Multi-Stage CMOS OTA Frequency Compensation: Genetic algorithm approach

Multistage amplifiers have become appropriate choices for high-speed electronics and data conversion. Because of the large number of high-impedance nodes, frequency compensation has become the biggest challenge in the design of multistage amplifiers. The new compensation technique in this study uses two differential stages to organize feedforward and feedback paths. Five Miller loops and a 500-pF load capacitor are driven by just two tiny compensating capacitors, each with a capacitance of less than 10 pF. The symbolic transfer function is calculated to estimate the circuit dynamics and HSPICE and TSMC 0.18 μm. CMOS technology is used to simulate the proposed five-stage amplifier. A straightforward iterative approach is also used to optimize the circuit parameters given a known cost function. According to simulation and mathematical results, the proposed structure has a DC gain of 190 dB, a gain bandwidth product of 15 MHz, a phase margin of 89°, and a power dissipation of 590 μW.     

Polymeric Nanoparticles as a Self-Adjuvanting Peptide Vaccine Delivery System: The Role of Shape

Antigens incorporated in subunit vaccines are typically poorly immunogenic, so a strong immunostimulant (adjuvant) and/or delivery system is required to boost immunogenicity. In this work, the various functional polymer nanostructures, that is, rods, worms, spheres, and tadpoles are used to develop potent peptide antigen delivery systems. The antigen PADRE-J8 (PJ8), derived from Group A Streptococcus (GAS) M-protein, is either physically mixed or chemically conjugated to polymeric nanoparticles of different shapes. The physical mixture of polymeric nanoparticles and antigen is more effective in inducing antibody production than their chemical conjugates. Moreover, rod-shaped polymeric nanoparticles in physical mixture with PJ8 elicited higher and more opsonic antibody titers than powerful complete Freund's adjuvant (CFA)-adjuvanted antigen. Herein, for the first time it is demonstrated that a) the block copolymer, in nanoparticle form, can act as an immune adjuvant, b) nanoparticle shape plays a crucial role in their immunogenicity, and c) antigen conjugation is not required, nor is antigen encapsulation or absorption.     

CO2 capture by benzene-based hypercrosslinked polymer adsorbent: Artificial neural network and response surface methodology

In this research, porous benzene-based hypercrosslinked polymeric adsorbents with different morphological properties were synthesized through Friedel–Crafts alkylation reaction. The resulting samples were applied for CO₂ capture at different operational conditions. Two modelling approaches, including artificial neural network (radial basis function [RBF] and multi layer perceptron [MLP]) and response surface methodology (RSM), were employed to investigate the effect of independent parameters on adsorption capacity. A semi-empirical quadratic model for adsorption capacity was presented based on RSM-central composite design technique. Additionally, the optimal structure of RBF was determined with 200 neurons, and the optimal structure of MLP was determined with three hidden layers and 10, 8, and 7 neurons. The modelling results demonstrate the better prediction of MLP and RBF approaches than the RSM method with correlation coefficient values of 0.999, 0.989, and 0.931, respectively. Finally, process optimization was carried out using RSM optimization module and the optimized values of synthesis time, crosslinker ratio (formaldehyde dimethyl acetal [FDA]/benzene), adsorption time, pressure, and temperature were obtained at 10.11 h, 1, 220 s, 9 bar, and 55°C, respectively. The optimum value of CO₂ uptake capacity was obtained around 167 (mg/g).     

Three-dimensional numerical modelling of a fast fluidized bed biomass gasifier to generate energy from wastes

The modelling of a biomass fluidized bed gasification system, one of the most effective ways to produce energy from biomass resources and wastes, has been performed in this study. The effect of the turbulence phenomena, including calculations relating to flow turbulence, chemical fuel reactions, and energy and momentum exchange between multiple solid and gas phases, has been taken into account in the current research as a novel approach. A computational fluid dynamics case study model that combines equations with comprehensive geometry has been considered. Results have been compared with published operational records of an existing power plant to validate the model. The solid particle distribution, the velocity of the mixture and gas phase, the turbulent flow viscosity ratio, and the temperature distribution in the model indicated the accuracy of the simulation performance compared with the experimental studies. The production of the molar fraction of the constituent elements of the synthesis gas has been evaluated in transient conditions. Additionally, 35 s after the process began, the system's performance was estimated, and the results indicated the average molecular weights of hydrogen, carbon monoxide, carbon dioxide, and methane are 26%, 23%, 12.5%, and 3.3%, respectively, which presented high precision with the experimental results.     

Theoretical and experimental investigation of CO2 solubility in nanofluids containing NaP zeolite nanocrystals and [C12mim][Cl] ionic liquid

Today, CO₂ separation is very important, both as an environmental issue and also in various industries. In this study, the water-based nanofluid of NaP zeolite nanocrystals and 1-dodecyl-3-methylimidazolium chloride ([C₁₂mim][Cl]) ionic liquid were mixed and tested experimentally for CO₂ absorption in an isothermal high pressure cell equipped with magnetic stirring. Zeolite nanocrystals were synthesized via the hydrothermal approach and characterized. A series of experiments were performed at different conditions to investigate the impact of various parameters, including nanoparticle type, nanoparticle concentration, stabilizer concentration, and the vessel's initial pressure, on CO₂ solubility. It was found that 0.02 wt.% of zeolite nanoparticles, 0.4 wt.% of [C₁₂mim][Cl] ionic liquid, and 0.05 wt.% of sodium dodecyl benzene sulphonate (SDBS) in nanofluids result in higher absorption of CO₂ compared to other concentrations. Furthermore, CO₂ absorption was increased by increasing ionic liquid and surfactant concentration up to a certain value near critical micelle concentration, but after that the CO₂ absorption was decreased. The overall CO₂ absorption enhancement at 20 bar for 0.02 wt.% zeolite and ZnO water-based nanofluids with 0.4% [C₁₂mim][Cl] ionic liquid and 0.02 wt.% SDBS were 26.9%, 21.5%, 21.2%, and 17% in comparison to pure water, respectively. In an absorption process using nanofluids, besides the influence of the mentioned parameters, the micro-convection caused by Brownian motion and the grazing effect of nanoparticles should be noted. Considering the micro-convection and grazing effects, a theoretical model should take into account the Brownian motion and grazing effects on the mass transfer rate in nanofluids to investigate the absorption enhancement by nano-particles.     

Innovative Diagnostic Peptide-Based Technologies for Cancer Diagnosis: Focus on EGFR-Targeting Peptides

Active targeting using biological ligands has emerged as a novel strategy for the targeted delivery of diagnostic agents to tumor cells. Conjugating functional targeting moieties with diagnostic probes can increase their accumulation in tumor cells and tissues, enhancing signal detection and, thus, the sensitivity of diagnosis. Due to their small size, ease of chemical synthesis and site-specific modification, high tissue penetration, low immunogenicity, rapid blood clearance, low cost, and biosafety, peptides offer several advantages over antibodies and proteins in diagnostic applications. Epidermal growth factor receptor (EGFR) is one of the most promising cancer biomarkers for actively targeting diagnostic and therapeutic agents to tumor cells due to its active involvement and overexpression in various cancers. Several peptides for EGFR-targeting have been identified in the last decades, which have been obtained by multiple means including derivation from natural proteins, phage display screening, positional scanning synthetic combinatorial library, and in silico screening. Many studies have used these peptides as a targeting moiety for diagnosing different cancers in vitro, in vivo, and in clinical trials. This review summarizes the progress of EGFR-targeting peptide-based assays in the molecular diagnosis of cancer.     

Interfacial Interactions in Particle-Induced Abnormal Grain Growth

The presence of secondary particles to polycrystalline alloys results in kinetic stabilization of the grain boundaries, which maintains desirable fine microstructures. In some instances, secondary particles trigger abnormal grain growth. The mechanisms influencing abnormal grain growth are still a subject of conjecture. As dispersed fine particles can contribute to abnormal grain growth, it is necessary to clarify the governing mechanism by which this occurs. The current work employs a multiphase field modeling approach to shed light onto abnormal grain growth. Particular attention is placed on understanding the role of grain boundary–particle interactions on abnormal grain growth. The results show that, in the presence of particles, normal grain growth occurs until a pinned state is achieved. In the pinned state, some grains overcome the pinning pressure exerted by some particles by piercing through the particles, which results in abnormal grain growth. The piercing events appear to be entirely random and not related to the size of the interacting particles. None-the-less, a bimodal particle size distribution is observed to lead to abnormal grain growth. A pinning parameter is introduced as a metric to identify the transition from normal to abnormal grain growth.     

High-Resolution 3D Deposition of Electrospun Fibers on Patterned Dielectric Elastomers

Electrostatic patterning has improved the performance of devices incorporating electrospun fibers in a wide variety of applications. However, the impact of process parameters on the final fiber pattern in these systems is rarely analyzed. Herein, a systematic analytical approach is developed to define quantitative metrics related to fiber patterning. Three-dimensional patterned dielectric elastomer collectors are fabricated via solution-casting polydimethylsiloxane with embedded carbon black or liquid metal droplets. Fiber patterning metrics are used to evaluate the effect of collector parameters such as insulating layer thickness, electrical ground surface area, and three-dimensional pattern geometry. Dielectric layer parameters such as conductive material concentration and particle diameter are also investigated. Using this framework, the best-performing collector is shown to improve selectivity 30-fold, uniformity ninefold, reproducibility eightfold, and increase fiber volume by one order of magnitude. Furthermore, eutectic gallium indium liquid metal and scaled-up pattern geometries demonstrate the tunability of this approach and broad applicability of systematic fiber pattern analysis. This rational approach to patterned fiber development can be applied to virtually any method or pattern to better understand the fiber patterning processes.     

Comprehensive review and future research directions on using various lanthanum-based adsorbents for selective phosphate removal

Clean Technologies and Environmental Policy - Due to the specific affinity of lanthanum (La) toward phosphate over a wide pH range, La compounds such as lanthanum oxide (LO), lanthanum hydroxide...     

An Unsupervised Writer Identification Based on Generating ClusterableEmbeddings

The writer identification system identifies individuals based on their handwriting is a frequent topic in biometric authentication and verification systems. Due to its importance, numerous studies have been conducted in various languages. Researchers have established several learning methods for writer identification including supervised and unsupervised learning. However, supervised methods require a large amount of annotation data, which is impossible in most scenarios. On the other hand, unsupervised writer identification methods may be limited and dependent on feature extraction that cannot provide the proper objectives to the architecture and be misinterpreted. This paper introduces an unsupervised writer identification system that analyzes the data and recognizes the writer based on the inter-feature relations of the data to resolve the uncertainty of the features. A pairwise architecture-based Autoembedder was applied to generate clusterable embeddings for handwritten text images. Furthermore, the trained baseline architecture generates the embedding of the data image, and the K-means algorithm is used to distinguish the embedding of individual writers. The proposed model utilized the IAM dataset for the experiment as it is inconsistent with contributions from the authors but is easily accessible for writer identification tasks. In addition, traditional evaluation metrics are used in the proposed model. Finally, the proposed model is compared with a few unsupervised models, and it outperformed the state-of-the-art deep convolutional architectures in recognizing writers based on unlabeled data.