellite and with Stepped-Frequency Microwave Radiometer (SFMR) and radar estimates by surveillance flights. The observations indicate that the rain-generated sound fluctuations have broadband acoustic spectra centered around 10 kHz. Acoustically detected rain events usually last for a few minutes. The data used in this study are insufficient to produce useful estimation of rain rate from ambient sound because of limited quantity and accuracy of the validation data. The frequency dependence of sound variations suggests that quantitative rainfall algorithms from ambient sound may be developed using multiple sound frequencies. Significance Statement Rain is an indispensable process in forecasting the intensity and path of tropical cyclones. However, its role in the air–sea interaction is still poorly understood, and its parameterization in numerical models is still in development. In this work, we analyzed sound measurements made by hydrophones on board Lagrangian floats beneath tropical cyclones. We find that wind, rain, and breaking waves each have distinctive signatures in underwater ambient sound. We suggest that the air–sea dynamic processes in tropical cyclones can be explored by listening to ambient sound using hydrophones beneath the sea surface.

Quantifying the synchronization of the spikes emitted by coupled lasers

Synchronization phenomena is ubiquitous in nature, and in spite of having been studied for decades, it still attracts a lot of attention as is still challenging to detect and quantify, directly from the analysis of noisy signals. Semiconductor lasers are ideal for performing experiments because they are stochastic, nonlinear, and inexpensive and display different synchronization regimes that can be controlled by tuning the lasers’ parameters. Here, we analyze experiments done with two mutually optically coupled lasers. Due to the delay in the coupling (due to the finite time the light takes to travel between the lasers), the lasers synchronize with a lag: the intensity time traces show well-defined spikes, and a spike in the intensity of one laser may occur shortly before (or shortly after) a spike in the intensity of the other laser. Measures that quantify the degree of synchronization of the lasers from the analysis of the intensity signals do not fully quantify the synchronicity of the spikes because they also take into account the synchronization of fast irregular fluctuations that occur between spikes. By analyzing only the coincidence of the spike times, we show that event synchronization measures quantify spike synchronization remarkably well. We show that these measures allow us to quantify the degree of synchronization and, also, to identify the leading laser and the lagging one.

Effects of Ramp Rate Limit on Sizing of Energy Storage Systems for PV, Wind and PV–Wind Power Plants

As the share of highly variable photovoltaic (PV) and wind power production increases, there is a growing need to smooth their fast power fluctuations. Some countries have set power ramp rate (RR) limits that the output powers of power plants may not exceed. In this study, the effects of RR limit on the sizing of energy storage systems (ESS) for PV, wind, and PV–wind power plants are examined. These effects have been studied prior for PV power plants. However, for the wind and PV–wind power plants, the effects of the RR limit are studied comprehensively for the first time. In addition, the effects of the size of the power plant are considered. The study is based on climatic measurements carried out with a sampling frequency of 10 Hz for a period of 153 days. The modeling of the PV and wind powers and the simulation of the RR-based control algorithm of the ESS were completed using MATLAB. The results show that as the applied RR limit increased from 1%/min to 20%/min, the required relative energy capacities of the ESSs of the PV, wind, and PV–wind power plants decreased roughly 88%, 89%, and 89%, respectively. The required relative power capacities of the ESSs of the PV, wind, and PV–wind power plants decreased roughly 15%, 12%, and 20%, respectively. The utilization of the ESSs was found to decrease as the applied RR limit increased and as the size of the power plant grew.

Perspective: How Fast Dynamics Affect Slow Function in Protein Machines.

Internal motions in proteins take place on a broad range of time- and space-scales. The potential roles of these dynamics in the biochemical functions of proteins have intrigued biophysicists for many years, and multiple mechanisms to couple motions to function have been proposed. Some of these mechanisms have relied on equilibrium concepts. For example, the modulation of dynamics was proposed to change the entropy of a protein, hence affecting processes such as binding. This so-called dynamic allostery scenario has been demonstrated in several recent experiments. Perhaps even more intriguing may be models that involve out-of-equilibrium operation, which by necessity require the input of energy. We discuss several recent experimental studies that expose such potential mechanisms for coupling dynamics and function. In Brownian ratchets, for example, directional motion is promoted by switching a protein between two free energy surfaces. An additional example involves the effect of microsecond domain-closure dynamics of an enzyme on its much slower chemical cycle. These observations lead us to propose a novel two-time-scale paradigm for the activity of protein machines: fast equilibrium fluctuations take place on the microsecond-millisecond time scale, while on a slower time scale, free energy is invested in order to push the system out of equilibrium and drive functional transitions. Motions on the two time scales affect each other and are essential for the overall function of these machines.

Wind and Turbulence Observations With the Mars Microphone on Perseverance

AbstractWe utilize SuperCam's Mars microphone to provide information on wind speed and turbulence at high frequencies on Mars. To do so, we first demonstrate the sensitivity of the microphone signal level to wind speed, yielding a power law dependence. We then show the relationship between the microphone signal level and pressure, air and ground temperatures. A calibration function is constructed using Gaussian process regression (a machine learning technique) taking the microphone signal and air temperature as inputs to produce an estimate of the wind speed. This provides a high rate wind speed estimate on Mars, with a sample every 0.01 s. As a result, we determine the fast fluctuations of the wind at Jezero crater which highlights the nature of wind gusts over the Martian day. To analyze the turbulent behavior of this wind speed estimate, we calculate its normalized standard deviation, known as gustiness. To characterize the behavior of this high frequency turbulent intensity at Jezero crater, correlations are shown between the evaluated gustiness statistic and pressure drop rates/sizes, temperature and energy fluxes. This has implications for future atmospheric models on Mars, taking into account turbulence at the finest scales.

Hydrogen Production System Using Alkaline Water Electrolysis Adapting to Fast Fluctuating Photovoltaic Power

Using photovoltaic (PV) energy to produce hydrogen through water electrolysis is an environmentally friendly approach that results in no contamination, making hydrogen a completely clean energy source. Alkaline water electrolysis (AWE) is an excellent method of hydrogen production due to its long service life, low cost, and high reliability. However, the fast fluctuations of photovoltaic power cannot integrate well with alkaline water electrolyzers. As a solution to the issues caused by the fluctuating power, a hydrogen production system comprising a photovoltaic array, a battery, and an alkaline electrolyzer, along with an electrical control strategy and energy management strategy is proposed. The energy management strategy takes into account the predicted PV power for the upcoming hour and determines the power flow accordingly. By analyzing the characteristics of PV panels and alkaline water electrolyzers and imposing the proposed strategy, this system offers an effective means of producing hydrogen while minimizing energy consumption and reducing damage to the electrolyzer. The proposed strategy has been validated under various scenarios through simulations. In addition, the system’s robustness was demonstrated by its ability to perform well despite inaccuracies in the predicted PV power.

Homogenization for stochastic Ginzburg-Landau equation on the half-line with fast boundary fluctuation

In this paper, we investigate the homogenization for complex stochastic Ginzburg-Landau equation on the half-line with fast boundary fluctuation. Precisely, we first verify the existence and uniqueness of the mild solution to the initial-boundary problem in a weighted space. Next, we discuss the tightness of the mild solution. Finally, we prove that the mild solution of the original system converges to the mild solution of the homogenized equation in probability, as ε goes to zero.

Two-photon fluorescence imaging and specifically biosensing of norepinephrine on a 100-ms timescale

Norepinephrine (NE) is a key neurotransmitter in the central nervous system of organisms; however, specifically tracking the transient NE dynamics with high spatiotemporal resolution in living systems remains a great challenge. Herein, we develop a small molecular fluorescent probe that can precisely anchor on neuronal cytomembranes and specifically respond to NE on a 100-ms timescale. A unique dual acceleration mechanism of molecular-folding and water-bridging is disclosed, which boosts the reaction kinetics by ˃105 and ˃103 times, respectively. Benefiting from its excellent spatiotemporal resolution, the probe is applied to monitor NE dynamics at the single-neuron level, thereby, successfully snapshotting the fast fluctuation of NE levels at neuronal cytomembranes within 2 s. Moreover, two-photon fluorescence imaging of acute brain tissue slices reveals a close correlation between downregulated NE levels and Alzheimer’s disease pathology as well as antioxidant therapy.

Growth and Saturation of the Electron Drift Instability in a Crossed Field Plasma.

The linear growth and nonlinear energy transfer of the electron drift instability (EDI) are experimentally measured in the plume of a low-temperature, Hall effect discharge. A frequency-based bispectral analysis technique applied to fast ion density fluctuation measurements shows a growth rate function that is qualitatively similar to predictions from the linear instability dispersion relation, but an order of magnitude smaller. Calculation of the nonlinear transfer function indicates multiple three-wave interactions between high-frequency resonances of the instability in addition to an inverse energy cascade toward lower-frequency modes. These results are discussed in the context of recent theoretical, numerical, and experimental efforts on the EDI in Hall effect discharges and how the EDI may impact anomalous cross field transport.