Assessment of dynamic characteristics of fluidized beds via numerical simulations

Euler–Lagrange simulations coupled with the multiphase particle-in-cell (MP-PIC) approach for considering inter-particulate collisions have been performed to simulate a non-reacting fluidized bed at laboratory-scale. The objective of this work is to assess dynamic properties of the fluidized bed in terms of the specific kinetic energy of the bed material $k_S$ in J/kg and the bubble frequency $f_B$ in Hz, which represent suitable measures for the efficiency of the multiphase momentum exchange and the characteristic timescale of the fluidized bed system. The simulations have reproduced the bubbling fluidization regime observed in the experiments, and the calculated pressure drop $\Delta p$ in Pa has shown a reasonably good agreement with measured data. While varying the bed inventory $m_S$ in kg and the superficial gas velocity $u_G$ in m/s, $k_S$ increases with $u_G$ due to the increased momentum of the gas flow, which leads to a reinforced gas-to-solid momentum transfer. In contrast, $f_B$ decreases with $m_S$, which is attributed to the increased bed height $h_B$ in m at larger $m_S$. An increased gas temperature $T_G$ from 20 to 500 °C has led to an increase in $k_S$ by approximately 50%, whereas $\Delta p$, $h_B$, and $f_B$ are not sensitive to $T_G$. This is due to the increased gas viscosity with $T_G$, which results in an increased drag force exerted by the gas on the solid phase. While up-scaling the reactor to increase the bed inventory, bubble formation is enhanced significantly. This has led to an increased $f_B$, whereas $k_S$, $h_B$, and $\Delta p$ remain almost unchanged during the scale-up process. The results reveal that the general parameters such as $h_B$ and $\Delta p$ are not sufficient for assessing the hydrodynamic behavior of a fluidized bed while varying the operating temperatures and up-scaling the reactor dimension. In these cases, the dynamic properties $k_S$ and $f_B$ can be used as more suitable parameters for characterizing the hydrodynamics of fluidized beds

Integrated In‐Memory Sensor and Computing of Artificial Vision Based on Full‐vdW Optoelectronic Ferroelectric Field‐Effect Transistor

Abstract The development and application of artificial intelligence have led to the exploitation of low‐power and compact intelligent information‐processing systems integrated with sensing, memory, and neuromorphic computing functions. The 2D van der Waals (vdW) materials with abundant reservoirs for arbitrary stacking based on functions and enabling continued device downscaling offer an attractive alternative for continuously promoting artificial intelligence. In this study, full 2D SnS2/h‐BN/CuInP2S6 (CIPS)‐based ferroelectric field‐effect transistors (Fe‐FETs) and utilized light‐induced ferroelectric polarization reversal to achieve excellent memory properties and multi‐functional sensing‐memory‐computing vision simulations are designed. The device exhibits a high on/off current ratio of over 105, long retention time (>104 s), stable cyclic endurance (>350 cycles), and 128 multilevel current states (7‐bit). In addition, fundamental synaptic plasticity characteristics are emulated including paired‐pulse facilitation (PPF), short‐term plasticity (STP), long‐term plasticity (LTP), long‐term potentiation, and long‐term depression. A ferroelectric optoelectronic reservoir computing system for the Modified National Institute of Standards and Technology (MNIST) handwritten digital recognition achieved a high accuracy of 93.62%. Furthermore, retina‐like light adaptation and Pavlovian conditioning are successfully mimicked. These results provide a strategy for developing a multilevel memory and novel neuromorphic vision systems with integrated sensing‐memory‐processing

Full hardware implementation of neuromorphic visual system based on multimodal optoelectronic resistive memory arrays for versatile image processing

Abstract In-sensor and near-sensor computing are becoming the next-generation computing paradigm for high-density and low-power sensory processing. To fulfil a high-density and efficient neuromorphic visual system with fully hierarchical emulation of the retina and visual cortex, emerging multimodal neuromorphic devices for multi-stage processing and a fully hardware-implemented system with versatile image processing functions are still lacking and highly desirable. Here we demonstrate an emerging multimodal-multifunctional resistive random-access memory (RRAM) device array based on modified silk fibroin protein (MSFP), exhibiting both optoelectronic RRAM (ORRAM) mode featured by unique negative and positive photoconductance memory and electrical RRAM (ERRAM) mode featured by analogue resistive switching. A full hardware implementation of the artificial visual system with versatile image processing functions is realised for the first time, including ORRAM mode array for the in-sensor image pre-processing (contrast enhancement, background denoising, feature extraction) and ERRAM mode array for near-sensor high-level image recognition, which hugely improves the integration density, and simply the circuit design and the fabrication and integration complexity

An Ultra-High Performance Liquid Chromatographic-Tandem Mass Spectrometric Method for the Determination of Sinomenine in Human Plasma after Transdermal Delivery of the Zhengqing Fengtongning Injection

A sensitive, precise and selective ultra-high performance liquid chromatography method coupled with triple-quadrupole mass spectrometry was developed and validated for the determination of trace amounts of sinomenine (ng/mL) in minute volumes of human plasma. Fifty microliter plasma samples were precipitated using methanol to extract sinomenine. Separation was carried out on a C18 column with a water and acetonitrile mobile phase gradient with formic acid as an additive. The mass spectrometry data were obtained in the positive ion mode, and the transition of multiple reactions was monitored at m/z 330.2→181.0 for sinomenine quantification. The working assay range for sinomenine was linear from 0.1173 to 15.02 ng/mL with the lower limit of quantification of 0.1173 ng/mL. The precision and accuracy of the method was less than 15% in intra-day and inter-day experiments with a matrix effect of less than 6.5%. After validation, the quantitative method was applied to analyze sinomenine levels in human plasma after transdermal delivery of the Zhengqing Fengtongning Injection. The results showed that some samples contained sinomenine within the concentration range 0.4131–4.407 ng/mL

Perovskite Photovoltachromic Supercapacitor with All-Transparent Electrodes

Photovoltachromic cells (PVCCs) are of great interest for the self-powered smart windows of architectures and vehicles, which require widely tunable transmittance and automatic color change under photostimuli. Organolead halide perovskite possesses high light absorption coefficient and enables thin and semitransparent photovoltaic device. In this work, we demonstrate co-anode and co-cathode photovoltachromic supercapacitors (PVCSs) by vertically integrating a perovskite solar cell (PSC) with MoO₃/Au/MoO₃ transparent electrode and electrochromic supercapacitor. The PVCSs provide a seamless integration of energy harvesting/storage device, automatic and wide color tunability, and enhanced photostability of PSCs. Compared with conventional PVCC, the counter electrodes of our PVCSs provide sufficient balancing charge, eliminate the necessity of reverse bias voltage for bleaching the device, and realize reasonable <i>in situ</i> energy storage. The color states of PVCSs not only indicate the amount of energy stored and energy consumed in real time, but also enhance the photostability of photovoltaic component by preventing its long-time photoexposure under fully charged state of PVCSs. This work designs PVCS devices for multifunctional smart window applications commonly made of glass

Analog HfxZr1‐xO2 Memristors with Tunable Linearity for Implementation in a Self‐Organizing Map Neural Network

Abstract Doped‐metal oxide‐based memristors, with the potential for improved switching performance and capability for multi‐bit information storage, are attractive candidates in the implementation of artificial neural network (ANN) hardware systems. However, performance and process considerations such as switching behavior and complementary‐metal‐oxide‐semiconductor (CMOS) process compatibility remain a challenge. This study shows that amorphous Zr‐doped HfO2 (HZO) memristors fabricated via a co‐sputtering approach improve the switching performance by providing a controllable knob to modulate defects in the switching layer. At the same time, it satisfies the CMOS process compatibility requirements for industry adoption. HZO memristors with optimized stoichiometry exhibit 30% reduced switching voltages and 50% faster switching as compared to control HfO2 memristors. Concurrently, this study shows that high linearity analog states tuning is achievable via a programming scheme that utilizes voltage pulses with increasing amplitudes. This study further shows via simulation evaluation that HZO memristors implemented in a self‐organizing‐map (SOM) network for Fashion MNIST database classification, achieve an accuracy of 92% with short training cycles. The results thus pave a potential pathway for further development of CMOS process compatible HZO memristors for use in future storage and computing applications

CMOS backend-of-line compatible memory array and logic circuitries enabled by high performance atomic layer deposited ZnO thin-film transistor

Abstract The development of high-performance oxide-based transistors is critical to enable very large-scale integration (VLSI) of monolithic 3-D integrated circuit (IC) in complementary metal oxide semiconductor (CMOS) backend-of-line (BEOL). Atomic layer deposition (ALD) deposited ZnO is an attractive candidate due to its excellent electrical properties, low processing temperature below copper interconnect thermal budget, and conformal sidewall deposition for novel 3D architecture. An optimized ALD deposited ZnO thin-film transistor achieving a record field-effect and intrinsic mobility (µ FE /µ o ) of 85/140 cm2/V·s is presented here. The ZnO TFT was integrated with HfO2 RRAM in a 1 kbit (32 × 32) 1T1R array, demonstrating functionalities in RRAM switching. In order to co-design for future technology requiring high performance BEOL circuitries implementation, a spice-compatible model of the ZnO TFTs was developed. We then present designs of various ZnO TFT-based inverters, and 5-stage ring oscillators through simulations and experiments with working frequency exceeding 10’s of MHz

Improved Performance of HfxZnyO‐Based RRAM and its Switching Characteristics down to 4 K Temperature

Abstract The search for high‐performance resistive random‐access memory (RRAM) devices is essential to pave the way for highly efficient non‐Von Neumann computing architecture. Here, it is reported on an alloying approach using atomic layer deposition for a Zn‐doped HfOx‐based resistive random‐access memory (HfZnO RRAM), with improved performance. As compared with HfOx RRAM, the HfZnO RRAM exhibits reduced switching voltages (>20%) and switching energy (>3×), as well as better uniformity both in voltages and resistance states. Furthermore, the HfZnO RRAM exhibits stable retention exceeding 10 years, as well as write/erase endurance exceeding 105 cycles. In addition, excellent linearity and repeatability of conductance tuning can be achieved using the constant voltage pulse scheme, achieving ≈90% accuracy in a simulated multi‐layer perceptron network for the recognition of modified national institute of standards and technology database handwriting. The HfZnO RRAM is also characterized down to the temperature of 4 K, showing functionality and the elucidation of its carrier conduction mechanism. Hence, a potential pathway for doped‐RRAM to be used in a wide range of temperatures including quantum computing and deep‐space exploration is shown