Adsorption of TNT, DNAN, NTO, FOX7, and NQ Onto Cellulose, Chitin, and Cellulose Triacetate. Insights From Density Functional Theory Calculations

Insensitive munitions (IM) compounds such as DNAN (2,4-dinitroanisole), NTO (3-nitro-1,2,4-triazol-5-one), NQ (nitroguanidine), and FOX7 (1,1-diamino-2,2-dinitroethene) reduce the risk of accidental explosions due to shock and high temperature exposure. These compounds are being used as replacements for sensitive munition compounds such as TNT (2,4,6-trinitromethylbenzene) and RDX (1,3,5-hexahydro-1,3,5-trinitro-1,3,5-triazine). NTO and NQ in IM compounds are more soluble than TNT or RDX, hence they can easily spread in the environment and get dissolved if exposed to precipitation. DNAN solubility is comparable to TNT solubility. Cellulosic biomass, due to its abundance in the environment and its chemical structure, has a high probability of adsorbing these IM compounds, and thus, it is important to investigate the interactions between cellulose and cellulose like biopolymers (e.g. cellulose triacetate and chitin) with IM compounds. Using Density Functional Theory methods, we have studied the adsorption of TNT, DNAN, NTO, NQ, and FOX7 onto cellulose Iα and Iβ, chitin, and cellulose triacetate I (CTA I). Solvent effects on the adsorption were also investigated. Our results show that all contaminants are more strongly adsorbed onto chitin and cellulose Iα than onto CTA I and cellulose Iβ. Dispersion forces were found to be the predominant contribution to the adsorption energies of all contaminants

Role of Stone-Wales Defects on the Interfacial Interactions Among Graphene, Carbon Nanotubes, and Nylon 6: A First-Principles Study

We investigate computationally the role of Stone-Wales (SW) defects on the interfacial interactions among graphene, carbon nanotubes (CNTs), and Nylon 6 using density functional theory (DFT) and the empirical force-field. Our first-principles DFT calculations were performed using the Quantum ESPRESSO electronic structure code with the highly accurate van der Waals functional (vdW-DF2). Both pristine and SW-defected carbon nanomaterials were investigated. The computed results show that the presence of SW defects on CNTs weakens the CNT-graphene interactions. Our result that CNT-graphene interaction is much stronger than CNT-CNT interaction indicates that graphene would be able to promote the dispersion of CNTs in the polymer matrix. Our results demonstrate that carbon nanomaterials form stable complexes with Nylon 6 and that the van der Waals interactions, as revealed by the electronic charge density difference maps, play a key stabilizing role on the interfacial interactions among graphene, CNTs, and Nylon 6. Using the density of states calculations, we observed that the bandgaps of graphene and CNTs were not significantly modified due to their interactions with Nylon 6. The Young’s moduli of complexes were found to be the averages of the moduli of their individual constituents. Published by AIP Publishing. https://doi.org/10.1063/1.503208

Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

As mortality rates decline, life expectancy increases, and populations age, non-fatal outcomes of diseases and injuries are becoming a larger component of the global burden of disease. The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of prevalence, incidence, and years lived with disability (YLDs) for 328 causes in 195 countries and territories from 1990 to 2016

The Effect of External Forces On the Initial Dissociation of RDX (1,3,5-trinitro-1,3,5-triazine): A Mechanochemical Study

Experimental and theoretical studies have proposed different initiation reactions for the decomposition of hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX). Three primary reactions are considered to start RDX decomposition: homolytic NN bond fission, HONO elimination, and concerted fission of CN bonds. The focus of this article is to study the effect of external forces on the energy barrier and reaction energies of all three mechanisms. We used the Nudged Elastic Band method along with ab initio Density Functional Theory within the framework of a generalized force-modified potential energy surface (G-FMPES) to calculate the minimum energy paths at different compressive (corresponding to pressure between approximately 6 and 294 MPa) and expansive force values (between 10 and 264 pN). For all three reactions, the application of an expansive force increases the exothermicity and lowers the energy barriers to different extents, while a compressive force decreases the exothermicity and raises the energy barrier to different extents

A Mechanochemical Study of the Effects of Compression On a Diels-Alder Reaction

We examine the effects of compressive external forces on the mechanisms of the parent Diels-Alder (DA) reaction between butadiene and ethylene. Reaction pathways and transition states were calculated using the nudged elastic band method within a mechanochemical framework at the CASSCF(6,6)/6-31G**, as well as the B3LYP/6-311++G** levels of theory. Our results suggest that compressive hydrostatic pressure lowers the energy barrier for the parent DA reaction while suppressing the undesirable side reaction, thereby leading to a direct increase in the yield of cyclohexene. Compressive pressure also increases the exothermicity of the parent DA reaction, which would lead to increased temperatures in a reaction vessel and thereby indirectly increase the yield of cyclohexene. Our estimates indicate that the compression used in our study corresponds to a range of 68 MPa–1410 MPa

First-Principles of the Interactions Between Graphene Oxide and Amine-Functionalized Carbon Nanotube

We applied plane-wave density functional theory to study the effects of chemical functionalizations of graphene and carbon nanotube (CNT) on the properties of graphene–CNT complexes. The functionalizations of graphene and CNT were modeled by covalently attaching oxygen-containing groups and amines (NH2), respectively, to the surfaces of these carbon nanomaterials. Our results show that both dispersion energy and hydrogen bonding play crucial roles in the formation of complexes between graphene oxide (GO) and CNT–NH2. At a lesser degree of functionalization, the interaction energies between functionalized graphene and CNT were either unchanged or decreased, with respect to those without functionalization. Our study indicated that the gain or loss of interaction energy between graphene and CNT is a competition between two contributions: dispersion energy and hydrogen bonds. It was found that the heavy functionalization of graphene and CNT could be a promising route for enhancing the interaction energy between them. Specifically, the carboxyl-functionalized GO produced the greatest increase in the hydrogen bond strength relative to the dispersion energy loss. The influence of Stone–Wales defects in CNT on the computed interaction energies was also examined. The computed electron density difference maps revealed that the enhancement in the interaction energy is due to the formation of several hydrogen bonds between oxygen-containing groups of GO and NH2-groups of CNT. Our results show that Young’s moduli of carbon nanomaterials decrease with the increasing concentration of functional groups. The moduli of GO–CNT–NH2 complexes were found to be the averages of the moduli of their constituents

Machine Learning and Artificial Intelligence in Cardiovascular Imaging

Artificial intelligence (AI) has captured the minds of science fiction writers and the general public for quite some time. As advancements have been made in computer science and engineering research, much improved computational power and the creation of newer, more efficient algorithms such as machine learning (ML) and deep learning (DL) have enabled the feasibility of big data analysis. AI has moved from the realm of science fiction to applications used in everyday life, such as Tesla’s self-driving cars, Facebook’s facial recognition, Amazon’s product recommendations, mobile check deposits, language translation software, and more. As AI continues to improve, ML algorithms can now master tasks that were previously thought to be too complex for machines and are now even capable of detecting patterns that are beyond human perception. This has led to a renewed and increased interest in ML as a useful tool in medical practice, particularly in the field of medical imaging. Indeed, now more than ever, medicine has become a big data science, with the introduction of electronic medical records (EMR) leading to a substantial amount of patient information being recorded. This available information will only increase in the future through the use of bidirectional patient portals. Moreover, in the era of evidence-based medicine, thousands of new evidence and data are being published daily. Going through such large volumes of data to determine what is clinically relevant and actionable can be overwhelming, resulting in important information being missed by physicians. However, AI machines can now consis- tently perform repetitive tasks at maximum capacity, sometimes producing results faster and more efficiently than humans. Medicine is thereby a perfect testing ground for the application of ML, as these systems can augment the ability of physicians to identify key information required for patient management while presenting it in an understandable manner. In particular, because radiology directly involves extracting data consisting of specific features seen on images and interpreting them through the knowledge base acquired by the radiologist, the medical imag- ing field serves as an attractive arena for the incorporation of ML systems. As advanced AI and ML systems transition from fiction to reality and steadily approach their implementation into med- ical and radiology practices, understanding the general meth- ods, capabilities, and limitations of machine learning is of fundamental importance to physicians and radiologists for the effective use of these systems. This chapter will introduce some of the basic concepts of machine learning techniques, provide a basic framework for their use, and highlight current and future applications in medicine and radiology with a special focus on cardiovascular imaging