Non-ideal Properties Research Articles

Surfactant effect on the hygroscopicity of aerosol particles at relative humidity ranging from 80% to 99.5%: Internally mixed adipic acid-ammonium sulfate particles

Surfactant, which is ubiquitous in the atmosphere, has an important effect on the hygroscopicty of nanoscale atmospheric aerosol particles. However, the knowledge on the high relative humidity (RH) section (90–100%) is insufficient due to the limitation of traditional measuring instruments. In this study, the hygroscopicity of aerosol particles consisted of adipic acid (AA) and ammonium sulfate (AS) at RH = 80–99.5% was studied using the experimental and the theoretical method. The growth factor (GF) values at different RH were measured by the high humidity tandem differential mobility analyzer (HHTDMA). With some assumptions, a new model (Model 3 (UNIFAC)) accounting for the reduction of surface tension, the effect of the bulk-surface partitioning of AA and the non-ideal of solution on the particle hygroscopicity was established to describe the experimental results. An overall better performance is shown after comparing with six traditional models. Moreover, the partitioning of AA and the non-ideal of solution were investigated in details by comparing the outcomes of corresponding models. The comparisons between Model 2 (UNIFAC) and Model 3 (UNIFAC) and between Model 3 (Ideal) and Model 3 (UNIFAC) indicate that both the partitioning of AA and the non-ideal properties of solution in particles result in the decrease of GF. In addition, the change of water activity affected by these two dynamic processes acts as a more important factor than surface tension in the decrease of GF. Moreover, RH, the mass ratio and the initial diameter have significant impact on the AA partitioning, the non-ideal properties of solution and their influences on the hygroscopicity of particles.

A nonlinear characteristic line model of the detonation process of aluminized explosives

Aiming at the non-ideal properties of aluminized explosive, a local isentropic hypothesis of the expansion of aluminized explosive detonation product was proposed, and a nonlinear characteristic line model for aluminized explosive detonation was established, which provides a new theoretical analysis method for studying the non-isentropic flow and the expansion of detonation products. A lot of studies have shown that for micron aluminum powder, the reaction mainly occurs in the expansion zone of detonation products. The aluminum powder was treated as inert in the detonation reaction zone in the model. Considering the reaction rate of the aluminum powder is relatively slow, the expansion process of the detonation products of aluminized explosive was divided into finite time regions. The energy released from the reaction of aluminum powder has a relaxation effect on the state of the products. Based on the relaxation effect, it was assumed that the detonation products flow was approximately isentropic in each time region. Due to the reaction of aluminum powder, the entropy in each time region was different. Based on the theory of isentropic flow of ideal explosive and local isentropic hypothesis, the non-isentropic expansion process of the detonation products of aluminized explosives can be analyzed theoretically. To verify the correctness of the model, metal plate experiments were conducted. Experiments on aluminized explosives and LiF explosives with a particle diameter of 5μm and 50μm to drive 0.5 mm and 1 mm metal plates were designed. The velocity history of the metal plate was measured by a laser displacement interferometer, and then the reaction degree of aluminum powder in the detonation products was calculated from the experimental results. Combined with the nonlinear characteristic line model of the detonation products of aluminized explosives, the velocity of the metal plate driven by aluminized explosives was calculated theoretically. Compared with the experimental results, the non-isentropic model can well describe the contribution of the secondary reaction of aluminum powder to the work ability of explosives, which verifies the correctness of the model for aluminized explosives (micron aluminum powder).

Chaos for communication

This work shows that chaotic signals with different power spectrum and different positive Lyapunov exponents are robust to linear superposition, meaning that the superposition preserves the Lyapunov exponents and the information content of the source signals, even after being transmitted over non-ideal physical medium. This work tackles with great detail how chaotic signals and their information content are affected when travelling through medium that presents the non-ideal properties of multi-path propagation, noise and chaotic interference (linear superposition), and how this impacts on the proposed communication system. Physical media with other non-ideal properties (dispersion and interference with periodic signals) are also discussed. These wonderful properties that chaotic signals have allow me to propose a novel communication system based on chaos, where information composed from and to multiple users each operating with different base frequencies and that is carried by chaotic wavesignals, can be fully preserved after transmission in the open air wireless physical medium, and it can be trivially decoded with low probability of errors.

Relativistic viscous effects on the primordial gravitational waves spectrum

We study the impact of the viscous effects of the primordial plasma on the evolution of the primordial gravitational waves (pGW) spectrum from Inflation until today, considering a self-consistent interaction that incorporates the back-reaction of the GW into the plasma. We use a relativistic causal hydrodynamic framework with a positive entropy production based on a Second-Order Theory (SOT) in which the viscous properties of the fluid are effectively described by a new set of independent variables. We study how the spin-2 modes typical of SOTs capture the simplest GW-fluid viscous interaction to first order. We consider that all non-ideal properties of the primordial plasma are due to an extra effectively massless self-interacting scalar field whose state becomes a many-particles one after Reheating and for which an effective fluid description is suitable. We numerically solve the evolution equations and explicitly compute the current GW spectrum obtaining two contributions. On the one hand we have the viscous evolution of the pGW: for the collision-dominated regime the GW source becomes negligible while in the collisionless limit there exists an absorption of the pGW energy due to the damping effect produced by the free-streaming spin-2 modes of the fluid and driven by the expansion of the Universe. The latter effect is characterized by a relative amplitude decrease of about 1 to 10 % with respect to the GW free evolution spectrum. On the other hand we get the GW production due to the decay of the initial spin-2 fluctuations of the fluid that is negligible compared with the above-mentioned contribution.This SOT framework captures the same qualitative effects on the evolution of GW coupled to matter reported in previous works in which a kinetic theory approach has been used.

Triple-layer Tissue Prediction for Cutaneous Skin Burn Injury: Analytical Solution and Parametric Analysis

This paper demonstrates a non-Fourier prediction methodology of triple-layer human skin tissue for determining skin burn injury with non-ideal properties of tissue, metabolism and blood perfusion. The dual-phase lag (DPL) bioheat model is employed and solved using joint integral transform (JIT) through Laplace and Fourier transforms methods. Parametric studies on the effects of skin tissue properties, initial temperature, blood perfusion rate and heat transfer parameters for the thermal response and exposure time of the layers of the skin tissue are carried out. The study demonstrates that the initial tissue temperature, the thermal conductivity of the epidermis and dermis, relaxation time, thermalisation time and convective heat transfer coefficient are critical parameters to examine skin burn injury threshold. The study also shows that thermal conductivity and the blood perfusion rate exhibits negligible effects on the burn injury threshold. The objective of the present study is to support the accurate quantification and assessment of skin burn injury for reliable experimentation, design and optimisation of thermal therapy delivery.

Spiking Neural Network (SNN) With Memristor Synapses Having Non-linear Weight Update.

Among many artificial neural networks, the research on Spike Neural Network (SNN), which mimics the energy-efficient signal system in the brain, is drawing much attention. Memristor is a promising candidate as a synaptic component for hardware implementation of SNN, but several non-ideal device properties are making it challengeable. In this work, we conducted an SNN simulation by adding a device model with a non-linear weight update to test the impact on SNN performance. We found that SNN has a strong tolerance for the device non-linearity and the network can keep the accuracy high if a device meets one of the two conditions: 1. symmetric LTP and LTD curves and 2. positive non-linearity factors for both LTP and LTD. The reason was analyzed in terms of the balance between network parameters as well as the variability of weight. The results are considered to be a piece of useful prior information for the future implementation of emerging device-based neuromorphic hardware.

Influence of non-ideal gas characteristics on working fluid properties and thermal cycle of space nuclear power generation system

Space nuclear Closed Brayton Cycle (CBC) power generation system with Helium-xenon gas (He–Xe) as a work fluid has attracted much attention with the development of space technology. The appropriate thermophysical property model of He–Xe is the key to achieve high-precision simulation of CBC power generation system. The thermophysical property model based on virial coefficient was developed and proposed. It was compared with the ideal gas model to clarify the differences and non-ideal gas characteristics of He–Xe. The deviations of He–Xe thermophysical parameters, main cycle parameters under different temperatures and pressures were compared numerically with these models. Based on non-ideal gas properties, a thermodynamic model was developed to analyze the influence of non-ideal gas characteristics on the efficiency of a 3.0 MWth lithium-cold fast reactor power system. The results showed that He–Xe physical parameters deviated from ideal gas characteristics significantly under the molar mass of more than 40 g/mol. Lower temperature (<500K) or higher pressure (>3.0 MPa) tends to make non-ideal gas characteristics on thermophysical parameters more effective. The thermophysical property model with non-ideal gas characteristics improves the system simulation accuracy about 4.91 %. This work can be used as reference for the development, accurate simulation and assessment of space nuclear CBC power generation system.

Optimized Design of OTA-Based Gyrator Realizing Fractional-Order Inductance Simulator: A Comprehensive Analysis

A detailed analysis of an operational transconductance amplifier based gyrator implementing a fractional-order inductance simulator is presented. The influence of active element non-ideal properties on the gyrator operation is investigated and demonstrated by admittance characteristics and formulas for important values and cut-off frequencies in these characteristics. Recommendations to optimize the performance of the gyrator in terms of operation bandwidth, the range of obtainable admittance magnitude, and signal dynamic range are proposed. The theoretical observations are verified by PSpice simulations of the gyrator with LT1228 integrated circuit.

DNN+NeuroSim V2.0: An End-to-End Benchmarking Framework for Compute-in-Memory Accelerators for On-Chip Training

DNN+NeuroSim is an integrated framework to benchmark compute-in-memory (CIM) accelerators for deep neural networks, with hierarchical design options from device-level, to circuit level and up to algorithm level. A python wrapper is developed to interface NeuroSim with a popular machine learning platform: Pytorch, to support flexible network structures. The framework provides automatic algorithm-to-hardware mapping, and evaluates chip-level area, energy efficiency and throughput for training or inference, as well as training/inference accuracy with hardware constraints. Our prior inference version of DNN+NeuroSim framework available at <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://github.com/neurosim/DNN_NeuroSim_V1.2</uri></i> was developed to estimate the impact of reliability in synaptic devices, and analog-to-digital converter (ADC) quantization loss on the accuracy and hardware performance of an inference engine. In this work, we further investigated the impact of the “analog” emerging nonvolatile memory (eNVM)’s nonideal device properties for on-chip training. By introducing the nonlinearity, asymmetry, device-to-device and cycle-to-cycle variation of weight update into the python wrapper, and peripheral circuits for error/weight gradient computation in NeuroSim core, we benchmarked CIM accelerators based on state-of-the-art SRAM and eNVM devices for VGG-8 on CIFAR-10 dataset, revealing the crucial specs of synaptic devices for on-chip training. The latest training version of the DNN+NeuroSim framework is available at <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://github.com/neurosim/DNN_NeuroSim_V2.1</uri></i> .

Creating Red Light-Switchable Protein Dimerization Systems as Genetically Encoded Actuators with High Specificity.

Protein dimerization systems controlled by red light with increased tissue penetration depth are a highly needed tool for clinical applications such as cell and gene therapies. However, mammalian applications of existing red light-induced dimerization systems are hampered by limitations of their two components: a photosensory protein (or photoreceptor) which often requires a mammalian exogenous chromophore and a naturally occurring photoreceptor binding protein typically having a complex structure and nonideal binding properties. Here, we introduce an efficient, generalizable method (COMBINES-LID) for creating highly specific, reversible light-induced heterodimerization systems independent of any existing binders to a photoreceptor. It involves a two-step binder screen (phage display and yeast two-hybrid) of a combinatorial nanobody library to obtain binders that selectively engage a light-activated form of a photoswitchable protein or domain not the dark form. Proof-of-principle was provided by engineering nanobody-based, red light-induced dimerization (nanoReD) systems comprising a truncated bacterial phytochrome sensory module using a mammalian endogenous chromophore, biliverdin, and light-form specific nanobodies. Selected nanoReD systems were biochemically characterized, exhibiting low dark activity and high induction specificity, and further demonstrated for the reversible control of protein translocation and activation of gene expression in mice. Overall, COMBINES-LID opens new opportunities for creating genetically encoded actuators for the optical manipulation of biological processes.

Insight into traps at Al2O3/p-GaN metal-oxide-semiconductor interface fabricated on free-standing GaN substrate

Vertical electronic devices fabricated from GaN-on-GaN substrate have been attracting growing interest due to their large current capacity and high breakdown voltage. However, the traps at the p-GaN metal-oxide-semiconductor (MOS) interfaces have been rarely understood. Here, the electrical characteristics of the Al2O3/p-GaN MOS structures fabricated on the free standing GaN substrates are investigated. The hysteresis in both current-voltage and capacitance-voltage curves are markedly reduced compared to the p-GaN MOS device fabricated on the sapphire substrate due to the suppression of the interfacial disorders, while a frequency dispersion is still observed. The interface state density is reduced from 7 × 1012 to 2 × 1012 cm−2eV−1 and the border trap density is reduced from 1.3 × 1013 to 2.2 × 1012 cm−2. The mechanism of the frequency dispersion is clarified by the impedance spectroscopy analysis. It reveals that the interface traps play an important role in the accumulation region of the capacitance-voltage property, and in the depletion region the leakage current induced shunt resistance dominates the non-ideal electrical properties.

Repetition-Frequency-Doubled Transform-Limited Optical Pulse Generation Based on Silicon Modulators

Compact and integrated optical pulse generation with a high repetition rate and a small duty cycle can benefit various applications such as integrated microwave photonics. In this work, a method to generate a repetition-frequency-doubled narrow optical pulse train based on two cascaded silicon Mach–Zehnder modulators is proposed and demonstrated. The generated frequency comb has even-order sidebands with linear phase and suppressed odd-order sidebands, which results in the transform-limited pulses in the time domain. The tunability of the repetition rate from 4.0 to 6.6 GHz and the duty cycle from 10.6% to 14.0% has been achieved. An analytic model that considers the non-ideal properties of the silicon modulators is also established and is able to reproduce the key features in the experiment. Our work shows the potential of an integrated silicon chip to generate high-speed optical pulse trains with tunable duty cycles demanded in integrated microwave photonics and integrated photonic signal processing.

Analog architectures for neural network acceleration based on non-volatile memory

Analog hardware accelerators, which perform computation within a dense memory array, have the potential to overcome the major bottlenecks faced by digital hardware for data-heavy workloads such as deep learning. Exploiting the intrinsic computational advantages of memory arrays, however, has proven to be challenging principally due to the overhead imposed by the peripheral circuitry and due to the non-ideal properties of memory devices that play the role of the synapse. We review the existing implementations of these accelerators for deep supervised learning, organizing our discussion around the different levels of the accelerator design hierarchy, with an emphasis on circuits and architecture. We explore and consolidate the various approaches that have been proposed to address the critical challenges faced by analog accelerators, for both neural network inference and training, and highlight the key design trade-offs underlying these techniques.

Nonlinear fluctuations in relativistic causal fluids

In the Second Order Theories (SOT) of real relativistic fluids, the non-ideal properties of the flows are described by a new set of dynamical tensor variables. In this work we explore the non-linear dynamics of those variables in a conformal fluid. Among all possible SOTs, we choose to work with the Divergence Type Theories (DTT) formalism, which ensures that the second law of thermodynamics is fulfilled non-perturbatively. The tensor modes include two divergence-free modes which have no analog in theories based on covariant generalizations of the Navier-Stokes equation, and that are particularly relevant because they couple linearly to a gravitational field. To study the dynamics of this irreducible tensor sector, we observe that in causal theories such as DTTs, thermal fluctuations induce a stochastic stirring force, which excites the tensor modes while preserving energy momentum conservation. From fluctuation-dissipation considerations it follows that the random force is Gaussian with a white spectrum. The irreducible tensor modes in turn excite vector modes, which back-react on the tensor sector, thus producing a consistent non-linear, second order description of the divergence-free tensor dynamics. Using the Martin-Siggia-Rose (MSR) formalism plus the Two-Particle Irreducible Effective Action (2PIEA) formalism, we obtain the one-loop corrected equations for the relevant two-point correlation functions of the model: the retarded propagator and the Hadamard function. The overall result of the self-consistent dynamics of the irreducible tensor modes at this order is a depletion of the spectrum in the UV sector, which suggests that tensor modes could sustain an inverse entropy cascade.

Energy saving potential of reactive dividing wall columns with azeotropic reaction systems

The reactive dividing wall column (RDWC) is a highly integrated process that enables savings in investment and operational costs. Nevertheless, the high grade of integration leads to an extremely complex process behavior, complicating the prediction of the advantageousness of RDWC for a given task during process design. So far, many applications have been proposed in literature for RDWC, but little general knowledge on the influence of reaction system properties on the RDWC has been published. Latter studies are limited to ideal reaction system properties. This article investigates the influence of non-ideal reaction system properties, focusing on azeotropic phase equilibrium. A comprehensive process understanding of how the non-ideal separation properties affect the energy saving potential of RDWC is developed. The results are combined in short heuristics, which allow the process engineer a quick and easy evaluation of the energy saving potential of RDWC for the investigated reaction systems during process synthesis.

Nonlinear waves propagation and stability analysis for planar waves at far field using quintic B-spline collocation method

A study of the dynamics of nonlinear waves at far field has been presented for real gases using quintic B-spline collocation method. To examine the dynamics at far field, “fast variable” in the governing system of equations has been introduced. Using asymptotic expansion of the flow variables in new fast variables, an evolution equation has been obtained for the behavior of waves at far from source. The evolution equation is modified Burger’s equation, which is known to have exact solution for planar flow. However, the solution for the corresponding cylindrical and spherical symmetric flow for the equation is unknown. The obtained numerical results have been compared with the exact solution for the planar flow. The error norms of numerically computed values are found very small when compared to corresponding exact solution. A Von Neumann stability analysis of the scheme is presented. The scheme is found unconditionally stable. It shows that the method is quite efficient to capture the dynamics of flow at far field. Results corresponding to other two geometries have been presented at different time. Further, a study on the effect of the real medium on the flow is presented. Schematic shows that with the increase in non-ideal property of the medium, the formation of shock becomes faster.

Device-aware Circuit Design for Robust Memristive Neuromorphic Systems with STDP-based Learning

In the past decade, complementary metal oxide semiconductor-memristor hybrid neuromorphic systems have gained importance owing to the advantages of memristors such as nano-scale size, non-volatility, and low-power operation. However, they are often accompanied by non-ideal properties that can impact the system’s performance. This article presents device-aware circuit design to mitigate such effects. A bi-memristor synapse with a robust spike-timing-dependent plasticity (STDP) is designed. A mixed-mode neuron is presented whose accumulation rate is tunable on-chip and can be used with a variety of memristors without needing a re-design. The proposed designs are employed together in an example pattern recognition system. A scalable winner-takes-all circuit is presented for the output stage. A pattern recognition task based on a simple STDP-based learning is demonstrated such that the recognition rate is directly dependent on the learnt weights. Device-level issues such as switching speed/threshold asymmetry, limited switching resolution, endurance, and varying resistance range (across devices) are shown to adversely affect learning at the system level and it is demonstrated that the proposed circuits can mitigate them. Last, the area and energy costs of the proposed designs are evaluated and compared against other implementations in the literature.

Bipolar-resistive switching and memristive properties of solution-processable cobalt oxide nanoparticles

This work reports the memristive properties of solution-processable cobalt oxide nanoparticles (CoO NPs) for resistive memory application. The memristive current???voltage hysteresis loop with good bipolar-resistive switching (BRS) property was shown by the developed device. Substantiate to this, the asymmetric device charge and double-valued charge???magnetic flux characteristics were calculated from the experimental electrical data and demonstrated the existence of the non-ideal memristor properties. The CoO memristive device can switch up to 103 BRS cycles and can retain the data up to 5????????103 s. The switching uniformity of the CoO memory device was elucidated by a cumulative probability distribution and statistical calculations. The low-resistance state shows little variation whereas broad variation was observed for high-resistance state. The theoretical model fitting results suggested that the conduction in the device during high-resistance sate was due to the Schottky and space charge-limited current and Ohmic current dominated in the low-resistance state. The formation and rupture of conductive filament with the assistance of interfacial dynamic is a possible reason for BRS in the CoO NPs-based memristive device.

On the mechanism of carbon dioxide corrosion of mild steel: Experimental investigation and mathematical modeling at elevated pressures and non-ideal solutions

The mechanistic study of uniform CO2 corrosion in the pH range from 4 to 6 and CO2 partial pressures up to 15 bar showed that neither H2CO3 nor HCO3− were reduced directly during the corrosion process to any appreciable extent. The significant effect of these carbonate species on the corrosion process is through their homogeneous dissociation insider the boundary layer. This mechanistic view was further quantitatively verified using a comprehensive mathematical model, developed with hydrogen ion reduction as the sole cathodic reaction while accounting for the effect of homogeneous chemical reactions, turbulent flow mixing, and non-ideal solution properties.

A mechanistic model to predict droplet drying history and particle shell formation in multicomponent systems

Spray drying allows tuning the physical properties of the resulting powders widely. However, targeted process design is complicated by the interplay between process characteristics and the non-ideal physical properties exhibited by multicomponent mixtures, such as pharmaceutical formulations. This work presents a mechanistic model describing the drying of single droplets. The model includes heat and mass balances, non-ideal vapour-liquid equilibria, and population balances describing the evolution of particulates within the drying droplet. The model is applied to (up to) ternary mixtures of solvents, polymers and solutes, and predicts properties of the drying droplet such as the time of shell formation and the size of the particle obtained after drying. A comparison with experimental data from single droplet drying experiments carried out at defined relative vapour saturation and temperature shows that the model can be used to predict shell formation (as well as other properties) for the systems studied here.