Shiv Nadar University

About: Shiv Nadar University is a education organization based out in Dadri, Uttar Pradesh, India. It is known for research contribution in the topics: Population & Graphene. The organization has 1015 authors who have published 1924 publications receiving 18420 citations.

Cellulose Nanocrystals/Graphene Hybrids-A Promising New Class of Materials for Advanced Applications.

Abstract: With the growth of global fossil-based resource consumption and the environmental concern, there is an urgent need to develop sustainable and environmentally friendly materials, which exhibit promising properties and could maintain an acceptable level of performance to substitute the petroleum-based ones. As elite nanomaterials, cellulose nanocrystals (CNC) derived from natural renewable resources, exhibit excellent physicochemical properties, biodegradability and biocompatibility and have attracted tremendous interest nowadays. Their combination with other nanomaterials such as graphene-based materials (GNM) has been revealed to be useful and generated new hybrid materials with fascinating physicochemical characteristics and performances. In this context, the review presented herein describes the quickly growing field of a new emerging generation of CNC/GNM hybrids, with a focus on strategies for their preparation and most relevant achievements. These hybrids showed great promise in a wide range of applications such as separation, energy storage, electronic, optic, biomedical, catalysis and food packaging. Some basic concepts and general background on the preparation of CNC and GNM as well as their key features are provided ahead.

Eco-friendly halogen-free flame retardant cardanol polyphosphazene polybenzoxazine networks

Abstract: We report on the preparation of hexa-functional cardanol (renewable phenolic compound) benzoxazine with a phosphazene core (CPN) for use as a greener eco-friendly halogen-free flame retardant reactive additive for the formation of sustainable polyphosphazene polybenzoxazine networks for flame resistant applications. The structure and purity of the monomer was confirmed by Fourier transform infrared (FTIR), nuclear magnetic resonance (1H-, 13C-, 31P NMR) and gel permeation chromatography studies. The CPN monomer showed good compatibility with benzoxazine monomer (CPN0) as suggested by the cocuring studies. The thermal properties of the copolymer can be directly tuned by altering the composition of the monomer blend. The occurrence of phosphazene–phosphazane thermal rearrangement is also suggested for the thermal behavior (thermogravimetry analysis) at higher loading of CPN in the monomer feed ratio. An improvement in mechanical properties of the copolymer with increase in glass transition temperature was c...

Assessing the (Un)Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging

Abstract: Purpose To evaluate the trustworthiness of saliency maps for abnormality localization in medical imaging. Materials and Methods Using two large publicly available radiology datasets (SIIM-ACR Pneumothorax Segmentation and RSNA Pneumonia Detection), we quantified the performance of eight commonly used saliency map techniques in regards to their 1) localization utility (segmentation and detection), 2) sensitivity to model weight randomization, 3) repeatability, and 4) reproducibility. We compared their performances versus baseline methods and localization network architectures, using area under the precision-recall curve (AUPRC) and structural similarity index (SSIM) as metrics. Results All eight saliency map techniques fail at least one of the criteria and were inferior in performance compared to localization networks. For pneumothorax segmentation, the AUPRC ranged from 0.024-0.224, while a U-Net achieved a significantly superior AUPRC of 0.404 (p Conclusion We suggest that the use of saliency maps in the high-risk domain of medical imaging warrants additional scrutiny and recommend that detection or segmentation models be used if localization is the desired output of the network. Supplemental material is available for this article. Summary The use of saliency maps to interpret deep neural networks trained on medical imaging fails several key criteria for utility and robustness, highlighting the need for scrutiny before clinical application. Key Points Eight popular saliency map techniques were evaluated for their utility and robustness in interpreting deep neural networks trained on chest radiographs. All the saliency map techniques fail at least one of the criteria defined in the paper, indicating their use for high-risk medical applications to be problematic. Instead, the use of detection or segmentation models are recommended if localization is the ultimate goal of interpretation.