Frontiers in Nanotechnology
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Published By Frontiers Media SA

2673-3013

2022 ◽  
Vol 3 ◽  
Author(s):  
Sujit K. Debnath ◽  
Rohit Srivastava

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a contagious virus that spreads exponentially across the world, resulting in serious viral pneumonia. Several companies and researchers have put their tremendous effort into developing novel vaccines or drugs for the complete eradication of COVID-19 caused by SARS-CoV-2. Bionanotechnology plays a vital role in designing functionalized biocompatible nanoparticulate systems with higher antiviral capabilities. Thus, several nanocarriers have been explored in designing and delivering drugs and vaccines. This problem can be overcome with the intervention of biomaterials or bionanoparticles. The present review describes the comparative analysis of SARS infection and its associated etiological agents. This review also highlighted some nanoparticles that have been explored in the treatment of COVID-19. However, these carriers elicit several problems once they come in contact with biological systems. Often, the body’s immune system treats these nanocarriers as foreign particles and antigens. In contrast, some bionanoparticles are highlighted here with their potential application in SARS-CoV-2. However, bionanoparticles have demonstrated some drawbacks discussed here with the possible outcomes. The scope of bioinspired nanoparticles is also discussed in detail to explore the new era of research. It is highly essential for the effective delivery of these nanoparticles to the target site. For effective management of SARS-CoV-2, different delivery patterns are also discussed here.


2022 ◽  
Vol 3 ◽  
Author(s):  
Karthikeyan Nagarajan ◽  
Junde Li ◽  
Sina Sayyah Ensan ◽  
Sachhidh Kannan ◽  
Swaroop Ghosh

Spiking Neural Networks (SNN) are fast emerging as an alternative option to Deep Neural Networks (DNN). They are computationally more powerful and provide higher energy-efficiency than DNNs. While exciting at first glance, SNNs contain security-sensitive assets (e.g., neuron threshold voltage) and vulnerabilities (e.g., sensitivity of classification accuracy to neuron threshold voltage change) that can be exploited by the adversaries. We explore global fault injection attacks using external power supply and laser-induced local power glitches on SNN designed using common analog neurons to corrupt critical training parameters such as spike amplitude and neuron’s membrane threshold potential. We also analyze the impact of power-based attacks on the SNN for digit classification task and observe a worst-case classification accuracy degradation of −85.65%. We explore the impact of various design parameters of SNN (e.g., learning rate, spike trace decay constant, and number of neurons) and identify design choices for robust implementation of SNN. We recover classification accuracy degradation by 30–47% for a subset of power-based attacks by modifying SNN training parameters such as learning rate, trace decay constant, and neurons per layer. We also propose hardware-level defenses, e.g., a robust current driver design that is immune to power-oriented attacks, improved circuit sizing of neuron components to reduce/recover the adversarial accuracy degradation at the cost of negligible area, and 25% power overhead. We also propose a dummy neuron-based detection of voltage fault injection at ∼1% power and area overhead each.


2022 ◽  
Vol 3 ◽  
Author(s):  
Xusheng Liu ◽  
Jie Cao ◽  
Jie Qiu ◽  
Xumeng Zhang ◽  
Ming Wang ◽  
...  

With the tremendous progress of Internet of Things (IoT) and artificial intelligence (AI) technologies, the demand for flexible and stretchable electronic systems is rapidly increasing. As the vital component of a system, existing computing units are usually rigid and brittle, which are incompatible with flexible and stretchable electronics. Emerging memristive devices with flexibility and stretchability as well as direct processing-in-memory ability are promising candidates to perform data computing in flexible and stretchable electronics. To execute the in-memory computing paradigm including digital and analogue computing, the array configuration of memristive devices is usually required. Herein, the recent progress on flexible and stretchable memristive arrays for in-memory computing is reviewed. The common materials used for flexible memristive arrays, including inorganic, organic and two-dimensional (2D) materials, will be highlighted, and effective strategies used for stretchable memristive arrays, including material innovation and structural design, will be discussed in detail. The current challenges and future perspectives of the in-memory computing utilizing flexible and stretchable memristive arrays are presented. These efforts aim to accelerate the development of flexible and stretchable memristive arrays for data computing in advanced intelligent systems, such as electronic skin, soft robotics, and wearable devices.


2022 ◽  
Vol 3 ◽  
Author(s):  
Teresita Arredondo-Ochoa ◽  
Guillermo A. Silva-Martínez

Most of the active pharmaceutical compounds are often prone to display low bioavailability and biological degradation represents an important drawback. Due to the above, the development of a drug delivery system (DDS) that enables the introduction of a pharmaceutical compound through the body to achieve a therapeutic effect in a controlled manner is an expanding application. Henceforth, new strategies have been developed to control several parameters considered essential for enhancing delivery of drugs. Nanostructure synthesis by microemulsions (ME) consist of enclosing a substance within a wall material at the nanoscale level, allowing to control the size and surface area of the resulting particle. This nanotechnology has shown the importance on targeted drug delivery to improve their stability by protecting a bioactive compound from an adverse environment, enhanced bioavailability as well as controlled release. Thus, a lower dose administration could be achieved by minimizing systemic side effects and decreasing toxicity. This review will focus on describing the different biocompatible nanostructures synthesized by ME as controlled DDS for therapeutic purposes.


2021 ◽  
Vol 3 ◽  
Author(s):  
Mark Buckwell ◽  
Wing H. Ng ◽  
Daniel J. Mannion ◽  
Horatio R. J. Cox ◽  
Stephen Hudziak ◽  
...  

Resistive random-access memories, also known as memristors, whose resistance can be modulated by the electrically driven formation and disruption of conductive filaments within an insulator, are promising candidates for neuromorphic applications due to their scalability, low-power operation and diverse functional behaviors. However, understanding the dynamics of individual filaments, and the surrounding material, is challenging, owing to the typically very large cross-sectional areas of test devices relative to the nanometer scale of individual filaments. In the present work, conductive atomic force microscopy is used to study the evolution of conductivity at the nanoscale in a fully CMOS-compatible silicon suboxide thin film. Distinct filamentary plasticity and background conductivity enhancement are reported, suggesting that device behavior might be best described by composite core (filament) and shell (background conductivity) dynamics. Furthermore, constant current measurements demonstrate an interplay between filament formation and rupture, resulting in current-controlled voltage spiking in nanoscale regions, with an estimated optimal energy consumption of 25 attojoules per spike. This is very promising for extremely low-power neuromorphic computation and suggests that the dynamic behavior observed in larger devices should persist and improve as dimensions are scaled down.


2021 ◽  
Vol 3 ◽  
Author(s):  
Revathy Sankaran ◽  
Kalaimani Markandan ◽  
Kuan Shiong Khoo ◽  
Chin Kui Cheng ◽  
Veeramuthu Ashokkumar ◽  
...  

Lignocellulosic biomass has arisen as a solution to our energy and environmental challenges because it is rich in feedstock that can be converted to biofuels. Converting lignocellulosic biomass to sugar is a complicated system involved in the bioconversion process. There are indeed a variety of techniques that have been utilized in the bioconversion process consisting of physical, chemical, and biological approaches. However, most of them have drawbacks when used on a large scale, which include the high cost of processing, the development of harmful inhibitors, and the detoxification of the inhibitors that have been produced. These constraints, taken together, hinder the effectiveness of current solutions and demand for the invention of a new, productive, cost-effective, and environmentally sustainable technique for LB processing. In this context, the approach of nanotechnology utilizing various nanomaterials and nanoparticles in treating lignocellulose biomass and bioenergy conversion has achieved increased interest and has been explored greatly in recent times. This mini review delves into the application of nanotechnological techniques in the bioconversion of lignocellulose biomass into bioenergy. This review on nanotechnological application in biomass conversion provides insights and development tools for the expansion of new sectors, resulting in excellent value and productivity, contributing to the long-term economic progress.


2021 ◽  
Vol 3 ◽  
Author(s):  
Pedro Henrique Correia de Lima ◽  
Débora Ribeiro Antunes ◽  
Mariana Monteiro de Lima Forini ◽  
Montcharles da Silva Pontes ◽  
Bruno Dufau Mattos ◽  
...  

Controlled release systems of agrochemicals have been developed in recent years. However, the design of intelligent nanocarriers that can be manufactured with renewable and low-cost materials is still a challenge for agricultural applications. Lignocellulosic building blocks (cellulose, lignin, and hemicellulose) are ideal candidates to manufacture ecofriendly nanocarriers given their low-cost, abundancy and sustainability. Complexity and heterogeneity of biopolymers have posed challenges in the development of nanocarriers; however, the current engineering toolbox for biopolymer modification has increased remarkably, which enables better control over their properties and tuned interactions with cargoes and plant tissues. In this mini-review, we explore recent advances on lignocellulosic-based nanocarriers for the controlled release of agrochemicals. We also offer a critical discussion regarding the future challenges of potential bio-based nanocarrier for sustainable agricultural development.


2021 ◽  
Vol 3 ◽  
Author(s):  
Kil Ho Lee ◽  
Faiz N. Khan ◽  
Lauren Cosby ◽  
Guolingzi Yang ◽  
Jessica O. Winter

Encapsulation in self-assembled block copolymer (BCP) based nanoparticles (NPs) is a common approach to enhance hydrophobic drug solubility, and nanoprecipitation processes in particular can yield high encapsulation efficiency (EE). However, guiding principles for optimizing polymer, drug, and solvent selection are critically needed to facilitate rapid design of drug nanocarriers. Here, we evaluated the relationship between drug-polymer compatibility and concentration ratios on EE and nanocarrier size. Our studies employed a panel of four drugs with differing molecular structures (i.e., coumarin 6, dexamethasone, vorinostat/SAHA, and lutein) and two BCPs [poly(caprolactone)-b-poly(ethylene oxide) (PCL-b-PEO) and poly(styrene)-b-poly(ethylene oxide) (PS-b-PEO)] synthesized using three nanoprecipitation processes [i.e., batch sonication, continuous flow flash nanoprecipitation (FNP), and electrohydrodynamic mixing-mediated nanoprecipitation (EM-NP)]. Continuous FNP and EM-NP processes demonstrated up to 50% higher EE than batch sonication methods, particularly for aliphatic compounds. Drug-polymer compatibilities were assessed using Hansen solubility parameters, Hansen interaction spheres, and Flory Huggins interaction parameters, but few correlations were EE observed. Although some Hansen solubility (i.e., hydrogen bonding and total) and Flory Huggins interaction parameters were predictive of drug-polymer preferences, no parameter was predictive of EE trends among drugs. Next, the relationship between polymer: drug molar ratio and EE was assessed using coumarin 6 as a model drug. As polymer:drug ratio increased from <1 to 3–6, EE approached a maximum (i.e., ∼51% for PCL BCPs vs. ∼44% PS BCPs) with Langmuir adsorption behavior. Langmuir behavior likely reflects a formation mechanism in which drug aggregate growth is controlled by BCP adsorption. These data suggest polymer:drug ratio is a better predictor of EE than solubility parameters and should serve as a first point of optimization.


2021 ◽  
Vol 3 ◽  
Author(s):  
Shima Hosseinzadeh ◽  
Mehrdad Biglari ◽  
Dietmar Fey

Non-volatile memory (NVM) technologies offer a number of advantages over conventional memory technologies such as SRAM and DRAM. These include a smaller area requirement, a lower energy requirement for reading and partly for writing, too, and, of course, the non-volatility and especially the qualitative advantage of multi-bit capability. It is expected that memristors based on resistive random access memories (ReRAMs), phase-change memories, or spin-transfer torque random access memories will replace conventional memory technologies in certain areas or complement them in hybrid solutions. To support the design of systems that use NVMs, there is still research to be done on the modeling side of NVMs. In this paper, we focus on multi-bit ternary memories in particular. Ternary NVMs allow the implementation of extremely memory-efficient ternary weights in neural networks, which have sufficiently high accuracy in interference, or they are part of carry-free fast ternary adders. Furthermore, we lay a focus on the technology side of memristive ReRAMs. In this paper, a novel memory model in the circuit level is presented to support the design of systems that profit from ternary data representations. This model considers two read methods of ternary ReRAMs, namely, serial read and parallel read. They are extensively studied and compared in this work, as well as the write-verification method that is often used in NVMs to reduce the device stress and to increase the endurance. In addition, a comprehensive tool for the ternary model was developed, which is capable of performing energy, performance, and area estimation for a given setup. In this work, three case studies were conducted, namely, area cost per trit, excessive parameter selection for the write-verification method, and the assessment of pulse width variation and their energy latency trade-off for the write-verification method in ReRAM.


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