Computational Study of Intramolecular Heterocyclic Ring Formation with Cyclic Phosphazenes.

Int J Eng Res Technol (Ahmedabad)

Department of Chemistry and Biochemistry, University of the Sciences in Philadelphia, Philadelphia, PA 19104 USA.

Published: August 2014

Polyphosphazenes, because of their unique properties, have generated many opportunities to explore a variety of applications. These applications include areas such as biomedical research (e.g. drug delivery) and material science (e.g. fire-resistant polymers). Phosphazenes potentially have more variations then benzene analogues because of different substitution patterns. Here we present A computational study of the chemical modifications to a group of cyclic phosphazenes mainly hexachlorophosphazene (PNCl). This study focuses on the relative energies of reactivity of hexachlorophosphazene to understand their geometry and the complexes they likely form. We compare diols, amino alcohols, and diamines with a carbon linker of 1-7 atoms. These heteroatom chains are attached to a single phosphorus atom or adjoining phosphorus atoms to form ring structures of geminal, vicinal (cis), and vicinal (trans) moieties. We find that the reactivities of "heteroatom caps" are predicted to be O,O (diol) > N,O (amino alcohol) > N,N (diamine). These results can be used to predict energetics and thus the stability of new compounds for biomedical and industrial applications.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4686155PMC

Publication Analysis

Top Keywords

computational study
8
cyclic phosphazenes
8
study intramolecular
4
intramolecular heterocyclic
4
heterocyclic ring
4
ring formation
4
formation cyclic
4
phosphazenes polyphosphazenes
4
polyphosphazenes unique
4
unique properties
4

Similar Publications

Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.

Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.

View Article and Find Full Text PDF

Background: X-ray grating-based dark-field imaging can sense the small angle scattering caused by object's micro-structures. This technique is sensitive to the porous microstructure of lung alveoli and has the potential to detect lung diseases at an early stage. Up to now, a human-scale dark-field CT (DF-CT) prototype has been built for lung imaging.

View Article and Find Full Text PDF

Objectives: This study evaluates the potential of pulp volume/total tooth-volume measurements of canine teeth in relation to chronologic age in patients with cleft lip and palate (CLP). The significance of this study lies in its exploration of the usability of these measurements for age determination in CLP patients, providing a novel perspective to the existing literature.

Methods: Cone beam computed tomography images of 33 patients (16 females, 17 males) with unilateral CLP aged 14-45 years and 33 age- and sex-matched healthy individuals (16 females, 17 males) were retrospectively evaluated.

View Article and Find Full Text PDF

Systematic Review of Hybrid Vision Transformer Architectures for Radiological Image Analysis.

J Imaging Inform Med

January 2025

School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.

Vision transformer (ViT)and convolutional neural networks (CNNs) each possess distinct strengths in medical imaging: ViT excels in capturing long-range dependencies through self-attention, while CNNs are adept at extracting local features via spatial convolution filters. While ViT may struggle with capturing detailed local spatial information, critical for tasks like anomaly detection in medical imaging, shallow CNNs often fail to effectively abstract global context. This study aims to explore and evaluate hybrid architectures that integrate ViT and CNN to leverage their complementary strengths for enhanced performance in medical vision tasks, such as segmentation, classification, reconstruction, and prediction.

View Article and Find Full Text PDF

Rib pathology is uniquely difficult and time-consuming for radiologists to diagnose. AI can reduce radiologist workload and serve as a tool to improve accurate diagnosis. To date, no reviews have been performed synthesizing identification of rib fracture data on AI and its diagnostic performance on X-ray and CT scans of rib fractures and its comparison to physicians.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!