http://nopr.niscpr.res.in/handle/123456789/46 2026-08-15T04:41:25Z http://nopr.niscpr.res.in/handle/123456789/68238 Title: Novel Biomaterial based Nanocomposite Coating for Corrosion Inhibition Studies of Carbon Steel Authors: Al-Shibli, Sara; Devi, Geetha; Al-Badi, Athari; N, Aravind; Al-Dhabari, Ola Abstract: Corrosion is a rising concern in oil and gas sectors, which causes destruction of the material and results in heavy financial loss accompanied by high risk to human health and safety. This study investigates the corrosion inhibition efficiency of a novel biomaterial based nanostructured coating on carbon steel specimen by dip-coating technique using Ziziphus Spina leaf extract, silica nanoparticles (SiO2NPs), and biopolymer (chitosan) matrix. The anti-corrosion performance of the fabricated composite thin film was investigated by exposing the coated specimen at different environmental conditions and the effectiveness was examined using potentiostatic, weight loss measurement, atmosphere and wet/dry tests. The main characterization tools employed are Atomic force microscopy (AFM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and Field emission scanning electron microscopy (FE-SEM). Tafal plot was used to study the corrosion rate. The results of atomic-level micro structural analysis performed by FE-SEM and AFM suggested the successful development of inhibitor layer at the carbon steel surface to prevent the corrosion. The experimental outcomes underscore the potential of biomaterial based nanostructured coating as an effective corrosion inhibitor for carbon steel in acidic environments. The nanocomposite thin films act as an efficient protective coating and offers superior corrosion resistance with extended lifespan of the carbon steel. The study validates that the green corrosion inhibitors developed from natural resources with minimum layer thickness could be a realistic solution in regulating the corrosion in oil pipelines with improved corrosion resistance, exceptional chemical stability, durability, and improved strength Page(s): 291-300 2026-04-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/68237 Title: Optimization Studies on Fenugreek Seed Extract via Spray-Freeze Drying Authors: Prabhakar, Butti; Venkateswara Prasad, Raghupatruni; Srilatha, Pathiam; Galipothula, Ranjith; Sree Meduri, Swapna Abstract: The fenugreek seed extract utilized in this study was encapsulated using Spray Freeze Drying (SFD), a novel encapsulation technique, using a unique extraction technique called Pulsed Electric Field (PEF) aided extraction. SFD has several significant industrial benefits, including enhanced bioavailability of components, production of high-quality and stable powder formulations, protection of thermolabile compounds, ease of handling and application, and prolonged shelf life. PEF extraction minimizes thermal degradation and reduces solvent usage, thereby enhancing extraction efficiency and promoting environmental sustainability. By PEF-assisted extraction, ethanol solvent produced 10.89 ± 0.09%extract yield with highest overall polyphenol content (148.0 ± 3.15 mg GAE/g) at sample to solvent ratio of 1:12.The encapsulated powder made from gum acacia binder, with a 1:5 extract to binder ratio and a 20h lyophilization duration was optimized with yield (87.11 ± 1.36%), particle size (42.67 ± 1.28 μm) and polyphenol EE (74.50 ± 0.94%). Physical characteristics of encapsulated powders were measured. Bulk density, true density, porosity and Carr index of spray freeze-dried powder was 0.54 ± 0.01 g/cm3, 0.57 ± 0.05 g/cm3, 4.11 ± 0.32% and 10.0 ± 0.24respectively. The solubility, wettability, hygrscopicity, dispersibility and water activity values of spray freeze dried powder were 87.6 ± 1.72%, 5:50 ± 0.00 min:sec, 3.12 ± 0.06 g/kg dry solids, 71 ± 1.56% and 0.36 ± 0.01. This successful extraction and encapsulation technology may be used by industrialists as a suitable extraction method for high value products, as well as to add substantial value to fenugreek seeds and open the door to the creation of new applications in a variety of sectors. Page(s): 301-311 2026-04-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/68236 Title: Seeing Beyond Text: A Visual-Linguistic Dataset and Multimodal Framework for English-Hindi Video-Guided Translation Authors: Paul, Binnu; Rudrapal, Dwijen; Chakma, Kunal; Jamatia, Anupam Abstract: Despite the progress in Neural Machine Translation (NMT), translating ambiguous and context rich content remains a major challenge, especially in low-resource language pairs like English-Hindi. Traditional NMT systems often fail to solve these challenges due to their reliance on textual data alone. Multimodal approaches, particularly those incorporating visual context, offer promising solutions to the task by resolving linguistic ambiguities. To address this, a novel solution is introduced through a Visual Scene-Aware Hindi Subtitles Dataset (VISA-HIN), designed specifically for English-Hindi Video-Guided Multimodal Machine Translation (VMMT). This dataset aligns English subtitles with corresponding video frames and provides Hindi translations. Alongside the dataset, this study propose a video-guided MMT framework that leverages visual cues to enhance translation quality. The results of the experiments show the potential of scene aware information to improve contextual understanding and fluency in English-to-Hindi translation, paving the way for more robust and accurate multimodal translation systems in low-resource settings. Page(s): 312-324 2026-04-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/68235 Title: BiSplit-DistilBERT: A Lightweight Early-Exit Transformer for Fake News Detection with Cross-Domain Evaluation on BoolQ Question-Answering Data Benchmarked against BERT, RoBERTa, and DeBERTa Authors: Saxena, Aradhana; Santhanavijayan, A. Abstract: In the age where people communicate more on digital mediums, authenticity of content is a benchmark. At the same time light weighted models are more preferable. By considering both things a light weighted model is developed in this study by modifying the classification layer of DistiBERT using Bi-Split method. The idea behind Bi-Split method is that prediction is possible by only the first half of the sentence in such cases. This generates an adaptive early-exit approach, in which the model determines whether to end prematurely or proceed with further processing to gain further insight. Upon reaching a predefined threshold, a prediction is made, otherwise the remaining text is analysed to maintain accuracy. The model is tested on four benchmark datasets, GossipCop, PolitiFact, ISOT, and BoolQ, using Accuracy, Precision, Recall, F1-score, and AUC. Accuracies of 99, 94.8, 91.5 and 85% are achieved, showing better performance than baseline models, with SHAP-based interpretability. Page(s): 325-340 2026-04-01T00:00:00Z